[ { "id": "L1_T1_Global_Aggregation_00007", "level": 1, "level_name": "Basic Operations", "category": "Atomic Retrieval", "subtask": "Global Aggregation", "question": "What is the median value of channel 147 in 2020-05-20 to 2020-05-31? (Output format: a single numeric value, rounded to 3 decimal places, e.g., x.xxx)", "answer": "0.15", "ground_truth": 0.15, "eval_metric": "rel_acc", "channel": "147", "ts_data_path": "ts_data/L1_T1_Global_Aggregation_00007.csv", "meta": { "source": "causal_rivers", "args": { "channel": "147", "time": "(Timestamp('2020-05-20 17:15:00'), Timestamp('2020-05-31 17:15:00'))", "agg": "median" } } }, { "id": "L1_T1_Global_Aggregation_00009", "level": 1, "level_name": "Basic Operations", "category": "Atomic Retrieval", "subtask": "Global Aggregation", "question": "What is the minimum value of channel 154 in 2019-06? (Output format: a single numeric value, rounded to 3 decimal places, e.g., x.xxx)", "answer": "0.313", "ground_truth": 0.313, "eval_metric": "rel_acc", "channel": "154", "ts_data_path": "ts_data/L1_T1_Global_Aggregation_00009.csv", "meta": { "source": "causal_rivers", "args": { "channel": "154", "time": "2019-06", "agg": "minimum" } } }, { "id": "L1_T1_Global_Aggregation_00010", "level": 1, "level_name": "Basic Operations", "category": "Atomic Retrieval", "subtask": "Global Aggregation", "question": "What is the range value of channel 155 in 2023-01? (Output format: a single numeric value, rounded to 3 decimal places, e.g., x.xxx)", "answer": "0.425", "ground_truth": 0.425, "eval_metric": "rel_acc", "channel": "155", "ts_data_path": "ts_data/L1_T1_Global_Aggregation_00010.csv", "meta": { "source": "causal_rivers", "args": { "channel": "155", "time": "2023-01", "agg": "range" } } }, { "id": "L1_T1_Global_Aggregation_00011", "level": 1, "level_name": "Basic Operations", "category": "Atomic Retrieval", "subtask": "Global Aggregation", "question": "What is the average value of channel 166 in 2021-06? (Output format: a single numeric value, rounded to 3 decimal places, e.g., x.xxx)", "answer": "433.709", "ground_truth": 433.709, "eval_metric": "rel_acc", "channel": "166", "ts_data_path": "ts_data/L1_T1_Global_Aggregation_00011.csv", "meta": { "source": "causal_rivers", "args": { "channel": "166", "time": "2021-06", "agg": "average" } } }, { "id": "L1_T1_Global_Aggregation_00013", "level": 1, "level_name": "Basic Operations", "category": "Atomic Retrieval", "subtask": "Global Aggregation", "question": "What is the range value of channel 170 in 2023-08-04 to 2023-09-02? (Output format: a single numeric value, rounded to 3 decimal places, e.g., x.xxx)", "answer": "109.0", "ground_truth": 109.0, "eval_metric": "rel_acc", "channel": "170", "ts_data_path": "ts_data/L1_T1_Global_Aggregation_00013.csv", "meta": { "source": "causal_rivers", "args": { "channel": "170", "time": "(Timestamp('2023-08-04 05:15:00'), Timestamp('2023-09-02 05:15:00'))", "agg": "range" } } }, { "id": "L1_T1_Global_Aggregation_00015", "level": 1, "level_name": "Basic Operations", "category": "Atomic Retrieval", "subtask": "Global Aggregation", "question": "What is the median value of channel 173 in 2023? (Output format: a single numeric value, rounded to 3 decimal places, e.g., x.xxx)", "answer": "268.5", "ground_truth": 268.5, "eval_metric": "rel_acc", "channel": "173", "ts_data_path": "ts_data/L1_T1_Global_Aggregation_00015.csv", "meta": { "source": "causal_rivers", "args": { "channel": "173", "time": "2023", "agg": "median" } } }, { "id": "L1_T1_Global_Aggregation_00022", "level": 1, "level_name": "Basic Operations", "category": "Atomic Retrieval", "subtask": "Global Aggregation", "question": "What is the average value of channel 495 in 2021-04? (Output format: a single numeric value, rounded to 3 decimal places, e.g., x.xxx)", "answer": "0.588", "ground_truth": 0.588, "eval_metric": "rel_acc", "channel": "495", "ts_data_path": "ts_data/L1_T1_Global_Aggregation_00022.csv", "meta": { "source": "causal_rivers", "args": { "channel": "495", "time": "2021-04", "agg": "average" } } }, { "id": "L1_T1_Global_Aggregation_00023", "level": 1, "level_name": "Basic Operations", "category": "Atomic Retrieval", "subtask": "Global Aggregation", "question": "What is the median value of channel 496 in 2019? (Output format: a single numeric value, rounded to 3 decimal places, e.g., x.xxx)", "answer": "0.436", "ground_truth": 0.436, "eval_metric": "rel_acc", "channel": "496", "ts_data_path": "ts_data/L1_T1_Global_Aggregation_00023.csv", "meta": { "source": "causal_rivers", "args": { "channel": "496", "time": "2019", "agg": "median" } } }, { "id": "L1_T1_Global_Aggregation_00025", "level": 1, "level_name": "Basic Operations", "category": "Atomic Retrieval", "subtask": "Global Aggregation", "question": "What is the minimum value of channel 578 in 2023-01? (Output format: a single numeric value, rounded to 3 decimal places, e.g., x.xxx)", "answer": "0.717", "ground_truth": 0.717, "eval_metric": "rel_acc", "channel": "578", "ts_data_path": "ts_data/L1_T1_Global_Aggregation_00025.csv", "meta": { "source": "causal_rivers", "args": { "channel": "578", "time": "2023-01", "agg": "minimum" } } }, { "id": "L1_T1_Global_Aggregation_00030", "level": 1, "level_name": "Basic Operations", "category": "Atomic Retrieval", "subtask": "Global Aggregation", "question": "What is the maximum value of channel 625 in 2022? (Output format: a single numeric value, rounded to 3 decimal places, e.g., x.xxx)", "answer": "1.95", "ground_truth": 1.95, "eval_metric": "rel_acc", "channel": "625", "ts_data_path": "ts_data/L1_T1_Global_Aggregation_00030.csv", "meta": { "source": "causal_rivers", "args": { "channel": "625", "time": "2022", "agg": "maximum" } } }, { "id": "L1_T1_Global_Aggregation_00033", "level": 1, "level_name": "Basic Operations", "category": "Atomic Retrieval", "subtask": "Global Aggregation", "question": "What is the maximum value of channel 647 in 2022-10-08 to 2022-10-30? (Output format: a single numeric value, rounded to 3 decimal places, e.g., x.xxx)", "answer": "0.772", "ground_truth": 0.772, "eval_metric": "rel_acc", "channel": "647", "ts_data_path": "ts_data/L1_T1_Global_Aggregation_00033.csv", "meta": { "source": "causal_rivers", "args": { "channel": "647", "time": "(Timestamp('2022-10-08 21:30:00'), Timestamp('2022-10-30 21:30:00'))", "agg": "maximum" } } }, { "id": "L1_T1_Global_Aggregation_00038", "level": 1, "level_name": "Basic Operations", "category": "Atomic Retrieval", "subtask": "Global Aggregation", "question": "What is the median value of channel 729 in 2023-01-28 to 2023-02-25? (Output format: a single numeric value, rounded to 3 decimal places, e.g., x.xxx)", "answer": "84.3", "ground_truth": 84.3, "eval_metric": "rel_acc", "channel": "729", "ts_data_path": "ts_data/L1_T1_Global_Aggregation_00038.csv", "meta": { "source": "causal_rivers", "args": { "channel": "729", "time": "(Timestamp('2023-01-28 08:15:00'), Timestamp('2023-02-25 08:15:00'))", "agg": "median" } } }, { "id": "L1_T1_Global_Aggregation_00045", "level": 1, "level_name": "Basic Operations", "category": "Atomic Retrieval", "subtask": "Global Aggregation", "question": "What is the minimum value of channel 894 in 2023-11? (Output format: a single numeric value, rounded to 3 decimal places, e.g., x.xxx)", "answer": "4.05", "ground_truth": 4.05, "eval_metric": "rel_acc", "channel": "894", "ts_data_path": "ts_data/L1_T1_Global_Aggregation_00045.csv", "meta": { "source": "causal_rivers", "args": { "channel": "894", "time": "2023-11", "agg": "minimum" } } }, { "id": "L1_T1_Global_Aggregation_00051", "level": 1, "level_name": "Basic Operations", "category": "Atomic Retrieval", "subtask": "Global Aggregation", "question": "What is the minimum value of channel MUFL in 2017-09? (Output format: a single numeric value, rounded to 3 decimal places, e.g., x.xxx)", "answer": "-23.986", "ground_truth": -23.986, "eval_metric": "rel_acc", "channel": "MUFL", "ts_data_path": "ts_data/L1_T1_Global_Aggregation_00051.csv", "meta": { "source": "ettm1", "args": { "channel": "MUFL", "time": "2017-09", "agg": "minimum" } } }, { "id": "L1_T1_Global_Aggregation_00054", "level": 1, "level_name": "Basic Operations", "category": "Atomic Retrieval", "subtask": "Global Aggregation", "question": "What is the range value of channel m_09 in 2020? (Output format: a single numeric value, rounded to 3 decimal places, e.g., x.xxx)", "answer": "1.0", "ground_truth": 1.0, "eval_metric": "rel_acc", "channel": "m_09", "ts_data_path": "ts_data/L1_T1_Global_Aggregation_00054.csv", "meta": { "source": "smd_machine_1_1", "args": { "channel": "m_09", "time": "2020", "agg": "range" } } }, { "id": "L1_T1_Global_Aggregation_00055", "level": 1, "level_name": "Basic Operations", "category": "Atomic Retrieval", "subtask": "Global Aggregation", "question": "What is the range value of channel m_21 in 2020-01-13 to 2020-02-08? (Output format: a single numeric value, rounded to 3 decimal places, e.g., x.xxx)", "answer": "0.989", "ground_truth": 0.989, "eval_metric": "rel_acc", "channel": "m_21", "ts_data_path": "ts_data/L1_T1_Global_Aggregation_00055.csv", "meta": { "source": "smd_machine_1_1", "args": { "channel": "m_21", "time": "(Timestamp('2020-01-13 19:29:00'), Timestamp('2020-02-08 19:29:00'))", "agg": "range" } } }, { "id": "L1_T1_Global_Aggregation_00057", "level": 1, "level_name": "Basic Operations", "category": "Atomic Retrieval", "subtask": "Global Aggregation", "question": "What is the maximum value of channel 67 in 2022? (Output format: a single numeric value, rounded to 3 decimal places, e.g., x.xxx)", "answer": "3.61", "ground_truth": 3.61, "eval_metric": "rel_acc", "channel": "67", "ts_data_path": "ts_data/L1_T1_Global_Aggregation_00057.csv", "meta": { "source": "causal_rivers", "args": { "channel": "67", "time": "2022", "agg": "maximum" } } }, { "id": "L1_T1_Global_Aggregation_00058", "level": 1, "level_name": "Basic Operations", "category": "Atomic Retrieval", "subtask": "Global Aggregation", "question": "What is the average value of channel 71 in 2022-09-17 to 2022-09-30? (Output format: a single numeric value, rounded to 3 decimal places, e.g., x.xxx)", "answer": "0.062", "ground_truth": 0.062, "eval_metric": "rel_acc", "channel": "71", "ts_data_path": "ts_data/L1_T1_Global_Aggregation_00058.csv", "meta": { "source": "causal_rivers", "args": { "channel": "71", "time": "(Timestamp('2022-09-17 06:00:00'), Timestamp('2022-09-30 06:00:00'))", "agg": "average" } } }, { "id": "L1_T1_Global_Aggregation_00059", "level": 1, "level_name": "Basic Operations", "category": "Atomic Retrieval", "subtask": "Global Aggregation", "question": "What is the average value of channel 99 in 2022-09-29 to 2022-10-23? (Output format: a single numeric value, rounded to 3 decimal places, e.g., x.xxx)", "answer": "0.796", "ground_truth": 0.796, "eval_metric": "rel_acc", "channel": "99", "ts_data_path": "ts_data/L1_T1_Global_Aggregation_00059.csv", "meta": { "source": "causal_rivers", "args": { "channel": "99", "time": "(Timestamp('2022-09-29 10:15:00'), Timestamp('2022-10-23 10:15:00'))", "agg": "average" } } }, { "id": "L1_T1_Global_Aggregation_00060", "level": 1, "level_name": "Basic Operations", "category": "Atomic Retrieval", "subtask": "Global Aggregation", "question": "What is the average value of channel 123 in 2019-12? (Output format: a single numeric value, rounded to 3 decimal places, e.g., x.xxx)", "answer": "0.594", "ground_truth": 0.594, "eval_metric": "rel_acc", "channel": "123", "ts_data_path": "ts_data/L1_T1_Global_Aggregation_00060.csv", "meta": { "source": "causal_rivers", "args": { "channel": "123", "time": "2019-12", "agg": "average" } } }, { "id": "L1_T1_Global_Aggregation_00062", "level": 1, "level_name": "Basic Operations", "category": "Atomic Retrieval", "subtask": "Global Aggregation", "question": "What is the median value of channel 146 in 2022-09-29 to 2022-10-10? (Output format: a single numeric value, rounded to 3 decimal places, e.g., x.xxx)", "answer": "0.452", "ground_truth": 0.452, "eval_metric": "rel_acc", "channel": "146", "ts_data_path": "ts_data/L1_T1_Global_Aggregation_00062.csv", "meta": { "source": "causal_rivers", "args": { "channel": "146", "time": "(Timestamp('2022-09-29 12:15:00'), Timestamp('2022-10-10 12:15:00'))", "agg": "median" } } }, { "id": "L1_T1_Global_Aggregation_00065", "level": 1, "level_name": "Basic Operations", "category": "Atomic Retrieval", "subtask": "Global Aggregation", "question": "What is the average value of channel 154 in 2023-12? (Output format: a single numeric value, rounded to 3 decimal places, e.g., x.xxx)", "answer": "1.022", "ground_truth": 1.022, "eval_metric": "rel_acc", "channel": "154", "ts_data_path": "ts_data/L1_T1_Global_Aggregation_00065.csv", "meta": { "source": "causal_rivers", "args": { "channel": "154", "time": "2023-12", "agg": "average" } } }, { "id": "L1_T1_Global_Aggregation_00067", "level": 1, "level_name": "Basic Operations", "category": "Atomic Retrieval", "subtask": "Global Aggregation", "question": "What is the average value of channel 166 in 2021-02? (Output format: a single numeric value, rounded to 3 decimal places, e.g., x.xxx)", "answer": "1071.591", "ground_truth": 1071.591, "eval_metric": "rel_acc", "channel": "166", "ts_data_path": "ts_data/L1_T1_Global_Aggregation_00067.csv", "meta": { "source": "causal_rivers", "args": { "channel": "166", "time": "2021-02", "agg": "average" } } }, { "id": "L1_T1_Global_Aggregation_00070", "level": 1, "level_name": "Basic Operations", "category": "Atomic Retrieval", "subtask": "Global Aggregation", "question": "What is the range value of channel 172 in 2020-02-10 to 2020-02-25? (Output format: a single numeric value, rounded to 3 decimal places, e.g., x.xxx)", "answer": "74.0", "ground_truth": 74.0, "eval_metric": "rel_acc", "channel": "172", "ts_data_path": "ts_data/L1_T1_Global_Aggregation_00070.csv", "meta": { "source": "causal_rivers", "args": { "channel": "172", "time": "(Timestamp('2020-02-10 03:00:00'), Timestamp('2020-02-25 03:00:00'))", "agg": "range" } } }, { "id": "L1_T1_Global_Aggregation_00071", "level": 1, "level_name": "Basic Operations", "category": "Atomic Retrieval", "subtask": "Global Aggregation", "question": "What is the average value of channel 173 in 2019? (Output format: a single numeric value, rounded to 3 decimal places, e.g., x.xxx)", "answer": "223.14", "ground_truth": 223.14, "eval_metric": "rel_acc", "channel": "173", "ts_data_path": "ts_data/L1_T1_Global_Aggregation_00071.csv", "meta": { "source": "causal_rivers", "args": { "channel": "173", "time": "2019", "agg": "average" } } }, { "id": "L1_T1_Global_Aggregation_00074", "level": 1, "level_name": "Basic Operations", "category": "Atomic Retrieval", "subtask": "Global Aggregation", "question": "What is the median value of channel 245 in 2023-12? (Output format: a single numeric value, rounded to 3 decimal places, e.g., x.xxx)", "answer": "0.761", "ground_truth": 0.761, "eval_metric": "rel_acc", "channel": "245", "ts_data_path": "ts_data/L1_T1_Global_Aggregation_00074.csv", "meta": { "source": "causal_rivers", "args": { "channel": "245", "time": "2023-12", "agg": "median" } } }, { "id": "L1_T1_Global_Aggregation_00077", "level": 1, "level_name": "Basic Operations", "category": "Atomic Retrieval", "subtask": "Global Aggregation", "question": "What is the median value of channel 441 in 2023? (Output format: a single numeric value, rounded to 3 decimal places, e.g., x.xxx)", "answer": "14.0", "ground_truth": 14.0, "eval_metric": "rel_acc", "channel": "441", "ts_data_path": "ts_data/L1_T1_Global_Aggregation_00077.csv", "meta": { "source": "causal_rivers", "args": { "channel": "441", "time": "2023", "agg": "median" } } }, { "id": "L1_T1_Global_Aggregation_00078", "level": 1, "level_name": "Basic Operations", "category": "Atomic Retrieval", "subtask": "Global Aggregation", "question": "What is the maximum value of channel 495 in 2023? (Output format: a single numeric value, rounded to 3 decimal places, e.g., x.xxx)", "answer": "1.93", "ground_truth": 1.93, "eval_metric": "rel_acc", "channel": "495", "ts_data_path": "ts_data/L1_T1_Global_Aggregation_00078.csv", "meta": { "source": "causal_rivers", "args": { "channel": "495", "time": "2023", "agg": "maximum" } } }, { "id": "L1_T1_Global_Aggregation_00079", "level": 1, "level_name": "Basic Operations", "category": "Atomic Retrieval", "subtask": "Global Aggregation", "question": "What is the maximum value of channel 496 in 2022? (Output format: a single numeric value, rounded to 3 decimal places, e.g., x.xxx)", "answer": "1.41", "ground_truth": 1.41, "eval_metric": "rel_acc", "channel": "496", "ts_data_path": "ts_data/L1_T1_Global_Aggregation_00079.csv", "meta": { "source": "causal_rivers", "args": { "channel": "496", "time": "2022", "agg": "maximum" } } }, { "id": "L1_T1_Global_Aggregation_00080", "level": 1, "level_name": "Basic Operations", "category": "Atomic Retrieval", "subtask": "Global Aggregation", "question": "What is the average value of channel 501 in 2021-12-10 to 2022-01-01? (Output format: a single numeric value, rounded to 3 decimal places, e.g., x.xxx)", "answer": "0.964", "ground_truth": 0.964, "eval_metric": "rel_acc", "channel": "501", "ts_data_path": "ts_data/L1_T1_Global_Aggregation_00080.csv", "meta": { "source": "causal_rivers", "args": { "channel": "501", "time": "(Timestamp('2021-12-10 20:15:00'), Timestamp('2022-01-01 20:15:00'))", "agg": "average" } } }, { "id": "L1_T1_Global_Aggregation_00082", "level": 1, "level_name": "Basic Operations", "category": "Atomic Retrieval", "subtask": "Global Aggregation", "question": "What is the median value of channel 580 in 2022-11-26 to 2022-12-23? (Output format: a single numeric value, rounded to 3 decimal places, e.g., x.xxx)", "answer": "0.23", "ground_truth": 0.23, "eval_metric": "rel_acc", "channel": "580", "ts_data_path": "ts_data/L1_T1_Global_Aggregation_00082.csv", "meta": { "source": "causal_rivers", "args": { "channel": "580", "time": "(Timestamp('2022-11-26 17:00:00'), Timestamp('2022-12-23 17:00:00'))", "agg": "median" } } }, { "id": "L1_T1_Global_Aggregation_00085", "level": 1, "level_name": "Basic Operations", "category": "Atomic Retrieval", "subtask": "Global Aggregation", "question": "What is the minimum value of channel 595 in 2022-03? (Output format: a single numeric value, rounded to 3 decimal places, e.g., x.xxx)", "answer": "0.936", "ground_truth": 0.936, "eval_metric": "rel_acc", "channel": "595", "ts_data_path": "ts_data/L1_T1_Global_Aggregation_00085.csv", "meta": { "source": "causal_rivers", "args": { "channel": "595", "time": "2022-03", "agg": "minimum" } } }, { "id": "L1_T1_Global_Aggregation_00086", "level": 1, "level_name": "Basic Operations", "category": "Atomic Retrieval", "subtask": "Global Aggregation", "question": "What is the minimum value of channel 625 in 2020-08-17 to 2020-09-14? (Output format: a single numeric value, rounded to 3 decimal places, e.g., x.xxx)", "answer": "0.05", "ground_truth": 0.05, "eval_metric": "rel_acc", "channel": "625", "ts_data_path": "ts_data/L1_T1_Global_Aggregation_00086.csv", "meta": { "source": "causal_rivers", "args": { "channel": "625", "time": "(Timestamp('2020-08-17 00:00:00'), Timestamp('2020-09-14 00:00:00'))", "agg": "minimum" } } }, { "id": "L1_T1_Global_Aggregation_00088", "level": 1, "level_name": "Basic Operations", "category": "Atomic Retrieval", "subtask": "Global Aggregation", "question": "What is the average value of channel 627 in 2019? (Output format: a single numeric value, rounded to 3 decimal places, e.g., x.xxx)", "answer": "0.379", "ground_truth": 0.379, "eval_metric": "rel_acc", "channel": "627", "ts_data_path": "ts_data/L1_T1_Global_Aggregation_00088.csv", "meta": { "source": "causal_rivers", "args": { "channel": "627", "time": "2019", "agg": "average" } } }, { "id": "L1_T1_Global_Aggregation_00090", "level": 1, "level_name": "Basic Operations", "category": "Atomic Retrieval", "subtask": "Global Aggregation", "question": "What is the minimum value of channel 680 in 2019-10-23 to 2019-11-22? (Output format: a single numeric value, rounded to 3 decimal places, e.g., x.xxx)", "answer": "0.704", "ground_truth": 0.704, "eval_metric": "rel_acc", "channel": "680", "ts_data_path": "ts_data/L1_T1_Global_Aggregation_00090.csv", "meta": { "source": "causal_rivers", "args": { "channel": "680", "time": "(Timestamp('2019-10-23 12:15:00'), Timestamp('2019-11-22 12:15:00'))", "agg": "minimum" } } }, { "id": "L1_T1_Global_Aggregation_00097", "level": 1, "level_name": "Basic Operations", "category": "Atomic Retrieval", "subtask": "Global Aggregation", "question": "What is the minimum value of channel 811 in 2023-02? (Output format: a single numeric value, rounded to 3 decimal places, e.g., x.xxx)", "answer": "7.76", "ground_truth": 7.76, "eval_metric": "rel_acc", "channel": "811", "ts_data_path": "ts_data/L1_T1_Global_Aggregation_00097.csv", "meta": { "source": "causal_rivers", "args": { "channel": "811", "time": "2023-02", "agg": "minimum" } } }, { "id": "L1_T1_Global_Aggregation_00099", "level": 1, "level_name": "Basic Operations", "category": "Atomic Retrieval", "subtask": "Global Aggregation", "question": "What is the minimum value of channel 865 in 2020-07? (Output format: a single numeric value, rounded to 3 decimal places, e.g., x.xxx)", "answer": "2.08", "ground_truth": 2.08, "eval_metric": "rel_acc", "channel": "865", "ts_data_path": "ts_data/L1_T1_Global_Aggregation_00099.csv", "meta": { "source": "causal_rivers", "args": { "channel": "865", "time": "2020-07", "agg": "minimum" } } }, { "id": "L1_T1_Global_Aggregation_00100", "level": 1, "level_name": "Basic Operations", "category": "Atomic Retrieval", "subtask": "Global Aggregation", "question": "What is the range value of channel 891 in 2020-11-25 to 2020-12-15? (Output format: a single numeric value, rounded to 3 decimal places, e.g., x.xxx)", "answer": "7.649", "ground_truth": 7.649, "eval_metric": "rel_acc", "channel": "891", "ts_data_path": "ts_data/L1_T1_Global_Aggregation_00100.csv", "meta": { "source": "causal_rivers", "args": { "channel": "891", "time": "(Timestamp('2020-11-25 17:30:00'), Timestamp('2020-12-15 17:30:00'))", "agg": "range" } } }, { "id": "L1_T1_Global_Aggregation_00103", "level": 1, "level_name": "Basic Operations", "category": "Atomic Retrieval", "subtask": "Global Aggregation", "question": "What is the maximum value of channel 897 in 2022-08-19 to 2022-08-28? (Output format: a single numeric value, rounded to 3 decimal places, e.g., x.xxx)", "answer": "0.319", "ground_truth": 0.319, "eval_metric": "rel_acc", "channel": "897", "ts_data_path": "ts_data/L1_T1_Global_Aggregation_00103.csv", "meta": { "source": "causal_rivers", "args": { "channel": "897", "time": "(Timestamp('2022-08-19 00:30:00'), Timestamp('2022-08-28 00:30:00'))", "agg": "maximum" } } }, { "id": "L1_T1_Global_Aggregation_00104", "level": 1, "level_name": "Basic Operations", "category": "Atomic Retrieval", "subtask": "Global Aggregation", "question": "What is the median value of channel 933 in 2022-08-13 to 2022-09-09? (Output format: a single numeric value, rounded to 3 decimal places, e.g., x.xxx)", "answer": "0.008", "ground_truth": 0.008, "eval_metric": "rel_acc", "channel": "933", "ts_data_path": "ts_data/L1_T1_Global_Aggregation_00104.csv", "meta": { "source": "causal_rivers", "args": { "channel": "933", "time": "(Timestamp('2022-08-13 04:00:00'), Timestamp('2022-09-09 04:00:00'))", "agg": "median" } } }, { "id": "L1_T1_Global_Aggregation_00107", "level": 1, "level_name": "Basic Operations", "category": "Atomic Retrieval", "subtask": "Global Aggregation", "question": "What is the minimum value of channel MUFL in 2017-11-24 to 2017-12-10? (Output format: a single numeric value, rounded to 3 decimal places, e.g., x.xxx)", "answer": "-19.793", "ground_truth": -19.793, "eval_metric": "rel_acc", "channel": "MUFL", "ts_data_path": "ts_data/L1_T1_Global_Aggregation_00107.csv", "meta": { "source": "ettm1", "args": { "channel": "MUFL", "time": "(Timestamp('2017-11-24 16:00:00'), Timestamp('2017-12-10 16:00:00'))", "agg": "minimum" } } }, { "id": "L1_T1_Global_Aggregation_00108", "level": 1, "level_name": "Basic Operations", "category": "Atomic Retrieval", "subtask": "Global Aggregation", "question": "What is the median value of channel MULL in 2017-09? (Output format: a single numeric value, rounded to 3 decimal places, e.g., x.xxx)", "answer": "-1.208", "ground_truth": -1.208, "eval_metric": "rel_acc", "channel": "MULL", "ts_data_path": "ts_data/L1_T1_Global_Aggregation_00108.csv", "meta": { "source": "ettm1", "args": { "channel": "MULL", "time": "2017-09", "agg": "median" } } }, { "id": "L1_T1_Global_Aggregation_00113", "level": 1, "level_name": "Basic Operations", "category": "Atomic Retrieval", "subtask": "Global Aggregation", "question": "What is the maximum value of channel 67 in 2019? (Output format: a single numeric value, rounded to 3 decimal places, e.g., x.xxx)", "answer": "3.45", "ground_truth": 3.45, "eval_metric": "rel_acc", "channel": "67", "ts_data_path": "ts_data/L1_T1_Global_Aggregation_00113.csv", "meta": { "source": "causal_rivers", "args": { "channel": "67", "time": "2019", "agg": "maximum" } } }, { "id": "L1_T1_Global_Aggregation_00114", "level": 1, "level_name": "Basic Operations", "category": "Atomic Retrieval", "subtask": "Global Aggregation", "question": "What is the minimum value of channel 71 in 2023? (Output format: a single numeric value, rounded to 3 decimal places, e.g., x.xxx)", "answer": "0.009", "ground_truth": 0.009, "eval_metric": "rel_acc", "channel": "71", "ts_data_path": "ts_data/L1_T1_Global_Aggregation_00114.csv", "meta": { "source": "causal_rivers", "args": { "channel": "71", "time": "2023", "agg": "minimum" } } }, { "id": "L1_T1_Global_Aggregation_00116", "level": 1, "level_name": "Basic Operations", "category": "Atomic Retrieval", "subtask": "Global Aggregation", "question": "What is the minimum value of channel 123 in 2020? (Output format: a single numeric value, rounded to 3 decimal places, e.g., x.xxx)", "answer": "0.096", "ground_truth": 0.096, "eval_metric": "rel_acc", "channel": "123", "ts_data_path": "ts_data/L1_T1_Global_Aggregation_00116.csv", "meta": { "source": "causal_rivers", "args": { "channel": "123", "time": "2020", "agg": "minimum" } } }, { "id": "L1_T1_Global_Aggregation_00117", "level": 1, "level_name": "Basic Operations", "category": "Atomic Retrieval", "subtask": "Global Aggregation", "question": "What is the minimum value of channel 124 in 2023? (Output format: a single numeric value, rounded to 3 decimal places, e.g., x.xxx)", "answer": "0.009", "ground_truth": 0.009, "eval_metric": "rel_acc", "channel": "124", "ts_data_path": "ts_data/L1_T1_Global_Aggregation_00117.csv", "meta": { "source": "causal_rivers", "args": { "channel": "124", "time": "2023", "agg": "minimum" } } }, { "id": "L1_T1_Global_Aggregation_00119", "level": 1, "level_name": "Basic Operations", "category": "Atomic Retrieval", "subtask": "Global Aggregation", "question": "What is the minimum value of channel 147 in 2020? (Output format: a single numeric value, rounded to 3 decimal places, e.g., x.xxx)", "answer": "0.039", "ground_truth": 0.039, "eval_metric": "rel_acc", "channel": "147", "ts_data_path": "ts_data/L1_T1_Global_Aggregation_00119.csv", "meta": { "source": "causal_rivers", "args": { "channel": "147", "time": "2020", "agg": "minimum" } } }, { "id": "L1_T1_Global_Aggregation_00120", "level": 1, "level_name": "Basic Operations", "category": "Atomic Retrieval", "subtask": "Global Aggregation", "question": "What is the maximum value of channel 151 in 2020? (Output format: a single numeric value, rounded to 3 decimal places, e.g., x.xxx)", "answer": "1.49", "ground_truth": 1.49, "eval_metric": "rel_acc", "channel": "151", "ts_data_path": "ts_data/L1_T1_Global_Aggregation_00120.csv", "meta": { "source": "causal_rivers", "args": { "channel": "151", "time": "2020", "agg": "maximum" } } }, { "id": "L1_T1_Global_Aggregation_00122", "level": 1, "level_name": "Basic Operations", "category": "Atomic Retrieval", "subtask": "Global Aggregation", "question": "What is the average value of channel 155 in 2020? (Output format: a single numeric value, rounded to 3 decimal places, e.g., x.xxx)", "answer": "0.166", "ground_truth": 0.166, "eval_metric": "rel_acc", "channel": "155", "ts_data_path": "ts_data/L1_T1_Global_Aggregation_00122.csv", "meta": { "source": "causal_rivers", "args": { "channel": "155", "time": "2020", "agg": "average" } } }, { "id": "L1_T1_Global_Aggregation_00131", "level": 1, "level_name": "Basic Operations", "category": "Atomic Retrieval", "subtask": "Global Aggregation", "question": "What is the range value of channel 312 in 2020? (Output format: a single numeric value, rounded to 3 decimal places, e.g., x.xxx)", "answer": "0.233", "ground_truth": 0.233, "eval_metric": "rel_acc", "channel": "312", "ts_data_path": "ts_data/L1_T1_Global_Aggregation_00131.csv", "meta": { "source": "causal_rivers", "args": { "channel": "312", "time": "2020", "agg": "range" } } }, { "id": "L1_T1_Global_Aggregation_00132", "level": 1, "level_name": "Basic Operations", "category": "Atomic Retrieval", "subtask": "Global Aggregation", "question": "What is the minimum value of channel 430 in 2021-03? (Output format: a single numeric value, rounded to 3 decimal places, e.g., x.xxx)", "answer": "0.243", "ground_truth": 0.243, "eval_metric": "rel_acc", "channel": "430", "ts_data_path": "ts_data/L1_T1_Global_Aggregation_00132.csv", "meta": { "source": "causal_rivers", "args": { "channel": "430", "time": "2021-03", "agg": "minimum" } } }, { "id": "L1_T1_Global_Aggregation_00133", "level": 1, "level_name": "Basic Operations", "category": "Atomic Retrieval", "subtask": "Global Aggregation", "question": "What is the average value of channel 441 in 2021-04? (Output format: a single numeric value, rounded to 3 decimal places, e.g., x.xxx)", "answer": "18.189", "ground_truth": 18.189, "eval_metric": "rel_acc", "channel": "441", "ts_data_path": "ts_data/L1_T1_Global_Aggregation_00133.csv", "meta": { "source": "causal_rivers", "args": { "channel": "441", "time": "2021-04", "agg": "average" } } }, { "id": "L1_T1_Global_Aggregation_00134", "level": 1, "level_name": "Basic Operations", "category": "Atomic Retrieval", "subtask": "Global Aggregation", "question": "What is the median value of channel 495 in 2022-08? (Output format: a single numeric value, rounded to 3 decimal places, e.g., x.xxx)", "answer": "0.0", "ground_truth": 0.0, "eval_metric": "rel_acc", "channel": "495", "ts_data_path": "ts_data/L1_T1_Global_Aggregation_00134.csv", "meta": { "source": "causal_rivers", "args": { "channel": "495", "time": "2022-08", "agg": "median" } } }, { "id": "L1_T1_Global_Aggregation_00135", "level": 1, "level_name": "Basic Operations", "category": "Atomic Retrieval", "subtask": "Global Aggregation", "question": "What is the maximum value of channel 496 in 2023-05-02 to 2023-05-19? (Output format: a single numeric value, rounded to 3 decimal places, e.g., x.xxx)", "answer": "0.564", "ground_truth": 0.564, "eval_metric": "rel_acc", "channel": "496", "ts_data_path": "ts_data/L1_T1_Global_Aggregation_00135.csv", "meta": { "source": "causal_rivers", "args": { "channel": "496", "time": "(Timestamp('2023-05-02 11:45:00'), Timestamp('2023-05-19 11:45:00'))", "agg": "maximum" } } }, { "id": "L1_T1_Global_Aggregation_00136", "level": 1, "level_name": "Basic Operations", "category": "Atomic Retrieval", "subtask": "Global Aggregation", "question": "What is the median value of channel 501 in 2023-11? (Output format: a single numeric value, rounded to 3 decimal places, e.g., x.xxx)", "answer": "1.14", "ground_truth": 1.14, "eval_metric": "rel_acc", "channel": "501", "ts_data_path": "ts_data/L1_T1_Global_Aggregation_00136.csv", "meta": { "source": "causal_rivers", "args": { "channel": "501", "time": "2023-11", "agg": "median" } } }, { "id": "L1_T1_Global_Aggregation_00139", "level": 1, "level_name": "Basic Operations", "category": "Atomic Retrieval", "subtask": "Global Aggregation", "question": "What is the range value of channel 589 in 2020-12? (Output format: a single numeric value, rounded to 3 decimal places, e.g., x.xxx)", "answer": "128.0", "ground_truth": 128.0, "eval_metric": "rel_acc", "channel": "589", "ts_data_path": "ts_data/L1_T1_Global_Aggregation_00139.csv", "meta": { "source": "causal_rivers", "args": { "channel": "589", "time": "2020-12", "agg": "range" } } }, { "id": "L1_T1_Global_Aggregation_00140", "level": 1, "level_name": "Basic Operations", "category": "Atomic Retrieval", "subtask": "Global Aggregation", "question": "What is the median value of channel 591 in 2022? (Output format: a single numeric value, rounded to 3 decimal places, e.g., x.xxx)", "answer": "160.0", "ground_truth": 160.0, "eval_metric": "rel_acc", "channel": "591", "ts_data_path": "ts_data/L1_T1_Global_Aggregation_00140.csv", "meta": { "source": "causal_rivers", "args": { "channel": "591", "time": "2022", "agg": "median" } } }, { "id": "L1_T1_Global_Aggregation_00141", "level": 1, "level_name": "Basic Operations", "category": "Atomic Retrieval", "subtask": "Global Aggregation", "question": "What is the minimum value of channel 595 in 2021-02-24 to 2021-03-25? (Output format: a single numeric value, rounded to 3 decimal places, e.g., x.xxx)", "answer": "0.522", "ground_truth": 0.522, "eval_metric": "rel_acc", "channel": "595", "ts_data_path": "ts_data/L1_T1_Global_Aggregation_00141.csv", "meta": { "source": "causal_rivers", "args": { "channel": "595", "time": "(Timestamp('2021-02-24 10:30:00'), Timestamp('2021-03-25 10:30:00'))", "agg": "minimum" } } }, { "id": "L1_T1_Global_Aggregation_00142", "level": 1, "level_name": "Basic Operations", "category": "Atomic Retrieval", "subtask": "Global Aggregation", "question": "What is the median value of channel 625 in 2022? (Output format: a single numeric value, rounded to 3 decimal places, e.g., x.xxx)", "answer": "0.308", "ground_truth": 0.308, "eval_metric": "rel_acc", "channel": "625", "ts_data_path": "ts_data/L1_T1_Global_Aggregation_00142.csv", "meta": { "source": "causal_rivers", "args": { "channel": "625", "time": "2022", "agg": "median" } } }, { "id": "L1_T1_Global_Aggregation_00143", "level": 1, "level_name": "Basic Operations", "category": "Atomic Retrieval", "subtask": "Global Aggregation", "question": "What is the average value of channel 626 in 2023? (Output format: a single numeric value, rounded to 3 decimal places, e.g., x.xxx)", "answer": "0.789", "ground_truth": 0.789, "eval_metric": "rel_acc", "channel": "626", "ts_data_path": "ts_data/L1_T1_Global_Aggregation_00143.csv", "meta": { "source": "causal_rivers", "args": { "channel": "626", "time": "2023", "agg": "average" } } }, { "id": "L1_T1_Global_Aggregation_00147", "level": 1, "level_name": "Basic Operations", "category": "Atomic Retrieval", "subtask": "Global Aggregation", "question": "What is the maximum value of channel 683 in 2020-11-01 to 2020-11-10? (Output format: a single numeric value, rounded to 3 decimal places, e.g., x.xxx)", "answer": "0.778", "ground_truth": 0.778, "eval_metric": "rel_acc", "channel": "683", "ts_data_path": "ts_data/L1_T1_Global_Aggregation_00147.csv", "meta": { "source": "causal_rivers", "args": { "channel": "683", "time": "(Timestamp('2020-11-01 04:15:00'), Timestamp('2020-11-10 04:15:00'))", "agg": "maximum" } } }, { "id": "L1_T1_Global_Aggregation_00148", "level": 1, "level_name": "Basic Operations", "category": "Atomic Retrieval", "subtask": "Global Aggregation", "question": "What is the minimum value of channel 727 in 2022? (Output format: a single numeric value, rounded to 3 decimal places, e.g., x.xxx)", "answer": "20.4", "ground_truth": 20.4, "eval_metric": "rel_acc", "channel": "727", "ts_data_path": "ts_data/L1_T1_Global_Aggregation_00148.csv", "meta": { "source": "causal_rivers", "args": { "channel": "727", "time": "2022", "agg": "minimum" } } }, { "id": "L1_T1_Global_Aggregation_00149", "level": 1, "level_name": "Basic Operations", "category": "Atomic Retrieval", "subtask": "Global Aggregation", "question": "What is the median value of channel 728 in 2020-02? (Output format: a single numeric value, rounded to 3 decimal places, e.g., x.xxx)", "answer": "131.0", "ground_truth": 131.0, "eval_metric": "rel_acc", "channel": "728", "ts_data_path": "ts_data/L1_T1_Global_Aggregation_00149.csv", "meta": { "source": "causal_rivers", "args": { "channel": "728", "time": "2020-02", "agg": "median" } } }, { "id": "L1_T1_Global_Aggregation_00150", "level": 1, "level_name": "Basic Operations", "category": "Atomic Retrieval", "subtask": "Global Aggregation", "question": "What is the maximum value of channel 729 in 2019-02-27 to 2019-03-27? (Output format: a single numeric value, rounded to 3 decimal places, e.g., x.xxx)", "answer": "155.0", "ground_truth": 155.0, "eval_metric": "rel_acc", "channel": "729", "ts_data_path": "ts_data/L1_T1_Global_Aggregation_00150.csv", "meta": { "source": "causal_rivers", "args": { "channel": "729", "time": "(Timestamp('2019-02-27 18:00:00'), Timestamp('2019-03-27 18:00:00'))", "agg": "maximum" } } }, { "id": "L1_T1_Global_Aggregation_00157", "level": 1, "level_name": "Basic Operations", "category": "Atomic Retrieval", "subtask": "Global Aggregation", "question": "What is the range value of channel 894 in 2022-03-27 to 2022-04-22? (Output format: a single numeric value, rounded to 3 decimal places, e.g., x.xxx)", "answer": "2.45", "ground_truth": 2.45, "eval_metric": "rel_acc", "channel": "894", "ts_data_path": "ts_data/L1_T1_Global_Aggregation_00157.csv", "meta": { "source": "causal_rivers", "args": { "channel": "894", "time": "(Timestamp('2022-03-27 19:45:00'), Timestamp('2022-04-22 19:45:00'))", "agg": "range" } } }, { "id": "L1_T1_Global_Aggregation_00158", "level": 1, "level_name": "Basic Operations", "category": "Atomic Retrieval", "subtask": "Global Aggregation", "question": "What is the median value of channel 895 in 2023? (Output format: a single numeric value, rounded to 3 decimal places, e.g., x.xxx)", "answer": "2.425", "ground_truth": 2.425, "eval_metric": "rel_acc", "channel": "895", "ts_data_path": "ts_data/L1_T1_Global_Aggregation_00158.csv", "meta": { "source": "causal_rivers", "args": { "channel": "895", "time": "2023", "agg": "median" } } }, { "id": "L1_T1_Global_Aggregation_00160", "level": 1, "level_name": "Basic Operations", "category": "Atomic Retrieval", "subtask": "Global Aggregation", "question": "What is the maximum value of channel 933 in 2022-06-19 to 2022-07-12? (Output format: a single numeric value, rounded to 3 decimal places, e.g., x.xxx)", "answer": "0.045", "ground_truth": 0.045, "eval_metric": "rel_acc", "channel": "933", "ts_data_path": "ts_data/L1_T1_Global_Aggregation_00160.csv", "meta": { "source": "causal_rivers", "args": { "channel": "933", "time": "(Timestamp('2022-06-19 10:15:00'), Timestamp('2022-07-12 10:15:00'))", "agg": "maximum" } } }, { "id": "L1_T1_Global_Aggregation_00161", "level": 1, "level_name": "Basic Operations", "category": "Atomic Retrieval", "subtask": "Global Aggregation", "question": "What is the range value of channel HUFL in 2018-01? (Output format: a single numeric value, rounded to 3 decimal places, e.g., x.xxx)", "answer": "35.566", "ground_truth": 35.566, "eval_metric": "rel_acc", "channel": "HUFL", "ts_data_path": "ts_data/L1_T1_Global_Aggregation_00161.csv", "meta": { "source": "ettm1", "args": { "channel": "HUFL", "time": "2018-01", "agg": "range" } } }, { "id": "L1_T1_Global_Aggregation_00163", "level": 1, "level_name": "Basic Operations", "category": "Atomic Retrieval", "subtask": "Global Aggregation", "question": "What is the median value of channel MUFL in 2018? (Output format: a single numeric value, rounded to 3 decimal places, e.g., x.xxx)", "answer": "6.432", "ground_truth": 6.432, "eval_metric": "rel_acc", "channel": "MUFL", "ts_data_path": "ts_data/L1_T1_Global_Aggregation_00163.csv", "meta": { "source": "ettm1", "args": { "channel": "MUFL", "time": "2018", "agg": "median" } } }, { "id": "L1_T1_Global_Aggregation_00164", "level": 1, "level_name": "Basic Operations", "category": "Atomic Retrieval", "subtask": "Global Aggregation", "question": "What is the median value of channel MULL in 2016-12-17 to 2017-01-06? (Output format: a single numeric value, rounded to 3 decimal places, e.g., x.xxx)", "answer": "-1.244", "ground_truth": -1.244, "eval_metric": "rel_acc", "channel": "MULL", "ts_data_path": "ts_data/L1_T1_Global_Aggregation_00164.csv", "meta": { "source": "ettm1", "args": { "channel": "MULL", "time": "(Timestamp('2016-12-17 13:15:00'), Timestamp('2017-01-06 13:15:00'))", "agg": "median" } } }, { "id": "L1_T1_Global_Aggregation_00165", "level": 1, "level_name": "Basic Operations", "category": "Atomic Retrieval", "subtask": "Global Aggregation", "question": "What is the maximum value of channel LUFL in 2018-02? (Output format: a single numeric value, rounded to 3 decimal places, e.g., x.xxx)", "answer": "6.153", "ground_truth": 6.153, "eval_metric": "rel_acc", "channel": "LUFL", "ts_data_path": "ts_data/L1_T1_Global_Aggregation_00165.csv", "meta": { "source": "ettm1", "args": { "channel": "LUFL", "time": "2018-02", "agg": "maximum" } } }, { "id": "L1_T1_Global_Aggregation_00167", "level": 1, "level_name": "Basic Operations", "category": "Atomic Retrieval", "subtask": "Global Aggregation", "question": "What is the maximum value of channel OT in 2016-12-20 to 2016-12-30? (Output format: a single numeric value, rounded to 3 decimal places, e.g., x.xxx)", "answer": "13.647", "ground_truth": 13.647, "eval_metric": "rel_acc", "channel": "OT", "ts_data_path": "ts_data/L1_T1_Global_Aggregation_00167.csv", "meta": { "source": "ettm1", "args": { "channel": "OT", "time": "(Timestamp('2016-12-20 23:00:00'), Timestamp('2016-12-30 23:00:00'))", "agg": "maximum" } } }, { "id": "L1_T3_Interval_Discovery_00340", "level": 1, "level_name": "Basic Operations", "category": "Atomic Retrieval", "subtask": "Interval Discovery", "question": "Find the longest period where channel 71 remained above 0.01 in 2020-08-22 to 2020-10-14. (Output format: a time interval, e.g., [YYYY-MM-DD HH:MM:SS, YYYY-MM-DD HH:MM:SS])", "answer": "[2020-09-02 07:45:00, 2020-09-10 17:00:00]", "ground_truth": [ "2020-09-02 07:45:00", "2020-09-10 17:00:00" ], "eval_metric": "iou", "channel": "71", "ts_data_path": "ts_data/L1_T3_Interval_Discovery_00340.csv", "meta": { "source": "causal_rivers", "args": { "channel": "71", "time": "(Timestamp('2020-08-22 09:45:00'), Timestamp('2020-10-14 09:45:00'))", "threshold": "0.01" } } }, { "id": "L1_T3_Interval_Discovery_00341", "level": 1, "level_name": "Basic Operations", "category": "Atomic Retrieval", "subtask": "Interval Discovery", "question": "Find the longest period where channel 99 remained above 0.807 in 2022-09-27 to 2022-11-02. (Output format: a time interval, e.g., [YYYY-MM-DD HH:MM:SS, YYYY-MM-DD HH:MM:SS])", "answer": "[2022-09-27 05:45:00, 2022-10-03 11:00:00]", "ground_truth": [ "2022-09-27 05:45:00", "2022-10-03 11:00:00" ], "eval_metric": "iou", "channel": "99", "ts_data_path": "ts_data/L1_T3_Interval_Discovery_00341.csv", "meta": { "source": "causal_rivers", "args": { "channel": "99", "time": "(Timestamp('2022-09-27 05:45:00'), Timestamp('2022-11-02 05:45:00'))", "threshold": "0.807" } } }, { "id": "L1_T3_Interval_Discovery_00344", "level": 1, "level_name": "Basic Operations", "category": "Atomic Retrieval", "subtask": "Interval Discovery", "question": "Find the longest period where channel 146 remained above 0.857 in 2020. (Output format: a time interval, e.g., [YYYY-MM-DD HH:MM:SS, YYYY-MM-DD HH:MM:SS])", "answer": "[2020-02-10 01:30:00, 2020-03-20 17:30:00]", "ground_truth": [ "2020-02-10 01:30:00", "2020-03-20 17:30:00" ], "eval_metric": "iou", "channel": "146", "ts_data_path": "ts_data/L1_T3_Interval_Discovery_00344.csv", "meta": { "source": "causal_rivers", "args": { "channel": "146", "time": "2020", "threshold": "0.857" } } }, { "id": "L1_T3_Interval_Discovery_00346", "level": 1, "level_name": "Basic Operations", "category": "Atomic Retrieval", "subtask": "Interval Discovery", "question": "Find the longest period where channel 151 remained above 0.648 in 2020-03-31 to 2020-05-03. (Output format: a time interval, e.g., [YYYY-MM-DD HH:MM:SS, YYYY-MM-DD HH:MM:SS])", "answer": "[2020-03-31 09:30:00, 2020-04-03 17:30:00]", "ground_truth": [ "2020-03-31 09:30:00", "2020-04-03 17:30:00" ], "eval_metric": "iou", "channel": "151", "ts_data_path": "ts_data/L1_T3_Interval_Discovery_00346.csv", "meta": { "source": "causal_rivers", "args": { "channel": "151", "time": "(Timestamp('2020-03-31 09:30:00'), Timestamp('2020-05-03 09:30:00'))", "threshold": "0.648" } } }, { "id": "L1_T3_Interval_Discovery_00352", "level": 1, "level_name": "Basic Operations", "category": "Atomic Retrieval", "subtask": "Interval Discovery", "question": "Find the longest period where channel 172 remained above 428.0 in 2019. (Output format: a time interval, e.g., [YYYY-MM-DD HH:MM:SS, YYYY-MM-DD HH:MM:SS])", "answer": "[2019-02-24 05:15:00, 2019-04-07 17:00:00]", "ground_truth": [ "2019-02-24 05:15:00", "2019-04-07 17:00:00" ], "eval_metric": "iou", "channel": "172", "ts_data_path": "ts_data/L1_T3_Interval_Discovery_00352.csv", "meta": { "source": "causal_rivers", "args": { "channel": "172", "time": "2019", "threshold": "428.0" } } }, { "id": "L1_T3_Interval_Discovery_00354", "level": 1, "level_name": "Basic Operations", "category": "Atomic Retrieval", "subtask": "Interval Discovery", "question": "Find the longest period where channel 177 remained above 316.0 in 2020-06-02 to 2020-08-22. (Output format: a time interval, e.g., [YYYY-MM-DD HH:MM:SS, YYYY-MM-DD HH:MM:SS])", "answer": "[2020-06-19 13:15:00, 2020-06-28 07:15:00]", "ground_truth": [ "2020-06-19 13:15:00", "2020-06-28 07:15:00" ], "eval_metric": "iou", "channel": "177", "ts_data_path": "ts_data/L1_T3_Interval_Discovery_00354.csv", "meta": { "source": "causal_rivers", "args": { "channel": "177", "time": "(Timestamp('2020-06-02 22:30:00'), Timestamp('2020-08-22 22:30:00'))", "threshold": "316.0" } } }, { "id": "L1_T3_Interval_Discovery_00355", "level": 1, "level_name": "Basic Operations", "category": "Atomic Retrieval", "subtask": "Interval Discovery", "question": "Find the longest period where channel 237 remained above 0.88 in 2022-09-19 to 2022-11-07. (Output format: a time interval, e.g., [YYYY-MM-DD HH:MM:SS, YYYY-MM-DD HH:MM:SS])", "answer": "[2022-09-19 17:30:00, 2022-09-22 14:00:00]", "ground_truth": [ "2022-09-19 17:30:00", "2022-09-22 14:00:00" ], "eval_metric": "iou", "channel": "237", "ts_data_path": "ts_data/L1_T3_Interval_Discovery_00355.csv", "meta": { "source": "causal_rivers", "args": { "channel": "237", "time": "(Timestamp('2022-09-19 17:30:00'), Timestamp('2022-11-07 17:30:00'))", "threshold": "0.88" } } }, { "id": "L1_T3_Interval_Discovery_00356", "level": 1, "level_name": "Basic Operations", "category": "Atomic Retrieval", "subtask": "Interval Discovery", "question": "Find the longest period where channel 245 remained above 0.679 in 2019-08-08 to 2019-09-08. (Output format: a time interval, e.g., [YYYY-MM-DD HH:MM:SS, YYYY-MM-DD HH:MM:SS])", "answer": "[2019-08-28 07:00:00, 2019-08-30 03:30:00]", "ground_truth": [ "2019-08-28 07:00:00", "2019-08-30 03:30:00" ], "eval_metric": "iou", "channel": "245", "ts_data_path": "ts_data/L1_T3_Interval_Discovery_00356.csv", "meta": { "source": "causal_rivers", "args": { "channel": "245", "time": "(Timestamp('2019-08-08 05:15:00'), Timestamp('2019-09-08 05:15:00'))", "threshold": "0.679" } } }, { "id": "L1_T3_Interval_Discovery_00359", "level": 1, "level_name": "Basic Operations", "category": "Atomic Retrieval", "subtask": "Interval Discovery", "question": "Find the longest period where channel 441 remained above 34.1 in 2023-02-15 to 2023-05-10. (Output format: a time interval, e.g., [YYYY-MM-DD HH:MM:SS, YYYY-MM-DD HH:MM:SS])", "answer": "[2023-04-15 15:30:00, 2023-04-20 08:15:00]", "ground_truth": [ "2023-04-15 15:30:00", "2023-04-20 08:15:00" ], "eval_metric": "iou", "channel": "441", "ts_data_path": "ts_data/L1_T3_Interval_Discovery_00359.csv", "meta": { "source": "causal_rivers", "args": { "channel": "441", "time": "(Timestamp('2023-02-15 12:00:00'), Timestamp('2023-05-10 12:00:00'))", "threshold": "34.1" } } }, { "id": "L1_T3_Interval_Discovery_00365", "level": 1, "level_name": "Basic Operations", "category": "Atomic Retrieval", "subtask": "Interval Discovery", "question": "Find the longest period where channel 589 remained above 386.0 in 2023-09-07 to 2023-12-05. (Output format: a time interval, e.g., [YYYY-MM-DD HH:MM:SS, YYYY-MM-DD HH:MM:SS])", "answer": "[2023-11-19 21:45:00, 2023-12-05 21:30:00]", "ground_truth": [ "2023-11-19 21:45:00", "2023-12-05 21:30:00" ], "eval_metric": "iou", "channel": "589", "ts_data_path": "ts_data/L1_T3_Interval_Discovery_00365.csv", "meta": { "source": "causal_rivers", "args": { "channel": "589", "time": "(Timestamp('2023-09-07 21:30:00'), Timestamp('2023-12-05 21:30:00'))", "threshold": "386.0" } } }, { "id": "L1_T3_Interval_Discovery_00366", "level": 1, "level_name": "Basic Operations", "category": "Atomic Retrieval", "subtask": "Interval Discovery", "question": "Find the longest period where channel 591 remained above 220.0 in 2022. (Output format: a time interval, e.g., [YYYY-MM-DD HH:MM:SS, YYYY-MM-DD HH:MM:SS])", "answer": "[2022-02-02 19:15:00, 2022-03-14 00:00:00]", "ground_truth": [ "2022-02-02 19:15:00", "2022-03-14 00:00:00" ], "eval_metric": "iou", "channel": "591", "ts_data_path": "ts_data/L1_T3_Interval_Discovery_00366.csv", "meta": { "source": "causal_rivers", "args": { "channel": "591", "time": "2022", "threshold": "220.0" } } }, { "id": "L1_T3_Interval_Discovery_00368", "level": 1, "level_name": "Basic Operations", "category": "Atomic Retrieval", "subtask": "Interval Discovery", "question": "Find the longest period where channel 625 remained above 0.045 in 2022-08-05 to 2022-09-11. (Output format: a time interval, e.g., [YYYY-MM-DD HH:MM:SS, YYYY-MM-DD HH:MM:SS])", "answer": "[2022-08-29 16:30:00, 2022-09-03 18:15:00]", "ground_truth": [ "2022-08-29 16:30:00", "2022-09-03 18:15:00" ], "eval_metric": "iou", "channel": "625", "ts_data_path": "ts_data/L1_T3_Interval_Discovery_00368.csv", "meta": { "source": "causal_rivers", "args": { "channel": "625", "time": "(Timestamp('2022-08-05 13:00:00'), Timestamp('2022-09-11 13:00:00'))", "threshold": "0.045" } } }, { "id": "L1_T3_Interval_Discovery_00369", "level": 1, "level_name": "Basic Operations", "category": "Atomic Retrieval", "subtask": "Interval Discovery", "question": "Find the longest period where channel 626 remained above 0.815 in 2021-06-09 to 2021-07-23. (Output format: a time interval, e.g., [YYYY-MM-DD HH:MM:SS, YYYY-MM-DD HH:MM:SS])", "answer": "[2021-07-14 05:15:00, 2021-07-19 05:15:00]", "ground_truth": [ "2021-07-14 05:15:00", "2021-07-19 05:15:00" ], "eval_metric": "iou", "channel": "626", "ts_data_path": "ts_data/L1_T3_Interval_Discovery_00369.csv", "meta": { "source": "causal_rivers", "args": { "channel": "626", "time": "(Timestamp('2021-06-09 13:30:00'), Timestamp('2021-07-23 13:30:00'))", "threshold": "0.815" } } }, { "id": "L1_T3_Interval_Discovery_00370", "level": 1, "level_name": "Basic Operations", "category": "Atomic Retrieval", "subtask": "Interval Discovery", "question": "Find the longest period where channel 627 remained above 0.378 in 2019-11-13 to 2020-01-06. (Output format: a time interval, e.g., [YYYY-MM-DD HH:MM:SS, YYYY-MM-DD HH:MM:SS])", "answer": "[2019-11-17 13:30:00, 2019-11-26 10:15:00]", "ground_truth": [ "2019-11-17 13:30:00", "2019-11-26 10:15:00" ], "eval_metric": "iou", "channel": "627", "ts_data_path": "ts_data/L1_T3_Interval_Discovery_00370.csv", "meta": { "source": "causal_rivers", "args": { "channel": "627", "time": "(Timestamp('2019-11-13 16:00:00'), Timestamp('2020-01-06 16:00:00'))", "threshold": "0.378" } } }, { "id": "L1_T3_Interval_Discovery_00371", "level": 1, "level_name": "Basic Operations", "category": "Atomic Retrieval", "subtask": "Interval Discovery", "question": "Find the longest period where channel 647 remained above 0.653 in 2019-12-08 to 2020-01-29. (Output format: a time interval, e.g., [YYYY-MM-DD HH:MM:SS, YYYY-MM-DD HH:MM:SS])", "answer": "[2020-01-10 03:45:00, 2020-01-13 12:45:00]", "ground_truth": [ "2020-01-10 03:45:00", "2020-01-13 12:45:00" ], "eval_metric": "iou", "channel": "647", "ts_data_path": "ts_data/L1_T3_Interval_Discovery_00371.csv", "meta": { "source": "causal_rivers", "args": { "channel": "647", "time": "(Timestamp('2019-12-08 11:15:00'), Timestamp('2020-01-29 11:15:00'))", "threshold": "0.653" } } }, { "id": "L1_T3_Interval_Discovery_00372", "level": 1, "level_name": "Basic Operations", "category": "Atomic Retrieval", "subtask": "Interval Discovery", "question": "Find the longest period where channel 680 remained above 6.84 in 2023. (Output format: a time interval, e.g., [YYYY-MM-DD HH:MM:SS, YYYY-MM-DD HH:MM:SS])", "answer": "[2023-03-10 08:30:00, 2023-04-05 12:00:00]", "ground_truth": [ "2023-03-10 08:30:00", "2023-04-05 12:00:00" ], "eval_metric": "iou", "channel": "680", "ts_data_path": "ts_data/L1_T3_Interval_Discovery_00372.csv", "meta": { "source": "causal_rivers", "args": { "channel": "680", "time": "2023", "threshold": "6.84" } } }, { "id": "L1_T3_Interval_Discovery_00373", "level": 1, "level_name": "Basic Operations", "category": "Atomic Retrieval", "subtask": "Interval Discovery", "question": "Find the longest period where channel 683 remained above 1.43 in 2020. (Output format: a time interval, e.g., [YYYY-MM-DD HH:MM:SS, YYYY-MM-DD HH:MM:SS])", "answer": "[2020-01-28 16:30:00, 2020-03-26 00:45:00]", "ground_truth": [ "2020-01-28 16:30:00", "2020-03-26 00:45:00" ], "eval_metric": "iou", "channel": "683", "ts_data_path": "ts_data/L1_T3_Interval_Discovery_00373.csv", "meta": { "source": "causal_rivers", "args": { "channel": "683", "time": "2020", "threshold": "1.43" } } }, { "id": "L1_T3_Interval_Discovery_00376", "level": 1, "level_name": "Basic Operations", "category": "Atomic Retrieval", "subtask": "Interval Discovery", "question": "Find the longest period where channel 729 remained above 89.7 in 2022. (Output format: a time interval, e.g., [YYYY-MM-DD HH:MM:SS, YYYY-MM-DD HH:MM:SS])", "answer": "[2022-01-01 00:00:00, 2022-03-10 11:15:00]", "ground_truth": [ "2022-01-01 00:00:00", "2022-03-10 11:15:00" ], "eval_metric": "iou", "channel": "729", "ts_data_path": "ts_data/L1_T3_Interval_Discovery_00376.csv", "meta": { "source": "causal_rivers", "args": { "channel": "729", "time": "2022", "threshold": "89.7" } } }, { "id": "L1_T3_Interval_Discovery_00379", "level": 1, "level_name": "Basic Operations", "category": "Atomic Retrieval", "subtask": "Interval Discovery", "question": "Find the longest period where channel 811 remained above 11.8 in 2019. (Output format: a time interval, e.g., [YYYY-MM-DD HH:MM:SS, YYYY-MM-DD HH:MM:SS])", "answer": "[2019-02-03 09:30:00, 2019-02-21 07:00:00]", "ground_truth": [ "2019-02-03 09:30:00", "2019-02-21 07:00:00" ], "eval_metric": "iou", "channel": "811", "ts_data_path": "ts_data/L1_T3_Interval_Discovery_00379.csv", "meta": { "source": "causal_rivers", "args": { "channel": "811", "time": "2019", "threshold": "11.8" } } }, { "id": "L1_T3_Interval_Discovery_00380", "level": 1, "level_name": "Basic Operations", "category": "Atomic Retrieval", "subtask": "Interval Discovery", "question": "Find the longest period where channel 813 remained above 0.789 in 2020-09-09 to 2020-11-23. (Output format: a time interval, e.g., [YYYY-MM-DD HH:MM:SS, YYYY-MM-DD HH:MM:SS])", "answer": "[2020-11-03 18:45:00, 2020-11-06 12:00:00]", "ground_truth": [ "2020-11-03 18:45:00", "2020-11-06 12:00:00" ], "eval_metric": "iou", "channel": "813", "ts_data_path": "ts_data/L1_T3_Interval_Discovery_00380.csv", "meta": { "source": "causal_rivers", "args": { "channel": "813", "time": "(Timestamp('2020-09-09 09:45:00'), Timestamp('2020-11-23 09:45:00'))", "threshold": "0.789" } } }, { "id": "L1_T3_Interval_Discovery_00382", "level": 1, "level_name": "Basic Operations", "category": "Atomic Retrieval", "subtask": "Interval Discovery", "question": "Find the longest period where channel 891 remained above 3.15 in 2023-04-06 to 2023-06-17. (Output format: a time interval, e.g., [YYYY-MM-DD HH:MM:SS, YYYY-MM-DD HH:MM:SS])", "answer": "[2023-04-06 19:30:00, 2023-04-19 10:30:00]", "ground_truth": [ "2023-04-06 19:30:00", "2023-04-19 10:30:00" ], "eval_metric": "iou", "channel": "891", "ts_data_path": "ts_data/L1_T3_Interval_Discovery_00382.csv", "meta": { "source": "causal_rivers", "args": { "channel": "891", "time": "(Timestamp('2023-04-06 19:30:00'), Timestamp('2023-06-17 19:30:00'))", "threshold": "3.15" } } }, { "id": "L1_T3_Interval_Discovery_00383", "level": 1, "level_name": "Basic Operations", "category": "Atomic Retrieval", "subtask": "Interval Discovery", "question": "Find the longest period where channel 894 remained above 8.34 in 2023. (Output format: a time interval, e.g., [YYYY-MM-DD HH:MM:SS, YYYY-MM-DD HH:MM:SS])", "answer": "[2023-03-10 13:30:00, 2023-04-08 17:15:00]", "ground_truth": [ "2023-03-10 13:30:00", "2023-04-08 17:15:00" ], "eval_metric": "iou", "channel": "894", "ts_data_path": "ts_data/L1_T3_Interval_Discovery_00383.csv", "meta": { "source": "causal_rivers", "args": { "channel": "894", "time": "2023", "threshold": "8.34" } } }, { "id": "L1_T3_Interval_Discovery_00385", "level": 1, "level_name": "Basic Operations", "category": "Atomic Retrieval", "subtask": "Interval Discovery", "question": "Find the longest period where channel 897 remained above 0.019 in 2019. (Output format: a time interval, e.g., [YYYY-MM-DD HH:MM:SS, YYYY-MM-DD HH:MM:SS])", "answer": "[2019-03-06 18:45:00, 2019-03-18 13:45:00]", "ground_truth": [ "2019-03-06 18:45:00", "2019-03-18 13:45:00" ], "eval_metric": "iou", "channel": "897", "ts_data_path": "ts_data/L1_T3_Interval_Discovery_00385.csv", "meta": { "source": "causal_rivers", "args": { "channel": "897", "time": "2019", "threshold": "0.019" } } }, { "id": "L1_T3_Interval_Discovery_00388", "level": 1, "level_name": "Basic Operations", "category": "Atomic Retrieval", "subtask": "Interval Discovery", "question": "Find the longest period where channel HULL remained above 1.474 in 2017-02-12 to 2017-04-13. (Output format: a time interval, e.g., [YYYY-MM-DD HH:MM:SS, YYYY-MM-DD HH:MM:SS])", "answer": "[2017-02-23 22:00:00, 2017-02-24 08:45:00]", "ground_truth": [ "2017-02-23 22:00:00", "2017-02-24 08:45:00" ], "eval_metric": "iou", "channel": "HULL", "ts_data_path": "ts_data/L1_T3_Interval_Discovery_00388.csv", "meta": { "source": "ettm1", "args": { "channel": "HULL", "time": "(Timestamp('2017-02-12 06:45:00'), Timestamp('2017-04-13 06:45:00'))", "threshold": "1.474" } } }, { "id": "L1_T3_Interval_Discovery_00389", "level": 1, "level_name": "Basic Operations", "category": "Atomic Retrieval", "subtask": "Interval Discovery", "question": "Find the longest period where channel MUFL remained above 10.554 in 2016-07-03 to 2016-09-28. (Output format: a time interval, e.g., [YYYY-MM-DD HH:MM:SS, YYYY-MM-DD HH:MM:SS])", "answer": "[2016-07-30 19:30:00, 2016-08-01 07:30:00]", "ground_truth": [ "2016-07-30 19:30:00", "2016-08-01 07:30:00" ], "eval_metric": "iou", "channel": "MUFL", "ts_data_path": "ts_data/L1_T3_Interval_Discovery_00389.csv", "meta": { "source": "ettm1", "args": { "channel": "MUFL", "time": "(Timestamp('2016-07-03 01:15:00'), Timestamp('2016-09-28 01:15:00'))", "threshold": "10.554" } } }, { "id": "L1_T3_Interval_Discovery_00390", "level": 1, "level_name": "Basic Operations", "category": "Atomic Retrieval", "subtask": "Interval Discovery", "question": "Find the longest period where channel MULL remained above 0.142 in 2017-02-22 to 2017-04-19. (Output format: a time interval, e.g., [YYYY-MM-DD HH:MM:SS, YYYY-MM-DD HH:MM:SS])", "answer": "[2017-02-23 21:15:00, 2017-02-24 08:45:00]", "ground_truth": [ "2017-02-23 21:15:00", "2017-02-24 08:45:00" ], "eval_metric": "iou", "channel": "MULL", "ts_data_path": "ts_data/L1_T3_Interval_Discovery_00390.csv", "meta": { "source": "ettm1", "args": { "channel": "MULL", "time": "(Timestamp('2017-02-22 03:00:00'), Timestamp('2017-04-19 03:00:00'))", "threshold": "0.142" } } }, { "id": "L1_T3_Interval_Discovery_00394", "level": 1, "level_name": "Basic Operations", "category": "Atomic Retrieval", "subtask": "Interval Discovery", "question": "Find the longest period where channel m_02 remained above 0.107 in 2020. (Output format: a time interval, e.g., [YYYY-MM-DD HH:MM:SS, YYYY-MM-DD HH:MM:SS])", "answer": "[2020-02-01 17:18:00, 2020-02-02 06:02:00]", "ground_truth": [ "2020-02-01 17:18:00", "2020-02-02 06:02:00" ], "eval_metric": "iou", "channel": "m_02", "ts_data_path": "ts_data/L1_T3_Interval_Discovery_00394.csv", "meta": { "source": "smd_machine_1_1", "args": { "channel": "m_02", "time": "2020", "threshold": "0.107" } } }, { "id": "L1_T3_Interval_Discovery_00397", "level": 1, "level_name": "Basic Operations", "category": "Atomic Retrieval", "subtask": "Interval Discovery", "question": "Find the longest period where channel 67 remained above 0.227 in 2022. (Output format: a time interval, e.g., [YYYY-MM-DD HH:MM:SS, YYYY-MM-DD HH:MM:SS])", "answer": "[2022-11-10 01:30:00, 2022-12-15 04:15:00]", "ground_truth": [ "2022-11-10 01:30:00", "2022-12-15 04:15:00" ], "eval_metric": "iou", "channel": "67", "ts_data_path": "ts_data/L1_T3_Interval_Discovery_00397.csv", "meta": { "source": "causal_rivers", "args": { "channel": "67", "time": "2022", "threshold": "0.227" } } }, { "id": "L1_T3_Interval_Discovery_00398", "level": 1, "level_name": "Basic Operations", "category": "Atomic Retrieval", "subtask": "Interval Discovery", "question": "Find the longest period where channel 71 remained above 0.776 in 2020. (Output format: a time interval, e.g., [YYYY-MM-DD HH:MM:SS, YYYY-MM-DD HH:MM:SS])", "answer": "[2020-02-01 09:15:00, 2020-03-31 00:00:00]", "ground_truth": [ "2020-02-01 09:15:00", "2020-03-31 00:00:00" ], "eval_metric": "iou", "channel": "71", "ts_data_path": "ts_data/L1_T3_Interval_Discovery_00398.csv", "meta": { "source": "causal_rivers", "args": { "channel": "71", "time": "2020", "threshold": "0.776" } } }, { "id": "L1_T3_Interval_Discovery_00399", "level": 1, "level_name": "Basic Operations", "category": "Atomic Retrieval", "subtask": "Interval Discovery", "question": "Find the longest period where channel 99 remained above 0.077 in 2022-05-13 to 2022-07-30. (Output format: a time interval, e.g., [YYYY-MM-DD HH:MM:SS, YYYY-MM-DD HH:MM:SS])", "answer": "[2022-05-20 02:30:00, 2022-05-30 15:45:00]", "ground_truth": [ "2022-05-20 02:30:00", "2022-05-30 15:45:00" ], "eval_metric": "iou", "channel": "99", "ts_data_path": "ts_data/L1_T3_Interval_Discovery_00399.csv", "meta": { "source": "causal_rivers", "args": { "channel": "99", "time": "(Timestamp('2022-05-13 04:30:00'), Timestamp('2022-07-30 04:30:00'))", "threshold": "0.077" } } }, { "id": "L1_T3_Interval_Discovery_00400", "level": 1, "level_name": "Basic Operations", "category": "Atomic Retrieval", "subtask": "Interval Discovery", "question": "Find the longest period where channel 123 remained above 1.25 in 2019-01-29 to 2019-04-20. (Output format: a time interval, e.g., [YYYY-MM-DD HH:MM:SS, YYYY-MM-DD HH:MM:SS])", "answer": "[2019-03-15 08:30:00, 2019-03-20 08:30:00]", "ground_truth": [ "2019-03-15 08:30:00", "2019-03-20 08:30:00" ], "eval_metric": "iou", "channel": "123", "ts_data_path": "ts_data/L1_T3_Interval_Discovery_00400.csv", "meta": { "source": "causal_rivers", "args": { "channel": "123", "time": "(Timestamp('2019-01-29 16:30:00'), Timestamp('2019-04-20 16:30:00'))", "threshold": "1.25" } } }, { "id": "L1_T3_Interval_Discovery_00405", "level": 1, "level_name": "Basic Operations", "category": "Atomic Retrieval", "subtask": "Interval Discovery", "question": "Find the longest period where channel 154 remained above 0.632 in 2021. (Output format: a time interval, e.g., [YYYY-MM-DD HH:MM:SS, YYYY-MM-DD HH:MM:SS])", "answer": "[2021-03-11 07:30:00, 2021-03-25 01:00:00]", "ground_truth": [ "2021-03-11 07:30:00", "2021-03-25 01:00:00" ], "eval_metric": "iou", "channel": "154", "ts_data_path": "ts_data/L1_T3_Interval_Discovery_00405.csv", "meta": { "source": "causal_rivers", "args": { "channel": "154", "time": "2021", "threshold": "0.632" } } }, { "id": "L1_T3_Interval_Discovery_00408", "level": 1, "level_name": "Basic Operations", "category": "Atomic Retrieval", "subtask": "Interval Discovery", "question": "Find the longest period where channel 169 remained above 577.0 in 2021. (Output format: a time interval, e.g., [YYYY-MM-DD HH:MM:SS, YYYY-MM-DD HH:MM:SS])", "answer": "[2021-02-01 06:45:00, 2021-03-17 10:00:00]", "ground_truth": [ "2021-02-01 06:45:00", "2021-03-17 10:00:00" ], "eval_metric": "iou", "channel": "169", "ts_data_path": "ts_data/L1_T3_Interval_Discovery_00408.csv", "meta": { "source": "causal_rivers", "args": { "channel": "169", "time": "2021", "threshold": "577.0" } } }, { "id": "L1_T3_Interval_Discovery_00409", "level": 1, "level_name": "Basic Operations", "category": "Atomic Retrieval", "subtask": "Interval Discovery", "question": "Find the longest period where channel 170 remained above 486.0 in 2022. (Output format: a time interval, e.g., [YYYY-MM-DD HH:MM:SS, YYYY-MM-DD HH:MM:SS])", "answer": "[2022-01-04 08:30:00, 2022-03-13 12:00:00]", "ground_truth": [ "2022-01-04 08:30:00", "2022-03-13 12:00:00" ], "eval_metric": "iou", "channel": "170", "ts_data_path": "ts_data/L1_T3_Interval_Discovery_00409.csv", "meta": { "source": "causal_rivers", "args": { "channel": "170", "time": "2022", "threshold": "486.0" } } }, { "id": "L1_T3_Interval_Discovery_00413", "level": 1, "level_name": "Basic Operations", "category": "Atomic Retrieval", "subtask": "Interval Discovery", "question": "Find the longest period where channel 237 remained above 1.67 in 2022. (Output format: a time interval, e.g., [YYYY-MM-DD HH:MM:SS, YYYY-MM-DD HH:MM:SS])", "answer": "[2022-01-01 00:00:00, 2022-03-08 19:30:00]", "ground_truth": [ "2022-01-01 00:00:00", "2022-03-08 19:30:00" ], "eval_metric": "iou", "channel": "237", "ts_data_path": "ts_data/L1_T3_Interval_Discovery_00413.csv", "meta": { "source": "causal_rivers", "args": { "channel": "237", "time": "2022", "threshold": "1.67" } } }, { "id": "L1_T3_Interval_Discovery_00420", "level": 1, "level_name": "Basic Operations", "category": "Atomic Retrieval", "subtask": "Interval Discovery", "question": "Find the longest period where channel 501 remained above 0.352 in 2022-10-08 to 2022-12-03. (Output format: a time interval, e.g., [YYYY-MM-DD HH:MM:SS, YYYY-MM-DD HH:MM:SS])", "answer": "[2022-11-25 14:00:00, 2022-12-03 17:15:00]", "ground_truth": [ "2022-11-25 14:00:00", "2022-12-03 17:15:00" ], "eval_metric": "iou", "channel": "501", "ts_data_path": "ts_data/L1_T3_Interval_Discovery_00420.csv", "meta": { "source": "causal_rivers", "args": { "channel": "501", "time": "(Timestamp('2022-10-08 17:15:00'), Timestamp('2022-12-03 17:15:00'))", "threshold": "0.352" } } }, { "id": "L1_T3_Interval_Discovery_00421", "level": 1, "level_name": "Basic Operations", "category": "Atomic Retrieval", "subtask": "Interval Discovery", "question": "Find the longest period where channel 578 remained above 1.11 in 2023. (Output format: a time interval, e.g., [YYYY-MM-DD HH:MM:SS, YYYY-MM-DD HH:MM:SS])", "answer": "[2023-12-06 06:00:00, 2023-12-31 23:45:00]", "ground_truth": [ "2023-12-06 06:00:00", "2023-12-31 23:45:00" ], "eval_metric": "iou", "channel": "578", "ts_data_path": "ts_data/L1_T3_Interval_Discovery_00421.csv", "meta": { "source": "causal_rivers", "args": { "channel": "578", "time": "2023", "threshold": "1.11" } } }, { "id": "L1_T3_Interval_Discovery_00422", "level": 1, "level_name": "Basic Operations", "category": "Atomic Retrieval", "subtask": "Interval Discovery", "question": "Find the longest period where channel 580 remained above 0.41 in 2023-02-11 to 2023-04-16. (Output format: a time interval, e.g., [YYYY-MM-DD HH:MM:SS, YYYY-MM-DD HH:MM:SS])", "answer": "[2023-03-10 04:00:00, 2023-03-17 23:15:00]", "ground_truth": [ "2023-03-10 04:00:00", "2023-03-17 23:15:00" ], "eval_metric": "iou", "channel": "580", "ts_data_path": "ts_data/L1_T3_Interval_Discovery_00422.csv", "meta": { "source": "causal_rivers", "args": { "channel": "580", "time": "(Timestamp('2023-02-11 06:15:00'), Timestamp('2023-04-16 06:15:00'))", "threshold": "0.41" } } }, { "id": "L1_T3_Interval_Discovery_00424", "level": 1, "level_name": "Basic Operations", "category": "Atomic Retrieval", "subtask": "Interval Discovery", "question": "Find the longest period where channel 591 remained above 158.0 in 2021-09-29 to 2021-12-21. (Output format: a time interval, e.g., [YYYY-MM-DD HH:MM:SS, YYYY-MM-DD HH:MM:SS])", "answer": "[2021-09-29 19:15:00, 2021-10-04 20:15:00]", "ground_truth": [ "2021-09-29 19:15:00", "2021-10-04 20:15:00" ], "eval_metric": "iou", "channel": "591", "ts_data_path": "ts_data/L1_T3_Interval_Discovery_00424.csv", "meta": { "source": "causal_rivers", "args": { "channel": "591", "time": "(Timestamp('2021-09-29 19:15:00'), Timestamp('2021-12-21 19:15:00'))", "threshold": "158.0" } } }, { "id": "L1_T3_Interval_Discovery_00425", "level": 1, "level_name": "Basic Operations", "category": "Atomic Retrieval", "subtask": "Interval Discovery", "question": "Find the longest period where channel 595 remained above 2.14 in 2020. (Output format: a time interval, e.g., [YYYY-MM-DD HH:MM:SS, YYYY-MM-DD HH:MM:SS])", "answer": "[2020-02-10 10:00:00, 2020-03-27 09:45:00]", "ground_truth": [ "2020-02-10 10:00:00", "2020-03-27 09:45:00" ], "eval_metric": "iou", "channel": "595", "ts_data_path": "ts_data/L1_T3_Interval_Discovery_00425.csv", "meta": { "source": "causal_rivers", "args": { "channel": "595", "time": "2020", "threshold": "2.14" } } }, { "id": "L1_T3_Interval_Discovery_00426", "level": 1, "level_name": "Basic Operations", "category": "Atomic Retrieval", "subtask": "Interval Discovery", "question": "Find the longest period where channel 625 remained above 0.906 in 2022. (Output format: a time interval, e.g., [YYYY-MM-DD HH:MM:SS, YYYY-MM-DD HH:MM:SS])", "answer": "[2022-01-01 00:00:00, 2022-03-07 20:00:00]", "ground_truth": [ "2022-01-01 00:00:00", "2022-03-07 20:00:00" ], "eval_metric": "iou", "channel": "625", "ts_data_path": "ts_data/L1_T3_Interval_Discovery_00426.csv", "meta": { "source": "causal_rivers", "args": { "channel": "625", "time": "2022", "threshold": "0.906" } } }, { "id": "L1_T3_Interval_Discovery_00427", "level": 1, "level_name": "Basic Operations", "category": "Atomic Retrieval", "subtask": "Interval Discovery", "question": "Find the longest period where channel 626 remained above 0.398 in 2022-09-14 to 2022-11-04. (Output format: a time interval, e.g., [YYYY-MM-DD HH:MM:SS, YYYY-MM-DD HH:MM:SS])", "answer": "[2022-10-20 05:30:00, 2022-10-28 10:30:00]", "ground_truth": [ "2022-10-20 05:30:00", "2022-10-28 10:30:00" ], "eval_metric": "iou", "channel": "626", "ts_data_path": "ts_data/L1_T3_Interval_Discovery_00427.csv", "meta": { "source": "causal_rivers", "args": { "channel": "626", "time": "(Timestamp('2022-09-14 06:00:00'), Timestamp('2022-11-04 06:00:00'))", "threshold": "0.398" } } }, { "id": "L1_T3_Interval_Discovery_00429", "level": 1, "level_name": "Basic Operations", "category": "Atomic Retrieval", "subtask": "Interval Discovery", "question": "Find the longest period where channel 647 remained above 1.49 in 2021. (Output format: a time interval, e.g., [YYYY-MM-DD HH:MM:SS, YYYY-MM-DD HH:MM:SS])", "answer": "[2021-02-16 17:30:00, 2021-03-02 20:30:00]", "ground_truth": [ "2021-02-16 17:30:00", "2021-03-02 20:30:00" ], "eval_metric": "iou", "channel": "647", "ts_data_path": "ts_data/L1_T3_Interval_Discovery_00429.csv", "meta": { "source": "causal_rivers", "args": { "channel": "647", "time": "2021", "threshold": "1.49" } } }, { "id": "L1_T3_Interval_Discovery_00436", "level": 1, "level_name": "Basic Operations", "category": "Atomic Retrieval", "subtask": "Interval Discovery", "question": "Find the longest period where channel 762 remained above 0.138 in 2019-03-31 to 2019-05-06. (Output format: a time interval, e.g., [YYYY-MM-DD HH:MM:SS, YYYY-MM-DD HH:MM:SS])", "answer": "[2019-03-31 03:15:00, 2019-04-07 07:45:00]", "ground_truth": [ "2019-03-31 03:15:00", "2019-04-07 07:45:00" ], "eval_metric": "iou", "channel": "762", "ts_data_path": "ts_data/L1_T3_Interval_Discovery_00436.csv", "meta": { "source": "causal_rivers", "args": { "channel": "762", "time": "(Timestamp('2019-03-31 03:15:00'), Timestamp('2019-05-06 03:15:00'))", "threshold": "0.138" } } }, { "id": "L1_T3_Interval_Discovery_00437", "level": 1, "level_name": "Basic Operations", "category": "Atomic Retrieval", "subtask": "Interval Discovery", "question": "Find the longest period where channel 811 remained above 9.64 in 2022. (Output format: a time interval, e.g., [YYYY-MM-DD HH:MM:SS, YYYY-MM-DD HH:MM:SS])", "answer": "[2022-01-01 00:00:00, 2022-03-10 03:15:00]", "ground_truth": [ "2022-01-01 00:00:00", "2022-03-10 03:15:00" ], "eval_metric": "iou", "channel": "811", "ts_data_path": "ts_data/L1_T3_Interval_Discovery_00437.csv", "meta": { "source": "causal_rivers", "args": { "channel": "811", "time": "2022", "threshold": "9.64" } } }, { "id": "L1_T3_Interval_Discovery_00438", "level": 1, "level_name": "Basic Operations", "category": "Atomic Retrieval", "subtask": "Interval Discovery", "question": "Find the longest period where channel 813 remained above 1.75 in 2021-02-11 to 2021-04-25. (Output format: a time interval, e.g., [YYYY-MM-DD HH:MM:SS, YYYY-MM-DD HH:MM:SS])", "answer": "[2021-02-16 12:15:00, 2021-02-19 08:30:00]", "ground_truth": [ "2021-02-16 12:15:00", "2021-02-19 08:30:00" ], "eval_metric": "iou", "channel": "813", "ts_data_path": "ts_data/L1_T3_Interval_Discovery_00438.csv", "meta": { "source": "causal_rivers", "args": { "channel": "813", "time": "(Timestamp('2021-02-11 21:15:00'), Timestamp('2021-04-25 21:15:00'))", "threshold": "1.75" } } }, { "id": "L1_T3_Interval_Discovery_00439", "level": 1, "level_name": "Basic Operations", "category": "Atomic Retrieval", "subtask": "Interval Discovery", "question": "Find the longest period where channel 865 remained above 15.3 in 2023. (Output format: a time interval, e.g., [YYYY-MM-DD HH:MM:SS, YYYY-MM-DD HH:MM:SS])", "answer": "[2023-12-11 19:15:00, 2023-12-31 23:45:00]", "ground_truth": [ "2023-12-11 19:15:00", "2023-12-31 23:45:00" ], "eval_metric": "iou", "channel": "865", "ts_data_path": "ts_data/L1_T3_Interval_Discovery_00439.csv", "meta": { "source": "causal_rivers", "args": { "channel": "865", "time": "2023", "threshold": "15.3" } } }, { "id": "L1_T3_Interval_Discovery_00444", "level": 1, "level_name": "Basic Operations", "category": "Atomic Retrieval", "subtask": "Interval Discovery", "question": "Find the longest period where channel 933 remained above 0.298 in 2019-01-30 to 2019-04-18. (Output format: a time interval, e.g., [YYYY-MM-DD HH:MM:SS, YYYY-MM-DD HH:MM:SS])", "answer": "[2019-03-01 11:30:00, 2019-03-08 02:15:00]", "ground_truth": [ "2019-03-01 11:30:00", "2019-03-08 02:15:00" ], "eval_metric": "iou", "channel": "933", "ts_data_path": "ts_data/L1_T3_Interval_Discovery_00444.csv", "meta": { "source": "causal_rivers", "args": { "channel": "933", "time": "(Timestamp('2019-01-30 10:00:00'), Timestamp('2019-04-18 10:00:00'))", "threshold": "0.298" } } }, { "id": "L1_T3_Interval_Discovery_00445", "level": 1, "level_name": "Basic Operations", "category": "Atomic Retrieval", "subtask": "Interval Discovery", "question": "Find the longest period where channel HUFL remained above 13.195 in 2017-12-05 to 2018-01-08. (Output format: a time interval, e.g., [YYYY-MM-DD HH:MM:SS, YYYY-MM-DD HH:MM:SS])", "answer": "[2017-12-30 23:15:00, 2018-01-01 04:00:00]", "ground_truth": [ "2017-12-30 23:15:00", "2018-01-01 04:00:00" ], "eval_metric": "iou", "channel": "HUFL", "ts_data_path": "ts_data/L1_T3_Interval_Discovery_00445.csv", "meta": { "source": "ettm1", "args": { "channel": "HUFL", "time": "(Timestamp('2017-12-05 02:30:00'), Timestamp('2018-01-08 02:30:00'))", "threshold": "13.195" } } }, { "id": "L1_T3_Interval_Discovery_00448", "level": 1, "level_name": "Basic Operations", "category": "Atomic Retrieval", "subtask": "Interval Discovery", "question": "Find the longest period where channel MULL remained above 2.523 in 2018. (Output format: a time interval, e.g., [YYYY-MM-DD HH:MM:SS, YYYY-MM-DD HH:MM:SS])", "answer": "[2018-03-30 22:45:00, 2018-04-01 06:30:00]", "ground_truth": [ "2018-03-30 22:45:00", "2018-04-01 06:30:00" ], "eval_metric": "iou", "channel": "MULL", "ts_data_path": "ts_data/L1_T3_Interval_Discovery_00448.csv", "meta": { "source": "ettm1", "args": { "channel": "MULL", "time": "2018", "threshold": "2.523" } } }, { "id": "L1_T3_Interval_Discovery_00463", "level": 1, "level_name": "Basic Operations", "category": "Atomic Retrieval", "subtask": "Interval Discovery", "question": "Find the longest period where channel 151 remained above 0.562 in 2021-06-10 to 2021-08-25. (Output format: a time interval, e.g., [YYYY-MM-DD HH:MM:SS, YYYY-MM-DD HH:MM:SS])", "answer": "[2021-07-07 07:00:00, 2021-07-15 14:30:00]", "ground_truth": [ "2021-07-07 07:00:00", "2021-07-15 14:30:00" ], "eval_metric": "iou", "channel": "151", "ts_data_path": "ts_data/L1_T3_Interval_Discovery_00463.csv", "meta": { "source": "causal_rivers", "args": { "channel": "151", "time": "(Timestamp('2021-06-10 00:00:00'), Timestamp('2021-08-25 00:00:00'))", "threshold": "0.562" } } }, { "id": "L1_T3_Interval_Discovery_00465", "level": 1, "level_name": "Basic Operations", "category": "Atomic Retrieval", "subtask": "Interval Discovery", "question": "Find the longest period where channel 155 remained above 0.248 in 2020-10-28 to 2020-12-29. (Output format: a time interval, e.g., [YYYY-MM-DD HH:MM:SS, YYYY-MM-DD HH:MM:SS])", "answer": "[2020-12-21 05:00:00, 2020-12-29 21:15:00]", "ground_truth": [ "2020-12-21 05:00:00", "2020-12-29 21:15:00" ], "eval_metric": "iou", "channel": "155", "ts_data_path": "ts_data/L1_T3_Interval_Discovery_00465.csv", "meta": { "source": "causal_rivers", "args": { "channel": "155", "time": "(Timestamp('2020-10-28 21:15:00'), Timestamp('2020-12-29 21:15:00'))", "threshold": "0.248" } } }, { "id": "L1_T3_Interval_Discovery_00467", "level": 1, "level_name": "Basic Operations", "category": "Atomic Retrieval", "subtask": "Interval Discovery", "question": "Find the longest period where channel 169 remained above 521.0 in 2019. (Output format: a time interval, e.g., [YYYY-MM-DD HH:MM:SS, YYYY-MM-DD HH:MM:SS])", "answer": "[2019-02-25 00:00:00, 2019-04-07 01:15:00]", "ground_truth": [ "2019-02-25 00:00:00", "2019-04-07 01:15:00" ], "eval_metric": "iou", "channel": "169", "ts_data_path": "ts_data/L1_T3_Interval_Discovery_00467.csv", "meta": { "source": "causal_rivers", "args": { "channel": "169", "time": "2019", "threshold": "521.0" } } }, { "id": "L1_T3_Interval_Discovery_00469", "level": 1, "level_name": "Basic Operations", "category": "Atomic Retrieval", "subtask": "Interval Discovery", "question": "Find the longest period where channel 172 remained above 628.0 in 2019-02-25 to 2019-05-07. (Output format: a time interval, e.g., [YYYY-MM-DD HH:MM:SS, YYYY-MM-DD HH:MM:SS])", "answer": "[2019-03-13 00:15:00, 2019-03-26 23:30:00]", "ground_truth": [ "2019-03-13 00:15:00", "2019-03-26 23:30:00" ], "eval_metric": "iou", "channel": "172", "ts_data_path": "ts_data/L1_T3_Interval_Discovery_00469.csv", "meta": { "source": "causal_rivers", "args": { "channel": "172", "time": "(Timestamp('2019-02-25 23:15:00'), Timestamp('2019-05-07 23:15:00'))", "threshold": "628.0" } } }, { "id": "L1_T3_Interval_Discovery_00470", "level": 1, "level_name": "Basic Operations", "category": "Atomic Retrieval", "subtask": "Interval Discovery", "question": "Find the longest period where channel 173 remained above 344.0 in 2019. (Output format: a time interval, e.g., [YYYY-MM-DD HH:MM:SS, YYYY-MM-DD HH:MM:SS])", "answer": "[2019-03-02 13:15:00, 2019-04-08 04:30:00]", "ground_truth": [ "2019-03-02 13:15:00", "2019-04-08 04:30:00" ], "eval_metric": "iou", "channel": "173", "ts_data_path": "ts_data/L1_T3_Interval_Discovery_00470.csv", "meta": { "source": "causal_rivers", "args": { "channel": "173", "time": "2019", "threshold": "344.0" } } }, { "id": "L1_T3_Interval_Discovery_00472", "level": 1, "level_name": "Basic Operations", "category": "Atomic Retrieval", "subtask": "Interval Discovery", "question": "Find the longest period where channel 237 remained above 1.21 in 2020. (Output format: a time interval, e.g., [YYYY-MM-DD HH:MM:SS, YYYY-MM-DD HH:MM:SS])", "answer": "[2020-02-22 00:15:00, 2020-03-27 10:00:00]", "ground_truth": [ "2020-02-22 00:15:00", "2020-03-27 10:00:00" ], "eval_metric": "iou", "channel": "237", "ts_data_path": "ts_data/L1_T3_Interval_Discovery_00472.csv", "meta": { "source": "causal_rivers", "args": { "channel": "237", "time": "2020", "threshold": "1.21" } } }, { "id": "L1_T3_Interval_Discovery_00474", "level": 1, "level_name": "Basic Operations", "category": "Atomic Retrieval", "subtask": "Interval Discovery", "question": "Find the longest period where channel 312 remained above 0.1 in 2020-07-25 to 2020-10-02. (Output format: a time interval, e.g., [YYYY-MM-DD HH:MM:SS, YYYY-MM-DD HH:MM:SS])", "answer": "[2020-07-25 00:45:00, 2020-08-01 12:45:00]", "ground_truth": [ "2020-07-25 00:45:00", "2020-08-01 12:45:00" ], "eval_metric": "iou", "channel": "312", "ts_data_path": "ts_data/L1_T3_Interval_Discovery_00474.csv", "meta": { "source": "causal_rivers", "args": { "channel": "312", "time": "(Timestamp('2020-07-25 00:45:00'), Timestamp('2020-10-02 00:45:00'))", "threshold": "0.1" } } }, { "id": "L1_T3_Interval_Discovery_00478", "level": 1, "level_name": "Basic Operations", "category": "Atomic Retrieval", "subtask": "Interval Discovery", "question": "Find the longest period where channel 496 remained above 0.264 in 2023-07-29 to 2023-10-24. (Output format: a time interval, e.g., [YYYY-MM-DD HH:MM:SS, YYYY-MM-DD HH:MM:SS])", "answer": "[2023-08-14 11:15:00, 2023-08-16 17:00:00]", "ground_truth": [ "2023-08-14 11:15:00", "2023-08-16 17:00:00" ], "eval_metric": "iou", "channel": "496", "ts_data_path": "ts_data/L1_T3_Interval_Discovery_00478.csv", "meta": { "source": "causal_rivers", "args": { "channel": "496", "time": "(Timestamp('2023-07-29 23:15:00'), Timestamp('2023-10-24 23:15:00'))", "threshold": "0.264" } } }, { "id": "L1_T3_Interval_Discovery_00481", "level": 1, "level_name": "Basic Operations", "category": "Atomic Retrieval", "subtask": "Interval Discovery", "question": "Find the longest period where channel 580 remained above 0.404 in 2019-02-08 to 2019-04-06. (Output format: a time interval, e.g., [YYYY-MM-DD HH:MM:SS, YYYY-MM-DD HH:MM:SS])", "answer": "[2019-03-14 13:45:00, 2019-03-19 00:45:00]", "ground_truth": [ "2019-03-14 13:45:00", "2019-03-19 00:45:00" ], "eval_metric": "iou", "channel": "580", "ts_data_path": "ts_data/L1_T3_Interval_Discovery_00481.csv", "meta": { "source": "causal_rivers", "args": { "channel": "580", "time": "(Timestamp('2019-02-08 18:00:00'), Timestamp('2019-04-06 18:00:00'))", "threshold": "0.404" } } }, { "id": "L1_T3_Interval_Discovery_00482", "level": 1, "level_name": "Basic Operations", "category": "Atomic Retrieval", "subtask": "Interval Discovery", "question": "Find the longest period where channel 589 remained above 484.0 in 2020. (Output format: a time interval, e.g., [YYYY-MM-DD HH:MM:SS, YYYY-MM-DD HH:MM:SS])", "answer": "[2020-10-18 00:15:00, 2020-11-30 08:15:00]", "ground_truth": [ "2020-10-18 00:15:00", "2020-11-30 08:15:00" ], "eval_metric": "iou", "channel": "589", "ts_data_path": "ts_data/L1_T3_Interval_Discovery_00482.csv", "meta": { "source": "causal_rivers", "args": { "channel": "589", "time": "2020", "threshold": "484.0" } } }, { "id": "L1_T3_Interval_Discovery_00486", "level": 1, "level_name": "Basic Operations", "category": "Atomic Retrieval", "subtask": "Interval Discovery", "question": "Find the longest period where channel 626 remained above 1.19 in 2021. (Output format: a time interval, e.g., [YYYY-MM-DD HH:MM:SS, YYYY-MM-DD HH:MM:SS])", "answer": "[2021-02-02 02:30:00, 2021-03-04 13:30:00]", "ground_truth": [ "2021-02-02 02:30:00", "2021-03-04 13:30:00" ], "eval_metric": "iou", "channel": "626", "ts_data_path": "ts_data/L1_T3_Interval_Discovery_00486.csv", "meta": { "source": "causal_rivers", "args": { "channel": "626", "time": "2021", "threshold": "1.19" } } }, { "id": "L1_T3_Interval_Discovery_00488", "level": 1, "level_name": "Basic Operations", "category": "Atomic Retrieval", "subtask": "Interval Discovery", "question": "Find the longest period where channel 647 remained above 0.249 in 2020-05-26 to 2020-07-03. (Output format: a time interval, e.g., [YYYY-MM-DD HH:MM:SS, YYYY-MM-DD HH:MM:SS])", "answer": "[2020-05-26 12:45:00, 2020-05-28 22:15:00]", "ground_truth": [ "2020-05-26 12:45:00", "2020-05-28 22:15:00" ], "eval_metric": "iou", "channel": "647", "ts_data_path": "ts_data/L1_T3_Interval_Discovery_00488.csv", "meta": { "source": "causal_rivers", "args": { "channel": "647", "time": "(Timestamp('2020-05-26 12:45:00'), Timestamp('2020-07-03 12:45:00'))", "threshold": "0.249" } } }, { "id": "L1_T3_Interval_Discovery_00489", "level": 1, "level_name": "Basic Operations", "category": "Atomic Retrieval", "subtask": "Interval Discovery", "question": "Find the longest period where channel 680 remained above 4.48 in 2022. (Output format: a time interval, e.g., [YYYY-MM-DD HH:MM:SS, YYYY-MM-DD HH:MM:SS])", "answer": "[2022-01-01 00:00:00, 2022-03-11 14:00:00]", "ground_truth": [ "2022-01-01 00:00:00", "2022-03-11 14:00:00" ], "eval_metric": "iou", "channel": "680", "ts_data_path": "ts_data/L1_T3_Interval_Discovery_00489.csv", "meta": { "source": "causal_rivers", "args": { "channel": "680", "time": "2022", "threshold": "4.48" } } }, { "id": "L1_T3_Interval_Discovery_00492", "level": 1, "level_name": "Basic Operations", "category": "Atomic Retrieval", "subtask": "Interval Discovery", "question": "Find the longest period where channel 728 remained above 73.5 in 2023-04-30 to 2023-06-08. (Output format: a time interval, e.g., [YYYY-MM-DD HH:MM:SS, YYYY-MM-DD HH:MM:SS])", "answer": "[2023-04-30 11:30:00, 2023-05-05 15:30:00]", "ground_truth": [ "2023-04-30 11:30:00", "2023-05-05 15:30:00" ], "eval_metric": "iou", "channel": "728", "ts_data_path": "ts_data/L1_T3_Interval_Discovery_00492.csv", "meta": { "source": "causal_rivers", "args": { "channel": "728", "time": "(Timestamp('2023-04-30 11:30:00'), Timestamp('2023-06-08 11:30:00'))", "threshold": "73.5" } } }, { "id": "L1_T3_Interval_Discovery_00494", "level": 1, "level_name": "Basic Operations", "category": "Atomic Retrieval", "subtask": "Interval Discovery", "question": "Find the longest period where channel 754 remained above 0.203 in 2019-09-06 to 2019-11-05. (Output format: a time interval, e.g., [YYYY-MM-DD HH:MM:SS, YYYY-MM-DD HH:MM:SS])", "answer": "[2019-10-13 13:45:00, 2019-10-17 08:15:00]", "ground_truth": [ "2019-10-13 13:45:00", "2019-10-17 08:15:00" ], "eval_metric": "iou", "channel": "754", "ts_data_path": "ts_data/L1_T3_Interval_Discovery_00494.csv", "meta": { "source": "causal_rivers", "args": { "channel": "754", "time": "(Timestamp('2019-09-06 04:00:00'), Timestamp('2019-11-05 04:00:00'))", "threshold": "0.203" } } }, { "id": "L1_T3_Interval_Discovery_00495", "level": 1, "level_name": "Basic Operations", "category": "Atomic Retrieval", "subtask": "Interval Discovery", "question": "Find the longest period where channel 762 remained above 0.199 in 2021. (Output format: a time interval, e.g., [YYYY-MM-DD HH:MM:SS, YYYY-MM-DD HH:MM:SS])", "answer": "[2021-01-11 16:45:00, 2021-01-31 05:30:00]", "ground_truth": [ "2021-01-11 16:45:00", "2021-01-31 05:30:00" ], "eval_metric": "iou", "channel": "762", "ts_data_path": "ts_data/L1_T3_Interval_Discovery_00495.csv", "meta": { "source": "causal_rivers", "args": { "channel": "762", "time": "2021", "threshold": "0.199" } } }, { "id": "L1_T3_Interval_Discovery_00498", "level": 1, "level_name": "Basic Operations", "category": "Atomic Retrieval", "subtask": "Interval Discovery", "question": "Find the longest period where channel 865 remained above 13.0 in 2020. (Output format: a time interval, e.g., [YYYY-MM-DD HH:MM:SS, YYYY-MM-DD HH:MM:SS])", "answer": "[2020-01-01 00:00:00, 2020-01-18 20:45:00]", "ground_truth": [ "2020-01-01 00:00:00", "2020-01-18 20:45:00" ], "eval_metric": "iou", "channel": "865", "ts_data_path": "ts_data/L1_T3_Interval_Discovery_00498.csv", "meta": { "source": "causal_rivers", "args": { "channel": "865", "time": "2020", "threshold": "13.0" } } }, { "id": "L1_T3_Interval_Discovery_00499", "level": 1, "level_name": "Basic Operations", "category": "Atomic Retrieval", "subtask": "Interval Discovery", "question": "Find the longest period where channel 891 remained above 4.93 in 2021. (Output format: a time interval, e.g., [YYYY-MM-DD HH:MM:SS, YYYY-MM-DD HH:MM:SS])", "answer": "[2021-11-04 13:45:00, 2021-11-17 11:30:00]", "ground_truth": [ "2021-11-04 13:45:00", "2021-11-17 11:30:00" ], "eval_metric": "iou", "channel": "891", "ts_data_path": "ts_data/L1_T3_Interval_Discovery_00499.csv", "meta": { "source": "causal_rivers", "args": { "channel": "891", "time": "2021", "threshold": "4.93" } } }, { "id": "L1_T3_Interval_Discovery_00502", "level": 1, "level_name": "Basic Operations", "category": "Atomic Retrieval", "subtask": "Interval Discovery", "question": "Find the longest period where channel 897 remained above 0.028 in 2022-10-11 to 2022-12-23. (Output format: a time interval, e.g., [YYYY-MM-DD HH:MM:SS, YYYY-MM-DD HH:MM:SS])", "answer": "[2022-12-05 09:45:00, 2022-12-10 06:15:00]", "ground_truth": [ "2022-12-05 09:45:00", "2022-12-10 06:15:00" ], "eval_metric": "iou", "channel": "897", "ts_data_path": "ts_data/L1_T3_Interval_Discovery_00502.csv", "meta": { "source": "causal_rivers", "args": { "channel": "897", "time": "(Timestamp('2022-10-11 23:00:00'), Timestamp('2022-12-23 23:00:00'))", "threshold": "0.028" } } }, { "id": "L1_T3_Interval_Discovery_00506", "level": 1, "level_name": "Basic Operations", "category": "Atomic Retrieval", "subtask": "Interval Discovery", "question": "Find the longest period where channel MUFL remained above 8.528 in 2017. (Output format: a time interval, e.g., [YYYY-MM-DD HH:MM:SS, YYYY-MM-DD HH:MM:SS])", "answer": "[2017-07-30 19:30:00, 2017-08-01 04:15:00]", "ground_truth": [ "2017-07-30 19:30:00", "2017-08-01 04:15:00" ], "eval_metric": "iou", "channel": "MUFL", "ts_data_path": "ts_data/L1_T3_Interval_Discovery_00506.csv", "meta": { "source": "ettm1", "args": { "channel": "MUFL", "time": "2017", "threshold": "8.528" } } }, { "id": "L1_T3_Interval_Discovery_00509", "level": 1, "level_name": "Basic Operations", "category": "Atomic Retrieval", "subtask": "Interval Discovery", "question": "Find the longest period where channel LULL remained above 1.34 in 2018. (Output format: a time interval, e.g., [YYYY-MM-DD HH:MM:SS, YYYY-MM-DD HH:MM:SS])", "answer": "[2018-05-30 23:15:00, 2018-06-01 12:15:00]", "ground_truth": [ "2018-05-30 23:15:00", "2018-06-01 12:15:00" ], "eval_metric": "iou", "channel": "LULL", "ts_data_path": "ts_data/L1_T3_Interval_Discovery_00509.csv", "meta": { "source": "ettm1", "args": { "channel": "LULL", "time": "2018", "threshold": "1.34" } } }, { "id": "L1_T3_Interval_Discovery_00511", "level": 1, "level_name": "Basic Operations", "category": "Atomic Retrieval", "subtask": "Interval Discovery", "question": "Find the longest period where channel m_06 remained above 0.301 in 2020-01-01 to 2020-03-29. (Output format: a time interval, e.g., [YYYY-MM-DD HH:MM:SS, YYYY-MM-DD HH:MM:SS])", "answer": "[2020-01-04 07:36:00, 2020-01-07 15:08:00]", "ground_truth": [ "2020-01-04 07:36:00", "2020-01-07 15:08:00" ], "eval_metric": "iou", "channel": "m_06", "ts_data_path": "ts_data/L1_T3_Interval_Discovery_00511.csv", "meta": { "source": "smd_machine_1_1", "args": { "channel": "m_06", "time": "(Timestamp('2020-01-01 00:00:00'), Timestamp('2020-03-29 00:00:00'))", "threshold": "0.301" } } }, { "id": "L1_T2_Temporal_Localization_00173", "level": 1, "level_name": "Basic Operations", "category": "Atomic Retrieval", "subtask": "Temporal Localization", "question": "At what exact timestamp did channel 99 reach its maximum value in 2020? (Output format: a specific timestamp, e.g., YYYY-MM-DD HH:MM:SS)", "answer": "2020-07-13 15:15:00", "ground_truth": "2020-07-13 15:15:00", "eval_metric": "hit", "channel": "99", "ts_data_path": "ts_data/L1_T2_Temporal_Localization_00173.csv", "meta": { "source": "causal_rivers", "args": { "channel": "99", "time": "2020", "threshold_high": "0.124", "threshold_low": "0.06", "action": "reach its maximum value" } } }, { "id": "L1_T2_Temporal_Localization_00174", "level": 1, "level_name": "Basic Operations", "category": "Atomic Retrieval", "subtask": "Temporal Localization", "question": "At what exact timestamp did channel 123 reach its minimum value in 2019? (Output format: a specific timestamp, e.g., YYYY-MM-DD HH:MM:SS)", "answer": "2019-08-28 08:00:00", "ground_truth": "2019-08-28 08:00:00", "eval_metric": "hit", "channel": "123", "ts_data_path": "ts_data/L1_T2_Temporal_Localization_00174.csv", "meta": { "source": "causal_rivers", "args": { "channel": "123", "time": "2019", "threshold_high": "1.04", "threshold_low": "0.205", "action": "reach its minimum value" } } }, { "id": "L1_T2_Temporal_Localization_00177", "level": 1, "level_name": "Basic Operations", "category": "Atomic Retrieval", "subtask": "Temporal Localization", "question": "At what exact timestamp did channel 147 reach its maximum value in 2020? (Output format: a specific timestamp, e.g., YYYY-MM-DD HH:MM:SS)", "answer": "2020-02-25 05:45:00", "ground_truth": "2020-02-25 05:45:00", "eval_metric": "hit", "channel": "147", "ts_data_path": "ts_data/L1_T2_Temporal_Localization_00177.csv", "meta": { "source": "causal_rivers", "args": { "channel": "147", "time": "2020", "threshold_high": "0.17", "threshold_low": "0.066", "action": "reach its maximum value" } } }, { "id": "L1_T2_Temporal_Localization_00182", "level": 1, "level_name": "Basic Operations", "category": "Atomic Retrieval", "subtask": "Temporal Localization", "question": "At what exact timestamp did channel 169 reach its minimum value in 2020? (Output format: a specific timestamp, e.g., YYYY-MM-DD HH:MM:SS)", "answer": "2020-08-06 05:45:00", "ground_truth": "2020-08-06 05:45:00", "eval_metric": "hit", "channel": "169", "ts_data_path": "ts_data/L1_T2_Temporal_Localization_00182.csv", "meta": { "source": "causal_rivers", "args": { "channel": "169", "time": "2020", "threshold_high": "476.0", "threshold_low": "213.0", "action": "reach its minimum value" } } }, { "id": "L1_T2_Temporal_Localization_00184", "level": 1, "level_name": "Basic Operations", "category": "Atomic Retrieval", "subtask": "Temporal Localization", "question": "At what exact timestamp did channel 172 reach its minimum value in 2020-03? (Output format: a specific timestamp, e.g., YYYY-MM-DD HH:MM:SS)", "answer": "2020-03-31 22:00:00", "ground_truth": "2020-03-31 22:00:00", "eval_metric": "hit", "channel": "172", "ts_data_path": "ts_data/L1_T2_Temporal_Localization_00184.csv", "meta": { "source": "causal_rivers", "args": { "channel": "172", "time": "2020-03", "threshold_high": "534.0", "threshold_low": "358.0", "action": "reach its minimum value" } } }, { "id": "L1_T2_Temporal_Localization_00188", "level": 1, "level_name": "Basic Operations", "category": "Atomic Retrieval", "subtask": "Temporal Localization", "question": "At what exact timestamp did channel 245 last fall below 0.546 in 2019-12? (Output format: a specific timestamp, e.g., YYYY-MM-DD HH:MM:SS)", "answer": "2019-12-06 18:00:00", "ground_truth": "2019-12-06 18:00:00", "eval_metric": "hit", "channel": "245", "ts_data_path": "ts_data/L1_T2_Temporal_Localization_00188.csv", "meta": { "source": "causal_rivers", "args": { "channel": "245", "time": "2019-12", "threshold_high": "0.679", "threshold_low": "0.546", "action": "last fall below 0.546" } } }, { "id": "L1_T2_Temporal_Localization_00191", "level": 1, "level_name": "Basic Operations", "category": "Atomic Retrieval", "subtask": "Temporal Localization", "question": "At what exact timestamp did channel 441 last fall below 6.07 in 2022-07? (Output format: a specific timestamp, e.g., YYYY-MM-DD HH:MM:SS)", "answer": "2022-07-30 16:45:00", "ground_truth": "2022-07-30 16:45:00", "eval_metric": "hit", "channel": "441", "ts_data_path": "ts_data/L1_T2_Temporal_Localization_00191.csv", "meta": { "source": "causal_rivers", "args": { "channel": "441", "time": "2022-07", "threshold_high": "9.05", "threshold_low": "6.07", "action": "last fall below 6.07" } } }, { "id": "L1_T2_Temporal_Localization_00192", "level": 1, "level_name": "Basic Operations", "category": "Atomic Retrieval", "subtask": "Temporal Localization", "question": "At what exact timestamp did channel 495 reach its minimum value in 2022-08? (Output format: a specific timestamp, e.g., YYYY-MM-DD HH:MM:SS)", "answer": "2022-08-01 00:00:00", "ground_truth": "2022-08-01 00:00:00", "eval_metric": "hit", "channel": "495", "ts_data_path": "ts_data/L1_T2_Temporal_Localization_00192.csv", "meta": { "source": "causal_rivers", "args": { "channel": "495", "time": "2022-08", "threshold_high": "0.0", "threshold_low": "0.0", "action": "reach its minimum value" } } }, { "id": "L1_T2_Temporal_Localization_00194", "level": 1, "level_name": "Basic Operations", "category": "Atomic Retrieval", "subtask": "Temporal Localization", "question": "At what exact timestamp did channel 501 reach its minimum value in 2020-01? (Output format: a specific timestamp, e.g., YYYY-MM-DD HH:MM:SS)", "answer": "2020-01-02 02:30:00", "ground_truth": "2020-01-02 02:30:00", "eval_metric": "hit", "channel": "501", "ts_data_path": "ts_data/L1_T2_Temporal_Localization_00194.csv", "meta": { "source": "causal_rivers", "args": { "channel": "501", "time": "2020-01", "threshold_high": "1.13", "threshold_low": "0.87", "action": "reach its minimum value" } } }, { "id": "L1_T2_Temporal_Localization_00200", "level": 1, "level_name": "Basic Operations", "category": "Atomic Retrieval", "subtask": "Temporal Localization", "question": "At what exact timestamp did channel 625 first rise above 2.86 in 2023-12? (Output format: a specific timestamp, e.g., YYYY-MM-DD HH:MM:SS)", "answer": "2023-12-25 07:00:00", "ground_truth": "2023-12-25 07:00:00", "eval_metric": "hit", "channel": "625", "ts_data_path": "ts_data/L1_T2_Temporal_Localization_00200.csv", "meta": { "source": "causal_rivers", "args": { "channel": "625", "time": "2023-12", "threshold_high": "2.86", "threshold_low": "1.17", "action": "first rise above 2.86" } } }, { "id": "L1_T2_Temporal_Localization_00202", "level": 1, "level_name": "Basic Operations", "category": "Atomic Retrieval", "subtask": "Temporal Localization", "question": "At what exact timestamp did channel 627 first rise above 0.478 in 2019? (Output format: a specific timestamp, e.g., YYYY-MM-DD HH:MM:SS)", "answer": "2019-01-01 00:15:00", "ground_truth": "2019-01-01 00:15:00", "eval_metric": "hit", "channel": "627", "ts_data_path": "ts_data/L1_T2_Temporal_Localization_00202.csv", "meta": { "source": "causal_rivers", "args": { "channel": "627", "time": "2019", "threshold_high": "0.478", "threshold_low": "0.266", "action": "first rise above 0.478" } } }, { "id": "L1_T2_Temporal_Localization_00204", "level": 1, "level_name": "Basic Operations", "category": "Atomic Retrieval", "subtask": "Temporal Localization", "question": "At what exact timestamp did channel 680 last fall below 4.32 in 2023-04? (Output format: a specific timestamp, e.g., YYYY-MM-DD HH:MM:SS)", "answer": "2023-04-30 23:45:00", "ground_truth": "2023-04-30 23:45:00", "eval_metric": "hit", "channel": "680", "ts_data_path": "ts_data/L1_T2_Temporal_Localization_00204.csv", "meta": { "source": "causal_rivers", "args": { "channel": "680", "time": "2023-04", "threshold_high": "6.1", "threshold_low": "4.32", "action": "last fall below 4.32" } } }, { "id": "L1_T2_Temporal_Localization_00209", "level": 1, "level_name": "Basic Operations", "category": "Atomic Retrieval", "subtask": "Temporal Localization", "question": "At what exact timestamp did channel 754 last fall below 0.093 in 2023? (Output format: a specific timestamp, e.g., YYYY-MM-DD HH:MM:SS)", "answer": "2023-10-12 00:00:00", "ground_truth": "2023-10-12 00:00:00", "eval_metric": "hit", "channel": "754", "ts_data_path": "ts_data/L1_T2_Temporal_Localization_00209.csv", "meta": { "source": "causal_rivers", "args": { "channel": "754", "time": "2023", "threshold_high": "0.338", "threshold_low": "0.093", "action": "last fall below 0.093" } } }, { "id": "L1_T2_Temporal_Localization_00210", "level": 1, "level_name": "Basic Operations", "category": "Atomic Retrieval", "subtask": "Temporal Localization", "question": "At what exact timestamp did channel 762 first rise above 0.691 in 2023? (Output format: a specific timestamp, e.g., YYYY-MM-DD HH:MM:SS)", "answer": "2023-10-08 03:45:00", "ground_truth": "2023-10-08 03:45:00", "eval_metric": "hit", "channel": "762", "ts_data_path": "ts_data/L1_T2_Temporal_Localization_00210.csv", "meta": { "source": "causal_rivers", "args": { "channel": "762", "time": "2023", "threshold_high": "0.691", "threshold_low": "0.075", "action": "first rise above 0.691" } } }, { "id": "L1_T2_Temporal_Localization_00211", "level": 1, "level_name": "Basic Operations", "category": "Atomic Retrieval", "subtask": "Temporal Localization", "question": "At what exact timestamp did channel 811 last fall below 13.7 in 2023-03? (Output format: a specific timestamp, e.g., YYYY-MM-DD HH:MM:SS)", "answer": "2023-03-08 18:15:00", "ground_truth": "2023-03-08 18:15:00", "eval_metric": "hit", "channel": "811", "ts_data_path": "ts_data/L1_T2_Temporal_Localization_00211.csv", "meta": { "source": "causal_rivers", "args": { "channel": "811", "time": "2023-03", "threshold_high": "30.6", "threshold_low": "13.7", "action": "last fall below 13.7" } } }, { "id": "L1_T2_Temporal_Localization_00213", "level": 1, "level_name": "Basic Operations", "category": "Atomic Retrieval", "subtask": "Temporal Localization", "question": "At what exact timestamp did channel 865 reach its minimum value in 2023-12? (Output format: a specific timestamp, e.g., YYYY-MM-DD HH:MM:SS)", "answer": "2023-12-02 21:30:00", "ground_truth": "2023-12-02 21:30:00", "eval_metric": "hit", "channel": "865", "ts_data_path": "ts_data/L1_T2_Temporal_Localization_00213.csv", "meta": { "source": "causal_rivers", "args": { "channel": "865", "time": "2023-12", "threshold_high": "20.1", "threshold_low": "13.7", "action": "reach its minimum value" } } }, { "id": "L1_T2_Temporal_Localization_00214", "level": 1, "level_name": "Basic Operations", "category": "Atomic Retrieval", "subtask": "Temporal Localization", "question": "At what exact timestamp did channel 891 reach its minimum value in 2021-01? (Output format: a specific timestamp, e.g., YYYY-MM-DD HH:MM:SS)", "answer": "2021-01-27 11:30:00", "ground_truth": "2021-01-27 11:30:00", "eval_metric": "hit", "channel": "891", "ts_data_path": "ts_data/L1_T2_Temporal_Localization_00214.csv", "meta": { "source": "causal_rivers", "args": { "channel": "891", "time": "2021-01", "threshold_high": "5.61", "threshold_low": "4.05", "action": "reach its minimum value" } } }, { "id": "L1_T2_Temporal_Localization_00217", "level": 1, "level_name": "Basic Operations", "category": "Atomic Retrieval", "subtask": "Temporal Localization", "question": "At what exact timestamp did channel 897 reach its minimum value in 2022? (Output format: a specific timestamp, e.g., YYYY-MM-DD HH:MM:SS)", "answer": "2022-08-16 19:30:00", "ground_truth": "2022-08-16 19:30:00", "eval_metric": "hit", "channel": "897", "ts_data_path": "ts_data/L1_T2_Temporal_Localization_00217.csv", "meta": { "source": "causal_rivers", "args": { "channel": "897", "time": "2022", "threshold_high": "0.126", "threshold_low": "0.023", "action": "reach its minimum value" } } }, { "id": "L1_T2_Temporal_Localization_00218", "level": 1, "level_name": "Basic Operations", "category": "Atomic Retrieval", "subtask": "Temporal Localization", "question": "At what exact timestamp did channel 933 reach its maximum value in 2019? (Output format: a specific timestamp, e.g., YYYY-MM-DD HH:MM:SS)", "answer": "2019-11-08 15:45:00", "ground_truth": "2019-11-08 15:45:00", "eval_metric": "hit", "channel": "933", "ts_data_path": "ts_data/L1_T2_Temporal_Localization_00218.csv", "meta": { "source": "causal_rivers", "args": { "channel": "933", "time": "2019", "threshold_high": "0.181", "threshold_low": "0.024", "action": "reach its maximum value" } } }, { "id": "L1_T2_Temporal_Localization_00220", "level": 1, "level_name": "Basic Operations", "category": "Atomic Retrieval", "subtask": "Temporal Localization", "question": "At what exact timestamp did channel HULL first rise above 4.287 in 2016-11? (Output format: a specific timestamp, e.g., YYYY-MM-DD HH:MM:SS)", "answer": "2016-11-02 23:15:00", "ground_truth": "2016-11-02 23:15:00", "eval_metric": "hit", "channel": "HULL", "ts_data_path": "ts_data/L1_T2_Temporal_Localization_00220.csv", "meta": { "source": "ettm1", "args": { "channel": "HULL", "time": "2016-11", "threshold_high": "4.287", "threshold_low": "1.34", "action": "first rise above 4.287" } } }, { "id": "L1_T2_Temporal_Localization_00222", "level": 1, "level_name": "Basic Operations", "category": "Atomic Retrieval", "subtask": "Temporal Localization", "question": "At what exact timestamp did channel MULL last fall below -1.528 in 2017? (Output format: a specific timestamp, e.g., YYYY-MM-DD HH:MM:SS)", "answer": "2017-10-22 20:45:00", "ground_truth": "2017-10-22 20:45:00", "eval_metric": "hit", "channel": "MULL", "ts_data_path": "ts_data/L1_T2_Temporal_Localization_00222.csv", "meta": { "source": "ettm1", "args": { "channel": "MULL", "time": "2017", "threshold_high": "1.777", "threshold_low": "-1.528", "action": "last fall below -1.528" } } }, { "id": "L1_T2_Temporal_Localization_00224", "level": 1, "level_name": "Basic Operations", "category": "Atomic Retrieval", "subtask": "Temporal Localization", "question": "At what exact timestamp did channel LULL reach its maximum value in 2016-09? (Output format: a specific timestamp, e.g., YYYY-MM-DD HH:MM:SS)", "answer": "2016-09-01 07:45:00", "ground_truth": "2016-09-01 07:45:00", "eval_metric": "hit", "channel": "LULL", "ts_data_path": "ts_data/L1_T2_Temporal_Localization_00224.csv", "meta": { "source": "ettm1", "args": { "channel": "LULL", "time": "2016-09", "threshold_high": "1.279", "threshold_low": "0.731", "action": "reach its maximum value" } } }, { "id": "L1_T2_Temporal_Localization_00227", "level": 1, "level_name": "Basic Operations", "category": "Atomic Retrieval", "subtask": "Temporal Localization", "question": "At what exact timestamp did channel m_18 last fall below 0.052 in 2020? (Output format: a specific timestamp, e.g., YYYY-MM-DD HH:MM:SS)", "answer": "2020-02-09 13:17:00", "ground_truth": "2020-02-09 13:17:00", "eval_metric": "hit", "channel": "m_18", "ts_data_path": "ts_data/L1_T2_Temporal_Localization_00227.csv", "meta": { "source": "smd_machine_1_1", "args": { "channel": "m_18", "time": "2020", "threshold_high": "0.14", "threshold_low": "0.052", "action": "last fall below 0.052" } } }, { "id": "L1_T2_Temporal_Localization_00230", "level": 1, "level_name": "Basic Operations", "category": "Atomic Retrieval", "subtask": "Temporal Localization", "question": "At what exact timestamp did channel 99 reach its minimum value in 2022-03? (Output format: a specific timestamp, e.g., YYYY-MM-DD HH:MM:SS)", "answer": "2022-03-06 16:00:00", "ground_truth": "2022-03-06 16:00:00", "eval_metric": "hit", "channel": "99", "ts_data_path": "ts_data/L1_T2_Temporal_Localization_00230.csv", "meta": { "source": "causal_rivers", "args": { "channel": "99", "time": "2022-03", "threshold_high": "0.269", "threshold_low": "0.092", "action": "reach its minimum value" } } }, { "id": "L1_T2_Temporal_Localization_00232", "level": 1, "level_name": "Basic Operations", "category": "Atomic Retrieval", "subtask": "Temporal Localization", "question": "At what exact timestamp did channel 124 first rise above 0.387 in 2021? (Output format: a specific timestamp, e.g., YYYY-MM-DD HH:MM:SS)", "answer": "2021-01-06 00:15:00", "ground_truth": "2021-01-06 00:15:00", "eval_metric": "hit", "channel": "124", "ts_data_path": "ts_data/L1_T2_Temporal_Localization_00232.csv", "meta": { "source": "causal_rivers", "args": { "channel": "124", "time": "2021", "threshold_high": "0.387", "threshold_low": "0.134", "action": "first rise above 0.387" } } }, { "id": "L1_T2_Temporal_Localization_00233", "level": 1, "level_name": "Basic Operations", "category": "Atomic Retrieval", "subtask": "Temporal Localization", "question": "At what exact timestamp did channel 146 first rise above 0.758 in 2019? (Output format: a specific timestamp, e.g., YYYY-MM-DD HH:MM:SS)", "answer": "2019-01-08 21:45:00", "ground_truth": "2019-01-08 21:45:00", "eval_metric": "hit", "channel": "146", "ts_data_path": "ts_data/L1_T2_Temporal_Localization_00233.csv", "meta": { "source": "causal_rivers", "args": { "channel": "146", "time": "2019", "threshold_high": "0.758", "threshold_low": "0.172", "action": "first rise above 0.758" } } }, { "id": "L1_T2_Temporal_Localization_00237", "level": 1, "level_name": "Basic Operations", "category": "Atomic Retrieval", "subtask": "Temporal Localization", "question": "At what exact timestamp did channel 155 reach its maximum value in 2020? (Output format: a specific timestamp, e.g., YYYY-MM-DD HH:MM:SS)", "answer": "2020-02-25 03:15:00", "ground_truth": "2020-02-25 03:15:00", "eval_metric": "hit", "channel": "155", "ts_data_path": "ts_data/L1_T2_Temporal_Localization_00237.csv", "meta": { "source": "causal_rivers", "args": { "channel": "155", "time": "2020", "threshold_high": "0.238", "threshold_low": "0.054", "action": "reach its maximum value" } } }, { "id": "L1_T2_Temporal_Localization_00238", "level": 1, "level_name": "Basic Operations", "category": "Atomic Retrieval", "subtask": "Temporal Localization", "question": "At what exact timestamp did channel 166 reach its maximum value in 2022-06? (Output format: a specific timestamp, e.g., YYYY-MM-DD HH:MM:SS)", "answer": "2022-06-01 19:30:00", "ground_truth": "2022-06-01 19:30:00", "eval_metric": "hit", "channel": "166", "ts_data_path": "ts_data/L1_T2_Temporal_Localization_00238.csv", "meta": { "source": "causal_rivers", "args": { "channel": "166", "time": "2022-06", "threshold_high": "282.0", "threshold_low": "222.0", "action": "reach its maximum value" } } }, { "id": "L1_T2_Temporal_Localization_00239", "level": 1, "level_name": "Basic Operations", "category": "Atomic Retrieval", "subtask": "Temporal Localization", "question": "At what exact timestamp did channel 169 reach its minimum value in 2022-04? (Output format: a specific timestamp, e.g., YYYY-MM-DD HH:MM:SS)", "answer": "2022-04-27 00:00:00", "ground_truth": "2022-04-27 00:00:00", "eval_metric": "hit", "channel": "169", "ts_data_path": "ts_data/L1_T2_Temporal_Localization_00239.csv", "meta": { "source": "causal_rivers", "args": { "channel": "169", "time": "2022-04", "threshold_high": "420.4", "threshold_low": "363.0", "action": "reach its minimum value" } } }, { "id": "L1_T2_Temporal_Localization_00243", "level": 1, "level_name": "Basic Operations", "category": "Atomic Retrieval", "subtask": "Temporal Localization", "question": "At what exact timestamp did channel 177 last fall below 112.0 in 2023? (Output format: a specific timestamp, e.g., YYYY-MM-DD HH:MM:SS)", "answer": "2023-10-12 19:45:00", "ground_truth": "2023-10-12 19:45:00", "eval_metric": "hit", "channel": "177", "ts_data_path": "ts_data/L1_T2_Temporal_Localization_00243.csv", "meta": { "source": "causal_rivers", "args": { "channel": "177", "time": "2023", "threshold_high": "369.0", "threshold_low": "112.0", "action": "last fall below 112.0" } } }, { "id": "L1_T2_Temporal_Localization_00246", "level": 1, "level_name": "Basic Operations", "category": "Atomic Retrieval", "subtask": "Temporal Localization", "question": "At what exact timestamp did channel 312 reach its minimum value in 2023? (Output format: a specific timestamp, e.g., YYYY-MM-DD HH:MM:SS)", "answer": "2023-09-25 16:30:00", "ground_truth": "2023-09-25 16:30:00", "eval_metric": "hit", "channel": "312", "ts_data_path": "ts_data/L1_T2_Temporal_Localization_00246.csv", "meta": { "source": "causal_rivers", "args": { "channel": "312", "time": "2023", "threshold_high": "0.731", "threshold_low": "0.234", "action": "reach its minimum value" } } }, { "id": "L1_T2_Temporal_Localization_00247", "level": 1, "level_name": "Basic Operations", "category": "Atomic Retrieval", "subtask": "Temporal Localization", "question": "At what exact timestamp did channel 430 reach its minimum value in 2019? (Output format: a specific timestamp, e.g., YYYY-MM-DD HH:MM:SS)", "answer": "2019-07-01 03:45:00", "ground_truth": "2019-07-01 03:45:00", "eval_metric": "hit", "channel": "430", "ts_data_path": "ts_data/L1_T2_Temporal_Localization_00247.csv", "meta": { "source": "causal_rivers", "args": { "channel": "430", "time": "2019", "threshold_high": "0.26", "threshold_low": "0.101", "action": "reach its minimum value" } } }, { "id": "L1_T2_Temporal_Localization_00248", "level": 1, "level_name": "Basic Operations", "category": "Atomic Retrieval", "subtask": "Temporal Localization", "question": "At what exact timestamp did channel 441 first rise above 20.5 in 2019? (Output format: a specific timestamp, e.g., YYYY-MM-DD HH:MM:SS)", "answer": "2019-01-01 00:00:00", "ground_truth": "2019-01-01 00:00:00", "eval_metric": "hit", "channel": "441", "ts_data_path": "ts_data/L1_T2_Temporal_Localization_00248.csv", "meta": { "source": "causal_rivers", "args": { "channel": "441", "time": "2019", "threshold_high": "20.5", "threshold_low": "6.4", "action": "first rise above 20.5" } } }, { "id": "L1_T2_Temporal_Localization_00254", "level": 1, "level_name": "Basic Operations", "category": "Atomic Retrieval", "subtask": "Temporal Localization", "question": "At what exact timestamp did channel 589 last fall below 209.0 in 2020-08? (Output format: a specific timestamp, e.g., YYYY-MM-DD HH:MM:SS)", "answer": "2020-08-25 13:45:00", "ground_truth": "2020-08-25 13:45:00", "eval_metric": "hit", "channel": "589", "ts_data_path": "ts_data/L1_T2_Temporal_Localization_00254.csv", "meta": { "source": "causal_rivers", "args": { "channel": "589", "time": "2020-08", "threshold_high": "254.0", "threshold_low": "209.0", "action": "last fall below 209.0" } } }, { "id": "L1_T2_Temporal_Localization_00255", "level": 1, "level_name": "Basic Operations", "category": "Atomic Retrieval", "subtask": "Temporal Localization", "question": "At what exact timestamp did channel 591 reach its minimum value in 2021-03? (Output format: a specific timestamp, e.g., YYYY-MM-DD HH:MM:SS)", "answer": "2021-03-31 00:30:00", "ground_truth": "2021-03-31 00:30:00", "eval_metric": "hit", "channel": "591", "ts_data_path": "ts_data/L1_T2_Temporal_Localization_00255.csv", "meta": { "source": "causal_rivers", "args": { "channel": "591", "time": "2021-03", "threshold_high": "420.0", "threshold_low": "303.0", "action": "reach its minimum value" } } }, { "id": "L1_T2_Temporal_Localization_00259", "level": 1, "level_name": "Basic Operations", "category": "Atomic Retrieval", "subtask": "Temporal Localization", "question": "At what exact timestamp did channel 627 reach its minimum value in 2019-06? (Output format: a specific timestamp, e.g., YYYY-MM-DD HH:MM:SS)", "answer": "2019-06-08 17:45:00", "ground_truth": "2019-06-08 17:45:00", "eval_metric": "hit", "channel": "627", "ts_data_path": "ts_data/L1_T2_Temporal_Localization_00259.csv", "meta": { "source": "causal_rivers", "args": { "channel": "627", "time": "2019-06", "threshold_high": "0.293", "threshold_low": "0.243", "action": "reach its minimum value" } } }, { "id": "L1_T2_Temporal_Localization_00260", "level": 1, "level_name": "Basic Operations", "category": "Atomic Retrieval", "subtask": "Temporal Localization", "question": "At what exact timestamp did channel 647 reach its maximum value in 2021-11? (Output format: a specific timestamp, e.g., YYYY-MM-DD HH:MM:SS)", "answer": "2021-11-06 02:00:00", "ground_truth": "2021-11-06 02:00:00", "eval_metric": "hit", "channel": "647", "ts_data_path": "ts_data/L1_T2_Temporal_Localization_00260.csv", "meta": { "source": "causal_rivers", "args": { "channel": "647", "time": "2021-11", "threshold_high": "0.95", "threshold_low": "0.701", "action": "reach its maximum value" } } }, { "id": "L1_T2_Temporal_Localization_00262", "level": 1, "level_name": "Basic Operations", "category": "Atomic Retrieval", "subtask": "Temporal Localization", "question": "At what exact timestamp did channel 683 reach its minimum value in 2022-05? (Output format: a specific timestamp, e.g., YYYY-MM-DD HH:MM:SS)", "answer": "2022-05-17 04:45:00", "ground_truth": "2022-05-17 04:45:00", "eval_metric": "hit", "channel": "683", "ts_data_path": "ts_data/L1_T2_Temporal_Localization_00262.csv", "meta": { "source": "causal_rivers", "args": { "channel": "683", "time": "2022-05", "threshold_high": "0.872", "threshold_low": "0.834", "action": "reach its minimum value" } } }, { "id": "L1_T2_Temporal_Localization_00264", "level": 1, "level_name": "Basic Operations", "category": "Atomic Retrieval", "subtask": "Temporal Localization", "question": "At what exact timestamp did channel 728 reach its maximum value in 2022? (Output format: a specific timestamp, e.g., YYYY-MM-DD HH:MM:SS)", "answer": "2022-02-23 21:15:00", "ground_truth": "2022-02-23 21:15:00", "eval_metric": "hit", "channel": "728", "ts_data_path": "ts_data/L1_T2_Temporal_Localization_00264.csv", "meta": { "source": "causal_rivers", "args": { "channel": "728", "time": "2022", "threshold_high": "107.0", "threshold_low": "38.0", "action": "reach its maximum value" } } }, { "id": "L1_T2_Temporal_Localization_00266", "level": 1, "level_name": "Basic Operations", "category": "Atomic Retrieval", "subtask": "Temporal Localization", "question": "At what exact timestamp did channel 754 reach its maximum value in 2022? (Output format: a specific timestamp, e.g., YYYY-MM-DD HH:MM:SS)", "answer": "2022-02-21 09:15:00", "ground_truth": "2022-02-21 09:15:00", "eval_metric": "hit", "channel": "754", "ts_data_path": "ts_data/L1_T2_Temporal_Localization_00266.csv", "meta": { "source": "causal_rivers", "args": { "channel": "754", "time": "2022", "threshold_high": "0.282", "threshold_low": "0.088", "action": "reach its maximum value" } } }, { "id": "L1_T2_Temporal_Localization_00267", "level": 1, "level_name": "Basic Operations", "category": "Atomic Retrieval", "subtask": "Temporal Localization", "question": "At what exact timestamp did channel 762 last fall below 0.056 in 2022? (Output format: a specific timestamp, e.g., YYYY-MM-DD HH:MM:SS)", "answer": "2022-09-06 06:30:00", "ground_truth": "2022-09-06 06:30:00", "eval_metric": "hit", "channel": "762", "ts_data_path": "ts_data/L1_T2_Temporal_Localization_00267.csv", "meta": { "source": "causal_rivers", "args": { "channel": "762", "time": "2022", "threshold_high": "0.268", "threshold_low": "0.056", "action": "last fall below 0.056" } } }, { "id": "L1_T2_Temporal_Localization_00268", "level": 1, "level_name": "Basic Operations", "category": "Atomic Retrieval", "subtask": "Temporal Localization", "question": "At what exact timestamp did channel 811 last fall below 11.3 in 2023-12? (Output format: a specific timestamp, e.g., YYYY-MM-DD HH:MM:SS)", "answer": "2023-12-10 20:30:00", "ground_truth": "2023-12-10 20:30:00", "eval_metric": "hit", "channel": "811", "ts_data_path": "ts_data/L1_T2_Temporal_Localization_00268.csv", "meta": { "source": "causal_rivers", "args": { "channel": "811", "time": "2023-12", "threshold_high": "36.9", "threshold_low": "11.3", "action": "last fall below 11.3" } } }, { "id": "L1_T2_Temporal_Localization_00269", "level": 1, "level_name": "Basic Operations", "category": "Atomic Retrieval", "subtask": "Temporal Localization", "question": "At what exact timestamp did channel 813 reach its maximum value in 2019-03? (Output format: a specific timestamp, e.g., YYYY-MM-DD HH:MM:SS)", "answer": "2019-03-16 17:00:00", "ground_truth": "2019-03-16 17:00:00", "eval_metric": "hit", "channel": "813", "ts_data_path": "ts_data/L1_T2_Temporal_Localization_00269.csv", "meta": { "source": "causal_rivers", "args": { "channel": "813", "time": "2019-03", "threshold_high": "3.63", "threshold_low": "1.56", "action": "reach its maximum value" } } }, { "id": "L1_T2_Temporal_Localization_00276", "level": 1, "level_name": "Basic Operations", "category": "Atomic Retrieval", "subtask": "Temporal Localization", "question": "At what exact timestamp did channel HUFL reach its minimum value in 2018-03? (Output format: a specific timestamp, e.g., YYYY-MM-DD HH:MM:SS)", "answer": "2018-03-11 11:45:00", "ground_truth": "2018-03-11 11:45:00", "eval_metric": "hit", "channel": "HUFL", "ts_data_path": "ts_data/L1_T2_Temporal_Localization_00276.csv", "meta": { "source": "ettm1", "args": { "channel": "HUFL", "time": "2018-03", "threshold_high": "13.128", "threshold_low": "-0.067", "action": "reach its minimum value" } } }, { "id": "L1_T2_Temporal_Localization_00280", "level": 1, "level_name": "Basic Operations", "category": "Atomic Retrieval", "subtask": "Temporal Localization", "question": "At what exact timestamp did channel LULL first rise above 1.34 in 2018? (Output format: a specific timestamp, e.g., YYYY-MM-DD HH:MM:SS)", "answer": "2018-01-01 23:15:00", "ground_truth": "2018-01-01 23:15:00", "eval_metric": "hit", "channel": "LULL", "ts_data_path": "ts_data/L1_T2_Temporal_Localization_00280.csv", "meta": { "source": "ettm1", "args": { "channel": "LULL", "time": "2018", "threshold_high": "1.34", "threshold_low": "0.64", "action": "first rise above 1.34" } } }, { "id": "L1_T2_Temporal_Localization_00283", "level": 1, "level_name": "Basic Operations", "category": "Atomic Retrieval", "subtask": "Temporal Localization", "question": "At what exact timestamp did channel m_06 reach its maximum value in 2020? (Output format: a specific timestamp, e.g., YYYY-MM-DD HH:MM:SS)", "answer": "2020-02-01 22:05:00", "ground_truth": "2020-02-01 22:05:00", "eval_metric": "hit", "channel": "m_06", "ts_data_path": "ts_data/L1_T2_Temporal_Localization_00283.csv", "meta": { "source": "smd_machine_1_1", "args": { "channel": "m_06", "time": "2020", "threshold_high": "0.301", "threshold_low": "0.237", "action": "reach its maximum value" } } }, { "id": "L1_T2_Temporal_Localization_00284", "level": 1, "level_name": "Basic Operations", "category": "Atomic Retrieval", "subtask": "Temporal Localization", "question": "At what exact timestamp did channel 67 reach its maximum value in 2022? (Output format: a specific timestamp, e.g., YYYY-MM-DD HH:MM:SS)", "answer": "2022-08-27 21:30:00", "ground_truth": "2022-08-27 21:30:00", "eval_metric": "hit", "channel": "67", "ts_data_path": "ts_data/L1_T2_Temporal_Localization_00284.csv", "meta": { "source": "causal_rivers", "args": { "channel": "67", "time": "2022", "threshold_high": "0.227", "threshold_low": "0.162", "action": "reach its maximum value" } } }, { "id": "L1_T2_Temporal_Localization_00287", "level": 1, "level_name": "Basic Operations", "category": "Atomic Retrieval", "subtask": "Temporal Localization", "question": "At what exact timestamp did channel 123 reach its maximum value in 2019? (Output format: a specific timestamp, e.g., YYYY-MM-DD HH:MM:SS)", "answer": "2019-03-17 07:15:00", "ground_truth": "2019-03-17 07:15:00", "eval_metric": "hit", "channel": "123", "ts_data_path": "ts_data/L1_T2_Temporal_Localization_00287.csv", "meta": { "source": "causal_rivers", "args": { "channel": "123", "time": "2019", "threshold_high": "1.04", "threshold_low": "0.205", "action": "reach its maximum value" } } }, { "id": "L1_T2_Temporal_Localization_00289", "level": 1, "level_name": "Basic Operations", "category": "Atomic Retrieval", "subtask": "Temporal Localization", "question": "At what exact timestamp did channel 146 last fall below 0.275 in 2023? (Output format: a specific timestamp, e.g., YYYY-MM-DD HH:MM:SS)", "answer": "2023-10-11 13:30:00", "ground_truth": "2023-10-11 13:30:00", "eval_metric": "hit", "channel": "146", "ts_data_path": "ts_data/L1_T2_Temporal_Localization_00289.csv", "meta": { "source": "causal_rivers", "args": { "channel": "146", "time": "2023", "threshold_high": "1.26", "threshold_low": "0.275", "action": "last fall below 0.275" } } }, { "id": "L1_T2_Temporal_Localization_00293", "level": 1, "level_name": "Basic Operations", "category": "Atomic Retrieval", "subtask": "Temporal Localization", "question": "At what exact timestamp did channel 155 last fall below 0.164 in 2021-05? (Output format: a specific timestamp, e.g., YYYY-MM-DD HH:MM:SS)", "answer": "2021-05-31 23:45:00", "ground_truth": "2021-05-31 23:45:00", "eval_metric": "hit", "channel": "155", "ts_data_path": "ts_data/L1_T2_Temporal_Localization_00293.csv", "meta": { "source": "causal_rivers", "args": { "channel": "155", "time": "2021-05", "threshold_high": "0.227", "threshold_low": "0.164", "action": "last fall below 0.164" } } }, { "id": "L1_T2_Temporal_Localization_00294", "level": 1, "level_name": "Basic Operations", "category": "Atomic Retrieval", "subtask": "Temporal Localization", "question": "At what exact timestamp did channel 166 first rise above 602.0 in 2022? (Output format: a specific timestamp, e.g., YYYY-MM-DD HH:MM:SS)", "answer": "2022-01-05 11:15:00", "ground_truth": "2022-01-05 11:15:00", "eval_metric": "hit", "channel": "166", "ts_data_path": "ts_data/L1_T2_Temporal_Localization_00294.csv", "meta": { "source": "causal_rivers", "args": { "channel": "166", "time": "2022", "threshold_high": "602.0", "threshold_low": "258.0", "action": "first rise above 602.0" } } }, { "id": "L1_T2_Temporal_Localization_00296", "level": 1, "level_name": "Basic Operations", "category": "Atomic Retrieval", "subtask": "Temporal Localization", "question": "At what exact timestamp did channel 170 first rise above 456.0 in 2021-04? (Output format: a specific timestamp, e.g., YYYY-MM-DD HH:MM:SS)", "answer": "2021-04-01 00:00:00", "ground_truth": "2021-04-01 00:00:00", "eval_metric": "hit", "channel": "170", "ts_data_path": "ts_data/L1_T2_Temporal_Localization_00296.csv", "meta": { "source": "causal_rivers", "args": { "channel": "170", "time": "2021-04", "threshold_high": "456.0", "threshold_low": "404.4", "action": "first rise above 456.0" } } }, { "id": "L1_T2_Temporal_Localization_00297", "level": 1, "level_name": "Basic Operations", "category": "Atomic Retrieval", "subtask": "Temporal Localization", "question": "At what exact timestamp did channel 172 reach its minimum value in 2019? (Output format: a specific timestamp, e.g., YYYY-MM-DD HH:MM:SS)", "answer": "2019-07-31 13:15:00", "ground_truth": "2019-07-31 13:15:00", "eval_metric": "hit", "channel": "172", "ts_data_path": "ts_data/L1_T2_Temporal_Localization_00297.csv", "meta": { "source": "causal_rivers", "args": { "channel": "172", "time": "2019", "threshold_high": "428.0", "threshold_low": "132.0", "action": "reach its minimum value" } } }, { "id": "L1_T2_Temporal_Localization_00301", "level": 1, "level_name": "Basic Operations", "category": "Atomic Retrieval", "subtask": "Temporal Localization", "question": "At what exact timestamp did channel 245 first rise above 0.421 in 2022? (Output format: a specific timestamp, e.g., YYYY-MM-DD HH:MM:SS)", "answer": "2022-01-01 00:00:00", "ground_truth": "2022-01-01 00:00:00", "eval_metric": "hit", "channel": "245", "ts_data_path": "ts_data/L1_T2_Temporal_Localization_00301.csv", "meta": { "source": "causal_rivers", "args": { "channel": "245", "time": "2022", "threshold_high": "0.421", "threshold_low": "0.16", "action": "first rise above 0.421" } } }, { "id": "L1_T2_Temporal_Localization_00304", "level": 1, "level_name": "Basic Operations", "category": "Atomic Retrieval", "subtask": "Temporal Localization", "question": "At what exact timestamp did channel 441 last fall below 10.9 in 2021? (Output format: a specific timestamp, e.g., YYYY-MM-DD HH:MM:SS)", "answer": "2021-12-13 13:45:00", "ground_truth": "2021-12-13 13:45:00", "eval_metric": "hit", "channel": "441", "ts_data_path": "ts_data/L1_T2_Temporal_Localization_00304.csv", "meta": { "source": "causal_rivers", "args": { "channel": "441", "time": "2021", "threshold_high": "23.5", "threshold_low": "10.9", "action": "last fall below 10.9" } } }, { "id": "L1_T2_Temporal_Localization_00305", "level": 1, "level_name": "Basic Operations", "category": "Atomic Retrieval", "subtask": "Temporal Localization", "question": "At what exact timestamp did channel 495 reach its minimum value in 2022? (Output format: a specific timestamp, e.g., YYYY-MM-DD HH:MM:SS)", "answer": "2022-04-30 07:00:00", "ground_truth": "2022-04-30 07:00:00", "eval_metric": "hit", "channel": "495", "ts_data_path": "ts_data/L1_T2_Temporal_Localization_00305.csv", "meta": { "source": "causal_rivers", "args": { "channel": "495", "time": "2022", "threshold_high": "0.79", "threshold_low": "0.0", "action": "reach its minimum value" } } }, { "id": "L1_T2_Temporal_Localization_00310", "level": 1, "level_name": "Basic Operations", "category": "Atomic Retrieval", "subtask": "Temporal Localization", "question": "At what exact timestamp did channel 589 first rise above 416.0 in 2022? (Output format: a specific timestamp, e.g., YYYY-MM-DD HH:MM:SS)", "answer": "2022-01-07 04:15:00", "ground_truth": "2022-01-07 04:15:00", "eval_metric": "hit", "channel": "589", "ts_data_path": "ts_data/L1_T2_Temporal_Localization_00310.csv", "meta": { "source": "causal_rivers", "args": { "channel": "589", "time": "2022", "threshold_high": "416.0", "threshold_low": "221.0", "action": "first rise above 416.0" } } }, { "id": "L1_T2_Temporal_Localization_00311", "level": 1, "level_name": "Basic Operations", "category": "Atomic Retrieval", "subtask": "Temporal Localization", "question": "At what exact timestamp did channel 591 reach its minimum value in 2019-05? (Output format: a specific timestamp, e.g., YYYY-MM-DD HH:MM:SS)", "answer": "2019-05-01 00:00:00", "ground_truth": "2019-05-01 00:00:00", "eval_metric": "hit", "channel": "591", "ts_data_path": "ts_data/L1_T2_Temporal_Localization_00311.csv", "meta": { "source": "causal_rivers", "args": { "channel": "591", "time": "2019-05", "threshold_high": "258.0", "threshold_low": "155.0", "action": "reach its minimum value" } } }, { "id": "L1_T2_Temporal_Localization_00313", "level": 1, "level_name": "Basic Operations", "category": "Atomic Retrieval", "subtask": "Temporal Localization", "question": "At what exact timestamp did channel 625 reach its maximum value in 2019? (Output format: a specific timestamp, e.g., YYYY-MM-DD HH:MM:SS)", "answer": "2019-02-11 21:15:00", "ground_truth": "2019-02-11 21:15:00", "eval_metric": "hit", "channel": "625", "ts_data_path": "ts_data/L1_T2_Temporal_Localization_00313.csv", "meta": { "source": "causal_rivers", "args": { "channel": "625", "time": "2019", "threshold_high": "0.978", "threshold_low": "0.011", "action": "reach its maximum value" } } }, { "id": "L1_T2_Temporal_Localization_00315", "level": 1, "level_name": "Basic Operations", "category": "Atomic Retrieval", "subtask": "Temporal Localization", "question": "At what exact timestamp did channel 627 reach its maximum value in 2019? (Output format: a specific timestamp, e.g., YYYY-MM-DD HH:MM:SS)", "answer": "2019-10-05 23:30:00", "ground_truth": "2019-10-05 23:30:00", "eval_metric": "hit", "channel": "627", "ts_data_path": "ts_data/L1_T2_Temporal_Localization_00315.csv", "meta": { "source": "causal_rivers", "args": { "channel": "627", "time": "2019", "threshold_high": "0.478", "threshold_low": "0.266", "action": "reach its maximum value" } } }, { "id": "L1_T2_Temporal_Localization_00316", "level": 1, "level_name": "Basic Operations", "category": "Atomic Retrieval", "subtask": "Temporal Localization", "question": "At what exact timestamp did channel 647 first rise above 0.606 in 2022-05? (Output format: a specific timestamp, e.g., YYYY-MM-DD HH:MM:SS)", "answer": "2022-05-01 00:00:00", "ground_truth": "2022-05-01 00:00:00", "eval_metric": "hit", "channel": "647", "ts_data_path": "ts_data/L1_T2_Temporal_Localization_00316.csv", "meta": { "source": "causal_rivers", "args": { "channel": "647", "time": "2022-05", "threshold_high": "0.606", "threshold_low": "0.343", "action": "first rise above 0.606" } } }, { "id": "L1_T2_Temporal_Localization_00317", "level": 1, "level_name": "Basic Operations", "category": "Atomic Retrieval", "subtask": "Temporal Localization", "question": "At what exact timestamp did channel 680 first rise above 6.84 in 2023? (Output format: a specific timestamp, e.g., YYYY-MM-DD HH:MM:SS)", "answer": "2023-01-06 12:15:00", "ground_truth": "2023-01-06 12:15:00", "eval_metric": "hit", "channel": "680", "ts_data_path": "ts_data/L1_T2_Temporal_Localization_00317.csv", "meta": { "source": "causal_rivers", "args": { "channel": "680", "time": "2023", "threshold_high": "6.84", "threshold_low": "0.227", "action": "first rise above 6.84" } } }, { "id": "L1_T2_Temporal_Localization_00318", "level": 1, "level_name": "Basic Operations", "category": "Atomic Retrieval", "subtask": "Temporal Localization", "question": "At what exact timestamp did channel 683 reach its minimum value in 2020-09? (Output format: a specific timestamp, e.g., YYYY-MM-DD HH:MM:SS)", "answer": "2020-09-01 00:00:00", "ground_truth": "2020-09-01 00:00:00", "eval_metric": "hit", "channel": "683", "ts_data_path": "ts_data/L1_T2_Temporal_Localization_00318.csv", "meta": { "source": "causal_rivers", "args": { "channel": "683", "time": "2020-09", "threshold_high": "1.04", "threshold_low": "0.985", "action": "reach its minimum value" } } }, { "id": "L1_T2_Temporal_Localization_00319", "level": 1, "level_name": "Basic Operations", "category": "Atomic Retrieval", "subtask": "Temporal Localization", "question": "At what exact timestamp did channel 727 last fall below 56.1 in 2021? (Output format: a specific timestamp, e.g., YYYY-MM-DD HH:MM:SS)", "answer": "2021-12-01 02:15:00", "ground_truth": "2021-12-01 02:15:00", "eval_metric": "hit", "channel": "727", "ts_data_path": "ts_data/L1_T2_Temporal_Localization_00319.csv", "meta": { "source": "causal_rivers", "args": { "channel": "727", "time": "2021", "threshold_high": "113.0", "threshold_low": "56.1", "action": "last fall below 56.1" } } }, { "id": "L1_T2_Temporal_Localization_00322", "level": 1, "level_name": "Basic Operations", "category": "Atomic Retrieval", "subtask": "Temporal Localization", "question": "At what exact timestamp did channel 754 last fall below 0.068 in 2020? (Output format: a specific timestamp, e.g., YYYY-MM-DD HH:MM:SS)", "answer": "2020-09-25 06:15:00", "ground_truth": "2020-09-25 06:15:00", "eval_metric": "hit", "channel": "754", "ts_data_path": "ts_data/L1_T2_Temporal_Localization_00322.csv", "meta": { "source": "causal_rivers", "args": { "channel": "754", "time": "2020", "threshold_high": "0.24", "threshold_low": "0.068", "action": "last fall below 0.068" } } }, { "id": "L1_T2_Temporal_Localization_00324", "level": 1, "level_name": "Basic Operations", "category": "Atomic Retrieval", "subtask": "Temporal Localization", "question": "At what exact timestamp did channel 811 first rise above 11.8 in 2019? (Output format: a specific timestamp, e.g., YYYY-MM-DD HH:MM:SS)", "answer": "2019-01-09 07:15:00", "ground_truth": "2019-01-09 07:15:00", "eval_metric": "hit", "channel": "811", "ts_data_path": "ts_data/L1_T2_Temporal_Localization_00324.csv", "meta": { "source": "causal_rivers", "args": { "channel": "811", "time": "2019", "threshold_high": "11.8", "threshold_low": "2.06", "action": "first rise above 11.8" } } }, { "id": "L1_T2_Temporal_Localization_00327", "level": 1, "level_name": "Basic Operations", "category": "Atomic Retrieval", "subtask": "Temporal Localization", "question": "At what exact timestamp did channel 891 first rise above 3.49 in 2020? (Output format: a specific timestamp, e.g., YYYY-MM-DD HH:MM:SS)", "answer": "2020-01-04 13:15:00", "ground_truth": "2020-01-04 13:15:00", "eval_metric": "hit", "channel": "891", "ts_data_path": "ts_data/L1_T2_Temporal_Localization_00327.csv", "meta": { "source": "causal_rivers", "args": { "channel": "891", "time": "2020", "threshold_high": "3.49", "threshold_low": "1.32", "action": "first rise above 3.49" } } }, { "id": "L1_T2_Temporal_Localization_00328", "level": 1, "level_name": "Basic Operations", "category": "Atomic Retrieval", "subtask": "Temporal Localization", "question": "At what exact timestamp did channel 894 last fall below 3.05 in 2020-10? (Output format: a specific timestamp, e.g., YYYY-MM-DD HH:MM:SS)", "answer": "2020-10-19 09:15:00", "ground_truth": "2020-10-19 09:15:00", "eval_metric": "hit", "channel": "894", "ts_data_path": "ts_data/L1_T2_Temporal_Localization_00328.csv", "meta": { "source": "causal_rivers", "args": { "channel": "894", "time": "2020-10", "threshold_high": "3.42", "threshold_low": "3.05", "action": "last fall below 3.05" } } }, { "id": "L1_T2_Temporal_Localization_00332", "level": 1, "level_name": "Basic Operations", "category": "Atomic Retrieval", "subtask": "Temporal Localization", "question": "At what exact timestamp did channel HULL first rise above 0.737 in 2017-02? (Output format: a specific timestamp, e.g., YYYY-MM-DD HH:MM:SS)", "answer": "2017-02-09 11:00:00", "ground_truth": "2017-02-09 11:00:00", "eval_metric": "hit", "channel": "HULL", "ts_data_path": "ts_data/L1_T2_Temporal_Localization_00332.csv", "meta": { "source": "ettm1", "args": { "channel": "HULL", "time": "2017-02", "threshold_high": "0.737", "threshold_low": "-1.273", "action": "first rise above 0.737" } } }, { "id": "L1_T2_Temporal_Localization_00334", "level": 1, "level_name": "Basic Operations", "category": "Atomic Retrieval", "subtask": "Temporal Localization", "question": "At what exact timestamp did channel LUFL reach its maximum value in 2017? (Output format: a specific timestamp, e.g., YYYY-MM-DD HH:MM:SS)", "answer": "2017-07-23 20:00:00", "ground_truth": "2017-07-23 20:00:00", "eval_metric": "hit", "channel": "LUFL", "ts_data_path": "ts_data/L1_T2_Temporal_Localization_00334.csv", "meta": { "source": "ettm1", "args": { "channel": "LUFL", "time": "2017", "threshold_high": "3.533", "threshold_low": "2.193", "action": "reach its maximum value" } } }, { "id": "L1_T2_Temporal_Localization_00335", "level": 1, "level_name": "Basic Operations", "category": "Atomic Retrieval", "subtask": "Temporal Localization", "question": "At what exact timestamp did channel LULL last fall below 0.67 in 2017-10? (Output format: a specific timestamp, e.g., YYYY-MM-DD HH:MM:SS)", "answer": "2017-10-18 05:15:00", "ground_truth": "2017-10-18 05:15:00", "eval_metric": "hit", "channel": "LULL", "ts_data_path": "ts_data/L1_T2_Temporal_Localization_00335.csv", "meta": { "source": "ettm1", "args": { "channel": "LULL", "time": "2017-10", "threshold_high": "1.127", "threshold_low": "0.67", "action": "last fall below 0.67" } } }, { "id": "L1_T2_Temporal_Localization_00338", "level": 1, "level_name": "Basic Operations", "category": "Atomic Retrieval", "subtask": "Temporal Localization", "question": "At what exact timestamp did channel m_11 first rise above 0.122 in 2020? (Output format: a specific timestamp, e.g., YYYY-MM-DD HH:MM:SS)", "answer": "2020-01-01 06:04:00", "ground_truth": "2020-01-01 06:04:00", "eval_metric": "hit", "channel": "m_11", "ts_data_path": "ts_data/L1_T2_Temporal_Localization_00338.csv", "meta": { "source": "smd_machine_1_1", "args": { "channel": "m_11", "time": "2020", "threshold_high": "0.122", "threshold_low": "0.041", "action": "first rise above 0.122" } } }, { "id": "L1_T4_Sliding_Window_00516", "level": 1, "level_name": "Basic Operations", "category": "Sliding Window", "subtask": "Sliding Window", "question": "Which 21-day period in 2023 had the largest range for channel 71? (Output format: [YYYY-MM-DD HH:MM:SS, YYYY-MM-DD HH:MM:SS])", "answer": "[2023-12-07 14:15:00, 2023-12-28 14:00:00]", "ground_truth": [ "2023-12-07 14:15:00", "2023-12-28 14:00:00" ], "eval_metric": "iou", "channel": "71", "ts_data_path": "ts_data/L1_T4_Sliding_Window_00516.csv", "meta": { "source": "causal_rivers", "args": { "channel": "71", "time": "2023", "metric": "largest range", "window": "21D" } } }, { "id": "L1_T4_Sliding_Window_00517", "level": 1, "level_name": "Basic Operations", "category": "Sliding Window", "subtask": "Sliding Window", "question": "Which 30-day period in 2023 had the largest range for channel 99? (Output format: [YYYY-MM-DD HH:MM:SS, YYYY-MM-DD HH:MM:SS])", "answer": "[2023-06-11 21:15:00, 2023-07-11 21:00:00]", "ground_truth": [ "2023-06-11 21:15:00", "2023-07-11 21:00:00" ], "eval_metric": "iou", "channel": "99", "ts_data_path": "ts_data/L1_T4_Sliding_Window_00517.csv", "meta": { "source": "causal_rivers", "args": { "channel": "99", "time": "2023", "metric": "largest range", "window": "30D" } } }, { "id": "L1_T4_Sliding_Window_00518", "level": 1, "level_name": "Basic Operations", "category": "Sliding Window", "subtask": "Sliding Window", "question": "Which 55-day period in 2020 had the lowest average for channel 123? (Output format: [YYYY-MM-DD HH:MM:SS, YYYY-MM-DD HH:MM:SS])", "answer": "[2020-07-10 06:00:00, 2020-09-03 05:45:00]", "ground_truth": [ "2020-07-10 06:00:00", "2020-09-03 05:45:00" ], "eval_metric": "iou", "channel": "123", "ts_data_path": "ts_data/L1_T4_Sliding_Window_00518.csv", "meta": { "source": "causal_rivers", "args": { "channel": "123", "time": "2020", "metric": "lowest average", "window": "55D" } } }, { "id": "L1_T4_Sliding_Window_00525", "level": 1, "level_name": "Basic Operations", "category": "Sliding Window", "subtask": "Sliding Window", "question": "Which 43-day period in 2022 had the highest variance for channel 166? (Output format: [YYYY-MM-DD HH:MM:SS, YYYY-MM-DD HH:MM:SS])", "answer": "[2022-02-22 19:45:00, 2022-04-06 19:30:00]", "ground_truth": [ "2022-02-22 19:45:00", "2022-04-06 19:30:00" ], "eval_metric": "iou", "channel": "166", "ts_data_path": "ts_data/L1_T4_Sliding_Window_00525.csv", "meta": { "source": "causal_rivers", "args": { "channel": "166", "time": "2022", "metric": "highest variance", "window": "43D" } } }, { "id": "L1_T4_Sliding_Window_00530", "level": 1, "level_name": "Basic Operations", "category": "Sliding Window", "subtask": "Sliding Window", "question": "Which 19-day period in 2023 had the largest range for channel 177? (Output format: [YYYY-MM-DD HH:MM:SS, YYYY-MM-DD HH:MM:SS])", "answer": "[2023-12-09 13:00:00, 2023-12-28 12:45:00]", "ground_truth": [ "2023-12-09 13:00:00", "2023-12-28 12:45:00" ], "eval_metric": "iou", "channel": "177", "ts_data_path": "ts_data/L1_T4_Sliding_Window_00530.csv", "meta": { "source": "causal_rivers", "args": { "channel": "177", "time": "2023", "metric": "largest range", "window": "19D" } } }, { "id": "L1_T4_Sliding_Window_00531", "level": 1, "level_name": "Basic Operations", "category": "Sliding Window", "subtask": "Sliding Window", "question": "Which 49-day period in 2019 had the largest range for channel 237? (Output format: [YYYY-MM-DD HH:MM:SS, YYYY-MM-DD HH:MM:SS])", "answer": "[2019-01-01 00:00:00, 2019-02-12 08:15:00]", "ground_truth": [ "2019-01-01 00:00:00", "2019-02-12 08:15:00" ], "eval_metric": "iou", "channel": "237", "ts_data_path": "ts_data/L1_T4_Sliding_Window_00531.csv", "meta": { "source": "causal_rivers", "args": { "channel": "237", "time": "2019", "metric": "largest range", "window": "49D" } } }, { "id": "L1_T4_Sliding_Window_00532", "level": 1, "level_name": "Basic Operations", "category": "Sliding Window", "subtask": "Sliding Window", "question": "Which 17-day period in 2023 had the largest range for channel 245? (Output format: [YYYY-MM-DD HH:MM:SS, YYYY-MM-DD HH:MM:SS])", "answer": "[2023-12-04 20:30:00, 2023-12-21 20:15:00]", "ground_truth": [ "2023-12-04 20:30:00", "2023-12-21 20:15:00" ], "eval_metric": "iou", "channel": "245", "ts_data_path": "ts_data/L1_T4_Sliding_Window_00532.csv", "meta": { "source": "causal_rivers", "args": { "channel": "245", "time": "2023", "metric": "largest range", "window": "17D" } } }, { "id": "L1_T4_Sliding_Window_00533", "level": 1, "level_name": "Basic Operations", "category": "Sliding Window", "subtask": "Sliding Window", "question": "Which 58-day period in 2021 had the lowest average for channel 312? (Output format: [YYYY-MM-DD HH:MM:SS, YYYY-MM-DD HH:MM:SS])", "answer": "[2021-01-01 00:00:00, 2021-01-01 00:00:00]", "ground_truth": [ "2021-01-01 00:00:00", "2021-01-01 00:00:00" ], "eval_metric": "iou", "channel": "312", "ts_data_path": "ts_data/L1_T4_Sliding_Window_00533.csv", "meta": { "source": "causal_rivers", "args": { "channel": "312", "time": "2021", "metric": "lowest average", "window": "58D" } } }, { "id": "L1_T4_Sliding_Window_00536", "level": 1, "level_name": "Basic Operations", "category": "Sliding Window", "subtask": "Sliding Window", "question": "Which 57-day period in 2021 had the highest variance for channel 495? (Output format: [YYYY-MM-DD HH:MM:SS, YYYY-MM-DD HH:MM:SS])", "answer": "[2021-09-25 15:15:00, 2021-11-21 15:00:00]", "ground_truth": [ "2021-09-25 15:15:00", "2021-11-21 15:00:00" ], "eval_metric": "iou", "channel": "495", "ts_data_path": "ts_data/L1_T4_Sliding_Window_00536.csv", "meta": { "source": "causal_rivers", "args": { "channel": "495", "time": "2021", "metric": "highest variance", "window": "57D" } } }, { "id": "L1_T4_Sliding_Window_00541", "level": 1, "level_name": "Basic Operations", "category": "Sliding Window", "subtask": "Sliding Window", "question": "Which 47-day period in 2020 had the highest variance for channel 589? (Output format: [YYYY-MM-DD HH:MM:SS, YYYY-MM-DD HH:MM:SS])", "answer": "[2020-09-20 09:30:00, 2020-11-06 09:15:00]", "ground_truth": [ "2020-09-20 09:30:00", "2020-11-06 09:15:00" ], "eval_metric": "iou", "channel": "589", "ts_data_path": "ts_data/L1_T4_Sliding_Window_00541.csv", "meta": { "source": "causal_rivers", "args": { "channel": "589", "time": "2020", "metric": "highest variance", "window": "47D" } } }, { "id": "L1_T4_Sliding_Window_00543", "level": 1, "level_name": "Basic Operations", "category": "Sliding Window", "subtask": "Sliding Window", "question": "Which 8-day period in 2019 had the highest variance for channel 595? (Output format: [YYYY-MM-DD HH:MM:SS, YYYY-MM-DD HH:MM:SS])", "answer": "[2019-03-06 19:15:00, 2019-03-14 19:00:00]", "ground_truth": [ "2019-03-06 19:15:00", "2019-03-14 19:00:00" ], "eval_metric": "iou", "channel": "595", "ts_data_path": "ts_data/L1_T4_Sliding_Window_00543.csv", "meta": { "source": "causal_rivers", "args": { "channel": "595", "time": "2019", "metric": "highest variance", "window": "8D" } } }, { "id": "L1_T4_Sliding_Window_00554", "level": 1, "level_name": "Basic Operations", "category": "Sliding Window", "subtask": "Sliding Window", "question": "Which 21-day period in 2019 had the highest variance for channel 762? (Output format: [YYYY-MM-DD HH:MM:SS, YYYY-MM-DD HH:MM:SS])", "answer": "[2019-11-26 19:00:00, 2019-12-17 18:45:00]", "ground_truth": [ "2019-11-26 19:00:00", "2019-12-17 18:45:00" ], "eval_metric": "iou", "channel": "762", "ts_data_path": "ts_data/L1_T4_Sliding_Window_00554.csv", "meta": { "source": "causal_rivers", "args": { "channel": "762", "time": "2019", "metric": "highest variance", "window": "21D" } } }, { "id": "L1_T4_Sliding_Window_00555", "level": 1, "level_name": "Basic Operations", "category": "Sliding Window", "subtask": "Sliding Window", "question": "Which 52-day period in 2022 had the highest variance for channel 811? (Output format: [YYYY-MM-DD HH:MM:SS, YYYY-MM-DD HH:MM:SS])", "answer": "[2022-02-12 08:45:00, 2022-04-05 08:30:00]", "ground_truth": [ "2022-02-12 08:45:00", "2022-04-05 08:30:00" ], "eval_metric": "iou", "channel": "811", "ts_data_path": "ts_data/L1_T4_Sliding_Window_00555.csv", "meta": { "source": "causal_rivers", "args": { "channel": "811", "time": "2022", "metric": "highest variance", "window": "52D" } } }, { "id": "L1_T4_Sliding_Window_00557", "level": 1, "level_name": "Basic Operations", "category": "Sliding Window", "subtask": "Sliding Window", "question": "Which 3-day period in 2021 had the lowest average for channel 865? (Output format: [YYYY-MM-DD HH:MM:SS, YYYY-MM-DD HH:MM:SS])", "answer": "[2021-06-27 04:15:00, 2021-06-30 04:00:00]", "ground_truth": [ "2021-06-27 04:15:00", "2021-06-30 04:00:00" ], "eval_metric": "iou", "channel": "865", "ts_data_path": "ts_data/L1_T4_Sliding_Window_00557.csv", "meta": { "source": "causal_rivers", "args": { "channel": "865", "time": "2021", "metric": "lowest average", "window": "3D" } } }, { "id": "L1_T4_Sliding_Window_00558", "level": 1, "level_name": "Basic Operations", "category": "Sliding Window", "subtask": "Sliding Window", "question": "Which 58-day period in 2020 had the highest average for channel 891? (Output format: [YYYY-MM-DD HH:MM:SS, YYYY-MM-DD HH:MM:SS])", "answer": "[2020-01-29 06:00:00, 2020-03-27 05:45:00]", "ground_truth": [ "2020-01-29 06:00:00", "2020-03-27 05:45:00" ], "eval_metric": "iou", "channel": "891", "ts_data_path": "ts_data/L1_T4_Sliding_Window_00558.csv", "meta": { "source": "causal_rivers", "args": { "channel": "891", "time": "2020", "metric": "highest average", "window": "58D" } } }, { "id": "L1_T4_Sliding_Window_00564", "level": 1, "level_name": "Basic Operations", "category": "Sliding Window", "subtask": "Sliding Window", "question": "Which 43-day period in 2018 had the largest range for channel HULL? (Output format: [YYYY-MM-DD HH:MM:SS, YYYY-MM-DD HH:MM:SS])", "answer": "[2018-03-03 21:00:00, 2018-04-15 20:45:00]", "ground_truth": [ "2018-03-03 21:00:00", "2018-04-15 20:45:00" ], "eval_metric": "iou", "channel": "HULL", "ts_data_path": "ts_data/L1_T4_Sliding_Window_00564.csv", "meta": { "source": "ettm1", "args": { "channel": "HULL", "time": "2018", "metric": "largest range", "window": "43D" } } }, { "id": "L1_T4_Sliding_Window_00566", "level": 1, "level_name": "Basic Operations", "category": "Sliding Window", "subtask": "Sliding Window", "question": "Which 46-day period in 2016 had the largest range for channel MULL? (Output format: [YYYY-MM-DD HH:MM:SS, YYYY-MM-DD HH:MM:SS])", "answer": "[2016-11-01 21:00:00, 2016-12-17 20:45:00]", "ground_truth": [ "2016-11-01 21:00:00", "2016-12-17 20:45:00" ], "eval_metric": "iou", "channel": "MULL", "ts_data_path": "ts_data/L1_T4_Sliding_Window_00566.csv", "meta": { "source": "ettm1", "args": { "channel": "MULL", "time": "2016", "metric": "largest range", "window": "46D" } } }, { "id": "L1_T4_Sliding_Window_00569", "level": 1, "level_name": "Basic Operations", "category": "Sliding Window", "subtask": "Sliding Window", "question": "Which 29-day period in 2017 had the largest range for channel OT? (Output format: [YYYY-MM-DD HH:MM:SS, YYYY-MM-DD HH:MM:SS])", "answer": "[2017-03-17 16:45:00, 2017-04-15 16:30:00]", "ground_truth": [ "2017-03-17 16:45:00", "2017-04-15 16:30:00" ], "eval_metric": "iou", "channel": "OT", "ts_data_path": "ts_data/L1_T4_Sliding_Window_00569.csv", "meta": { "source": "ettm1", "args": { "channel": "OT", "time": "2017", "metric": "largest range", "window": "29D" } } }, { "id": "L1_T4_Sliding_Window_00574", "level": 1, "level_name": "Basic Operations", "category": "Sliding Window", "subtask": "Sliding Window", "question": "Which 24-day period in 2019 had the highest variance for channel 67? (Output format: [YYYY-MM-DD HH:MM:SS, YYYY-MM-DD HH:MM:SS])", "answer": "[2019-07-20 12:30:00, 2019-08-13 12:15:00]", "ground_truth": [ "2019-07-20 12:30:00", "2019-08-13 12:15:00" ], "eval_metric": "iou", "channel": "67", "ts_data_path": "ts_data/L1_T4_Sliding_Window_00574.csv", "meta": { "source": "causal_rivers", "args": { "channel": "67", "time": "2019", "metric": "highest variance", "window": "24D" } } }, { "id": "L1_T4_Sliding_Window_00579", "level": 1, "level_name": "Basic Operations", "category": "Sliding Window", "subtask": "Sliding Window", "question": "Which 51-day period in 2019 had the highest average for channel 146? (Output format: [YYYY-MM-DD HH:MM:SS, YYYY-MM-DD HH:MM:SS])", "answer": "[2019-02-05 09:15:00, 2019-03-28 09:00:00]", "ground_truth": [ "2019-02-05 09:15:00", "2019-03-28 09:00:00" ], "eval_metric": "iou", "channel": "146", "ts_data_path": "ts_data/L1_T4_Sliding_Window_00579.csv", "meta": { "source": "causal_rivers", "args": { "channel": "146", "time": "2019", "metric": "highest average", "window": "51D" } } }, { "id": "L1_T4_Sliding_Window_00580", "level": 1, "level_name": "Basic Operations", "category": "Sliding Window", "subtask": "Sliding Window", "question": "Which 43-day period in 2022 had the highest variance for channel 147? (Output format: [YYYY-MM-DD HH:MM:SS, YYYY-MM-DD HH:MM:SS])", "answer": "[2022-02-17 04:15:00, 2022-04-01 04:00:00]", "ground_truth": [ "2022-02-17 04:15:00", "2022-04-01 04:00:00" ], "eval_metric": "iou", "channel": "147", "ts_data_path": "ts_data/L1_T4_Sliding_Window_00580.csv", "meta": { "source": "causal_rivers", "args": { "channel": "147", "time": "2022", "metric": "highest variance", "window": "43D" } } }, { "id": "L1_T4_Sliding_Window_00583", "level": 1, "level_name": "Basic Operations", "category": "Sliding Window", "subtask": "Sliding Window", "question": "Which 41-day period in 2023 had the lowest average for channel 155? (Output format: [YYYY-MM-DD HH:MM:SS, YYYY-MM-DD HH:MM:SS])", "answer": "[2023-08-27 16:15:00, 2023-10-07 16:00:00]", "ground_truth": [ "2023-08-27 16:15:00", "2023-10-07 16:00:00" ], "eval_metric": "iou", "channel": "155", "ts_data_path": "ts_data/L1_T4_Sliding_Window_00583.csv", "meta": { "source": "causal_rivers", "args": { "channel": "155", "time": "2023", "metric": "lowest average", "window": "41D" } } }, { "id": "L1_T4_Sliding_Window_00585", "level": 1, "level_name": "Basic Operations", "category": "Sliding Window", "subtask": "Sliding Window", "question": "Which 50-day period in 2023 had the highest average for channel 169? (Output format: [YYYY-MM-DD HH:MM:SS, YYYY-MM-DD HH:MM:SS])", "answer": "[2023-03-11 21:15:00, 2023-04-30 21:00:00]", "ground_truth": [ "2023-03-11 21:15:00", "2023-04-30 21:00:00" ], "eval_metric": "iou", "channel": "169", "ts_data_path": "ts_data/L1_T4_Sliding_Window_00585.csv", "meta": { "source": "causal_rivers", "args": { "channel": "169", "time": "2023", "metric": "highest average", "window": "50D" } } }, { "id": "L1_T4_Sliding_Window_00586", "level": 1, "level_name": "Basic Operations", "category": "Sliding Window", "subtask": "Sliding Window", "question": "Which 43-day period in 2021 had the highest variance for channel 170? (Output format: [YYYY-MM-DD HH:MM:SS, YYYY-MM-DD HH:MM:SS])", "answer": "[2021-01-04 01:30:00, 2021-02-16 01:15:00]", "ground_truth": [ "2021-01-04 01:30:00", "2021-02-16 01:15:00" ], "eval_metric": "iou", "channel": "170", "ts_data_path": "ts_data/L1_T4_Sliding_Window_00586.csv", "meta": { "source": "causal_rivers", "args": { "channel": "170", "time": "2021", "metric": "highest variance", "window": "43D" } } }, { "id": "L1_T4_Sliding_Window_00587", "level": 1, "level_name": "Basic Operations", "category": "Sliding Window", "subtask": "Sliding Window", "question": "Which 18-day period in 2020 had the lowest average for channel 172? (Output format: [YYYY-MM-DD HH:MM:SS, YYYY-MM-DD HH:MM:SS])", "answer": "[2020-05-22 00:00:00, 2020-06-08 23:45:00]", "ground_truth": [ "2020-05-22 00:00:00", "2020-06-08 23:45:00" ], "eval_metric": "iou", "channel": "172", "ts_data_path": "ts_data/L1_T4_Sliding_Window_00587.csv", "meta": { "source": "causal_rivers", "args": { "channel": "172", "time": "2020", "metric": "lowest average", "window": "18D" } } }, { "id": "L1_T4_Sliding_Window_00591", "level": 1, "level_name": "Basic Operations", "category": "Sliding Window", "subtask": "Sliding Window", "question": "Which 42-day period in 2020 had the lowest average for channel 245? (Output format: [YYYY-MM-DD HH:MM:SS, YYYY-MM-DD HH:MM:SS])", "answer": "[2020-11-10 15:30:00, 2020-12-22 15:15:00]", "ground_truth": [ "2020-11-10 15:30:00", "2020-12-22 15:15:00" ], "eval_metric": "iou", "channel": "245", "ts_data_path": "ts_data/L1_T4_Sliding_Window_00591.csv", "meta": { "source": "causal_rivers", "args": { "channel": "245", "time": "2020", "metric": "lowest average", "window": "42D" } } }, { "id": "L1_T4_Sliding_Window_00596", "level": 1, "level_name": "Basic Operations", "category": "Sliding Window", "subtask": "Sliding Window", "question": "Which 11-day period in 2023 had the highest average for channel 496? (Output format: [YYYY-MM-DD HH:MM:SS, YYYY-MM-DD HH:MM:SS])", "answer": "[2023-04-07 15:45:00, 2023-04-18 15:30:00]", "ground_truth": [ "2023-04-07 15:45:00", "2023-04-18 15:30:00" ], "eval_metric": "iou", "channel": "496", "ts_data_path": "ts_data/L1_T4_Sliding_Window_00596.csv", "meta": { "source": "causal_rivers", "args": { "channel": "496", "time": "2023", "metric": "highest average", "window": "11D" } } }, { "id": "L1_T4_Sliding_Window_00600", "level": 1, "level_name": "Basic Operations", "category": "Sliding Window", "subtask": "Sliding Window", "question": "Which 34-day period in 2022 had the largest range for channel 589? (Output format: [YYYY-MM-DD HH:MM:SS, YYYY-MM-DD HH:MM:SS])", "answer": "[2022-03-01 14:00:00, 2022-04-04 13:45:00]", "ground_truth": [ "2022-03-01 14:00:00", "2022-04-04 13:45:00" ], "eval_metric": "iou", "channel": "589", "ts_data_path": "ts_data/L1_T4_Sliding_Window_00600.csv", "meta": { "source": "causal_rivers", "args": { "channel": "589", "time": "2022", "metric": "largest range", "window": "34D" } } }, { "id": "L1_T4_Sliding_Window_00601", "level": 1, "level_name": "Basic Operations", "category": "Sliding Window", "subtask": "Sliding Window", "question": "Which 21-day period in 2023 had the lowest average for channel 591? (Output format: [YYYY-MM-DD HH:MM:SS, YYYY-MM-DD HH:MM:SS])", "answer": "[2023-07-09 08:30:00, 2023-07-30 08:15:00]", "ground_truth": [ "2023-07-09 08:30:00", "2023-07-30 08:15:00" ], "eval_metric": "iou", "channel": "591", "ts_data_path": "ts_data/L1_T4_Sliding_Window_00601.csv", "meta": { "source": "causal_rivers", "args": { "channel": "591", "time": "2023", "metric": "lowest average", "window": "21D" } } }, { "id": "L1_T4_Sliding_Window_00605", "level": 1, "level_name": "Basic Operations", "category": "Sliding Window", "subtask": "Sliding Window", "question": "Which 12-day period in 2020 had the lowest average for channel 627? (Output format: [YYYY-MM-DD HH:MM:SS, YYYY-MM-DD HH:MM:SS])", "answer": "[2020-09-12 14:15:00, 2020-09-24 14:00:00]", "ground_truth": [ "2020-09-12 14:15:00", "2020-09-24 14:00:00" ], "eval_metric": "iou", "channel": "627", "ts_data_path": "ts_data/L1_T4_Sliding_Window_00605.csv", "meta": { "source": "causal_rivers", "args": { "channel": "627", "time": "2020", "metric": "lowest average", "window": "12D" } } }, { "id": "L1_T4_Sliding_Window_00608", "level": 1, "level_name": "Basic Operations", "category": "Sliding Window", "subtask": "Sliding Window", "question": "Which 54-day period in 2023 had the highest average for channel 683? (Output format: [YYYY-MM-DD HH:MM:SS, YYYY-MM-DD HH:MM:SS])", "answer": "[2023-02-09 17:30:00, 2023-04-04 17:15:00]", "ground_truth": [ "2023-02-09 17:30:00", "2023-04-04 17:15:00" ], "eval_metric": "iou", "channel": "683", "ts_data_path": "ts_data/L1_T4_Sliding_Window_00608.csv", "meta": { "source": "causal_rivers", "args": { "channel": "683", "time": "2023", "metric": "highest average", "window": "54D" } } }, { "id": "L1_T4_Sliding_Window_00609", "level": 1, "level_name": "Basic Operations", "category": "Sliding Window", "subtask": "Sliding Window", "question": "Which 56-day period in 2023 had the lowest average for channel 727? (Output format: [YYYY-MM-DD HH:MM:SS, YYYY-MM-DD HH:MM:SS])", "answer": "[2023-05-31 03:45:00, 2023-07-26 03:30:00]", "ground_truth": [ "2023-05-31 03:45:00", "2023-07-26 03:30:00" ], "eval_metric": "iou", "channel": "727", "ts_data_path": "ts_data/L1_T4_Sliding_Window_00609.csv", "meta": { "source": "causal_rivers", "args": { "channel": "727", "time": "2023", "metric": "lowest average", "window": "56D" } } }, { "id": "L1_T4_Sliding_Window_00616", "level": 1, "level_name": "Basic Operations", "category": "Sliding Window", "subtask": "Sliding Window", "question": "Which 55-day period in 2019 had the lowest average for channel 865? (Output format: [YYYY-MM-DD HH:MM:SS, YYYY-MM-DD HH:MM:SS])", "answer": "[2019-07-17 12:30:00, 2019-09-10 12:15:00]", "ground_truth": [ "2019-07-17 12:30:00", "2019-09-10 12:15:00" ], "eval_metric": "iou", "channel": "865", "ts_data_path": "ts_data/L1_T4_Sliding_Window_00616.csv", "meta": { "source": "causal_rivers", "args": { "channel": "865", "time": "2019", "metric": "lowest average", "window": "55D" } } }, { "id": "L1_T4_Sliding_Window_00619", "level": 1, "level_name": "Basic Operations", "category": "Sliding Window", "subtask": "Sliding Window", "question": "Which 4-day period in 2020 had the largest range for channel 895? (Output format: [YYYY-MM-DD HH:MM:SS, YYYY-MM-DD HH:MM:SS])", "answer": "[2020-02-21 16:30:00, 2020-02-25 16:15:00]", "ground_truth": [ "2020-02-21 16:30:00", "2020-02-25 16:15:00" ], "eval_metric": "iou", "channel": "895", "ts_data_path": "ts_data/L1_T4_Sliding_Window_00619.csv", "meta": { "source": "causal_rivers", "args": { "channel": "895", "time": "2020", "metric": "largest range", "window": "4D" } } }, { "id": "L1_T4_Sliding_Window_00624", "level": 1, "level_name": "Basic Operations", "category": "Sliding Window", "subtask": "Sliding Window", "question": "Which 37-day period in 2017 had the highest variance for channel MUFL? (Output format: [YYYY-MM-DD HH:MM:SS, YYYY-MM-DD HH:MM:SS])", "answer": "[2017-03-24 17:15:00, 2017-04-30 17:00:00]", "ground_truth": [ "2017-03-24 17:15:00", "2017-04-30 17:00:00" ], "eval_metric": "iou", "channel": "MUFL", "ts_data_path": "ts_data/L1_T4_Sliding_Window_00624.csv", "meta": { "source": "ettm1", "args": { "channel": "MUFL", "time": "2017", "metric": "highest variance", "window": "37D" } } }, { "id": "L1_T4_Sliding_Window_00627", "level": 1, "level_name": "Basic Operations", "category": "Sliding Window", "subtask": "Sliding Window", "question": "Which 41-day period in 2018 had the highest variance for channel LULL? (Output format: [YYYY-MM-DD HH:MM:SS, YYYY-MM-DD HH:MM:SS])", "answer": "[2018-01-01 00:00:00, 2018-01-02 07:45:00]", "ground_truth": [ "2018-01-01 00:00:00", "2018-01-02 07:45:00" ], "eval_metric": "iou", "channel": "LULL", "ts_data_path": "ts_data/L1_T4_Sliding_Window_00627.csv", "meta": { "source": "ettm1", "args": { "channel": "LULL", "time": "2018", "metric": "highest variance", "window": "41D" } } }, { "id": "L1_T4_Sliding_Window_00629", "level": 1, "level_name": "Basic Operations", "category": "Sliding Window", "subtask": "Sliding Window", "question": "Which 4-day period in 2020 had the largest range for channel m_06? (Output format: [YYYY-MM-DD HH:MM:SS, YYYY-MM-DD HH:MM:SS])", "answer": "[2020-01-30 22:11:00, 2020-02-03 22:10:00]", "ground_truth": [ "2020-01-30 22:11:00", "2020-02-03 22:10:00" ], "eval_metric": "iou", "channel": "m_06", "ts_data_path": "ts_data/L1_T4_Sliding_Window_00629.csv", "meta": { "source": "smd_machine_1_1", "args": { "channel": "m_06", "time": "2020", "metric": "largest range", "window": "4D" } } }, { "id": "L1_T4_Sliding_Window_00633", "level": 1, "level_name": "Basic Operations", "category": "Sliding Window", "subtask": "Sliding Window", "question": "Which 14-day period in 2021 had the highest variance for channel 67? (Output format: [YYYY-MM-DD HH:MM:SS, YYYY-MM-DD HH:MM:SS])", "answer": "[2021-06-30 09:30:00, 2021-07-14 09:15:00]", "ground_truth": [ "2021-06-30 09:30:00", "2021-07-14 09:15:00" ], "eval_metric": "iou", "channel": "67", "ts_data_path": "ts_data/L1_T4_Sliding_Window_00633.csv", "meta": { "source": "causal_rivers", "args": { "channel": "67", "time": "2021", "metric": "highest variance", "window": "14D" } } }, { "id": "L1_T4_Sliding_Window_00640", "level": 1, "level_name": "Basic Operations", "category": "Sliding Window", "subtask": "Sliding Window", "question": "Which 31-day period in 2021 had the largest range for channel 151? (Output format: [YYYY-MM-DD HH:MM:SS, YYYY-MM-DD HH:MM:SS])", "answer": "[2021-10-06 10:45:00, 2021-11-06 10:30:00]", "ground_truth": [ "2021-10-06 10:45:00", "2021-11-06 10:30:00" ], "eval_metric": "iou", "channel": "151", "ts_data_path": "ts_data/L1_T4_Sliding_Window_00640.csv", "meta": { "source": "causal_rivers", "args": { "channel": "151", "time": "2021", "metric": "largest range", "window": "31D" } } }, { "id": "L1_T4_Sliding_Window_00642", "level": 1, "level_name": "Basic Operations", "category": "Sliding Window", "subtask": "Sliding Window", "question": "Which 18-day period in 2021 had the highest average for channel 155? (Output format: [YYYY-MM-DD HH:MM:SS, YYYY-MM-DD HH:MM:SS])", "answer": "[2021-01-01 00:00:00, 2021-01-01 11:15:00]", "ground_truth": [ "2021-01-01 00:00:00", "2021-01-01 11:15:00" ], "eval_metric": "iou", "channel": "155", "ts_data_path": "ts_data/L1_T4_Sliding_Window_00642.csv", "meta": { "source": "causal_rivers", "args": { "channel": "155", "time": "2021", "metric": "highest average", "window": "18D" } } }, { "id": "L1_T4_Sliding_Window_00643", "level": 1, "level_name": "Basic Operations", "category": "Sliding Window", "subtask": "Sliding Window", "question": "Which 9-day period in 2019 had the lowest average for channel 166? (Output format: [YYYY-MM-DD HH:MM:SS, YYYY-MM-DD HH:MM:SS])", "answer": "[2019-09-01 12:45:00, 2019-09-10 12:30:00]", "ground_truth": [ "2019-09-01 12:45:00", "2019-09-10 12:30:00" ], "eval_metric": "iou", "channel": "166", "ts_data_path": "ts_data/L1_T4_Sliding_Window_00643.csv", "meta": { "source": "causal_rivers", "args": { "channel": "166", "time": "2019", "metric": "lowest average", "window": "9D" } } }, { "id": "L1_T4_Sliding_Window_00645", "level": 1, "level_name": "Basic Operations", "category": "Sliding Window", "subtask": "Sliding Window", "question": "Which 59-day period in 2022 had the highest average for channel 170? (Output format: [YYYY-MM-DD HH:MM:SS, YYYY-MM-DD HH:MM:SS])", "answer": "[2022-01-06 07:00:00, 2022-03-06 06:45:00]", "ground_truth": [ "2022-01-06 07:00:00", "2022-03-06 06:45:00" ], "eval_metric": "iou", "channel": "170", "ts_data_path": "ts_data/L1_T4_Sliding_Window_00645.csv", "meta": { "source": "causal_rivers", "args": { "channel": "170", "time": "2022", "metric": "highest average", "window": "59D" } } }, { "id": "L1_T4_Sliding_Window_00646", "level": 1, "level_name": "Basic Operations", "category": "Sliding Window", "subtask": "Sliding Window", "question": "Which 32-day period in 2019 had the lowest average for channel 172? (Output format: [YYYY-MM-DD HH:MM:SS, YYYY-MM-DD HH:MM:SS])", "answer": "[2019-07-02 18:15:00, 2019-08-03 18:00:00]", "ground_truth": [ "2019-07-02 18:15:00", "2019-08-03 18:00:00" ], "eval_metric": "iou", "channel": "172", "ts_data_path": "ts_data/L1_T4_Sliding_Window_00646.csv", "meta": { "source": "causal_rivers", "args": { "channel": "172", "time": "2019", "metric": "lowest average", "window": "32D" } } }, { "id": "L1_T4_Sliding_Window_00650", "level": 1, "level_name": "Basic Operations", "category": "Sliding Window", "subtask": "Sliding Window", "question": "Which 59-day period in 2020 had the largest range for channel 245? (Output format: [YYYY-MM-DD HH:MM:SS, YYYY-MM-DD HH:MM:SS])", "answer": "[2020-09-28 10:00:00, 2020-11-26 09:45:00]", "ground_truth": [ "2020-09-28 10:00:00", "2020-11-26 09:45:00" ], "eval_metric": "iou", "channel": "245", "ts_data_path": "ts_data/L1_T4_Sliding_Window_00650.csv", "meta": { "source": "causal_rivers", "args": { "channel": "245", "time": "2020", "metric": "largest range", "window": "59D" } } }, { "id": "L1_T4_Sliding_Window_00653", "level": 1, "level_name": "Basic Operations", "category": "Sliding Window", "subtask": "Sliding Window", "question": "Which 39-day period in 2019 had the lowest average for channel 441? (Output format: [YYYY-MM-DD HH:MM:SS, YYYY-MM-DD HH:MM:SS])", "answer": "[2019-08-26 04:30:00, 2019-10-04 04:15:00]", "ground_truth": [ "2019-08-26 04:30:00", "2019-10-04 04:15:00" ], "eval_metric": "iou", "channel": "441", "ts_data_path": "ts_data/L1_T4_Sliding_Window_00653.csv", "meta": { "source": "causal_rivers", "args": { "channel": "441", "time": "2019", "metric": "lowest average", "window": "39D" } } }, { "id": "L1_T4_Sliding_Window_00655", "level": 1, "level_name": "Basic Operations", "category": "Sliding Window", "subtask": "Sliding Window", "question": "Which 41-day period in 2023 had the lowest average for channel 496? (Output format: [YYYY-MM-DD HH:MM:SS, YYYY-MM-DD HH:MM:SS])", "answer": "[2023-06-10 17:15:00, 2023-07-21 17:00:00]", "ground_truth": [ "2023-06-10 17:15:00", "2023-07-21 17:00:00" ], "eval_metric": "iou", "channel": "496", "ts_data_path": "ts_data/L1_T4_Sliding_Window_00655.csv", "meta": { "source": "causal_rivers", "args": { "channel": "496", "time": "2023", "metric": "lowest average", "window": "41D" } } }, { "id": "L1_T4_Sliding_Window_00656", "level": 1, "level_name": "Basic Operations", "category": "Sliding Window", "subtask": "Sliding Window", "question": "Which 60-day period in 2022 had the highest variance for channel 501? (Output format: [YYYY-MM-DD HH:MM:SS, YYYY-MM-DD HH:MM:SS])", "answer": "[2022-01-01 00:00:00, 2022-02-25 16:15:00]", "ground_truth": [ "2022-01-01 00:00:00", "2022-02-25 16:15:00" ], "eval_metric": "iou", "channel": "501", "ts_data_path": "ts_data/L1_T4_Sliding_Window_00656.csv", "meta": { "source": "causal_rivers", "args": { "channel": "501", "time": "2022", "metric": "highest variance", "window": "60D" } } }, { "id": "L1_T4_Sliding_Window_00657", "level": 1, "level_name": "Basic Operations", "category": "Sliding Window", "subtask": "Sliding Window", "question": "Which 20-day period in 2023 had the highest variance for channel 578? (Output format: [YYYY-MM-DD HH:MM:SS, YYYY-MM-DD HH:MM:SS])", "answer": "[2023-12-07 05:30:00, 2023-12-27 05:15:00]", "ground_truth": [ "2023-12-07 05:30:00", "2023-12-27 05:15:00" ], "eval_metric": "iou", "channel": "578", "ts_data_path": "ts_data/L1_T4_Sliding_Window_00657.csv", "meta": { "source": "causal_rivers", "args": { "channel": "578", "time": "2023", "metric": "highest variance", "window": "20D" } } }, { "id": "L1_T4_Sliding_Window_00664", "level": 1, "level_name": "Basic Operations", "category": "Sliding Window", "subtask": "Sliding Window", "question": "Which 12-day period in 2023 had the highest average for channel 627? (Output format: [YYYY-MM-DD HH:MM:SS, YYYY-MM-DD HH:MM:SS])", "answer": "[2023-12-20 00:00:00, 2023-12-31 23:45:00]", "ground_truth": [ "2023-12-20 00:00:00", "2023-12-31 23:45:00" ], "eval_metric": "iou", "channel": "627", "ts_data_path": "ts_data/L1_T4_Sliding_Window_00664.csv", "meta": { "source": "causal_rivers", "args": { "channel": "627", "time": "2023", "metric": "highest average", "window": "12D" } } }, { "id": "L1_T4_Sliding_Window_00665", "level": 1, "level_name": "Basic Operations", "category": "Sliding Window", "subtask": "Sliding Window", "question": "Which 14-day period in 2021 had the highest average for channel 647? (Output format: [YYYY-MM-DD HH:MM:SS, YYYY-MM-DD HH:MM:SS])", "answer": "[2021-02-12 18:45:00, 2021-02-26 18:30:00]", "ground_truth": [ "2021-02-12 18:45:00", "2021-02-26 18:30:00" ], "eval_metric": "iou", "channel": "647", "ts_data_path": "ts_data/L1_T4_Sliding_Window_00665.csv", "meta": { "source": "causal_rivers", "args": { "channel": "647", "time": "2021", "metric": "highest average", "window": "14D" } } }, { "id": "L1_T4_Sliding_Window_00666", "level": 1, "level_name": "Basic Operations", "category": "Sliding Window", "subtask": "Sliding Window", "question": "Which 17-day period in 2021 had the largest range for channel 680? (Output format: [YYYY-MM-DD HH:MM:SS, YYYY-MM-DD HH:MM:SS])", "answer": "[2021-06-14 19:15:00, 2021-07-01 19:00:00]", "ground_truth": [ "2021-06-14 19:15:00", "2021-07-01 19:00:00" ], "eval_metric": "iou", "channel": "680", "ts_data_path": "ts_data/L1_T4_Sliding_Window_00666.csv", "meta": { "source": "causal_rivers", "args": { "channel": "680", "time": "2021", "metric": "largest range", "window": "17D" } } }, { "id": "L1_T4_Sliding_Window_00674", "level": 1, "level_name": "Basic Operations", "category": "Sliding Window", "subtask": "Sliding Window", "question": "Which 38-day period in 2023 had the highest average for channel 813? (Output format: [YYYY-MM-DD HH:MM:SS, YYYY-MM-DD HH:MM:SS])", "answer": "[2023-03-10 09:15:00, 2023-04-17 09:00:00]", "ground_truth": [ "2023-03-10 09:15:00", "2023-04-17 09:00:00" ], "eval_metric": "iou", "channel": "813", "ts_data_path": "ts_data/L1_T4_Sliding_Window_00674.csv", "meta": { "source": "causal_rivers", "args": { "channel": "813", "time": "2023", "metric": "highest average", "window": "38D" } } }, { "id": "L1_T4_Sliding_Window_00675", "level": 1, "level_name": "Basic Operations", "category": "Sliding Window", "subtask": "Sliding Window", "question": "Which 46-day period in 2020 had the highest variance for channel 865? (Output format: [YYYY-MM-DD HH:MM:SS, YYYY-MM-DD HH:MM:SS])", "answer": "[2020-09-11 19:45:00, 2020-10-27 19:30:00]", "ground_truth": [ "2020-09-11 19:45:00", "2020-10-27 19:30:00" ], "eval_metric": "iou", "channel": "865", "ts_data_path": "ts_data/L1_T4_Sliding_Window_00675.csv", "meta": { "source": "causal_rivers", "args": { "channel": "865", "time": "2020", "metric": "highest variance", "window": "46D" } } }, { "id": "L1_T4_Sliding_Window_00680", "level": 1, "level_name": "Basic Operations", "category": "Sliding Window", "subtask": "Sliding Window", "question": "Which 57-day period in 2021 had the highest average for channel 933? (Output format: [YYYY-MM-DD HH:MM:SS, YYYY-MM-DD HH:MM:SS])", "answer": "[2021-02-02 12:30:00, 2021-03-31 12:15:00]", "ground_truth": [ "2021-02-02 12:30:00", "2021-03-31 12:15:00" ], "eval_metric": "iou", "channel": "933", "ts_data_path": "ts_data/L1_T4_Sliding_Window_00680.csv", "meta": { "source": "causal_rivers", "args": { "channel": "933", "time": "2021", "metric": "highest average", "window": "57D" } } }, { "id": "L1_T4_Sliding_Window_00681", "level": 1, "level_name": "Basic Operations", "category": "Sliding Window", "subtask": "Sliding Window", "question": "Which 28-day period in 2018 had the highest average for channel HUFL? (Output format: [YYYY-MM-DD HH:MM:SS, YYYY-MM-DD HH:MM:SS])", "answer": "[2018-01-01 00:00:00, 2018-01-01 02:30:00]", "ground_truth": [ "2018-01-01 00:00:00", "2018-01-01 02:30:00" ], "eval_metric": "iou", "channel": "HUFL", "ts_data_path": "ts_data/L1_T4_Sliding_Window_00681.csv", "meta": { "source": "ettm1", "args": { "channel": "HUFL", "time": "2018", "metric": "highest average", "window": "28D" } } }, { "id": "L1_T4_Sliding_Window_00688", "level": 1, "level_name": "Basic Operations", "category": "Sliding Window", "subtask": "Sliding Window", "question": "Which 19-day period in 2020 had the largest range for channel m_06? (Output format: [YYYY-MM-DD HH:MM:SS, YYYY-MM-DD HH:MM:SS])", "answer": "[2020-01-13 22:06:00, 2020-02-01 22:05:00]", "ground_truth": [ "2020-01-13 22:06:00", "2020-02-01 22:05:00" ], "eval_metric": "iou", "channel": "m_06", "ts_data_path": "ts_data/L1_T4_Sliding_Window_00688.csv", "meta": { "source": "smd_machine_1_1", "args": { "channel": "m_06", "time": "2020", "metric": "largest range", "window": "19D" } } }, { "id": "L1_T4_Sliding_Window_00689", "level": 1, "level_name": "Basic Operations", "category": "Sliding Window", "subtask": "Sliding Window", "question": "Which 4-day period in 2020 had the lowest average for channel m_14? (Output format: [YYYY-MM-DD HH:MM:SS, YYYY-MM-DD HH:MM:SS])", "answer": "[2020-01-01 00:00:00, 2020-01-01 05:36:00]", "ground_truth": [ "2020-01-01 00:00:00", "2020-01-01 05:36:00" ], "eval_metric": "iou", "channel": "m_14", "ts_data_path": "ts_data/L1_T4_Sliding_Window_00689.csv", "meta": { "source": "smd_machine_1_1", "args": { "channel": "m_14", "time": "2020", "metric": "lowest average", "window": "4D" } } }, { "id": "L1_T4_Sliding_Window_00690", "level": 1, "level_name": "Basic Operations", "category": "Sliding Window", "subtask": "Sliding Window", "question": "Which 6-day period in 2020 had the highest average for channel m_24? (Output format: [YYYY-MM-DD HH:MM:SS, YYYY-MM-DD HH:MM:SS])", "answer": "[2020-01-29 05:17:00, 2020-02-04 05:16:00]", "ground_truth": [ "2020-01-29 05:17:00", "2020-02-04 05:16:00" ], "eval_metric": "iou", "channel": "m_24", "ts_data_path": "ts_data/L1_T4_Sliding_Window_00690.csv", "meta": { "source": "smd_machine_1_1", "args": { "channel": "m_24", "time": "2020", "metric": "highest average", "window": "6D" } } }, { "id": "L2_T2_Periodicity_Detection_00166", "level": 2, "level_name": "Pattern Recognition", "category": "Periodicity Detection", "subtask": "Periodicity Detection", "question": "What is the dominant cycle period (in data points) of channel 67 within [2019-11-07 09:00:00 to 2019-11-15 11:45:00]? (Output format: integer)", "answer": "51", "ground_truth": 51, "eval_metric": "rel_acc", "channel": "67", "ts_data_path": "ts_data/L2_T2_Periodicity_Detection_00166.csv", "meta": { "period": 51, "wave_type": "cosine", "sub_period": 12, "amp": 1.0, "search_window": [ "2019-11-07 09:00:00", "2019-11-15 11:45:00" ], "source": "causal_rivers" } }, { "id": "L2_T2_Periodicity_Detection_00167", "level": 2, "level_name": "Pattern Recognition", "category": "Periodicity Detection", "subtask": "Periodicity Detection", "question": "What is the dominant cycle period (in data points) of channel 71 within [2022-08-01 23:00:00 to 2022-08-07 08:15:00]? (Output format: integer)", "answer": "35", "ground_truth": 35, "eval_metric": "rel_acc", "channel": "71", "ts_data_path": "ts_data/L2_T2_Periodicity_Detection_00167.csv", "meta": { "period": 35, "wave_type": "composite", "sub_period": 8, "amp": 1.0, "search_window": [ "2022-08-01 23:00:00", "2022-08-07 08:15:00" ], "source": "causal_rivers" } }, { "id": "L2_T2_Periodicity_Detection_00168", "level": 2, "level_name": "Pattern Recognition", "category": "Periodicity Detection", "subtask": "Periodicity Detection", "question": "What is the dominant cycle period (in data points) of channel 99 within [2019-12-20 06:00:00 to 2019-12-24 00:15:00]? (Output format: integer)", "answer": "33", "ground_truth": 33, "eval_metric": "rel_acc", "channel": "99", "ts_data_path": "ts_data/L2_T2_Periodicity_Detection_00168.csv", "meta": { "period": 33, "wave_type": "cosine", "sub_period": 16, "amp": 1.0, "search_window": [ "2019-12-20 06:00:00", "2019-12-24 00:15:00" ], "source": "causal_rivers" } }, { "id": "L2_T2_Periodicity_Detection_00169", "level": 2, "level_name": "Pattern Recognition", "category": "Periodicity Detection", "subtask": "Periodicity Detection", "question": "What is the dominant cycle period (in data points) of channel 123 within [2022-10-26 06:00:00 to 2022-10-30 20:15:00]? (Output format: integer)", "answer": "49", "ground_truth": 49, "eval_metric": "rel_acc", "channel": "123", "ts_data_path": "ts_data/L2_T2_Periodicity_Detection_00169.csv", "meta": { "period": 49, "wave_type": "cosine", "sub_period": 24, "amp": 1.0, "search_window": [ "2022-10-26 06:00:00", "2022-10-30 20:15:00" ], "source": "causal_rivers" } }, { "id": "L2_T2_Periodicity_Detection_00170", "level": 2, "level_name": "Pattern Recognition", "category": "Periodicity Detection", "subtask": "Periodicity Detection", "question": "What is the dominant cycle period (in data points) of channel 124 within [2022-08-16 00:00:00 to 2022-08-25 20:00:00]? (Output format: integer)", "answer": "100", "ground_truth": 100, "eval_metric": "rel_acc", "channel": "124", "ts_data_path": "ts_data/L2_T2_Periodicity_Detection_00170.csv", "meta": { "period": 100, "wave_type": "cosine", "sub_period": 25, "amp": 1.0, "search_window": [ "2022-08-16 00:00:00", "2022-08-25 20:00:00" ], "source": "causal_rivers" } }, { "id": "L2_T2_Periodicity_Detection_00171", "level": 2, "level_name": "Pattern Recognition", "category": "Periodicity Detection", "subtask": "Periodicity Detection", "question": "What is the dominant cycle period (in data points) of channel 146 within [2019-05-08 23:15:00 to 2019-05-12 06:45:00]? (Output format: integer)", "answer": "97", "ground_truth": 97, "eval_metric": "rel_acc", "channel": "146", "ts_data_path": "ts_data/L2_T2_Periodicity_Detection_00171.csv", "meta": { "period": 97, "wave_type": "sine", "sub_period": 24, "amp": 1.0, "search_window": [ "2019-05-08 23:15:00", "2019-05-12 06:45:00" ], "source": "causal_rivers" } }, { "id": "L2_T2_Periodicity_Detection_00172", "level": 2, "level_name": "Pattern Recognition", "category": "Periodicity Detection", "subtask": "Periodicity Detection", "question": "What is the dominant cycle period (in data points) of channel 147 within [2022-07-16 00:00:00 to 2022-07-23 22:00:00]? (Output format: integer)", "answer": "98", "ground_truth": 98, "eval_metric": "rel_acc", "channel": "147", "ts_data_path": "ts_data/L2_T2_Periodicity_Detection_00172.csv", "meta": { "period": 98, "wave_type": "cosine", "sub_period": 32, "amp": 1.0, "search_window": [ "2022-07-16 00:00:00", "2022-07-23 22:00:00" ], "source": "causal_rivers" } }, { "id": "L2_T2_Periodicity_Detection_00173", "level": 2, "level_name": "Pattern Recognition", "category": "Periodicity Detection", "subtask": "Periodicity Detection", "question": "What is the dominant cycle period (in data points) of channel 151 within [2021-10-01 05:00:00 to 2021-10-05 06:00:00]? (Output format: integer)", "answer": "61", "ground_truth": 61, "eval_metric": "rel_acc", "channel": "151", "ts_data_path": "ts_data/L2_T2_Periodicity_Detection_00173.csv", "meta": { "period": 61, "wave_type": "cosine", "sub_period": 30, "amp": 1.0, "search_window": [ "2021-10-01 05:00:00", "2021-10-05 06:00:00" ], "source": "causal_rivers" } }, { "id": "L2_T2_Periodicity_Detection_00175", "level": 2, "level_name": "Pattern Recognition", "category": "Periodicity Detection", "subtask": "Periodicity Detection", "question": "What is the dominant cycle period (in data points) of channel 155 within [2021-03-25 04:00:00 to 2021-03-27 06:45:00]? (Output format: integer)", "answer": "68", "ground_truth": 68, "eval_metric": "rel_acc", "channel": "155", "ts_data_path": "ts_data/L2_T2_Periodicity_Detection_00175.csv", "meta": { "period": 68, "wave_type": "cosine", "sub_period": 34, "amp": 1.0, "search_window": [ "2021-03-25 04:00:00", "2021-03-27 06:45:00" ], "source": "causal_rivers" } }, { "id": "L2_T2_Periodicity_Detection_00176", "level": 2, "level_name": "Pattern Recognition", "category": "Periodicity Detection", "subtask": "Periodicity Detection", "question": "What is the dominant cycle period (in data points) of channel 166 within [2019-10-19 02:15:00 to 2019-10-26 03:00:00]? (Output format: integer)", "answer": "34", "ground_truth": 34, "eval_metric": "rel_acc", "channel": "166", "ts_data_path": "ts_data/L2_T2_Periodicity_Detection_00176.csv", "meta": { "period": 34, "wave_type": "cosine", "sub_period": 8, "amp": 22.238695329873984, "search_window": [ "2019-10-19 02:15:00", "2019-10-26 03:00:00" ], "source": "causal_rivers" } }, { "id": "L2_T2_Periodicity_Detection_00177", "level": 2, "level_name": "Pattern Recognition", "category": "Periodicity Detection", "subtask": "Periodicity Detection", "question": "What is the dominant cycle period (in data points) of channel 169 within [2023-09-30 19:30:00 to 2023-10-07 08:45:00]? (Output format: integer)", "answer": "82", "ground_truth": 82, "eval_metric": "rel_acc", "channel": "169", "ts_data_path": "ts_data/L2_T2_Periodicity_Detection_00177.csv", "meta": { "period": 82, "wave_type": "sine", "sub_period": 20, "amp": 13.343217197924389, "search_window": [ "2023-09-30 19:30:00", "2023-10-07 08:45:00" ], "source": "causal_rivers" } }, { "id": "L2_T2_Periodicity_Detection_00178", "level": 2, "level_name": "Pattern Recognition", "category": "Periodicity Detection", "subtask": "Periodicity Detection", "question": "What is the dominant cycle period (in data points) of channel 170 within [2019-12-21 14:00:00 to 2019-12-31 05:00:00]? (Output format: integer)", "answer": "113", "ground_truth": 113, "eval_metric": "rel_acc", "channel": "170", "ts_data_path": "ts_data/L2_T2_Periodicity_Detection_00178.csv", "meta": { "period": 113, "wave_type": "composite", "sub_period": 28, "amp": 37.805782060785766, "search_window": [ "2019-12-21 14:00:00", "2019-12-31 05:00:00" ], "source": "causal_rivers" } }, { "id": "L2_T2_Periodicity_Detection_00179", "level": 2, "level_name": "Pattern Recognition", "category": "Periodicity Detection", "subtask": "Periodicity Detection", "question": "What is the dominant cycle period (in data points) of channel 172 within [2019-08-16 12:00:00 to 2019-08-21 06:15:00]? (Output format: integer)", "answer": "32", "ground_truth": 32, "eval_metric": "rel_acc", "channel": "172", "ts_data_path": "ts_data/L2_T2_Periodicity_Detection_00179.csv", "meta": { "period": 32, "wave_type": "cosine", "sub_period": 10, "amp": 13.343217197924389, "search_window": [ "2019-08-16 12:00:00", "2019-08-21 06:15:00" ], "source": "causal_rivers" } }, { "id": "L2_T2_Periodicity_Detection_00180", "level": 2, "level_name": "Pattern Recognition", "category": "Periodicity Detection", "subtask": "Periodicity Detection", "question": "What is the dominant cycle period (in data points) of channel 173 within [2022-11-07 14:30:00 to 2022-11-13 04:45:00]? (Output format: integer)", "answer": "72", "ground_truth": 72, "eval_metric": "rel_acc", "channel": "173", "ts_data_path": "ts_data/L2_T2_Periodicity_Detection_00180.csv", "meta": { "period": 72, "wave_type": "cosine", "sub_period": 24, "amp": 13.343217197924389, "search_window": [ "2022-11-07 14:30:00", "2022-11-13 04:45:00" ], "source": "causal_rivers" } }, { "id": "L2_T2_Periodicity_Detection_00182", "level": 2, "level_name": "Pattern Recognition", "category": "Periodicity Detection", "subtask": "Periodicity Detection", "question": "What is the dominant cycle period (in data points) of channel 237 within [2022-06-23 07:00:00 to 2022-06-25 10:00:00]? (Output format: integer)", "answer": "68", "ground_truth": 68, "eval_metric": "rel_acc", "channel": "237", "ts_data_path": "ts_data/L2_T2_Periodicity_Detection_00182.csv", "meta": { "period": 68, "wave_type": "cosine", "sub_period": 34, "amp": 1.0, "search_window": [ "2022-06-23 07:00:00", "2022-06-25 10:00:00" ], "source": "causal_rivers" } }, { "id": "L2_T2_Periodicity_Detection_00184", "level": 2, "level_name": "Pattern Recognition", "category": "Periodicity Detection", "subtask": "Periodicity Detection", "question": "What is the dominant cycle period (in data points) of channel 312 within [2020-11-08 09:30:00 to 2020-11-12 01:30:00]? (Output format: integer)", "answer": "111", "ground_truth": 111, "eval_metric": "rel_acc", "channel": "312", "ts_data_path": "ts_data/L2_T2_Periodicity_Detection_00184.csv", "meta": { "period": 111, "wave_type": "sine", "sub_period": 55, "amp": 1.0, "search_window": [ "2020-11-08 09:30:00", "2020-11-12 01:30:00" ], "source": "causal_rivers" } }, { "id": "L2_T2_Periodicity_Detection_00185", "level": 2, "level_name": "Pattern Recognition", "category": "Periodicity Detection", "subtask": "Periodicity Detection", "question": "What is the dominant cycle period (in data points) of channel 430 within [2022-06-23 08:30:00 to 2022-07-03 08:00:00]? (Output format: integer)", "answer": "39", "ground_truth": 39, "eval_metric": "rel_acc", "channel": "430", "ts_data_path": "ts_data/L2_T2_Periodicity_Detection_00185.csv", "meta": { "period": 39, "wave_type": "cosine", "sub_period": 13, "amp": 1.0, "search_window": [ "2022-06-23 08:30:00", "2022-07-03 08:00:00" ], "source": "causal_rivers" } }, { "id": "L2_T2_Periodicity_Detection_00186", "level": 2, "level_name": "Pattern Recognition", "category": "Periodicity Detection", "subtask": "Periodicity Detection", "question": "What is the dominant cycle period (in data points) of channel 441 within [2022-09-08 07:00:00 to 2022-09-16 12:45:00]? (Output format: integer)", "answer": "54", "ground_truth": 54, "eval_metric": "rel_acc", "channel": "441", "ts_data_path": "ts_data/L2_T2_Periodicity_Detection_00186.csv", "meta": { "period": 54, "wave_type": "sine", "sub_period": 18, "amp": 2.268346923647145, "search_window": [ "2022-09-08 07:00:00", "2022-09-16 12:45:00" ], "source": "causal_rivers" } }, { "id": "L2_T2_Periodicity_Detection_00189", "level": 2, "level_name": "Pattern Recognition", "category": "Periodicity Detection", "subtask": "Periodicity Detection", "question": "What is the dominant cycle period (in data points) of channel 501 within [2021-07-17 06:00:00 to 2021-07-21 08:45:00]? (Output format: integer)", "answer": "84", "ground_truth": 84, "eval_metric": "rel_acc", "channel": "501", "ts_data_path": "ts_data/L2_T2_Periodicity_Detection_00189.csv", "meta": { "period": 84, "wave_type": "composite", "sub_period": 42, "amp": 1.0, "search_window": [ "2021-07-17 06:00:00", "2021-07-21 08:45:00" ], "source": "causal_rivers" } }, { "id": "L2_T2_Periodicity_Detection_00190", "level": 2, "level_name": "Pattern Recognition", "category": "Periodicity Detection", "subtask": "Periodicity Detection", "question": "What is the dominant cycle period (in data points) of channel 578 within [2020-12-07 08:00:00 to 2020-12-15 20:00:00]? (Output format: integer)", "answer": "80", "ground_truth": 80, "eval_metric": "rel_acc", "channel": "578", "ts_data_path": "ts_data/L2_T2_Periodicity_Detection_00190.csv", "meta": { "period": 80, "wave_type": "sine", "sub_period": 20, "amp": 1.0, "search_window": [ "2020-12-07 08:00:00", "2020-12-15 20:00:00" ], "source": "causal_rivers" } }, { "id": "L2_T2_Periodicity_Detection_00191", "level": 2, "level_name": "Pattern Recognition", "category": "Periodicity Detection", "subtask": "Periodicity Detection", "question": "What is the dominant cycle period (in data points) of channel 580 within [2022-06-23 16:00:00 to 2022-06-29 02:15:00]? (Output format: integer)", "answer": "53", "ground_truth": 53, "eval_metric": "rel_acc", "channel": "580", "ts_data_path": "ts_data/L2_T2_Periodicity_Detection_00191.csv", "meta": { "period": 53, "wave_type": "sine", "sub_period": 17, "amp": 1.0, "search_window": [ "2022-06-23 16:00:00", "2022-06-29 02:15:00" ], "source": "causal_rivers" } }, { "id": "L2_T2_Periodicity_Detection_00192", "level": 2, "level_name": "Pattern Recognition", "category": "Periodicity Detection", "subtask": "Periodicity Detection", "question": "What is the dominant cycle period (in data points) of channel 589 within [2021-11-15 04:00:00 to 2021-11-19 08:00:00]? (Output format: integer)", "answer": "86", "ground_truth": 86, "eval_metric": "rel_acc", "channel": "589", "ts_data_path": "ts_data/L2_T2_Periodicity_Detection_00192.csv", "meta": { "period": 86, "wave_type": "composite", "sub_period": 21, "amp": 11.119347664936992, "search_window": [ "2021-11-15 04:00:00", "2021-11-19 08:00:00" ], "source": "causal_rivers" } }, { "id": "L2_T2_Periodicity_Detection_00193", "level": 2, "level_name": "Pattern Recognition", "category": "Periodicity Detection", "subtask": "Periodicity Detection", "question": "What is the dominant cycle period (in data points) of channel 591 within [2021-12-05 20:30:00 to 2021-12-15 03:30:00]? (Output format: integer)", "answer": "72", "ground_truth": 72, "eval_metric": "rel_acc", "channel": "591", "ts_data_path": "ts_data/L2_T2_Periodicity_Detection_00193.csv", "meta": { "period": 72, "wave_type": "cosine", "sub_period": 24, "amp": 17.790956263899183, "search_window": [ "2021-12-05 20:30:00", "2021-12-15 03:30:00" ], "source": "causal_rivers" } }, { "id": "L2_T2_Periodicity_Detection_00194", "level": 2, "level_name": "Pattern Recognition", "category": "Periodicity Detection", "subtask": "Periodicity Detection", "question": "What is the dominant cycle period (in data points) of channel 595 within [2023-05-25 18:30:00 to 2023-06-02 18:45:00]? (Output format: integer)", "answer": "89", "ground_truth": 89, "eval_metric": "rel_acc", "channel": "595", "ts_data_path": "ts_data/L2_T2_Periodicity_Detection_00194.csv", "meta": { "period": 89, "wave_type": "cosine", "sub_period": 29, "amp": 1.0, "search_window": [ "2023-05-25 18:30:00", "2023-06-02 18:45:00" ], "source": "causal_rivers" } }, { "id": "L2_T2_Periodicity_Detection_00198", "level": 2, "level_name": "Pattern Recognition", "category": "Periodicity Detection", "subtask": "Periodicity Detection", "question": "What is the dominant cycle period (in data points) of channel 647 within [2020-07-09 14:00:00 to 2020-07-15 11:00:00]? (Output format: integer)", "answer": "74", "ground_truth": 74, "eval_metric": "rel_acc", "channel": "647", "ts_data_path": "ts_data/L2_T2_Periodicity_Detection_00198.csv", "meta": { "period": 74, "wave_type": "sine", "sub_period": 24, "amp": 1.0, "search_window": [ "2020-07-09 14:00:00", "2020-07-15 11:00:00" ], "source": "causal_rivers" } }, { "id": "L2_T2_Periodicity_Detection_00200", "level": 2, "level_name": "Pattern Recognition", "category": "Periodicity Detection", "subtask": "Periodicity Detection", "question": "What is the dominant cycle period (in data points) of channel 683 within [2020-04-29 02:45:00 to 2020-05-02 12:15:00]? (Output format: integer)", "answer": "74", "ground_truth": 74, "eval_metric": "rel_acc", "channel": "683", "ts_data_path": "ts_data/L2_T2_Periodicity_Detection_00200.csv", "meta": { "period": 74, "wave_type": "composite", "sub_period": 24, "amp": 1.0, "search_window": [ "2020-04-29 02:45:00", "2020-05-02 12:15:00" ], "source": "causal_rivers" } }, { "id": "L2_T2_Periodicity_Detection_00202", "level": 2, "level_name": "Pattern Recognition", "category": "Periodicity Detection", "subtask": "Periodicity Detection", "question": "What is the dominant cycle period (in data points) of channel 728 within [2020-07-22 18:00:00 to 2020-07-28 23:45:00]? (Output format: integer)", "answer": "76", "ground_truth": 76, "eval_metric": "rel_acc", "channel": "728", "ts_data_path": "ts_data/L2_T2_Periodicity_Detection_00202.csv", "meta": { "period": 76, "wave_type": "sine", "sub_period": 25, "amp": 7.56115641215715, "search_window": [ "2020-07-22 18:00:00", "2020-07-28 23:45:00" ], "source": "causal_rivers" } }, { "id": "L2_T2_Periodicity_Detection_00205", "level": 2, "level_name": "Pattern Recognition", "category": "Periodicity Detection", "subtask": "Periodicity Detection", "question": "What is the dominant cycle period (in data points) of channel 762 within [2022-10-07 00:00:00 to 2022-10-12 18:00:00]? (Output format: integer)", "answer": "35", "ground_truth": 35, "eval_metric": "rel_acc", "channel": "762", "ts_data_path": "ts_data/L2_T2_Periodicity_Detection_00205.csv", "meta": { "period": 35, "wave_type": "cosine", "sub_period": 8, "amp": 1.0, "search_window": [ "2022-10-07 00:00:00", "2022-10-12 18:00:00" ], "source": "causal_rivers" } }, { "id": "L2_T2_Periodicity_Detection_00207", "level": 2, "level_name": "Pattern Recognition", "category": "Periodicity Detection", "subtask": "Periodicity Detection", "question": "What is the dominant cycle period (in data points) of channel 813 within [2021-09-30 11:15:00 to 2021-10-08 23:15:00]? (Output format: integer)", "answer": "105", "ground_truth": 105, "eval_metric": "rel_acc", "channel": "813", "ts_data_path": "ts_data/L2_T2_Periodicity_Detection_00207.csv", "meta": { "period": 105, "wave_type": "composite", "sub_period": 52, "amp": 1.0, "search_window": [ "2021-09-30 11:15:00", "2021-10-08 23:15:00" ], "source": "causal_rivers" } }, { "id": "L2_T2_Periodicity_Detection_00209", "level": 2, "level_name": "Pattern Recognition", "category": "Periodicity Detection", "subtask": "Periodicity Detection", "question": "What is the dominant cycle period (in data points) of channel 891 within [2020-01-24 05:00:00 to 2020-01-26 12:00:00]? (Output format: integer)", "answer": "60", "ground_truth": 60, "eval_metric": "rel_acc", "channel": "891", "ts_data_path": "ts_data/L2_T2_Periodicity_Detection_00209.csv", "meta": { "period": 60, "wave_type": "sine", "sub_period": 15, "amp": 1.0, "search_window": [ "2020-01-24 05:00:00", "2020-01-26 12:00:00" ], "source": "causal_rivers" } }, { "id": "L2_T2_Periodicity_Detection_00210", "level": 2, "level_name": "Pattern Recognition", "category": "Periodicity Detection", "subtask": "Periodicity Detection", "question": "What is the dominant cycle period (in data points) of channel 894 within [2020-12-06 13:30:00 to 2020-12-12 02:00:00]? (Output format: integer)", "answer": "84", "ground_truth": 84, "eval_metric": "rel_acc", "channel": "894", "ts_data_path": "ts_data/L2_T2_Periodicity_Detection_00210.csv", "meta": { "period": 84, "wave_type": "composite", "sub_period": 42, "amp": 1.0, "search_window": [ "2020-12-06 13:30:00", "2020-12-12 02:00:00" ], "source": "causal_rivers" } }, { "id": "L2_T2_Periodicity_Detection_00211", "level": 2, "level_name": "Pattern Recognition", "category": "Periodicity Detection", "subtask": "Periodicity Detection", "question": "What is the dominant cycle period (in data points) of channel 895 within [2021-08-03 15:00:00 to 2021-08-12 11:45:00]? (Output format: integer)", "answer": "116", "ground_truth": 116, "eval_metric": "rel_acc", "channel": "895", "ts_data_path": "ts_data/L2_T2_Periodicity_Detection_00211.csv", "meta": { "period": 116, "wave_type": "composite", "sub_period": 38, "amp": 1.0, "search_window": [ "2021-08-03 15:00:00", "2021-08-12 11:45:00" ], "source": "causal_rivers" } }, { "id": "L2_T2_Periodicity_Detection_00214", "level": 2, "level_name": "Pattern Recognition", "category": "Periodicity Detection", "subtask": "Periodicity Detection", "question": "What is the dominant cycle period (in data points) of channel HUFL within [2016-08-14 09:45:00 to 2016-08-22 02:00:00]? (Output format: integer)", "answer": "116", "ground_truth": 116, "eval_metric": "rel_acc", "channel": "HUFL", "ts_data_path": "ts_data/L2_T2_Periodicity_Detection_00214.csv", "meta": { "period": 116, "wave_type": "sine", "sub_period": 29, "amp": 9.086729775012383, "search_window": [ "2016-08-14 09:45:00", "2016-08-22 02:00:00" ], "source": "ettm1" } }, { "id": "L2_T2_Periodicity_Detection_00215", "level": 2, "level_name": "Pattern Recognition", "category": "Periodicity Detection", "subtask": "Periodicity Detection", "question": "What is the dominant cycle period (in data points) of channel HULL within [2016-12-29 12:00:00 to 2017-01-03 15:45:00]? (Output format: integer)", "answer": "47", "ground_truth": 47, "eval_metric": "rel_acc", "channel": "HULL", "ts_data_path": "ts_data/L2_T2_Periodicity_Detection_00215.csv", "meta": { "period": 47, "wave_type": "sine", "sub_period": 15, "amp": 3.277983640723091, "search_window": [ "2016-12-29 12:00:00", "2017-01-03 15:45:00" ], "source": "ettm1" } }, { "id": "L2_T2_Periodicity_Detection_00216", "level": 2, "level_name": "Pattern Recognition", "category": "Periodicity Detection", "subtask": "Periodicity Detection", "question": "What is the dominant cycle period (in data points) of channel MUFL within [2016-12-19 18:45:00 to 2016-12-23 09:30:00]? (Output format: integer)", "answer": "77", "ground_truth": 77, "eval_metric": "rel_acc", "channel": "MUFL", "ts_data_path": "ts_data/L2_T2_Periodicity_Detection_00216.csv", "meta": { "period": 77, "wave_type": "cosine", "sub_period": 25, "amp": 4.76074855004177, "search_window": [ "2016-12-19 18:45:00", "2016-12-23 09:30:00" ], "source": "ettm1" } }, { "id": "L2_T2_Periodicity_Detection_00218", "level": 2, "level_name": "Pattern Recognition", "category": "Periodicity Detection", "subtask": "Periodicity Detection", "question": "What is the dominant cycle period (in data points) of channel LUFL within [2017-05-02 11:15:00 to 2017-05-06 02:00:00]? (Output format: integer)", "answer": "65", "ground_truth": 65, "eval_metric": "rel_acc", "channel": "LUFL", "ts_data_path": "ts_data/L2_T2_Periodicity_Detection_00218.csv", "meta": { "period": 65, "wave_type": "cosine", "sub_period": 32, "amp": 1.2853967427677166, "search_window": [ "2017-05-02 11:15:00", "2017-05-06 02:00:00" ], "source": "ettm1" } }, { "id": "L2_T2_Periodicity_Detection_00219", "level": 2, "level_name": "Pattern Recognition", "category": "Periodicity Detection", "subtask": "Periodicity Detection", "question": "What is the dominant cycle period (in data points) of channel LULL within [2017-04-24 06:00:00 to 2017-04-30 07:30:00]? (Output format: integer)", "answer": "67", "ground_truth": 67, "eval_metric": "rel_acc", "channel": "LULL", "ts_data_path": "ts_data/L2_T2_Periodicity_Detection_00219.csv", "meta": { "period": 67, "wave_type": "composite", "sub_period": 16, "amp": 1.0, "search_window": [ "2017-04-24 06:00:00", "2017-04-30 07:30:00" ], "source": "ettm1" } }, { "id": "L2_T2_Periodicity_Detection_00222", "level": 2, "level_name": "Pattern Recognition", "category": "Periodicity Detection", "subtask": "Periodicity Detection", "question": "What is the dominant cycle period (in data points) of channel 71 within [2023-09-23 04:45:00 to 2023-09-30 01:15:00]? (Output format: integer)", "answer": "95", "ground_truth": 95, "eval_metric": "rel_acc", "channel": "71", "ts_data_path": "ts_data/L2_T2_Periodicity_Detection_00222.csv", "meta": { "period": 95, "wave_type": "sine", "sub_period": 23, "amp": 1.0, "search_window": [ "2023-09-23 04:45:00", "2023-09-30 01:15:00" ], "source": "causal_rivers" } }, { "id": "L2_T2_Periodicity_Detection_00225", "level": 2, "level_name": "Pattern Recognition", "category": "Periodicity Detection", "subtask": "Periodicity Detection", "question": "What is the dominant cycle period (in data points) of channel 124 within [2023-09-12 06:45:00 to 2023-09-19 02:45:00]? (Output format: integer)", "answer": "92", "ground_truth": 92, "eval_metric": "rel_acc", "channel": "124", "ts_data_path": "ts_data/L2_T2_Periodicity_Detection_00225.csv", "meta": { "period": 92, "wave_type": "sine", "sub_period": 23, "amp": 1.0, "search_window": [ "2023-09-12 06:45:00", "2023-09-19 02:45:00" ], "source": "causal_rivers" } }, { "id": "L2_T2_Periodicity_Detection_00226", "level": 2, "level_name": "Pattern Recognition", "category": "Periodicity Detection", "subtask": "Periodicity Detection", "question": "What is the dominant cycle period (in data points) of channel 146 within [2022-11-15 12:00:00 to 2022-11-25 01:45:00]? (Output format: integer)", "answer": "93", "ground_truth": 93, "eval_metric": "rel_acc", "channel": "146", "ts_data_path": "ts_data/L2_T2_Periodicity_Detection_00226.csv", "meta": { "period": 93, "wave_type": "cosine", "sub_period": 23, "amp": 1.0, "search_window": [ "2022-11-15 12:00:00", "2022-11-25 01:45:00" ], "source": "causal_rivers" } }, { "id": "L2_T2_Periodicity_Detection_00228", "level": 2, "level_name": "Pattern Recognition", "category": "Periodicity Detection", "subtask": "Periodicity Detection", "question": "What is the dominant cycle period (in data points) of channel 151 within [2020-09-04 12:00:00 to 2020-09-10 17:45:00]? (Output format: integer)", "answer": "99", "ground_truth": 99, "eval_metric": "rel_acc", "channel": "151", "ts_data_path": "ts_data/L2_T2_Periodicity_Detection_00228.csv", "meta": { "period": 99, "wave_type": "cosine", "sub_period": 49, "amp": 1.0, "search_window": [ "2020-09-04 12:00:00", "2020-09-10 17:45:00" ], "source": "causal_rivers" } }, { "id": "L2_T2_Periodicity_Detection_00229", "level": 2, "level_name": "Pattern Recognition", "category": "Periodicity Detection", "subtask": "Periodicity Detection", "question": "What is the dominant cycle period (in data points) of channel 154 within [2022-12-10 22:15:00 to 2022-12-18 01:45:00]? (Output format: integer)", "answer": "31", "ground_truth": 31, "eval_metric": "rel_acc", "channel": "154", "ts_data_path": "ts_data/L2_T2_Periodicity_Detection_00229.csv", "meta": { "period": 31, "wave_type": "composite", "sub_period": 10, "amp": 1.0, "search_window": [ "2022-12-10 22:15:00", "2022-12-18 01:45:00" ], "source": "causal_rivers" } }, { "id": "L2_T2_Periodicity_Detection_00230", "level": 2, "level_name": "Pattern Recognition", "category": "Periodicity Detection", "subtask": "Periodicity Detection", "question": "What is the dominant cycle period (in data points) of channel 155 within [2019-11-25 23:15:00 to 2019-12-02 06:30:00]? (Output format: integer)", "answer": "81", "ground_truth": 81, "eval_metric": "rel_acc", "channel": "155", "ts_data_path": "ts_data/L2_T2_Periodicity_Detection_00230.csv", "meta": { "period": 81, "wave_type": "composite", "sub_period": 27, "amp": 1.0, "search_window": [ "2019-11-25 23:15:00", "2019-12-02 06:30:00" ], "source": "causal_rivers" } }, { "id": "L2_T2_Periodicity_Detection_00231", "level": 2, "level_name": "Pattern Recognition", "category": "Periodicity Detection", "subtask": "Periodicity Detection", "question": "What is the dominant cycle period (in data points) of channel 166 within [2020-06-08 16:30:00 to 2020-06-12 11:45:00]? (Output format: integer)", "answer": "89", "ground_truth": 89, "eval_metric": "rel_acc", "channel": "166", "ts_data_path": "ts_data/L2_T2_Periodicity_Detection_00231.csv", "meta": { "period": 89, "wave_type": "sine", "sub_period": 29, "amp": 6.6716085989621945, "search_window": [ "2020-06-08 16:30:00", "2020-06-12 11:45:00" ], "source": "causal_rivers" } }, { "id": "L2_T2_Periodicity_Detection_00233", "level": 2, "level_name": "Pattern Recognition", "category": "Periodicity Detection", "subtask": "Periodicity Detection", "question": "What is the dominant cycle period (in data points) of channel 170 within [2019-09-03 13:00:00 to 2019-09-07 21:00:00]? (Output format: integer)", "answer": "48", "ground_truth": 48, "eval_metric": "rel_acc", "channel": "170", "ts_data_path": "ts_data/L2_T2_Periodicity_Detection_00233.csv", "meta": { "period": 48, "wave_type": "sine", "sub_period": 16, "amp": 13.343217197924389, "search_window": [ "2019-09-03 13:00:00", "2019-09-07 21:00:00" ], "source": "causal_rivers" } }, { "id": "L2_T2_Periodicity_Detection_00234", "level": 2, "level_name": "Pattern Recognition", "category": "Periodicity Detection", "subtask": "Periodicity Detection", "question": "What is the dominant cycle period (in data points) of channel 172 within [2019-08-05 17:30:00 to 2019-08-11 03:00:00]? (Output format: integer)", "answer": "98", "ground_truth": 98, "eval_metric": "rel_acc", "channel": "172", "ts_data_path": "ts_data/L2_T2_Periodicity_Detection_00234.csv", "meta": { "period": 98, "wave_type": "sine", "sub_period": 32, "amp": 13.343217197924389, "search_window": [ "2019-08-05 17:30:00", "2019-08-11 03:00:00" ], "source": "causal_rivers" } }, { "id": "L2_T2_Periodicity_Detection_00235", "level": 2, "level_name": "Pattern Recognition", "category": "Periodicity Detection", "subtask": "Periodicity Detection", "question": "What is the dominant cycle period (in data points) of channel 173 within [2023-10-10 01:00:00 to 2023-10-12 08:00:00]? (Output format: integer)", "answer": "45", "ground_truth": 45, "eval_metric": "rel_acc", "channel": "173", "ts_data_path": "ts_data/L2_T2_Periodicity_Detection_00235.csv", "meta": { "period": 45, "wave_type": "composite", "sub_period": 11, "amp": 8.895478131949591, "search_window": [ "2023-10-10 01:00:00", "2023-10-12 08:00:00" ], "source": "causal_rivers" } }, { "id": "L2_T2_Periodicity_Detection_00237", "level": 2, "level_name": "Pattern Recognition", "category": "Periodicity Detection", "subtask": "Periodicity Detection", "question": "What is the dominant cycle period (in data points) of channel 237 within [2020-07-24 09:00:00 to 2020-08-01 11:45:00]? (Output format: integer)", "answer": "54", "ground_truth": 54, "eval_metric": "rel_acc", "channel": "237", "ts_data_path": "ts_data/L2_T2_Periodicity_Detection_00237.csv", "meta": { "period": 54, "wave_type": "composite", "sub_period": 27, "amp": 1.0, "search_window": [ "2020-07-24 09:00:00", "2020-08-01 11:45:00" ], "source": "causal_rivers" } }, { "id": "L2_T2_Periodicity_Detection_00238", "level": 2, "level_name": "Pattern Recognition", "category": "Periodicity Detection", "subtask": "Periodicity Detection", "question": "What is the dominant cycle period (in data points) of channel 245 within [2021-09-15 15:00:00 to 2021-09-19 02:15:00]? (Output format: integer)", "answer": "111", "ground_truth": 111, "eval_metric": "rel_acc", "channel": "245", "ts_data_path": "ts_data/L2_T2_Periodicity_Detection_00238.csv", "meta": { "period": 111, "wave_type": "cosine", "sub_period": 37, "amp": 1.0, "search_window": [ "2021-09-15 15:00:00", "2021-09-19 02:15:00" ], "source": "causal_rivers" } }, { "id": "L2_T2_Periodicity_Detection_00239", "level": 2, "level_name": "Pattern Recognition", "category": "Periodicity Detection", "subtask": "Periodicity Detection", "question": "What is the dominant cycle period (in data points) of channel 312 within [2020-06-02 18:00:00 to 2020-06-11 09:15:00]? (Output format: integer)", "answer": "71", "ground_truth": 71, "eval_metric": "rel_acc", "channel": "312", "ts_data_path": "ts_data/L2_T2_Periodicity_Detection_00239.csv", "meta": { "period": 71, "wave_type": "sine", "sub_period": 35, "amp": 1.0, "search_window": [ "2020-06-02 18:00:00", "2020-06-11 09:15:00" ], "source": "causal_rivers" } }, { "id": "L2_T2_Periodicity_Detection_00241", "level": 2, "level_name": "Pattern Recognition", "category": "Periodicity Detection", "subtask": "Periodicity Detection", "question": "What is the dominant cycle period (in data points) of channel 441 within [2022-11-27 02:00:00 to 2022-12-02 18:30:00]? (Output format: integer)", "answer": "82", "ground_truth": 82, "eval_metric": "rel_acc", "channel": "441", "ts_data_path": "ts_data/L2_T2_Periodicity_Detection_00241.csv", "meta": { "period": 82, "wave_type": "sine", "sub_period": 20, "amp": 2.668643439584876, "search_window": [ "2022-11-27 02:00:00", "2022-12-02 18:30:00" ], "source": "causal_rivers" } }, { "id": "L2_T2_Periodicity_Detection_00242", "level": 2, "level_name": "Pattern Recognition", "category": "Periodicity Detection", "subtask": "Periodicity Detection", "question": "What is the dominant cycle period (in data points) of channel 495 within [2022-05-23 14:00:00 to 2022-06-01 06:30:00]? (Output format: integer)", "answer": "82", "ground_truth": 82, "eval_metric": "rel_acc", "channel": "495", "ts_data_path": "ts_data/L2_T2_Periodicity_Detection_00242.csv", "meta": { "period": 82, "wave_type": "cosine", "sub_period": 27, "amp": 3.0, "search_window": [ "2022-05-23 14:00:00", "2022-06-01 06:30:00" ], "source": "causal_rivers" } }, { "id": "L2_T2_Periodicity_Detection_00244", "level": 2, "level_name": "Pattern Recognition", "category": "Periodicity Detection", "subtask": "Periodicity Detection", "question": "What is the dominant cycle period (in data points) of channel 501 within [2021-06-09 02:30:00 to 2021-06-11 17:00:00]? (Output format: integer)", "answer": "83", "ground_truth": 83, "eval_metric": "rel_acc", "channel": "501", "ts_data_path": "ts_data/L2_T2_Periodicity_Detection_00244.csv", "meta": { "period": 83, "wave_type": "sine", "sub_period": 20, "amp": 1.0, "search_window": [ "2021-06-09 02:30:00", "2021-06-11 17:00:00" ], "source": "causal_rivers" } }, { "id": "L2_T2_Periodicity_Detection_00245", "level": 2, "level_name": "Pattern Recognition", "category": "Periodicity Detection", "subtask": "Periodicity Detection", "question": "What is the dominant cycle period (in data points) of channel 578 within [2022-10-28 10:00:00 to 2022-10-31 03:45:00]? (Output format: integer)", "answer": "88", "ground_truth": 88, "eval_metric": "rel_acc", "channel": "578", "ts_data_path": "ts_data/L2_T2_Periodicity_Detection_00245.csv", "meta": { "period": 88, "wave_type": "composite", "sub_period": 22, "amp": 1.0, "search_window": [ "2022-10-28 10:00:00", "2022-10-31 03:45:00" ], "source": "causal_rivers" } }, { "id": "L2_T2_Periodicity_Detection_00247", "level": 2, "level_name": "Pattern Recognition", "category": "Periodicity Detection", "subtask": "Periodicity Detection", "question": "What is the dominant cycle period (in data points) of channel 589 within [2022-07-13 20:15:00 to 2022-07-17 18:15:00]? (Output format: integer)", "answer": "62", "ground_truth": 62, "eval_metric": "rel_acc", "channel": "589", "ts_data_path": "ts_data/L2_T2_Periodicity_Detection_00247.csv", "meta": { "period": 62, "wave_type": "cosine", "sub_period": 31, "amp": 20.014825796886583, "search_window": [ "2022-07-13 20:15:00", "2022-07-17 18:15:00" ], "source": "causal_rivers" } }, { "id": "L2_T2_Periodicity_Detection_00248", "level": 2, "level_name": "Pattern Recognition", "category": "Periodicity Detection", "subtask": "Periodicity Detection", "question": "What is the dominant cycle period (in data points) of channel 591 within [2019-12-10 18:45:00 to 2019-12-19 17:15:00]? (Output format: integer)", "answer": "96", "ground_truth": 96, "eval_metric": "rel_acc", "channel": "591", "ts_data_path": "ts_data/L2_T2_Periodicity_Detection_00248.csv", "meta": { "period": 96, "wave_type": "cosine", "sub_period": 32, "amp": 29.577464788732385, "search_window": [ "2019-12-10 18:45:00", "2019-12-19 17:15:00" ], "source": "causal_rivers" } }, { "id": "L2_T2_Periodicity_Detection_00249", "level": 2, "level_name": "Pattern Recognition", "category": "Periodicity Detection", "subtask": "Periodicity Detection", "question": "What is the dominant cycle period (in data points) of channel 595 within [2023-06-08 19:30:00 to 2023-06-17 07:30:00]? (Output format: integer)", "answer": "73", "ground_truth": 73, "eval_metric": "rel_acc", "channel": "595", "ts_data_path": "ts_data/L2_T2_Periodicity_Detection_00249.csv", "meta": { "period": 73, "wave_type": "cosine", "sub_period": 24, "amp": 1.0, "search_window": [ "2023-06-08 19:30:00", "2023-06-17 07:30:00" ], "source": "causal_rivers" } }, { "id": "L2_T2_Periodicity_Detection_00250", "level": 2, "level_name": "Pattern Recognition", "category": "Periodicity Detection", "subtask": "Periodicity Detection", "question": "What is the dominant cycle period (in data points) of channel 625 within [2019-07-20 14:00:00 to 2019-07-26 15:00:00]? (Output format: integer)", "answer": "68", "ground_truth": 68, "eval_metric": "rel_acc", "channel": "625", "ts_data_path": "ts_data/L2_T2_Periodicity_Detection_00250.csv", "meta": { "period": 68, "wave_type": "composite", "sub_period": 17, "amp": 1.0, "search_window": [ "2019-07-20 14:00:00", "2019-07-26 15:00:00" ], "source": "causal_rivers" } }, { "id": "L2_T2_Periodicity_Detection_00251", "level": 2, "level_name": "Pattern Recognition", "category": "Periodicity Detection", "subtask": "Periodicity Detection", "question": "What is the dominant cycle period (in data points) of channel 626 within [2019-01-30 15:00:00 to 2019-02-07 20:15:00]? (Output format: integer)", "answer": "54", "ground_truth": 54, "eval_metric": "rel_acc", "channel": "626", "ts_data_path": "ts_data/L2_T2_Periodicity_Detection_00251.csv", "meta": { "period": 54, "wave_type": "cosine", "sub_period": 18, "amp": 1.0, "search_window": [ "2019-01-30 15:00:00", "2019-02-07 20:15:00" ], "source": "causal_rivers" } }, { "id": "L2_T2_Periodicity_Detection_00253", "level": 2, "level_name": "Pattern Recognition", "category": "Periodicity Detection", "subtask": "Periodicity Detection", "question": "What is the dominant cycle period (in data points) of channel 647 within [2023-07-06 04:30:00 to 2023-07-09 12:00:00]? (Output format: integer)", "answer": "81", "ground_truth": 81, "eval_metric": "rel_acc", "channel": "647", "ts_data_path": "ts_data/L2_T2_Periodicity_Detection_00253.csv", "meta": { "period": 81, "wave_type": "cosine", "sub_period": 20, "amp": 1.0, "search_window": [ "2023-07-06 04:30:00", "2023-07-09 12:00:00" ], "source": "causal_rivers" } }, { "id": "L2_T2_Periodicity_Detection_00254", "level": 2, "level_name": "Pattern Recognition", "category": "Periodicity Detection", "subtask": "Periodicity Detection", "question": "What is the dominant cycle period (in data points) of channel 680 within [2023-09-13 16:00:00 to 2023-09-17 00:30:00]? (Output format: integer)", "answer": "56", "ground_truth": 56, "eval_metric": "rel_acc", "channel": "680", "ts_data_path": "ts_data/L2_T2_Periodicity_Detection_00254.csv", "meta": { "period": 56, "wave_type": "cosine", "sub_period": 18, "amp": 1.0, "search_window": [ "2023-09-13 16:00:00", "2023-09-17 00:30:00" ], "source": "causal_rivers" } }, { "id": "L2_T2_Periodicity_Detection_00255", "level": 2, "level_name": "Pattern Recognition", "category": "Periodicity Detection", "subtask": "Periodicity Detection", "question": "What is the dominant cycle period (in data points) of channel 683 within [2020-03-20 04:30:00 to 2020-03-23 18:45:00]? (Output format: integer)", "answer": "85", "ground_truth": 85, "eval_metric": "rel_acc", "channel": "683", "ts_data_path": "ts_data/L2_T2_Periodicity_Detection_00255.csv", "meta": { "period": 85, "wave_type": "sine", "sub_period": 28, "amp": 1.0, "search_window": [ "2020-03-20 04:30:00", "2020-03-23 18:45:00" ], "source": "causal_rivers" } }, { "id": "L2_T2_Periodicity_Detection_00256", "level": 2, "level_name": "Pattern Recognition", "category": "Periodicity Detection", "subtask": "Periodicity Detection", "question": "What is the dominant cycle period (in data points) of channel 727 within [2023-05-21 00:30:00 to 2023-05-24 12:45:00]? (Output format: integer)", "answer": "61", "ground_truth": 61, "eval_metric": "rel_acc", "channel": "727", "ts_data_path": "ts_data/L2_T2_Periodicity_Detection_00256.csv", "meta": { "period": 61, "wave_type": "sine", "sub_period": 15, "amp": 4.225352112676053, "search_window": [ "2023-05-21 00:30:00", "2023-05-24 12:45:00" ], "source": "causal_rivers" } }, { "id": "L2_T2_Periodicity_Detection_00258", "level": 2, "level_name": "Pattern Recognition", "category": "Periodicity Detection", "subtask": "Periodicity Detection", "question": "What is the dominant cycle period (in data points) of channel 729 within [2022-06-10 03:15:00 to 2022-06-18 13:00:00]? (Output format: integer)", "answer": "79", "ground_truth": 79, "eval_metric": "rel_acc", "channel": "729", "ts_data_path": "ts_data/L2_T2_Periodicity_Detection_00258.csv", "meta": { "period": 79, "wave_type": "composite", "sub_period": 19, "amp": 7.56115641215715, "search_window": [ "2022-06-10 03:15:00", "2022-06-18 13:00:00" ], "source": "causal_rivers" } }, { "id": "L2_T2_Periodicity_Detection_00259", "level": 2, "level_name": "Pattern Recognition", "category": "Periodicity Detection", "subtask": "Periodicity Detection", "question": "What is the dominant cycle period (in data points) of channel 754 within [2020-02-14 03:30:00 to 2020-02-18 10:15:00]? (Output format: integer)", "answer": "117", "ground_truth": 117, "eval_metric": "rel_acc", "channel": "754", "ts_data_path": "ts_data/L2_T2_Periodicity_Detection_00259.csv", "meta": { "period": 117, "wave_type": "sine", "sub_period": 29, "amp": 1.0, "search_window": [ "2020-02-14 03:30:00", "2020-02-18 10:15:00" ], "source": "causal_rivers" } }, { "id": "L2_T2_Periodicity_Detection_00260", "level": 2, "level_name": "Pattern Recognition", "category": "Periodicity Detection", "subtask": "Periodicity Detection", "question": "What is the dominant cycle period (in data points) of channel 762 within [2022-10-11 00:00:00 to 2022-10-16 00:00:00]? (Output format: integer)", "answer": "38", "ground_truth": 38, "eval_metric": "rel_acc", "channel": "762", "ts_data_path": "ts_data/L2_T2_Periodicity_Detection_00260.csv", "meta": { "period": 38, "wave_type": "sine", "sub_period": 19, "amp": 1.0, "search_window": [ "2022-10-11 00:00:00", "2022-10-16 00:00:00" ], "source": "causal_rivers" } }, { "id": "L2_T2_Periodicity_Detection_00261", "level": 2, "level_name": "Pattern Recognition", "category": "Periodicity Detection", "subtask": "Periodicity Detection", "question": "What is the dominant cycle period (in data points) of channel 811 within [2022-10-07 22:30:00 to 2022-10-13 18:00:00]? (Output format: integer)", "answer": "44", "ground_truth": 44, "eval_metric": "rel_acc", "channel": "811", "ts_data_path": "ts_data/L2_T2_Periodicity_Detection_00261.csv", "meta": { "period": 44, "wave_type": "sine", "sub_period": 11, "amp": 1.0, "search_window": [ "2022-10-07 22:30:00", "2022-10-13 18:00:00" ], "source": "causal_rivers" } }, { "id": "L2_T2_Periodicity_Detection_00262", "level": 2, "level_name": "Pattern Recognition", "category": "Periodicity Detection", "subtask": "Periodicity Detection", "question": "What is the dominant cycle period (in data points) of channel 813 within [2022-09-30 15:15:00 to 2022-10-04 23:00:00]? (Output format: integer)", "answer": "82", "ground_truth": 82, "eval_metric": "rel_acc", "channel": "813", "ts_data_path": "ts_data/L2_T2_Periodicity_Detection_00262.csv", "meta": { "period": 82, "wave_type": "composite", "sub_period": 41, "amp": 1.0, "search_window": [ "2022-09-30 15:15:00", "2022-10-04 23:00:00" ], "source": "causal_rivers" } }, { "id": "L2_T2_Periodicity_Detection_00264", "level": 2, "level_name": "Pattern Recognition", "category": "Periodicity Detection", "subtask": "Periodicity Detection", "question": "What is the dominant cycle period (in data points) of channel 891 within [2020-06-17 00:00:00 to 2020-06-19 11:45:00]? (Output format: integer)", "answer": "32", "ground_truth": 32, "eval_metric": "rel_acc", "channel": "891", "ts_data_path": "ts_data/L2_T2_Periodicity_Detection_00264.csv", "meta": { "period": 32, "wave_type": "sine", "sub_period": 8, "amp": 1.0, "search_window": [ "2020-06-17 00:00:00", "2020-06-19 11:45:00" ], "source": "causal_rivers" } }, { "id": "L2_T2_Periodicity_Detection_00265", "level": 2, "level_name": "Pattern Recognition", "category": "Periodicity Detection", "subtask": "Periodicity Detection", "question": "What is the dominant cycle period (in data points) of channel 894 within [2019-07-15 09:45:00 to 2019-07-24 04:45:00]? (Output format: integer)", "answer": "100", "ground_truth": 100, "eval_metric": "rel_acc", "channel": "894", "ts_data_path": "ts_data/L2_T2_Periodicity_Detection_00265.csv", "meta": { "period": 100, "wave_type": "sine", "sub_period": 25, "amp": 1.0, "search_window": [ "2019-07-15 09:45:00", "2019-07-24 04:45:00" ], "source": "causal_rivers" } }, { "id": "L2_T2_Periodicity_Detection_00266", "level": 2, "level_name": "Pattern Recognition", "category": "Periodicity Detection", "subtask": "Periodicity Detection", "question": "What is the dominant cycle period (in data points) of channel 895 within [2021-07-21 18:00:00 to 2021-07-31 05:30:00]? (Output format: integer)", "answer": "98", "ground_truth": 98, "eval_metric": "rel_acc", "channel": "895", "ts_data_path": "ts_data/L2_T2_Periodicity_Detection_00266.csv", "meta": { "period": 98, "wave_type": "cosine", "sub_period": 24, "amp": 1.0, "search_window": [ "2021-07-21 18:00:00", "2021-07-31 05:30:00" ], "source": "causal_rivers" } }, { "id": "L2_T2_Periodicity_Detection_00267", "level": 2, "level_name": "Pattern Recognition", "category": "Periodicity Detection", "subtask": "Periodicity Detection", "question": "What is the dominant cycle period (in data points) of channel 897 within [2019-06-22 19:00:00 to 2019-06-30 06:00:00]? (Output format: integer)", "answer": "48", "ground_truth": 48, "eval_metric": "rel_acc", "channel": "897", "ts_data_path": "ts_data/L2_T2_Periodicity_Detection_00267.csv", "meta": { "period": 48, "wave_type": "sine", "sub_period": 24, "amp": 1.0, "search_window": [ "2019-06-22 19:00:00", "2019-06-30 06:00:00" ], "source": "causal_rivers" } }, { "id": "L2_T2_Periodicity_Detection_00268", "level": 2, "level_name": "Pattern Recognition", "category": "Periodicity Detection", "subtask": "Periodicity Detection", "question": "What is the dominant cycle period (in data points) of channel 933 within [2023-08-29 22:45:00 to 2023-09-05 03:15:00]? (Output format: integer)", "answer": "32", "ground_truth": 32, "eval_metric": "rel_acc", "channel": "933", "ts_data_path": "ts_data/L2_T2_Periodicity_Detection_00268.csv", "meta": { "period": 32, "wave_type": "composite", "sub_period": 10, "amp": 1.0, "search_window": [ "2023-08-29 22:45:00", "2023-09-05 03:15:00" ], "source": "causal_rivers" } }, { "id": "L2_T2_Periodicity_Detection_00270", "level": 2, "level_name": "Pattern Recognition", "category": "Periodicity Detection", "subtask": "Periodicity Detection", "question": "What is the dominant cycle period (in data points) of channel HULL within [2017-06-11 03:00:00 to 2017-06-14 14:00:00]? (Output format: integer)", "answer": "100", "ground_truth": 100, "eval_metric": "rel_acc", "channel": "HULL", "ts_data_path": "ts_data/L2_T2_Periodicity_Detection_00270.csv", "meta": { "period": 100, "wave_type": "composite", "sub_period": 50, "amp": 2.977761407531218, "search_window": [ "2017-06-11 03:00:00", "2017-06-14 14:00:00" ], "source": "ettm1" } }, { "id": "L2_T2_Periodicity_Detection_00272", "level": 2, "level_name": "Pattern Recognition", "category": "Periodicity Detection", "subtask": "Periodicity Detection", "question": "What is the dominant cycle period (in data points) of channel MULL within [2018-02-19 03:00:00 to 2018-02-25 04:45:00]? (Output format: integer)", "answer": "71", "ground_truth": 71, "eval_metric": "rel_acc", "channel": "MULL", "ts_data_path": "ts_data/L2_T2_Periodicity_Detection_00272.csv", "meta": { "period": 71, "wave_type": "sine", "sub_period": 35, "amp": 1.1052630946669781, "search_window": [ "2018-02-19 03:00:00", "2018-02-25 04:45:00" ], "source": "ettm1" } }, { "id": "L2_T2_Periodicity_Detection_00273", "level": 2, "level_name": "Pattern Recognition", "category": "Periodicity Detection", "subtask": "Periodicity Detection", "question": "What is the dominant cycle period (in data points) of channel LUFL within [2017-04-19 21:45:00 to 2017-04-24 15:45:00]? (Output format: integer)", "answer": "116", "ground_truth": 116, "eval_metric": "rel_acc", "channel": "LUFL", "ts_data_path": "ts_data/L2_T2_Periodicity_Detection_00273.csv", "meta": { "period": 116, "wave_type": "cosine", "sub_period": 58, "amp": 1.5567082489217285, "search_window": [ "2017-04-19 21:45:00", "2017-04-24 15:45:00" ], "source": "ettm1" } }, { "id": "L2_T2_Periodicity_Detection_00274", "level": 2, "level_name": "Pattern Recognition", "category": "Periodicity Detection", "subtask": "Periodicity Detection", "question": "What is the dominant cycle period (in data points) of channel LULL within [2018-02-14 03:00:00 to 2018-02-16 20:30:00]? (Output format: integer)", "answer": "56", "ground_truth": 56, "eval_metric": "rel_acc", "channel": "LULL", "ts_data_path": "ts_data/L2_T2_Periodicity_Detection_00274.csv", "meta": { "period": 56, "wave_type": "sine", "sub_period": 28, "amp": 1.0, "search_window": [ "2018-02-14 03:00:00", "2018-02-16 20:30:00" ], "source": "ettm1" } }, { "id": "L2_T2_Periodicity_Detection_00275", "level": 2, "level_name": "Pattern Recognition", "category": "Periodicity Detection", "subtask": "Periodicity Detection", "question": "What is the dominant cycle period (in data points) of channel OT within [2017-12-01 08:30:00 to 2017-12-10 16:15:00]? (Output format: integer)", "answer": "72", "ground_truth": 72, "eval_metric": "rel_acc", "channel": "OT", "ts_data_path": "ts_data/L2_T2_Periodicity_Detection_00275.csv", "meta": { "period": 72, "wave_type": "sine", "sub_period": 24, "amp": 3.911786648500752, "search_window": [ "2017-12-01 08:30:00", "2017-12-10 16:15:00" ], "source": "ettm1" } }, { "id": "L2_T2_Periodicity_Detection_00277", "level": 2, "level_name": "Pattern Recognition", "category": "Periodicity Detection", "subtask": "Periodicity Detection", "question": "What is the dominant cycle period (in data points) of channel 71 within [2020-09-01 00:00:00 to 2020-09-05 12:45:00]? (Output format: integer)", "answer": "33", "ground_truth": 33, "eval_metric": "rel_acc", "channel": "71", "ts_data_path": "ts_data/L2_T2_Periodicity_Detection_00277.csv", "meta": { "period": 33, "wave_type": "cosine", "sub_period": 11, "amp": 1.0, "search_window": [ "2020-09-01 00:00:00", "2020-09-05 12:45:00" ], "source": "causal_rivers" } }, { "id": "L2_T2_Periodicity_Detection_00278", "level": 2, "level_name": "Pattern Recognition", "category": "Periodicity Detection", "subtask": "Periodicity Detection", "question": "What is the dominant cycle period (in data points) of channel 99 within [2020-08-03 03:00:00 to 2020-08-11 06:00:00]? (Output format: integer)", "answer": "70", "ground_truth": 70, "eval_metric": "rel_acc", "channel": "99", "ts_data_path": "ts_data/L2_T2_Periodicity_Detection_00278.csv", "meta": { "period": 70, "wave_type": "cosine", "sub_period": 17, "amp": 1.0, "search_window": [ "2020-08-03 03:00:00", "2020-08-11 06:00:00" ], "source": "causal_rivers" } }, { "id": "L2_T2_Periodicity_Detection_00279", "level": 2, "level_name": "Pattern Recognition", "category": "Periodicity Detection", "subtask": "Periodicity Detection", "question": "What is the dominant cycle period (in data points) of channel 123 within [2022-09-11 07:00:00 to 2022-09-21 02:45:00]? (Output format: integer)", "answer": "59", "ground_truth": 59, "eval_metric": "rel_acc", "channel": "123", "ts_data_path": "ts_data/L2_T2_Periodicity_Detection_00279.csv", "meta": { "period": 59, "wave_type": "composite", "sub_period": 29, "amp": 1.0, "search_window": [ "2022-09-11 07:00:00", "2022-09-21 02:45:00" ], "source": "causal_rivers" } }, { "id": "L2_T2_Periodicity_Detection_00281", "level": 2, "level_name": "Pattern Recognition", "category": "Periodicity Detection", "subtask": "Periodicity Detection", "question": "What is the dominant cycle period (in data points) of channel 146 within [2020-04-19 00:00:00 to 2020-04-27 05:45:00]? (Output format: integer)", "answer": "95", "ground_truth": 95, "eval_metric": "rel_acc", "channel": "146", "ts_data_path": "ts_data/L2_T2_Periodicity_Detection_00281.csv", "meta": { "period": 95, "wave_type": "composite", "sub_period": 23, "amp": 1.0, "search_window": [ "2020-04-19 00:00:00", "2020-04-27 05:45:00" ], "source": "causal_rivers" } }, { "id": "L2_T2_Periodicity_Detection_00282", "level": 2, "level_name": "Pattern Recognition", "category": "Periodicity Detection", "subtask": "Periodicity Detection", "question": "What is the dominant cycle period (in data points) of channel 147 within [2020-10-04 16:30:00 to 2020-10-09 10:45:00]? (Output format: integer)", "answer": "77", "ground_truth": 77, "eval_metric": "rel_acc", "channel": "147", "ts_data_path": "ts_data/L2_T2_Periodicity_Detection_00282.csv", "meta": { "period": 77, "wave_type": "composite", "sub_period": 38, "amp": 1.0, "search_window": [ "2020-10-04 16:30:00", "2020-10-09 10:45:00" ], "source": "causal_rivers" } }, { "id": "L2_T2_Periodicity_Detection_00283", "level": 2, "level_name": "Pattern Recognition", "category": "Periodicity Detection", "subtask": "Periodicity Detection", "question": "What is the dominant cycle period (in data points) of channel 151 within [2022-07-29 00:00:00 to 2022-08-02 13:15:00]? (Output format: integer)", "answer": "31", "ground_truth": 31, "eval_metric": "rel_acc", "channel": "151", "ts_data_path": "ts_data/L2_T2_Periodicity_Detection_00283.csv", "meta": { "period": 31, "wave_type": "composite", "sub_period": 15, "amp": 1.0, "search_window": [ "2022-07-29 00:00:00", "2022-08-02 13:15:00" ], "source": "causal_rivers" } }, { "id": "L2_T2_Periodicity_Detection_00284", "level": 2, "level_name": "Pattern Recognition", "category": "Periodicity Detection", "subtask": "Periodicity Detection", "question": "What is the dominant cycle period (in data points) of channel 154 within [2019-04-03 04:00:00 to 2019-04-11 09:30:00]? (Output format: integer)", "answer": "38", "ground_truth": 38, "eval_metric": "rel_acc", "channel": "154", "ts_data_path": "ts_data/L2_T2_Periodicity_Detection_00284.csv", "meta": { "period": 38, "wave_type": "composite", "sub_period": 19, "amp": 1.0, "search_window": [ "2019-04-03 04:00:00", "2019-04-11 09:30:00" ], "source": "causal_rivers" } }, { "id": "L2_T2_Periodicity_Detection_00285", "level": 2, "level_name": "Pattern Recognition", "category": "Periodicity Detection", "subtask": "Periodicity Detection", "question": "What is the dominant cycle period (in data points) of channel 155 within [2022-07-17 08:00:00 to 2022-07-23 23:45:00]? (Output format: integer)", "answer": "86", "ground_truth": 86, "eval_metric": "rel_acc", "channel": "155", "ts_data_path": "ts_data/L2_T2_Periodicity_Detection_00285.csv", "meta": { "period": 86, "wave_type": "sine", "sub_period": 43, "amp": 1.0, "search_window": [ "2022-07-17 08:00:00", "2022-07-23 23:45:00" ], "source": "causal_rivers" } }, { "id": "L2_T2_Periodicity_Detection_00286", "level": 2, "level_name": "Pattern Recognition", "category": "Periodicity Detection", "subtask": "Periodicity Detection", "question": "What is the dominant cycle period (in data points) of channel 166 within [2020-11-30 12:45:00 to 2020-12-09 03:45:00]? (Output format: integer)", "answer": "90", "ground_truth": 90, "eval_metric": "rel_acc", "channel": "166", "ts_data_path": "ts_data/L2_T2_Periodicity_Detection_00286.csv", "meta": { "period": 90, "wave_type": "composite", "sub_period": 45, "amp": 37.805782060785766, "search_window": [ "2020-11-30 12:45:00", "2020-12-09 03:45:00" ], "source": "causal_rivers" } }, { "id": "L2_T2_Periodicity_Detection_00287", "level": 2, "level_name": "Pattern Recognition", "category": "Periodicity Detection", "subtask": "Periodicity Detection", "question": "What is the dominant cycle period (in data points) of channel 169 within [2019-04-24 05:00:00 to 2019-05-01 16:30:00]? (Output format: integer)", "answer": "104", "ground_truth": 104, "eval_metric": "rel_acc", "channel": "169", "ts_data_path": "ts_data/L2_T2_Periodicity_Detection_00287.csv", "meta": { "period": 104, "wave_type": "sine", "sub_period": 26, "amp": 33.35804299481097, "search_window": [ "2019-04-24 05:00:00", "2019-05-01 16:30:00" ], "source": "causal_rivers" } }, { "id": "L2_T2_Periodicity_Detection_00288", "level": 2, "level_name": "Pattern Recognition", "category": "Periodicity Detection", "subtask": "Periodicity Detection", "question": "What is the dominant cycle period (in data points) of channel 170 within [2020-05-04 20:15:00 to 2020-05-10 00:30:00]? (Output format: integer)", "answer": "106", "ground_truth": 106, "eval_metric": "rel_acc", "channel": "170", "ts_data_path": "ts_data/L2_T2_Periodicity_Detection_00288.csv", "meta": { "period": 106, "wave_type": "sine", "sub_period": 26, "amp": 8.895478131949591, "search_window": [ "2020-05-04 20:15:00", "2020-05-10 00:30:00" ], "source": "causal_rivers" } }, { "id": "L2_T2_Periodicity_Detection_00289", "level": 2, "level_name": "Pattern Recognition", "category": "Periodicity Detection", "subtask": "Periodicity Detection", "question": "What is the dominant cycle period (in data points) of channel 172 within [2019-12-27 03:00:00 to 2020-01-03 02:45:00]? (Output format: integer)", "answer": "63", "ground_truth": 63, "eval_metric": "rel_acc", "channel": "172", "ts_data_path": "ts_data/L2_T2_Periodicity_Detection_00289.csv", "meta": { "period": 63, "wave_type": "cosine", "sub_period": 15, "amp": 31.134173461823572, "search_window": [ "2019-12-27 03:00:00", "2020-01-03 02:45:00" ], "source": "causal_rivers" } }, { "id": "L2_T2_Periodicity_Detection_00291", "level": 2, "level_name": "Pattern Recognition", "category": "Periodicity Detection", "subtask": "Periodicity Detection", "question": "What is the dominant cycle period (in data points) of channel 177 within [2021-09-13 02:45:00 to 2021-09-20 06:00:00]? (Output format: integer)", "answer": "67", "ground_truth": 67, "eval_metric": "rel_acc", "channel": "177", "ts_data_path": "ts_data/L2_T2_Periodicity_Detection_00291.csv", "meta": { "period": 67, "wave_type": "sine", "sub_period": 33, "amp": 40.02965159377317, "search_window": [ "2021-09-13 02:45:00", "2021-09-20 06:00:00" ], "source": "causal_rivers" } }, { "id": "L2_T2_Periodicity_Detection_00292", "level": 2, "level_name": "Pattern Recognition", "category": "Periodicity Detection", "subtask": "Periodicity Detection", "question": "What is the dominant cycle period (in data points) of channel 237 within [2020-12-08 03:45:00 to 2020-12-17 01:45:00]? (Output format: integer)", "answer": "54", "ground_truth": 54, "eval_metric": "rel_acc", "channel": "237", "ts_data_path": "ts_data/L2_T2_Periodicity_Detection_00292.csv", "meta": { "period": 54, "wave_type": "cosine", "sub_period": 13, "amp": 1.0, "search_window": [ "2020-12-08 03:45:00", "2020-12-17 01:45:00" ], "source": "causal_rivers" } }, { "id": "L2_T2_Periodicity_Detection_00293", "level": 2, "level_name": "Pattern Recognition", "category": "Periodicity Detection", "subtask": "Periodicity Detection", "question": "What is the dominant cycle period (in data points) of channel 245 within [2022-08-14 13:00:00 to 2022-08-19 08:15:00]? (Output format: integer)", "answer": "65", "ground_truth": 65, "eval_metric": "rel_acc", "channel": "245", "ts_data_path": "ts_data/L2_T2_Periodicity_Detection_00293.csv", "meta": { "period": 65, "wave_type": "cosine", "sub_period": 21, "amp": 1.0, "search_window": [ "2022-08-14 13:00:00", "2022-08-19 08:15:00" ], "source": "causal_rivers" } }, { "id": "L2_T2_Periodicity_Detection_00294", "level": 2, "level_name": "Pattern Recognition", "category": "Periodicity Detection", "subtask": "Periodicity Detection", "question": "What is the dominant cycle period (in data points) of channel 312 within [2020-07-16 11:15:00 to 2020-07-22 21:45:00]? (Output format: integer)", "answer": "30", "ground_truth": 30, "eval_metric": "rel_acc", "channel": "312", "ts_data_path": "ts_data/L2_T2_Periodicity_Detection_00294.csv", "meta": { "period": 30, "wave_type": "sine", "sub_period": 15, "amp": 1.0, "search_window": [ "2020-07-16 11:15:00", "2020-07-22 21:45:00" ], "source": "causal_rivers" } }, { "id": "L2_T2_Periodicity_Detection_00295", "level": 2, "level_name": "Pattern Recognition", "category": "Periodicity Detection", "subtask": "Periodicity Detection", "question": "What is the dominant cycle period (in data points) of channel 430 within [2022-08-02 18:00:00 to 2022-08-04 19:15:00]? (Output format: integer)", "answer": "35", "ground_truth": 35, "eval_metric": "rel_acc", "channel": "430", "ts_data_path": "ts_data/L2_T2_Periodicity_Detection_00295.csv", "meta": { "period": 35, "wave_type": "cosine", "sub_period": 8, "amp": 1.0, "search_window": [ "2022-08-02 18:00:00", "2022-08-04 19:15:00" ], "source": "causal_rivers" } }, { "id": "L2_T2_Periodicity_Detection_00296", "level": 2, "level_name": "Pattern Recognition", "category": "Periodicity Detection", "subtask": "Periodicity Detection", "question": "What is the dominant cycle period (in data points) of channel 441 within [2019-12-18 16:00:00 to 2019-12-26 23:45:00]? (Output format: integer)", "answer": "69", "ground_truth": 69, "eval_metric": "rel_acc", "channel": "441", "ts_data_path": "ts_data/L2_T2_Periodicity_Detection_00296.csv", "meta": { "period": 69, "wave_type": "composite", "sub_period": 23, "amp": 1.9570051890289082, "search_window": [ "2019-12-18 16:00:00", "2019-12-26 23:45:00" ], "source": "causal_rivers" } }, { "id": "L2_T2_Periodicity_Detection_00298", "level": 2, "level_name": "Pattern Recognition", "category": "Periodicity Detection", "subtask": "Periodicity Detection", "question": "What is the dominant cycle period (in data points) of channel 496 within [2022-10-24 04:30:00 to 2022-11-01 15:30:00]? (Output format: integer)", "answer": "59", "ground_truth": 59, "eval_metric": "rel_acc", "channel": "496", "ts_data_path": "ts_data/L2_T2_Periodicity_Detection_00298.csv", "meta": { "period": 59, "wave_type": "composite", "sub_period": 29, "amp": 1.0, "search_window": [ "2022-10-24 04:30:00", "2022-11-01 15:30:00" ], "source": "causal_rivers" } }, { "id": "L2_T2_Periodicity_Detection_00299", "level": 2, "level_name": "Pattern Recognition", "category": "Periodicity Detection", "subtask": "Periodicity Detection", "question": "What is the dominant cycle period (in data points) of channel 501 within [2019-09-13 05:00:00 to 2019-09-22 08:00:00]? (Output format: integer)", "answer": "39", "ground_truth": 39, "eval_metric": "rel_acc", "channel": "501", "ts_data_path": "ts_data/L2_T2_Periodicity_Detection_00299.csv", "meta": { "period": 39, "wave_type": "sine", "sub_period": 9, "amp": 1.0, "search_window": [ "2019-09-13 05:00:00", "2019-09-22 08:00:00" ], "source": "causal_rivers" } }, { "id": "L2_T2_Periodicity_Detection_00300", "level": 2, "level_name": "Pattern Recognition", "category": "Periodicity Detection", "subtask": "Periodicity Detection", "question": "What is the dominant cycle period (in data points) of channel 578 within [2020-12-28 14:30:00 to 2021-01-04 05:00:00]? (Output format: integer)", "answer": "65", "ground_truth": 65, "eval_metric": "rel_acc", "channel": "578", "ts_data_path": "ts_data/L2_T2_Periodicity_Detection_00300.csv", "meta": { "period": 65, "wave_type": "sine", "sub_period": 16, "amp": 1.0, "search_window": [ "2020-12-28 14:30:00", "2021-01-04 05:00:00" ], "source": "causal_rivers" } }, { "id": "L2_T2_Periodicity_Detection_00301", "level": 2, "level_name": "Pattern Recognition", "category": "Periodicity Detection", "subtask": "Periodicity Detection", "question": "What is the dominant cycle period (in data points) of channel 580 within [2020-09-29 12:00:00 to 2020-10-08 21:00:00]? (Output format: integer)", "answer": "85", "ground_truth": 85, "eval_metric": "rel_acc", "channel": "580", "ts_data_path": "ts_data/L2_T2_Periodicity_Detection_00301.csv", "meta": { "period": 85, "wave_type": "composite", "sub_period": 28, "amp": 1.0, "search_window": [ "2020-09-29 12:00:00", "2020-10-08 21:00:00" ], "source": "causal_rivers" } }, { "id": "L2_T2_Periodicity_Detection_00303", "level": 2, "level_name": "Pattern Recognition", "category": "Periodicity Detection", "subtask": "Periodicity Detection", "question": "What is the dominant cycle period (in data points) of channel 591 within [2020-05-29 12:45:00 to 2020-06-04 08:00:00]? (Output format: integer)", "answer": "40", "ground_truth": 40, "eval_metric": "rel_acc", "channel": "591", "ts_data_path": "ts_data/L2_T2_Periodicity_Detection_00303.csv", "meta": { "period": 40, "wave_type": "sine", "sub_period": 20, "amp": 17.790956263899183, "search_window": [ "2020-05-29 12:45:00", "2020-06-04 08:00:00" ], "source": "causal_rivers" } }, { "id": "L2_T2_Periodicity_Detection_00304", "level": 2, "level_name": "Pattern Recognition", "category": "Periodicity Detection", "subtask": "Periodicity Detection", "question": "What is the dominant cycle period (in data points) of channel 595 within [2020-10-03 21:00:00 to 2020-10-07 00:30:00]? (Output format: integer)", "answer": "33", "ground_truth": 33, "eval_metric": "rel_acc", "channel": "595", "ts_data_path": "ts_data/L2_T2_Periodicity_Detection_00304.csv", "meta": { "period": 33, "wave_type": "composite", "sub_period": 11, "amp": 1.0, "search_window": [ "2020-10-03 21:00:00", "2020-10-07 00:30:00" ], "source": "causal_rivers" } }, { "id": "L2_T2_Periodicity_Detection_00305", "level": 2, "level_name": "Pattern Recognition", "category": "Periodicity Detection", "subtask": "Periodicity Detection", "question": "What is the dominant cycle period (in data points) of channel 625 within [2023-09-12 19:45:00 to 2023-09-19 11:00:00]? (Output format: integer)", "answer": "43", "ground_truth": 43, "eval_metric": "rel_acc", "channel": "625", "ts_data_path": "ts_data/L2_T2_Periodicity_Detection_00305.csv", "meta": { "period": 43, "wave_type": "composite", "sub_period": 21, "amp": 1.0, "search_window": [ "2023-09-12 19:45:00", "2023-09-19 11:00:00" ], "source": "causal_rivers" } }, { "id": "L2_T2_Periodicity_Detection_00306", "level": 2, "level_name": "Pattern Recognition", "category": "Periodicity Detection", "subtask": "Periodicity Detection", "question": "What is the dominant cycle period (in data points) of channel 626 within [2020-07-21 00:00:00 to 2020-07-28 12:15:00]? (Output format: integer)", "answer": "66", "ground_truth": 66, "eval_metric": "rel_acc", "channel": "626", "ts_data_path": "ts_data/L2_T2_Periodicity_Detection_00306.csv", "meta": { "period": 66, "wave_type": "sine", "sub_period": 22, "amp": 1.0, "search_window": [ "2020-07-21 00:00:00", "2020-07-28 12:15:00" ], "source": "causal_rivers" } }, { "id": "L2_T2_Periodicity_Detection_00308", "level": 2, "level_name": "Pattern Recognition", "category": "Periodicity Detection", "subtask": "Periodicity Detection", "question": "What is the dominant cycle period (in data points) of channel 647 within [2022-06-02 10:30:00 to 2022-06-06 21:30:00]? (Output format: integer)", "answer": "39", "ground_truth": 39, "eval_metric": "rel_acc", "channel": "647", "ts_data_path": "ts_data/L2_T2_Periodicity_Detection_00308.csv", "meta": { "period": 39, "wave_type": "composite", "sub_period": 19, "amp": 1.0, "search_window": [ "2022-06-02 10:30:00", "2022-06-06 21:30:00" ], "source": "causal_rivers" } }, { "id": "L2_T2_Periodicity_Detection_00309", "level": 2, "level_name": "Pattern Recognition", "category": "Periodicity Detection", "subtask": "Periodicity Detection", "question": "What is the dominant cycle period (in data points) of channel 680 within [2019-07-09 05:15:00 to 2019-07-18 05:30:00]? (Output format: integer)", "answer": "120", "ground_truth": 120, "eval_metric": "rel_acc", "channel": "680", "ts_data_path": "ts_data/L2_T2_Periodicity_Detection_00309.csv", "meta": { "period": 120, "wave_type": "cosine", "sub_period": 30, "amp": 1.0, "search_window": [ "2019-07-09 05:15:00", "2019-07-18 05:30:00" ], "source": "causal_rivers" } }, { "id": "L2_T2_Periodicity_Detection_00310", "level": 2, "level_name": "Pattern Recognition", "category": "Periodicity Detection", "subtask": "Periodicity Detection", "question": "What is the dominant cycle period (in data points) of channel 683 within [2023-09-20 09:15:00 to 2023-09-27 23:15:00]? (Output format: integer)", "answer": "92", "ground_truth": 92, "eval_metric": "rel_acc", "channel": "683", "ts_data_path": "ts_data/L2_T2_Periodicity_Detection_00310.csv", "meta": { "period": 92, "wave_type": "sine", "sub_period": 30, "amp": 1.0, "search_window": [ "2023-09-20 09:15:00", "2023-09-27 23:15:00" ], "source": "causal_rivers" } }, { "id": "L2_T2_Periodicity_Detection_00311", "level": 2, "level_name": "Pattern Recognition", "category": "Periodicity Detection", "subtask": "Periodicity Detection", "question": "What is the dominant cycle period (in data points) of channel 727 within [2021-01-06 03:30:00 to 2021-01-16 02:30:00]? (Output format: integer)", "answer": "52", "ground_truth": 52, "eval_metric": "rel_acc", "channel": "727", "ts_data_path": "ts_data/L2_T2_Periodicity_Detection_00311.csv", "meta": { "period": 52, "wave_type": "sine", "sub_period": 17, "amp": 7.338769458858423, "search_window": [ "2021-01-06 03:30:00", "2021-01-16 02:30:00" ], "source": "causal_rivers" } }, { "id": "L2_T2_Periodicity_Detection_00313", "level": 2, "level_name": "Pattern Recognition", "category": "Periodicity Detection", "subtask": "Periodicity Detection", "question": "What is the dominant cycle period (in data points) of channel 729 within [2020-04-28 14:30:00 to 2020-05-05 20:15:00]? (Output format: integer)", "answer": "90", "ground_truth": 90, "eval_metric": "rel_acc", "channel": "729", "ts_data_path": "ts_data/L2_T2_Periodicity_Detection_00313.csv", "meta": { "period": 90, "wave_type": "sine", "sub_period": 30, "amp": 7.56115641215715, "search_window": [ "2020-04-28 14:30:00", "2020-05-05 20:15:00" ], "source": "causal_rivers" } }, { "id": "L2_T2_Periodicity_Detection_00314", "level": 2, "level_name": "Pattern Recognition", "category": "Periodicity Detection", "subtask": "Periodicity Detection", "question": "What is the dominant cycle period (in data points) of channel 754 within [2019-06-19 16:30:00 to 2019-06-22 01:30:00]? (Output format: integer)", "answer": "40", "ground_truth": 40, "eval_metric": "rel_acc", "channel": "754", "ts_data_path": "ts_data/L2_T2_Periodicity_Detection_00314.csv", "meta": { "period": 40, "wave_type": "cosine", "sub_period": 13, "amp": 1.0, "search_window": [ "2019-06-19 16:30:00", "2019-06-22 01:30:00" ], "source": "causal_rivers" } }, { "id": "L2_T2_Periodicity_Detection_00316", "level": 2, "level_name": "Pattern Recognition", "category": "Periodicity Detection", "subtask": "Periodicity Detection", "question": "What is the dominant cycle period (in data points) of channel 811 within [2019-11-19 21:00:00 to 2019-11-26 10:45:00]? (Output format: integer)", "answer": "98", "ground_truth": 98, "eval_metric": "rel_acc", "channel": "811", "ts_data_path": "ts_data/L2_T2_Periodicity_Detection_00316.csv", "meta": { "period": 98, "wave_type": "cosine", "sub_period": 24, "amp": 1.0, "search_window": [ "2019-11-19 21:00:00", "2019-11-26 10:45:00" ], "source": "causal_rivers" } }, { "id": "L2_T2_Periodicity_Detection_00318", "level": 2, "level_name": "Pattern Recognition", "category": "Periodicity Detection", "subtask": "Periodicity Detection", "question": "What is the dominant cycle period (in data points) of channel 865 within [2020-07-16 23:30:00 to 2020-07-23 08:45:00]? (Output format: integer)", "answer": "54", "ground_truth": 54, "eval_metric": "rel_acc", "channel": "865", "ts_data_path": "ts_data/L2_T2_Periodicity_Detection_00318.csv", "meta": { "period": 54, "wave_type": "cosine", "sub_period": 13, "amp": 1.0, "search_window": [ "2020-07-16 23:30:00", "2020-07-23 08:45:00" ], "source": "causal_rivers" } }, { "id": "L2_T2_Periodicity_Detection_00319", "level": 2, "level_name": "Pattern Recognition", "category": "Periodicity Detection", "subtask": "Periodicity Detection", "question": "What is the dominant cycle period (in data points) of channel 891 within [2020-04-21 09:00:00 to 2020-04-26 18:00:00]? (Output format: integer)", "answer": "89", "ground_truth": 89, "eval_metric": "rel_acc", "channel": "891", "ts_data_path": "ts_data/L2_T2_Periodicity_Detection_00319.csv", "meta": { "period": 89, "wave_type": "cosine", "sub_period": 44, "amp": 1.0, "search_window": [ "2020-04-21 09:00:00", "2020-04-26 18:00:00" ], "source": "causal_rivers" } }, { "id": "L2_T2_Periodicity_Detection_00320", "level": 2, "level_name": "Pattern Recognition", "category": "Periodicity Detection", "subtask": "Periodicity Detection", "question": "What is the dominant cycle period (in data points) of channel 894 within [2021-09-28 21:00:00 to 2021-10-03 06:45:00]? (Output format: integer)", "answer": "49", "ground_truth": 49, "eval_metric": "rel_acc", "channel": "894", "ts_data_path": "ts_data/L2_T2_Periodicity_Detection_00320.csv", "meta": { "period": 49, "wave_type": "composite", "sub_period": 16, "amp": 1.0, "search_window": [ "2021-09-28 21:00:00", "2021-10-03 06:45:00" ], "source": "causal_rivers" } }, { "id": "L2_T2_Periodicity_Detection_00321", "level": 2, "level_name": "Pattern Recognition", "category": "Periodicity Detection", "subtask": "Periodicity Detection", "question": "What is the dominant cycle period (in data points) of channel 895 within [2019-02-25 12:30:00 to 2019-03-01 19:45:00]? (Output format: integer)", "answer": "55", "ground_truth": 55, "eval_metric": "rel_acc", "channel": "895", "ts_data_path": "ts_data/L2_T2_Periodicity_Detection_00321.csv", "meta": { "period": 55, "wave_type": "sine", "sub_period": 18, "amp": 1.0, "search_window": [ "2019-02-25 12:30:00", "2019-03-01 19:45:00" ], "source": "causal_rivers" } }, { "id": "L2_T2_Periodicity_Detection_00323", "level": 2, "level_name": "Pattern Recognition", "category": "Periodicity Detection", "subtask": "Periodicity Detection", "question": "What is the dominant cycle period (in data points) of channel 933 within [2022-11-05 15:00:00 to 2022-11-10 22:30:00]? (Output format: integer)", "answer": "86", "ground_truth": 86, "eval_metric": "rel_acc", "channel": "933", "ts_data_path": "ts_data/L2_T2_Periodicity_Detection_00323.csv", "meta": { "period": 86, "wave_type": "cosine", "sub_period": 21, "amp": 1.0, "search_window": [ "2022-11-05 15:00:00", "2022-11-10 22:30:00" ], "source": "causal_rivers" } }, { "id": "L2_T2_Periodicity_Detection_00324", "level": 2, "level_name": "Pattern Recognition", "category": "Periodicity Detection", "subtask": "Periodicity Detection", "question": "What is the dominant cycle period (in data points) of channel HUFL within [2016-07-14 23:00:00 to 2016-07-18 11:00:00]? (Output format: integer)", "answer": "85", "ground_truth": 85, "eval_metric": "rel_acc", "channel": "HUFL", "ts_data_path": "ts_data/L2_T2_Periodicity_Detection_00324.csv", "meta": { "period": 85, "wave_type": "sine", "sub_period": 42, "amp": 6.851742760705626, "search_window": [ "2016-07-14 23:00:00", "2016-07-18 11:00:00" ], "source": "ettm1" } }, { "id": "L2_T2_Periodicity_Detection_00325", "level": 2, "level_name": "Pattern Recognition", "category": "Periodicity Detection", "subtask": "Periodicity Detection", "question": "What is the dominant cycle period (in data points) of channel HULL within [2018-01-24 06:00:00 to 2018-01-27 13:00:00]? (Output format: integer)", "answer": "57", "ground_truth": 57, "eval_metric": "rel_acc", "channel": "HULL", "ts_data_path": "ts_data/L2_T2_Periodicity_Detection_00325.csv", "meta": { "period": 57, "wave_type": "composite", "sub_period": 19, "amp": 2.0859895413499836, "search_window": [ "2018-01-24 06:00:00", "2018-01-27 13:00:00" ], "source": "ettm1" } }, { "id": "L2_T2_Periodicity_Detection_00328", "level": 2, "level_name": "Pattern Recognition", "category": "Periodicity Detection", "subtask": "Periodicity Detection", "question": "What is the dominant cycle period (in data points) of channel LUFL within [2017-09-03 11:15:00 to 2017-09-07 16:15:00]? (Output format: integer)", "answer": "101", "ground_truth": 101, "eval_metric": "rel_acc", "channel": "LUFL", "ts_data_path": "ts_data/L2_T2_Periodicity_Detection_00328.csv", "meta": { "period": 101, "wave_type": "sine", "sub_period": 33, "amp": 1.3543364734805197, "search_window": [ "2017-09-03 11:15:00", "2017-09-07 16:15:00" ], "source": "ettm1" } }, { "id": "L2_T2_Periodicity_Detection_00329", "level": 2, "level_name": "Pattern Recognition", "category": "Periodicity Detection", "subtask": "Periodicity Detection", "question": "What is the dominant cycle period (in data points) of channel LULL within [2018-02-10 09:00:00 to 2018-02-18 21:45:00]? (Output format: integer)", "answer": "60", "ground_truth": 60, "eval_metric": "rel_acc", "channel": "LULL", "ts_data_path": "ts_data/L2_T2_Periodicity_Detection_00329.csv", "meta": { "period": 60, "wave_type": "composite", "sub_period": 20, "amp": 1.0, "search_window": [ "2018-02-10 09:00:00", "2018-02-18 21:45:00" ], "source": "ettm1" } }, { "id": "L2_T1_Shape_Identification_00001", "level": 2, "level_name": "Pattern Recognition", "category": "Shape Identification", "subtask": "Shape Identification", "question": "Identify the time range of the largest step ascent in channel 67 within [2019-08-19 10:00:00 to 2019-08-26 17:45:00]. (Output format: [YYYY-MM-DD HH:MM:SS, YYYY-MM-DD HH:MM:SS])", "answer": "['2019-08-20 11:00:00', '2019-08-21 13:00:00']", "ground_truth": [ "2019-08-20 11:00:00", "2019-08-21 13:00:00" ], "eval_metric": "iou", "channel": "67", "ts_data_path": "ts_data/L2_T1_Shape_Identification_00001.csv", "meta": { "pattern": "step_up", "superlative": "largest", "direction": "up", "primary_amp": 0.08804694870779693, "local_std": 0.0037064492216456464, "search_window": [ "2019-08-19 10:00:00", "2019-08-26 17:45:00" ], "source": "causal_rivers" } }, { "id": "L2_T1_Shape_Identification_00003", "level": 2, "level_name": "Pattern Recognition", "category": "Shape Identification", "subtask": "Shape Identification", "question": "Identify the time range of the largest step ascent in channel 99 within [2019-04-26 15:00:00 to 2019-05-01 20:00:00]. (Output format: [YYYY-MM-DD HH:MM:SS, YYYY-MM-DD HH:MM:SS])", "answer": "['2019-04-30 11:30:00', '2019-05-01 06:00:00']", "ground_truth": [ "2019-04-30 11:30:00", "2019-05-01 06:00:00" ], "eval_metric": "iou", "channel": "99", "ts_data_path": "ts_data/L2_T1_Shape_Identification_00003.csv", "meta": { "pattern": "step_up", "superlative": "largest", "direction": "up", "primary_amp": 0.0628141308191623, "local_std": 0.0044477390659748, "search_window": [ "2019-04-26 15:00:00", "2019-05-01 20:00:00" ], "source": "causal_rivers" } }, { "id": "L2_T1_Shape_Identification_00004", "level": 2, "level_name": "Pattern Recognition", "category": "Shape Identification", "subtask": "Shape Identification", "question": "Identify the time range of the largest step descent in channel 123 within [2022-06-16 15:00:00 to 2022-06-26 03:15:00]. (Output format: [YYYY-MM-DD HH:MM:SS, YYYY-MM-DD HH:MM:SS])", "answer": "['2022-06-20 19:30:00', '2022-06-22 05:30:00']", "ground_truth": [ "2022-06-20 19:30:00", "2022-06-22 05:30:00" ], "eval_metric": "iou", "channel": "123", "ts_data_path": "ts_data/L2_T1_Shape_Identification_00004.csv", "meta": { "pattern": "step_down", "superlative": "largest", "direction": "down", "primary_amp": 0.2409191994069681, "local_std": 0.019273535952557447, "search_window": [ "2022-06-16 15:00:00", "2022-06-26 03:15:00" ], "source": "causal_rivers" } }, { "id": "L2_T1_Shape_Identification_00006", "level": 2, "level_name": "Pattern Recognition", "category": "Shape Identification", "subtask": "Shape Identification", "question": "Identify the time range of the longest plateau (stable period) in channel 146 within [2022-07-19 00:00:00 to 2022-07-27 18:15:00]. (Output format: [YYYY-MM-DD HH:MM:SS, YYYY-MM-DD HH:MM:SS])", "answer": "['2022-07-22 06:15:00', '2022-07-23 13:30:00']", "ground_truth": [ "2022-07-22 06:15:00", "2022-07-23 13:30:00" ], "eval_metric": "iou", "channel": "146", "ts_data_path": "ts_data/L2_T1_Shape_Identification_00006.csv", "meta": { "pattern": "plateau", "superlative": "longest", "direction": "up", "primary_amp": 0.5782060785767238, "local_std": 0.038547071905114916, "search_window": [ "2022-07-19 00:00:00", "2022-07-27 18:15:00" ], "source": "causal_rivers" } }, { "id": "L2_T1_Shape_Identification_00007", "level": 2, "level_name": "Pattern Recognition", "category": "Shape Identification", "subtask": "Shape Identification", "question": "Identify the time range of the largest step descent in channel 147 within [2021-04-20 05:15:00 to 2021-04-28 22:00:00]. (Output format: [YYYY-MM-DD HH:MM:SS, YYYY-MM-DD HH:MM:SS])", "answer": "['2021-04-25 12:45:00', '2021-04-26 19:45:00']", "ground_truth": [ "2021-04-25 12:45:00", "2021-04-26 19:45:00" ], "eval_metric": "iou", "channel": "147", "ts_data_path": "ts_data/L2_T1_Shape_Identification_00007.csv", "meta": { "pattern": "step_down", "superlative": "largest", "direction": "down", "primary_amp": 0.12045959970348405, "local_std": 0.009636767976278724, "search_window": [ "2021-04-20 05:15:00", "2021-04-28 22:00:00" ], "source": "causal_rivers" } }, { "id": "L2_T1_Shape_Identification_00008", "level": 2, "level_name": "Pattern Recognition", "category": "Shape Identification", "subtask": "Shape Identification", "question": "Identify the time range of the longest low plateau (bottom out) in channel 151 within [2023-06-18 21:00:00 to 2023-06-27 00:15:00]. (Output format: [YYYY-MM-DD HH:MM:SS, YYYY-MM-DD HH:MM:SS])", "answer": "['2023-06-25 03:30:00', '2023-06-26 08:30:00']", "ground_truth": [ "2023-06-25 03:30:00", "2023-06-26 08:30:00" ], "eval_metric": "iou", "channel": "151", "ts_data_path": "ts_data/L2_T1_Shape_Identification_00008.csv", "meta": { "pattern": "depression", "superlative": "longest", "direction": "down", "primary_amp": 0.42253521126760596, "local_std": 0.028169014084507067, "search_window": [ "2023-06-18 21:00:00", "2023-06-27 00:15:00" ], "source": "causal_rivers" } }, { "id": "L2_T1_Shape_Identification_00009", "level": 2, "level_name": "Pattern Recognition", "category": "Shape Identification", "subtask": "Shape Identification", "question": "Identify the time range of the largest step descent in channel 154 within [2020-11-29 16:00:00 to 2020-12-06 08:30:00]. (Output format: [YYYY-MM-DD HH:MM:SS, YYYY-MM-DD HH:MM:SS])", "answer": "['2020-12-04 00:45:00', '2020-12-05 00:30:00']", "ground_truth": [ "2020-12-04 00:45:00", "2020-12-05 00:30:00" ], "eval_metric": "iou", "channel": "154", "ts_data_path": "ts_data/L2_T1_Shape_Identification_00009.csv", "meta": { "pattern": "step_down", "superlative": "largest", "direction": "down", "primary_amp": 0.3338146654338736, "local_std": 0.0266864343958488, "search_window": [ "2020-11-29 16:00:00", "2020-12-06 08:30:00" ], "source": "causal_rivers" } }, { "id": "L2_T1_Shape_Identification_00011", "level": 2, "level_name": "Pattern Recognition", "category": "Shape Identification", "subtask": "Shape Identification", "question": "Identify the time range of the largest step ascent in channel 166 within [2019-08-27 06:15:00 to 2019-08-31 21:45:00]. (Output format: [YYYY-MM-DD HH:MM:SS, YYYY-MM-DD HH:MM:SS])", "answer": "['2019-08-27 16:45:00', '2019-08-28 09:15:00']", "ground_truth": [ "2019-08-27 16:45:00", "2019-08-28 09:15:00" ], "eval_metric": "iou", "channel": "166", "ts_data_path": "ts_data/L2_T1_Shape_Identification_00011.csv", "meta": { "pattern": "step_up", "superlative": "largest", "direction": "up", "primary_amp": 74.12898443291327, "local_std": 5.930318754633062, "search_window": [ "2019-08-27 06:15:00", "2019-08-31 21:45:00" ], "source": "causal_rivers" } }, { "id": "L2_T1_Shape_Identification_00013", "level": 2, "level_name": "Pattern Recognition", "category": "Shape Identification", "subtask": "Shape Identification", "question": "Identify the time range of the largest step ascent in channel 170 within [2019-07-09 20:15:00 to 2019-07-14 01:15:00]. (Output format: [YYYY-MM-DD HH:MM:SS, YYYY-MM-DD HH:MM:SS])", "answer": "['2019-07-11 06:30:00', '2019-07-11 21:15:00']", "ground_truth": [ "2019-07-11 06:30:00", "2019-07-11 21:15:00" ], "eval_metric": "iou", "channel": "170", "ts_data_path": "ts_data/L2_T1_Shape_Identification_00013.csv", "meta": { "pattern": "step_up", "superlative": "largest", "direction": "up", "primary_amp": 25.724285936541875, "local_std": 1.4825796886582654, "search_window": [ "2019-07-09 20:15:00", "2019-07-14 01:15:00" ], "source": "causal_rivers" } }, { "id": "L2_T1_Shape_Identification_00014", "level": 2, "level_name": "Pattern Recognition", "category": "Shape Identification", "subtask": "Shape Identification", "question": "Identify the time range of the largest step ascent in channel 172 within [2020-02-22 15:00:00 to 2020-02-25 08:00:00]. (Output format: [YYYY-MM-DD HH:MM:SS, YYYY-MM-DD HH:MM:SS])", "answer": "['2020-02-23 07:00:00', '2020-02-23 16:30:00']", "ground_truth": [ "2020-02-23 07:00:00", "2020-02-23 16:30:00" ], "eval_metric": "iou", "channel": "172", "ts_data_path": "ts_data/L2_T1_Shape_Identification_00014.csv", "meta": { "pattern": "step_up", "superlative": "largest", "direction": "up", "primary_amp": 37.064492216456635, "local_std": 2.965159377316531, "search_window": [ "2020-02-22 15:00:00", "2020-02-25 08:00:00" ], "source": "causal_rivers" } }, { "id": "L2_T1_Shape_Identification_00015", "level": 2, "level_name": "Pattern Recognition", "category": "Shape Identification", "subtask": "Shape Identification", "question": "Identify the time range of the longest low plateau (bottom out) in channel 173 within [2020-05-27 06:45:00 to 2020-06-05 02:00:00]. (Output format: [YYYY-MM-DD HH:MM:SS, YYYY-MM-DD HH:MM:SS])", "answer": "['2020-05-27 19:00:00', '2020-05-29 02:15:00']", "ground_truth": [ "2020-05-27 19:00:00", "2020-05-29 02:15:00" ], "eval_metric": "iou", "channel": "173", "ts_data_path": "ts_data/L2_T1_Shape_Identification_00015.csv", "meta": { "pattern": "depression", "superlative": "longest", "direction": "down", "primary_amp": 111.1934766493699, "local_std": 7.412898443291327, "search_window": [ "2020-05-27 06:45:00", "2020-06-05 02:00:00" ], "source": "causal_rivers" } }, { "id": "L2_T1_Shape_Identification_00016", "level": 2, "level_name": "Pattern Recognition", "category": "Shape Identification", "subtask": "Shape Identification", "question": "Identify the time range of the deepest deep valley in channel 177 within [2019-07-29 19:00:00 to 2019-08-08 16:30:00]. (Output format: [YYYY-MM-DD HH:MM:SS, YYYY-MM-DD HH:MM:SS])", "answer": "['2019-08-04 08:45:00', '2019-08-05 20:00:00']", "ground_truth": [ "2019-08-04 08:45:00", "2019-08-05 20:00:00" ], "eval_metric": "iou", "channel": "177", "ts_data_path": "ts_data/L2_T1_Shape_Identification_00016.csv", "meta": { "pattern": "valley", "superlative": "deepest", "direction": "down", "primary_amp": 147.88732394366195, "local_std": 9.859154929577462, "search_window": [ "2019-07-29 19:00:00", "2019-08-08 16:30:00" ], "source": "causal_rivers" } }, { "id": "L2_T1_Shape_Identification_00017", "level": 2, "level_name": "Pattern Recognition", "category": "Shape Identification", "subtask": "Shape Identification", "question": "Identify the time range of the longest low plateau (bottom out) in channel 237 within [2020-07-30 23:30:00 to 2020-08-09 22:30:00]. (Output format: [YYYY-MM-DD HH:MM:SS, YYYY-MM-DD HH:MM:SS])", "answer": "['2020-07-31 12:30:00', '2020-08-02 00:00:00']", "ground_truth": [ "2020-07-31 12:30:00", "2020-08-02 00:00:00" ], "eval_metric": "iou", "channel": "237", "ts_data_path": "ts_data/L2_T1_Shape_Identification_00017.csv", "meta": { "pattern": "depression", "superlative": "longest", "direction": "down", "primary_amp": 0.015411420511187962, "local_std": 0.0007412898443291333, "search_window": [ "2020-07-30 23:30:00", "2020-08-09 22:30:00" ], "source": "causal_rivers" } }, { "id": "L2_T1_Shape_Identification_00018", "level": 2, "level_name": "Pattern Recognition", "category": "Shape Identification", "subtask": "Shape Identification", "question": "Identify the time range of the largest step descent in channel 245 within [2022-12-11 00:00:00 to 2022-12-20 09:15:00]. (Output format: [YYYY-MM-DD HH:MM:SS, YYYY-MM-DD HH:MM:SS])", "answer": "['2022-12-11 16:15:00', '2022-12-13 01:45:00']", "ground_truth": [ "2022-12-11 16:15:00", "2022-12-13 01:45:00" ], "eval_metric": "iou", "channel": "245", "ts_data_path": "ts_data/L2_T1_Shape_Identification_00018.csv", "meta": { "pattern": "step_down", "superlative": "largest", "direction": "down", "primary_amp": 0.14825796886582668, "local_std": 0.011860637509266133, "search_window": [ "2022-12-11 00:00:00", "2022-12-20 09:15:00" ], "source": "causal_rivers" } }, { "id": "L2_T1_Shape_Identification_00019", "level": 2, "level_name": "Pattern Recognition", "category": "Shape Identification", "subtask": "Shape Identification", "question": "Identify the time range of the deepest deep valley in channel 312 within [2020-09-02 20:00:00 to 2020-09-07 00:15:00]. (Output format: [YYYY-MM-DD HH:MM:SS, YYYY-MM-DD HH:MM:SS])", "answer": "['2020-09-03 04:45:00', '2020-09-03 19:30:00']", "ground_truth": [ "2020-09-03 04:45:00", "2020-09-03 19:30:00" ], "eval_metric": "iou", "channel": "312", "ts_data_path": "ts_data/L2_T1_Shape_Identification_00019.csv", "meta": { "pattern": "valley", "superlative": "deepest", "direction": "down", "primary_amp": 0.044477390659748, "local_std": 0.0029651593773165332, "search_window": [ "2020-09-02 20:00:00", "2020-09-07 00:15:00" ], "source": "causal_rivers" } }, { "id": "L2_T1_Shape_Identification_00021", "level": 2, "level_name": "Pattern Recognition", "category": "Shape Identification", "subtask": "Shape Identification", "question": "Identify the time range of the longest plateau (stable period) in channel 441 within [2023-09-29 02:30:00 to 2023-10-06 12:15:00]. (Output format: [YYYY-MM-DD HH:MM:SS, YYYY-MM-DD HH:MM:SS])", "answer": "['2023-10-04 05:30:00', '2023-10-05 07:45:00']", "ground_truth": [ "2023-10-04 05:30:00", "2023-10-05 07:45:00" ], "eval_metric": "iou", "channel": "441", "ts_data_path": "ts_data/L2_T1_Shape_Identification_00021.csv", "meta": { "pattern": "plateau", "superlative": "longest", "direction": "up", "primary_amp": 11.714031492324732, "local_std": 0.2816901408450703, "search_window": [ "2023-09-29 02:30:00", "2023-10-06 12:15:00" ], "source": "causal_rivers" } }, { "id": "L2_T1_Shape_Identification_00022", "level": 2, "level_name": "Pattern Recognition", "category": "Shape Identification", "subtask": "Shape Identification", "question": "Identify the time range of the largest step descent in channel 495 within [2021-04-12 11:30:00 to 2021-04-15 17:15:00]. (Output format: [YYYY-MM-DD HH:MM:SS, YYYY-MM-DD HH:MM:SS])", "answer": "['2021-04-12 13:30:00', '2021-04-13 00:45:00']", "ground_truth": [ "2021-04-12 13:30:00", "2021-04-13 00:45:00" ], "eval_metric": "iou", "channel": "495", "ts_data_path": "ts_data/L2_T1_Shape_Identification_00022.csv", "meta": { "pattern": "step_down", "superlative": "largest", "direction": "down", "primary_amp": 12.5, "local_std": 1.0, "search_window": [ "2021-04-12 11:30:00", "2021-04-15 17:15:00" ], "source": "causal_rivers" } }, { "id": "L2_T1_Shape_Identification_00024", "level": 2, "level_name": "Pattern Recognition", "category": "Shape Identification", "subtask": "Shape Identification", "question": "Identify the time range of the longest plateau (stable period) in channel 501 within [2023-08-19 09:45:00 to 2023-08-25 21:45:00]. (Output format: [YYYY-MM-DD HH:MM:SS, YYYY-MM-DD HH:MM:SS])", "answer": "['2023-08-23 22:00:00', '2023-08-24 21:00:00']", "ground_truth": [ "2023-08-23 22:00:00", "2023-08-24 21:00:00" ], "eval_metric": "iou", "channel": "501", "ts_data_path": "ts_data/L2_T1_Shape_Identification_00024.csv", "meta": { "pattern": "plateau", "superlative": "longest", "direction": "up", "primary_amp": 0.170468085989622, "local_std": 0.0074128984432913336, "search_window": [ "2023-08-19 09:45:00", "2023-08-25 21:45:00" ], "source": "causal_rivers" } }, { "id": "L2_T1_Shape_Identification_00025", "level": 2, "level_name": "Pattern Recognition", "category": "Shape Identification", "subtask": "Shape Identification", "question": "Identify the time range of the largest step descent in channel 578 within [2019-11-10 02:15:00 to 2019-11-16 20:00:00]. (Output format: [YYYY-MM-DD HH:MM:SS, YYYY-MM-DD HH:MM:SS])", "answer": "['2019-11-11 17:00:00', '2019-11-12 17:00:00']", "ground_truth": [ "2019-11-11 17:00:00", "2019-11-12 17:00:00" ], "eval_metric": "iou", "channel": "578", "ts_data_path": "ts_data/L2_T1_Shape_Identification_00025.csv", "meta": { "pattern": "step_down", "superlative": "largest", "direction": "down", "primary_amp": 0.25886401049245444, "local_std": 0.017049666419570068, "search_window": [ "2019-11-10 02:15:00", "2019-11-16 20:00:00" ], "source": "causal_rivers" } }, { "id": "L2_T1_Shape_Identification_00026", "level": 2, "level_name": "Pattern Recognition", "category": "Shape Identification", "subtask": "Shape Identification", "question": "Identify the time range of the longest plateau (stable period) in channel 580 within [2019-08-30 23:30:00 to 2019-09-05 23:30:00]. (Output format: [YYYY-MM-DD HH:MM:SS, YYYY-MM-DD HH:MM:SS])", "answer": "['2019-09-02 06:15:00', '2019-09-03 03:30:00']", "ground_truth": [ "2019-09-02 06:15:00", "2019-09-03 03:30:00" ], "eval_metric": "iou", "channel": "580", "ts_data_path": "ts_data/L2_T1_Shape_Identification_00026.csv", "meta": { "pattern": "plateau", "superlative": "longest", "direction": "up", "primary_amp": 0.12231282431430701, "local_std": 0.008154188287620467, "search_window": [ "2019-08-30 23:30:00", "2019-09-05 23:30:00" ], "source": "causal_rivers" } }, { "id": "L2_T1_Shape_Identification_00027", "level": 2, "level_name": "Pattern Recognition", "category": "Shape Identification", "subtask": "Shape Identification", "question": "Identify the time range of the largest step descent in channel 589 within [2023-07-04 21:00:00 to 2023-07-12 21:30:00]. (Output format: [YYYY-MM-DD HH:MM:SS, YYYY-MM-DD HH:MM:SS])", "answer": "['2023-07-10 17:00:00', '2023-07-11 21:30:00']", "ground_truth": [ "2023-07-10 17:00:00", "2023-07-11 21:30:00" ], "eval_metric": "iou", "channel": "589", "ts_data_path": "ts_data/L2_T1_Shape_Identification_00027.csv", "meta": { "pattern": "step_down", "superlative": "largest", "direction": "down", "primary_amp": 101.92735359525574, "local_std": 8.15418828762046, "search_window": [ "2023-07-04 21:00:00", "2023-07-12 21:30:00" ], "source": "causal_rivers" } }, { "id": "L2_T1_Shape_Identification_00028", "level": 2, "level_name": "Pattern Recognition", "category": "Shape Identification", "subtask": "Shape Identification", "question": "Identify the time range of the longest plateau (stable period) in channel 591 within [2022-12-15 09:30:00 to 2022-12-24 16:15:00]. (Output format: [YYYY-MM-DD HH:MM:SS, YYYY-MM-DD HH:MM:SS])", "answer": "['2022-12-15 17:00:00', '2022-12-17 02:00:00']", "ground_truth": [ "2022-12-15 17:00:00", "2022-12-17 02:00:00" ], "eval_metric": "iou", "channel": "591", "ts_data_path": "ts_data/L2_T1_Shape_Identification_00028.csv", "meta": { "pattern": "plateau", "superlative": "longest", "direction": "up", "primary_amp": 88.95478131949594, "local_std": 5.930318754633062, "search_window": [ "2022-12-15 09:30:00", "2022-12-24 16:15:00" ], "source": "causal_rivers" } }, { "id": "L2_T1_Shape_Identification_00029", "level": 2, "level_name": "Pattern Recognition", "category": "Shape Identification", "subtask": "Shape Identification", "question": "Identify the time range of the longest plateau (stable period) in channel 595 within [2020-04-11 15:45:00 to 2020-04-17 08:30:00]. (Output format: [YYYY-MM-DD HH:MM:SS, YYYY-MM-DD HH:MM:SS])", "answer": "['2020-04-16 03:45:00', '2020-04-17 00:00:00']", "ground_truth": [ "2020-04-16 03:45:00", "2020-04-17 00:00:00" ], "eval_metric": "iou", "channel": "595", "ts_data_path": "ts_data/L2_T1_Shape_Identification_00029.csv", "meta": { "pattern": "plateau", "superlative": "longest", "direction": "up", "primary_amp": 0.8895478131949576, "local_std": 0.0593031875463305, "search_window": [ "2020-04-11 15:45:00", "2020-04-17 08:30:00" ], "source": "causal_rivers" } }, { "id": "L2_T1_Shape_Identification_00030", "level": 2, "level_name": "Pattern Recognition", "category": "Shape Identification", "subtask": "Shape Identification", "question": "Identify the time range of the largest step descent in channel 625 within [2022-04-19 20:45:00 to 2022-04-22 07:45:00]. (Output format: [YYYY-MM-DD HH:MM:SS, YYYY-MM-DD HH:MM:SS])", "answer": "['2022-04-21 01:30:00', '2022-04-21 10:00:00']", "ground_truth": [ "2022-04-21 01:30:00", "2022-04-21 10:00:00" ], "eval_metric": "iou", "channel": "625", "ts_data_path": "ts_data/L2_T1_Shape_Identification_00030.csv", "meta": { "pattern": "step_down", "superlative": "largest", "direction": "down", "primary_amp": 0.03706449221645667, "local_std": 0.0029651593773165332, "search_window": [ "2022-04-19 20:45:00", "2022-04-22 07:45:00" ], "source": "causal_rivers" } }, { "id": "L2_T1_Shape_Identification_00031", "level": 2, "level_name": "Pattern Recognition", "category": "Shape Identification", "subtask": "Shape Identification", "question": "Identify the time range of the deepest deep valley in channel 626 within [2023-01-07 09:00:00 to 2023-01-11 21:00:00]. (Output format: [YYYY-MM-DD HH:MM:SS, YYYY-MM-DD HH:MM:SS])", "answer": "['2023-01-07 12:45:00', '2023-01-08 04:30:00']", "ground_truth": [ "2023-01-07 12:45:00", "2023-01-08 04:30:00" ], "eval_metric": "iou", "channel": "626", "ts_data_path": "ts_data/L2_T1_Shape_Identification_00031.csv", "meta": { "pattern": "valley", "superlative": "deepest", "direction": "down", "primary_amp": 0.233506300963677, "local_std": 0.0155670867309118, "search_window": [ "2023-01-07 09:00:00", "2023-01-11 21:00:00" ], "source": "causal_rivers" } }, { "id": "L2_T1_Shape_Identification_00032", "level": 2, "level_name": "Pattern Recognition", "category": "Shape Identification", "subtask": "Shape Identification", "question": "Identify the time range of the largest step ascent in channel 627 within [2021-09-19 04:30:00 to 2021-09-25 17:15:00]. (Output format: [YYYY-MM-DD HH:MM:SS, YYYY-MM-DD HH:MM:SS])", "answer": "['2021-09-24 10:45:00', '2021-09-25 10:00:00']", "ground_truth": [ "2021-09-24 10:45:00", "2021-09-25 10:00:00" ], "eval_metric": "iou", "channel": "627", "ts_data_path": "ts_data/L2_T1_Shape_Identification_00032.csv", "meta": { "pattern": "step_up", "superlative": "largest", "direction": "up", "primary_amp": 0.13770569612782294, "local_std": 0.009636767976278734, "search_window": [ "2021-09-19 04:30:00", "2021-09-25 17:15:00" ], "source": "causal_rivers" } }, { "id": "L2_T1_Shape_Identification_00035", "level": 2, "level_name": "Pattern Recognition", "category": "Shape Identification", "subtask": "Shape Identification", "question": "Identify the time range of the deepest deep valley in channel 683 within [2023-09-10 13:30:00 to 2023-09-19 12:00:00]. (Output format: [YYYY-MM-DD HH:MM:SS, YYYY-MM-DD HH:MM:SS])", "answer": "['2023-09-16 19:45:00', '2023-09-18 03:30:00']", "ground_truth": [ "2023-09-16 19:45:00", "2023-09-18 03:30:00" ], "eval_metric": "iou", "channel": "683", "ts_data_path": "ts_data/L2_T1_Shape_Identification_00035.csv", "meta": { "pattern": "valley", "superlative": "deepest", "direction": "down", "primary_amp": 0.24147757360904576, "local_std": 0.014825796886582667, "search_window": [ "2023-09-10 13:30:00", "2023-09-19 12:00:00" ], "source": "causal_rivers" } }, { "id": "L2_T1_Shape_Identification_00036", "level": 2, "level_name": "Pattern Recognition", "category": "Shape Identification", "subtask": "Shape Identification", "question": "Identify the time range of the highest upward spike in channel 727 within [2019-05-02 02:15:00 to 2019-05-10 04:45:00]. (Output format: [YYYY-MM-DD HH:MM:SS, YYYY-MM-DD HH:MM:SS])", "answer": "['2019-05-03 01:30:00', '2019-05-04 06:15:00']", "ground_truth": [ "2019-05-03 01:30:00", "2019-05-04 06:15:00" ], "eval_metric": "iou", "channel": "727", "ts_data_path": "ts_data/L2_T1_Shape_Identification_00036.csv", "meta": { "pattern": "spike", "superlative": "highest", "direction": "up", "primary_amp": 46.231644036232794, "local_std": 3.039288361749445, "search_window": [ "2019-05-02 02:15:00", "2019-05-10 04:45:00" ], "source": "causal_rivers" } }, { "id": "L2_T1_Shape_Identification_00037", "level": 2, "level_name": "Pattern Recognition", "category": "Shape Identification", "subtask": "Shape Identification", "question": "Identify the time range of the longest low plateau (bottom out) in channel 728 within [2021-01-09 09:00:00 to 2021-01-18 20:45:00]. (Output format: [YYYY-MM-DD HH:MM:SS, YYYY-MM-DD HH:MM:SS])", "answer": "['2021-01-13 19:00:00', '2021-01-15 04:45:00']", "ground_truth": [ "2021-01-13 19:00:00", "2021-01-15 04:45:00" ], "eval_metric": "iou", "channel": "728", "ts_data_path": "ts_data/L2_T1_Shape_Identification_00037.csv", "meta": { "pattern": "depression", "superlative": "longest", "direction": "down", "primary_amp": 34.12162249814674, "local_std": 2.0756115641215693, "search_window": [ "2021-01-09 09:00:00", "2021-01-18 20:45:00" ], "source": "causal_rivers" } }, { "id": "L2_T1_Shape_Identification_00038", "level": 2, "level_name": "Pattern Recognition", "category": "Shape Identification", "subtask": "Shape Identification", "question": "Identify the time range of the largest step descent in channel 729 within [2023-10-09 06:00:00 to 2023-10-14 01:45:00]. (Output format: [YYYY-MM-DD HH:MM:SS, YYYY-MM-DD HH:MM:SS])", "answer": "['2023-10-10 06:45:00', '2023-10-10 23:45:00']", "ground_truth": [ "2023-10-10 06:45:00", "2023-10-10 23:45:00" ], "eval_metric": "iou", "channel": "729", "ts_data_path": "ts_data/L2_T1_Shape_Identification_00038.csv", "meta": { "pattern": "step_down", "superlative": "largest", "direction": "down", "primary_amp": 37.89157637522048, "local_std": 2.52038547071905, "search_window": [ "2023-10-09 06:00:00", "2023-10-14 01:45:00" ], "source": "causal_rivers" } }, { "id": "L2_T1_Shape_Identification_00039", "level": 2, "level_name": "Pattern Recognition", "category": "Shape Identification", "subtask": "Shape Identification", "question": "Identify the time range of the highest upward spike in channel 754 within [2020-07-07 14:00:00 to 2020-07-17 02:15:00]. (Output format: [YYYY-MM-DD HH:MM:SS, YYYY-MM-DD HH:MM:SS])", "answer": "['2020-07-15 04:45:00', '2020-07-16 14:45:00']", "ground_truth": [ "2020-07-15 04:45:00", "2020-07-16 14:45:00" ], "eval_metric": "iou", "channel": "754", "ts_data_path": "ts_data/L2_T1_Shape_Identification_00039.csv", "meta": { "pattern": "spike", "superlative": "highest", "direction": "up", "primary_amp": 0.12012653428343875, "local_std": 0.006671608598962195, "search_window": [ "2020-07-07 14:00:00", "2020-07-17 02:15:00" ], "source": "causal_rivers" } }, { "id": "L2_T1_Shape_Identification_00040", "level": 2, "level_name": "Pattern Recognition", "category": "Shape Identification", "subtask": "Shape Identification", "question": "Identify the time range of the largest step ascent in channel 762 within [2019-05-05 18:45:00 to 2019-05-12 02:00:00]. (Output format: [YYYY-MM-DD HH:MM:SS, YYYY-MM-DD HH:MM:SS])", "answer": "['2019-05-08 09:30:00', '2019-05-09 07:45:00']", "ground_truth": [ "2019-05-08 09:30:00", "2019-05-09 07:45:00" ], "eval_metric": "iou", "channel": "762", "ts_data_path": "ts_data/L2_T1_Shape_Identification_00040.csv", "meta": { "pattern": "step_up", "superlative": "largest", "direction": "up", "primary_amp": 0.009071875041735592, "local_std": 0.0007257500033388475, "search_window": [ "2019-05-05 18:45:00", "2019-05-12 02:00:00" ], "source": "causal_rivers" } }, { "id": "L2_T1_Shape_Identification_00041", "level": 2, "level_name": "Pattern Recognition", "category": "Shape Identification", "subtask": "Shape Identification", "question": "Identify the time range of the longest low plateau (bottom out) in channel 811 within [2020-01-26 09:45:00 to 2020-01-28 10:30:00]. (Output format: [YYYY-MM-DD HH:MM:SS, YYYY-MM-DD HH:MM:SS])", "answer": "['2020-01-26 12:45:00', '2020-01-26 19:45:00']", "ground_truth": [ "2020-01-26 12:45:00", "2020-01-26 19:45:00" ], "eval_metric": "iou", "channel": "811", "ts_data_path": "ts_data/L2_T1_Shape_Identification_00041.csv", "meta": { "pattern": "depression", "superlative": "longest", "direction": "down", "primary_amp": 0.9054017792229448, "local_std": 0.060360118614862984, "search_window": [ "2020-01-26 09:45:00", "2020-01-28 10:30:00" ], "source": "causal_rivers" } }, { "id": "L2_T1_Shape_Identification_00044", "level": 2, "level_name": "Pattern Recognition", "category": "Shape Identification", "subtask": "Shape Identification", "question": "Identify the time range of the largest step ascent in channel 891 within [2023-05-20 00:00:00 to 2023-05-26 16:30:00]. (Output format: [YYYY-MM-DD HH:MM:SS, YYYY-MM-DD HH:MM:SS])", "answer": "['2023-05-25 03:45:00', '2023-05-26 03:30:00']", "ground_truth": [ "2023-05-25 03:45:00", "2023-05-26 03:30:00" ], "eval_metric": "iou", "channel": "891", "ts_data_path": "ts_data/L2_T1_Shape_Identification_00044.csv", "meta": { "pattern": "step_up", "superlative": "largest", "direction": "up", "primary_amp": 1.0506549117081132, "local_std": 0.022238695329874002, "search_window": [ "2023-05-20 00:00:00", "2023-05-26 16:30:00" ], "source": "causal_rivers" } }, { "id": "L2_T1_Shape_Identification_00045", "level": 2, "level_name": "Pattern Recognition", "category": "Shape Identification", "subtask": "Shape Identification", "question": "Identify the time range of the deepest deep valley in channel 894 within [2020-07-24 00:00:00 to 2020-07-31 22:30:00]. (Output format: [YYYY-MM-DD HH:MM:SS, YYYY-MM-DD HH:MM:SS])", "answer": "['2020-07-29 15:15:00', '2020-07-30 19:30:00']", "ground_truth": [ "2020-07-29 15:15:00", "2020-07-30 19:30:00" ], "eval_metric": "iou", "channel": "894", "ts_data_path": "ts_data/L2_T1_Shape_Identification_00045.csv", "meta": { "pattern": "valley", "superlative": "deepest", "direction": "down", "primary_amp": 0.9099279015473822, "local_std": 0.05930318754633067, "search_window": [ "2020-07-24 00:00:00", "2020-07-31 22:30:00" ], "source": "causal_rivers" } }, { "id": "L2_T1_Shape_Identification_00046", "level": 2, "level_name": "Pattern Recognition", "category": "Shape Identification", "subtask": "Shape Identification", "question": "Identify the time range of the longest low plateau (bottom out) in channel 895 within [2021-04-23 00:00:00 to 2021-04-26 18:00:00]. (Output format: [YYYY-MM-DD HH:MM:SS, YYYY-MM-DD HH:MM:SS])", "answer": "['2021-04-23 07:15:00', '2021-04-23 20:30:00']", "ground_truth": [ "2021-04-23 07:15:00", "2021-04-23 20:30:00" ], "eval_metric": "iou", "channel": "895", "ts_data_path": "ts_data/L2_T1_Shape_Identification_00046.csv", "meta": { "pattern": "depression", "superlative": "longest", "direction": "down", "primary_amp": 0.8895478131949576, "local_std": 0.0593031875463305, "search_window": [ "2021-04-23 00:00:00", "2021-04-26 18:00:00" ], "source": "causal_rivers" } }, { "id": "L2_T1_Shape_Identification_00047", "level": 2, "level_name": "Pattern Recognition", "category": "Shape Identification", "subtask": "Shape Identification", "question": "Identify the time range of the highest upward spike in channel 897 within [2019-10-30 19:30:00 to 2019-11-08 16:15:00]. (Output format: [YYYY-MM-DD HH:MM:SS, YYYY-MM-DD HH:MM:SS])", "answer": "['2019-11-03 01:15:00', '2019-11-04 08:45:00']", "ground_truth": [ "2019-11-03 01:15:00", "2019-11-04 08:45:00" ], "eval_metric": "iou", "channel": "897", "ts_data_path": "ts_data/L2_T1_Shape_Identification_00047.csv", "meta": { "pattern": "spike", "superlative": "highest", "direction": "up", "primary_amp": 0.04447739065974796, "local_std": 0.002965159377316531, "search_window": [ "2019-10-30 19:30:00", "2019-11-08 16:15:00" ], "source": "causal_rivers" } }, { "id": "L2_T1_Shape_Identification_00048", "level": 2, "level_name": "Pattern Recognition", "category": "Shape Identification", "subtask": "Shape Identification", "question": "Identify the time range of the deepest deep valley in channel 933 within [2020-07-02 06:00:00 to 2020-07-05 19:15:00]. (Output format: [YYYY-MM-DD HH:MM:SS, YYYY-MM-DD HH:MM:SS])", "answer": "['2020-07-04 02:30:00', '2020-07-04 15:00:00']", "ground_truth": [ "2020-07-04 02:30:00", "2020-07-04 15:00:00" ], "eval_metric": "iou", "channel": "933", "ts_data_path": "ts_data/L2_T1_Shape_Identification_00048.csv", "meta": { "pattern": "valley", "superlative": "deepest", "direction": "down", "primary_amp": 0.02120197424082872, "local_std": 0.0011471979175969498, "search_window": [ "2020-07-02 06:00:00", "2020-07-05 19:15:00" ], "source": "causal_rivers" } }, { "id": "L2_T1_Shape_Identification_00049", "level": 2, "level_name": "Pattern Recognition", "category": "Shape Identification", "subtask": "Shape Identification", "question": "Identify the time range of the largest step descent in channel HUFL within [2016-07-24 03:45:00 to 2016-07-27 03:00:00]. (Output format: [YYYY-MM-DD HH:MM:SS, YYYY-MM-DD HH:MM:SS])", "answer": "['2016-07-26 09:30:00', '2016-07-26 19:45:00']", "ground_truth": [ "2016-07-26 09:30:00", "2016-07-26 19:45:00" ], "eval_metric": "iou", "channel": "HUFL", "ts_data_path": "ts_data/L2_T1_Shape_Identification_00049.csv", "meta": { "pattern": "step_down", "superlative": "largest", "direction": "down", "primary_amp": 45.19247656561093, "local_std": 2.4701635249019818, "search_window": [ "2016-07-24 03:45:00", "2016-07-27 03:00:00" ], "source": "ettm1" } }, { "id": "L2_T1_Shape_Identification_00051", "level": 2, "level_name": "Pattern Recognition", "category": "Shape Identification", "subtask": "Shape Identification", "question": "Identify the time range of the deepest deep valley in channel MUFL within [2017-01-10 18:00:00 to 2017-01-20 10:15:00]. (Output format: [YYYY-MM-DD HH:MM:SS, YYYY-MM-DD HH:MM:SS])", "answer": "['2017-01-11 06:15:00', '2017-01-12 16:45:00']", "ground_truth": [ "2017-01-11 06:15:00", "2017-01-12 16:45:00" ], "eval_metric": "iou", "channel": "MUFL", "ts_data_path": "ts_data/L2_T1_Shape_Identification_00051.csv", "meta": { "pattern": "valley", "superlative": "deepest", "direction": "down", "primary_amp": 53.95004294898372, "local_std": 2.1275022986025327, "search_window": [ "2017-01-10 18:00:00", "2017-01-20 10:15:00" ], "source": "ettm1" } }, { "id": "L2_T1_Shape_Identification_00052", "level": 2, "level_name": "Pattern Recognition", "category": "Shape Identification", "subtask": "Shape Identification", "question": "Identify the time range of the deepest deep valley in channel MULL within [2018-02-05 01:30:00 to 2018-02-08 17:30:00]. (Output format: [YYYY-MM-DD HH:MM:SS, YYYY-MM-DD HH:MM:SS])", "answer": "['2018-02-05 20:15:00', '2018-02-06 09:00:00']", "ground_truth": [ "2018-02-05 20:15:00", "2018-02-06 09:00:00" ], "eval_metric": "iou", "channel": "MULL", "ts_data_path": "ts_data/L2_T1_Shape_Identification_00052.csv", "meta": { "pattern": "valley", "superlative": "deepest", "direction": "down", "primary_amp": 13.031876025330675, "local_std": 0.8687917350220451, "search_window": [ "2018-02-05 01:30:00", "2018-02-08 17:30:00" ], "source": "ettm1" } }, { "id": "L2_T1_Shape_Identification_00053", "level": 2, "level_name": "Pattern Recognition", "category": "Shape Identification", "subtask": "Shape Identification", "question": "Identify the time range of the longest plateau (stable period) in channel LUFL within [2017-11-19 22:00:00 to 2017-11-27 22:15:00]. (Output format: [YYYY-MM-DD HH:MM:SS, YYYY-MM-DD HH:MM:SS])", "answer": "['2017-11-25 16:30:00', '2017-11-26 21:00:00']", "ground_truth": [ "2017-11-25 16:30:00", "2017-11-26 21:00:00" ], "eval_metric": "iou", "channel": "LUFL", "ts_data_path": "ts_data/L2_T1_Shape_Identification_00053.csv", "meta": { "pattern": "plateau", "superlative": "longest", "direction": "up", "primary_amp": 9.31460405846456, "local_std": 0.5418828366154472, "search_window": [ "2017-11-19 22:00:00", "2017-11-27 22:15:00" ], "source": "ettm1" } }, { "id": "L2_T1_Shape_Identification_00054", "level": 2, "level_name": "Pattern Recognition", "category": "Shape Identification", "subtask": "Shape Identification", "question": "Identify the time range of the deepest deep valley in channel LULL within [2018-02-18 23:45:00 to 2018-02-22 01:45:00]. (Output format: [YYYY-MM-DD HH:MM:SS, YYYY-MM-DD HH:MM:SS])", "answer": "['2018-02-20 09:45:00', '2018-02-20 20:30:00']", "ground_truth": [ "2018-02-20 09:45:00", "2018-02-20 20:30:00" ], "eval_metric": "iou", "channel": "LULL", "ts_data_path": "ts_data/L2_T1_Shape_Identification_00054.csv", "meta": { "pattern": "valley", "superlative": "deepest", "direction": "down", "primary_amp": 1.3565605132114986, "local_std": 0.09043736754743323, "search_window": [ "2018-02-18 23:45:00", "2018-02-22 01:45:00" ], "source": "ettm1" } }, { "id": "L2_T1_Shape_Identification_00055", "level": 2, "level_name": "Pattern Recognition", "category": "Shape Identification", "subtask": "Shape Identification", "question": "Identify the time range of the longest low plateau (bottom out) in channel OT within [2017-11-19 06:45:00 to 2017-11-21 23:15:00]. (Output format: [YYYY-MM-DD HH:MM:SS, YYYY-MM-DD HH:MM:SS])", "answer": "['2017-11-21 07:00:00', '2017-11-21 16:15:00']", "ground_truth": [ "2017-11-21 07:00:00", "2017-11-21 16:15:00" ], "eval_metric": "iou", "channel": "OT", "ts_data_path": "ts_data/L2_T1_Shape_Identification_00055.csv", "meta": { "pattern": "depression", "superlative": "longest", "direction": "down", "primary_amp": 14.088213046097243, "local_std": 0.9392142030731495, "search_window": [ "2017-11-19 06:45:00", "2017-11-21 23:15:00" ], "source": "ettm1" } }, { "id": "L2_T1_Shape_Identification_00058", "level": 2, "level_name": "Pattern Recognition", "category": "Shape Identification", "subtask": "Shape Identification", "question": "Identify the time range of the longest low plateau (bottom out) in channel 99 within [2019-01-27 16:00:00 to 2019-01-31 20:45:00]. (Output format: [YYYY-MM-DD HH:MM:SS, YYYY-MM-DD HH:MM:SS])", "answer": "['2019-01-30 19:15:00', '2019-01-31 10:00:00']", "ground_truth": [ "2019-01-30 19:15:00", "2019-01-31 10:00:00" ], "eval_metric": "iou", "channel": "99", "ts_data_path": "ts_data/L2_T1_Shape_Identification_00058.csv", "meta": { "pattern": "depression", "superlative": "longest", "direction": "down", "primary_amp": 0.04231500510820034, "local_std": 0.0027008421517362366, "search_window": [ "2019-01-27 16:00:00", "2019-01-31 20:45:00" ], "source": "causal_rivers" } }, { "id": "L2_T1_Shape_Identification_00059", "level": 2, "level_name": "Pattern Recognition", "category": "Shape Identification", "subtask": "Shape Identification", "question": "Identify the time range of the longest low plateau (bottom out) in channel 123 within [2021-10-16 14:30:00 to 2021-10-22 07:00:00]. (Output format: [YYYY-MM-DD HH:MM:SS, YYYY-MM-DD HH:MM:SS])", "answer": "['2021-10-20 07:45:00', '2021-10-21 04:00:00']", "ground_truth": [ "2021-10-20 07:45:00", "2021-10-21 04:00:00" ], "eval_metric": "iou", "channel": "123", "ts_data_path": "ts_data/L2_T1_Shape_Identification_00059.csv", "meta": { "pattern": "depression", "superlative": "longest", "direction": "down", "primary_amp": 0.11052440496921684, "local_std": 0.0037064492216456668, "search_window": [ "2021-10-16 14:30:00", "2021-10-22 07:00:00" ], "source": "causal_rivers" } }, { "id": "L2_T1_Shape_Identification_00060", "level": 2, "level_name": "Pattern Recognition", "category": "Shape Identification", "subtask": "Shape Identification", "question": "Identify the time range of the highest upward spike in channel 124 within [2022-05-12 15:00:00 to 2022-05-21 00:15:00]. (Output format: [YYYY-MM-DD HH:MM:SS, YYYY-MM-DD HH:MM:SS])", "answer": "['2022-05-15 00:15:00', '2022-05-16 06:00:00']", "ground_truth": [ "2022-05-15 00:15:00", "2022-05-16 06:00:00" ], "eval_metric": "iou", "channel": "124", "ts_data_path": "ts_data/L2_T1_Shape_Identification_00060.csv", "meta": { "pattern": "spike", "superlative": "highest", "direction": "up", "primary_amp": 0.05033329163961178, "local_std": 0.0014825796886582666, "search_window": [ "2022-05-12 15:00:00", "2022-05-21 00:15:00" ], "source": "causal_rivers" } }, { "id": "L2_T1_Shape_Identification_00062", "level": 2, "level_name": "Pattern Recognition", "category": "Shape Identification", "subtask": "Shape Identification", "question": "Identify the time range of the longest plateau (stable period) in channel 147 within [2019-10-28 16:00:00 to 2019-10-30 19:00:00]. (Output format: [YYYY-MM-DD HH:MM:SS, YYYY-MM-DD HH:MM:SS])", "answer": "['2019-10-30 01:45:00', '2019-10-30 09:00:00']", "ground_truth": [ "2019-10-30 01:45:00", "2019-10-30 09:00:00" ], "eval_metric": "iou", "channel": "147", "ts_data_path": "ts_data/L2_T1_Shape_Identification_00062.csv", "meta": { "pattern": "plateau", "superlative": "longest", "direction": "up", "primary_amp": 0.037322876657662804, "local_std": 0.0020299131245641567, "search_window": [ "2019-10-28 16:00:00", "2019-10-30 19:00:00" ], "source": "causal_rivers" } }, { "id": "L2_T1_Shape_Identification_00063", "level": 2, "level_name": "Pattern Recognition", "category": "Shape Identification", "subtask": "Shape Identification", "question": "Identify the time range of the highest upward spike in channel 151 within [2020-08-20 21:45:00 to 2020-08-23 15:45:00]. (Output format: [YYYY-MM-DD HH:MM:SS, YYYY-MM-DD HH:MM:SS])", "answer": "['2020-08-22 06:45:00', '2020-08-22 16:15:00']", "ground_truth": [ "2020-08-22 06:45:00", "2020-08-22 16:15:00" ], "eval_metric": "iou", "channel": "151", "ts_data_path": "ts_data/L2_T1_Shape_Identification_00063.csv", "meta": { "pattern": "spike", "superlative": "highest", "direction": "up", "primary_amp": 0.2891030392883619, "local_std": 0.019273535952557458, "search_window": [ "2020-08-20 21:45:00", "2020-08-23 15:45:00" ], "source": "causal_rivers" } }, { "id": "L2_T1_Shape_Identification_00064", "level": 2, "level_name": "Pattern Recognition", "category": "Shape Identification", "subtask": "Shape Identification", "question": "Identify the time range of the largest step ascent in channel 154 within [2023-01-25 18:45:00 to 2023-01-31 14:15:00]. (Output format: [YYYY-MM-DD HH:MM:SS, YYYY-MM-DD HH:MM:SS])", "answer": "['2023-01-29 09:30:00', '2023-01-30 06:00:00']", "ground_truth": [ "2023-01-29 09:30:00", "2023-01-30 06:00:00" ], "eval_metric": "iou", "channel": "154", "ts_data_path": "ts_data/L2_T1_Shape_Identification_00064.csv", "meta": { "pattern": "step_up", "superlative": "largest", "direction": "up", "primary_amp": 0.4133427431172093, "local_std": 0.03076352853965891, "search_window": [ "2023-01-25 18:45:00", "2023-01-31 14:15:00" ], "source": "causal_rivers" } }, { "id": "L2_T1_Shape_Identification_00065", "level": 2, "level_name": "Pattern Recognition", "category": "Shape Identification", "subtask": "Shape Identification", "question": "Identify the time range of the highest upward spike in channel 155 within [2019-09-29 21:00:00 to 2019-10-04 09:30:00]. (Output format: [YYYY-MM-DD HH:MM:SS, YYYY-MM-DD HH:MM:SS])", "answer": "['2019-09-30 14:45:00', '2019-10-01 06:45:00']", "ground_truth": [ "2019-09-30 14:45:00", "2019-10-01 06:45:00" ], "eval_metric": "iou", "channel": "155", "ts_data_path": "ts_data/L2_T1_Shape_Identification_00065.csv", "meta": { "pattern": "spike", "superlative": "highest", "direction": "up", "primary_amp": 0.033358042994811, "local_std": 0.0022238695329874, "search_window": [ "2019-09-29 21:00:00", "2019-10-04 09:30:00" ], "source": "causal_rivers" } }, { "id": "L2_T1_Shape_Identification_00066", "level": 2, "level_name": "Pattern Recognition", "category": "Shape Identification", "subtask": "Shape Identification", "question": "Identify the time range of the longest plateau (stable period) in channel 166 within [2022-06-06 23:15:00 to 2022-06-11 05:00:00]. (Output format: [YYYY-MM-DD HH:MM:SS, YYYY-MM-DD HH:MM:SS])", "answer": "['2022-06-07 14:30:00', '2022-06-08 05:30:00']", "ground_truth": [ "2022-06-07 14:30:00", "2022-06-08 05:30:00" ], "eval_metric": "iou", "channel": "166", "ts_data_path": "ts_data/L2_T1_Shape_Identification_00066.csv", "meta": { "pattern": "plateau", "superlative": "longest", "direction": "up", "primary_amp": 122.3128243143069, "local_std": 8.15418828762046, "search_window": [ "2022-06-06 23:15:00", "2022-06-11 05:00:00" ], "source": "causal_rivers" } }, { "id": "L2_T1_Shape_Identification_00067", "level": 2, "level_name": "Pattern Recognition", "category": "Shape Identification", "subtask": "Shape Identification", "question": "Identify the time range of the deepest deep valley in channel 169 within [2019-08-17 23:45:00 to 2019-08-22 15:00:00]. (Output format: [YYYY-MM-DD HH:MM:SS, YYYY-MM-DD HH:MM:SS])", "answer": "['2019-08-18 03:45:00', '2019-08-18 20:00:00']", "ground_truth": [ "2019-08-18 03:45:00", "2019-08-18 20:00:00" ], "eval_metric": "iou", "channel": "169", "ts_data_path": "ts_data/L2_T1_Shape_Identification_00067.csv", "meta": { "pattern": "valley", "superlative": "deepest", "direction": "down", "primary_amp": 66.71608598962194, "local_std": 4.447739065974797, "search_window": [ "2019-08-17 23:45:00", "2019-08-22 15:00:00" ], "source": "causal_rivers" } }, { "id": "L2_T1_Shape_Identification_00068", "level": 2, "level_name": "Pattern Recognition", "category": "Shape Identification", "subtask": "Shape Identification", "question": "Identify the time range of the highest upward spike in channel 170 within [2023-09-27 21:15:00 to 2023-10-06 14:45:00]. (Output format: [YYYY-MM-DD HH:MM:SS, YYYY-MM-DD HH:MM:SS])", "answer": "['2023-10-03 06:30:00', '2023-10-04 13:30:00']", "ground_truth": [ "2023-10-03 06:30:00", "2023-10-04 13:30:00" ], "eval_metric": "iou", "channel": "170", "ts_data_path": "ts_data/L2_T1_Shape_Identification_00068.csv", "meta": { "pattern": "spike", "superlative": "highest", "direction": "up", "primary_amp": 104.28767258844454, "local_std": 5.189028910303929, "search_window": [ "2023-09-27 21:15:00", "2023-10-06 14:45:00" ], "source": "causal_rivers" } }, { "id": "L2_T1_Shape_Identification_00070", "level": 2, "level_name": "Pattern Recognition", "category": "Shape Identification", "subtask": "Shape Identification", "question": "Identify the time range of the largest step ascent in channel 173 within [2019-07-22 22:30:00 to 2019-08-01 21:15:00]. (Output format: [YYYY-MM-DD HH:MM:SS, YYYY-MM-DD HH:MM:SS])", "answer": "['2019-07-30 13:00:00', '2019-08-01 00:30:00']", "ground_truth": [ "2019-07-30 13:00:00", "2019-08-01 00:30:00" ], "eval_metric": "iou", "channel": "173", "ts_data_path": "ts_data/L2_T1_Shape_Identification_00070.csv", "meta": { "pattern": "step_up", "superlative": "largest", "direction": "up", "primary_amp": 70.42253521126756, "local_std": 5.6338028169014045, "search_window": [ "2019-07-22 22:30:00", "2019-08-01 21:15:00" ], "source": "causal_rivers" } }, { "id": "L2_T1_Shape_Identification_00071", "level": 2, "level_name": "Pattern Recognition", "category": "Shape Identification", "subtask": "Shape Identification", "question": "Identify the time range of the deepest deep valley in channel 177 within [2020-10-28 16:15:00 to 2020-10-30 19:15:00]. (Output format: [YYYY-MM-DD HH:MM:SS, YYYY-MM-DD HH:MM:SS])", "answer": "['2020-10-29 22:15:00', '2020-10-30 05:30:00']", "ground_truth": [ "2020-10-29 22:15:00", "2020-10-30 05:30:00" ], "eval_metric": "iou", "channel": "177", "ts_data_path": "ts_data/L2_T1_Shape_Identification_00071.csv", "meta": { "pattern": "valley", "superlative": "deepest", "direction": "down", "primary_amp": 66.71608598962194, "local_std": 4.447739065974797, "search_window": [ "2020-10-28 16:15:00", "2020-10-30 19:15:00" ], "source": "causal_rivers" } }, { "id": "L2_T1_Shape_Identification_00073", "level": 2, "level_name": "Pattern Recognition", "category": "Shape Identification", "subtask": "Shape Identification", "question": "Identify the time range of the deepest deep valley in channel 245 within [2021-05-06 09:45:00 to 2021-05-10 23:00:00]. (Output format: [YYYY-MM-DD HH:MM:SS, YYYY-MM-DD HH:MM:SS])", "answer": "['2021-05-09 17:00:00', '2021-05-10 09:00:00']", "ground_truth": [ "2021-05-09 17:00:00", "2021-05-10 09:00:00" ], "eval_metric": "iou", "channel": "245", "ts_data_path": "ts_data/L2_T1_Shape_Identification_00073.csv", "meta": { "pattern": "valley", "superlative": "deepest", "direction": "down", "primary_amp": 0.2446256486286134, "local_std": 0.016308376575240893, "search_window": [ "2021-05-06 09:45:00", "2021-05-10 23:00:00" ], "source": "causal_rivers" } }, { "id": "L2_T1_Shape_Identification_00074", "level": 2, "level_name": "Pattern Recognition", "category": "Shape Identification", "subtask": "Shape Identification", "question": "Identify the time range of the largest step descent in channel 312 within [2021-01-21 19:30:00 to 2021-01-24 05:30:00]. (Output format: [YYYY-MM-DD HH:MM:SS, YYYY-MM-DD HH:MM:SS])", "answer": "['2021-01-22 15:45:00', '2021-01-23 00:00:00']", "ground_truth": [ "2021-01-22 15:45:00", "2021-01-23 00:00:00" ], "eval_metric": "iou", "channel": "312", "ts_data_path": "ts_data/L2_T1_Shape_Identification_00074.csv", "meta": { "pattern": "step_down", "superlative": "largest", "direction": "down", "primary_amp": 0.009266123054114167, "local_std": 0.0007412898443291333, "search_window": [ "2021-01-21 19:30:00", "2021-01-24 05:30:00" ], "source": "causal_rivers" } }, { "id": "L2_T1_Shape_Identification_00075", "level": 2, "level_name": "Pattern Recognition", "category": "Shape Identification", "subtask": "Shape Identification", "question": "Identify the time range of the longest plateau (stable period) in channel 430 within [2022-08-04 09:00:00 to 2022-08-12 12:15:00]. (Output format: [YYYY-MM-DD HH:MM:SS, YYYY-MM-DD HH:MM:SS])", "answer": "['2022-08-09 10:45:00', '2022-08-10 15:45:00']", "ground_truth": [ "2022-08-09 10:45:00", "2022-08-10 15:45:00" ], "eval_metric": "iou", "channel": "430", "ts_data_path": "ts_data/L2_T1_Shape_Identification_00075.csv", "meta": { "pattern": "plateau", "superlative": "longest", "direction": "up", "primary_amp": 0.033358042994811, "local_std": 0.0022238695329874, "search_window": [ "2022-08-04 09:00:00", "2022-08-12 12:15:00" ], "source": "causal_rivers" } }, { "id": "L2_T1_Shape_Identification_00076", "level": 2, "level_name": "Pattern Recognition", "category": "Shape Identification", "subtask": "Shape Identification", "question": "Identify the time range of the highest upward spike in channel 441 within [2020-06-04 02:15:00 to 2020-06-06 18:30:00]. (Output format: [YYYY-MM-DD HH:MM:SS, YYYY-MM-DD HH:MM:SS])", "answer": "['2020-06-05 11:00:00', '2020-06-05 20:15:00']", "ground_truth": [ "2020-06-05 11:00:00", "2020-06-05 20:15:00" ], "eval_metric": "iou", "channel": "441", "ts_data_path": "ts_data/L2_T1_Shape_Identification_00076.csv", "meta": { "pattern": "spike", "superlative": "highest", "direction": "up", "primary_amp": 10.21247677004499, "local_std": 0.266864343958488, "search_window": [ "2020-06-04 02:15:00", "2020-06-06 18:30:00" ], "source": "causal_rivers" } }, { "id": "L2_T1_Shape_Identification_00079", "level": 2, "level_name": "Pattern Recognition", "category": "Shape Identification", "subtask": "Shape Identification", "question": "Identify the time range of the largest step descent in channel 501 within [2022-08-19 05:15:00 to 2022-08-25 00:45:00]. (Output format: [YYYY-MM-DD HH:MM:SS, YYYY-MM-DD HH:MM:SS])", "answer": "['2022-08-22 15:00:00', '2022-08-23 11:30:00']", "ground_truth": [ "2022-08-22 15:00:00", "2022-08-23 11:30:00" ], "eval_metric": "iou", "channel": "501", "ts_data_path": "ts_data/L2_T1_Shape_Identification_00079.csv", "meta": { "pattern": "step_down", "superlative": "largest", "direction": "down", "primary_amp": 0.06442691598228863, "local_std": 0.0044477390659748, "search_window": [ "2022-08-19 05:15:00", "2022-08-25 00:45:00" ], "source": "causal_rivers" } }, { "id": "L2_T1_Shape_Identification_00080", "level": 2, "level_name": "Pattern Recognition", "category": "Shape Identification", "subtask": "Shape Identification", "question": "Identify the time range of the largest step descent in channel 578 within [2023-09-25 20:00:00 to 2023-10-04 09:30:00]. (Output format: [YYYY-MM-DD HH:MM:SS, YYYY-MM-DD HH:MM:SS])", "answer": "['2023-10-02 22:30:00', '2023-10-04 05:00:00']", "ground_truth": [ "2023-10-02 22:30:00", "2023-10-04 05:00:00" ], "eval_metric": "iou", "channel": "578", "ts_data_path": "ts_data/L2_T1_Shape_Identification_00080.csv", "meta": { "pattern": "step_down", "superlative": "largest", "direction": "down", "primary_amp": 0.2687175685693106, "local_std": 0.021497405485544848, "search_window": [ "2023-09-25 20:00:00", "2023-10-04 09:30:00" ], "source": "causal_rivers" } }, { "id": "L2_T1_Shape_Identification_00081", "level": 2, "level_name": "Pattern Recognition", "category": "Shape Identification", "subtask": "Shape Identification", "question": "Identify the time range of the longest plateau (stable period) in channel 580 within [2021-05-01 18:30:00 to 2021-05-10 15:15:00]. (Output format: [YYYY-MM-DD HH:MM:SS, YYYY-MM-DD HH:MM:SS])", "answer": "['2021-05-08 12:30:00', '2021-05-09 20:00:00']", "ground_truth": [ "2021-05-08 12:30:00", "2021-05-09 20:00:00" ], "eval_metric": "iou", "channel": "580", "ts_data_path": "ts_data/L2_T1_Shape_Identification_00081.csv", "meta": { "pattern": "plateau", "superlative": "longest", "direction": "up", "primary_amp": 0.1735355819125276, "local_std": 0.008154188287620467, "search_window": [ "2021-05-01 18:30:00", "2021-05-10 15:15:00" ], "source": "causal_rivers" } }, { "id": "L2_T1_Shape_Identification_00082", "level": 2, "level_name": "Pattern Recognition", "category": "Shape Identification", "subtask": "Shape Identification", "question": "Identify the time range of the longest low plateau (bottom out) in channel 589 within [2022-12-31 21:00:00 to 2023-01-03 13:45:00]. (Output format: [YYYY-MM-DD HH:MM:SS, YYYY-MM-DD HH:MM:SS])", "answer": "['2023-01-01 10:15:00', '2023-01-01 19:45:00']", "ground_truth": [ "2023-01-01 10:15:00", "2023-01-01 19:45:00" ], "eval_metric": "iou", "channel": "589", "ts_data_path": "ts_data/L2_T1_Shape_Identification_00082.csv", "meta": { "pattern": "depression", "superlative": "longest", "direction": "down", "primary_amp": 18.68195814563495, "local_std": 0.37064492216456635, "search_window": [ "2022-12-31 21:00:00", "2023-01-03 13:45:00" ], "source": "causal_rivers" } }, { "id": "L2_T1_Shape_Identification_00083", "level": 2, "level_name": "Pattern Recognition", "category": "Shape Identification", "subtask": "Shape Identification", "question": "Identify the time range of the longest plateau (stable period) in channel 591 within [2021-10-21 19:30:00 to 2021-10-27 18:15:00]. (Output format: [YYYY-MM-DD HH:MM:SS, YYYY-MM-DD HH:MM:SS])", "answer": "['2021-10-26 09:30:00', '2021-10-27 06:30:00']", "ground_truth": [ "2021-10-26 09:30:00", "2021-10-27 06:30:00" ], "eval_metric": "iou", "channel": "591", "ts_data_path": "ts_data/L2_T1_Shape_Identification_00083.csv", "meta": { "pattern": "plateau", "superlative": "longest", "direction": "up", "primary_amp": 111.1934766493699, "local_std": 7.412898443291327, "search_window": [ "2021-10-21 19:30:00", "2021-10-27 18:15:00" ], "source": "causal_rivers" } }, { "id": "L2_T1_Shape_Identification_00084", "level": 2, "level_name": "Pattern Recognition", "category": "Shape Identification", "subtask": "Shape Identification", "question": "Identify the time range of the largest step descent in channel 595 within [2022-08-19 17:30:00 to 2022-08-24 05:30:00]. (Output format: [YYYY-MM-DD HH:MM:SS, YYYY-MM-DD HH:MM:SS])", "answer": "['2022-08-22 08:00:00', '2022-08-22 23:45:00']", "ground_truth": [ "2022-08-22 08:00:00", "2022-08-22 23:45:00" ], "eval_metric": "iou", "channel": "595", "ts_data_path": "ts_data/L2_T1_Shape_Identification_00084.csv", "meta": { "pattern": "step_down", "superlative": "largest", "direction": "down", "primary_amp": 0.3984432913269087, "local_std": 0.03187546330615269, "search_window": [ "2022-08-19 17:30:00", "2022-08-24 05:30:00" ], "source": "causal_rivers" } }, { "id": "L2_T1_Shape_Identification_00085", "level": 2, "level_name": "Pattern Recognition", "category": "Shape Identification", "subtask": "Shape Identification", "question": "Identify the time range of the largest step ascent in channel 625 within [2019-09-07 23:30:00 to 2019-09-15 09:30:00]. (Output format: [YYYY-MM-DD HH:MM:SS, YYYY-MM-DD HH:MM:SS])", "answer": "['2019-09-13 09:00:00', '2019-09-14 11:15:00']", "ground_truth": [ "2019-09-13 09:00:00", "2019-09-14 11:15:00" ], "eval_metric": "iou", "channel": "625", "ts_data_path": "ts_data/L2_T1_Shape_Identification_00085.csv", "meta": { "pattern": "step_up", "superlative": "largest", "direction": "up", "primary_amp": 0.009266123054114159, "local_std": 0.0007412898443291328, "search_window": [ "2019-09-07 23:30:00", "2019-09-15 09:30:00" ], "source": "causal_rivers" } }, { "id": "L2_T1_Shape_Identification_00086", "level": 2, "level_name": "Pattern Recognition", "category": "Shape Identification", "subtask": "Shape Identification", "question": "Identify the time range of the longest low plateau (bottom out) in channel 626 within [2023-08-03 09:45:00 to 2023-08-07 00:15:00]. (Output format: [YYYY-MM-DD HH:MM:SS, YYYY-MM-DD HH:MM:SS])", "answer": "['2023-08-03 17:45:00', '2023-08-04 06:30:00']", "ground_truth": [ "2023-08-03 17:45:00", "2023-08-04 06:30:00" ], "eval_metric": "iou", "channel": "626", "ts_data_path": "ts_data/L2_T1_Shape_Identification_00086.csv", "meta": { "pattern": "depression", "superlative": "longest", "direction": "down", "primary_amp": 0.2335063009636767, "local_std": 0.01556708673091178, "search_window": [ "2023-08-03 09:45:00", "2023-08-07 00:15:00" ], "source": "causal_rivers" } }, { "id": "L2_T1_Shape_Identification_00087", "level": 2, "level_name": "Pattern Recognition", "category": "Shape Identification", "subtask": "Shape Identification", "question": "Identify the time range of the largest step descent in channel 627 within [2019-06-24 00:00:00 to 2019-06-27 18:30:00]. (Output format: [YYYY-MM-DD HH:MM:SS, YYYY-MM-DD HH:MM:SS])", "answer": "['2019-06-24 07:45:00', '2019-06-24 21:00:00']", "ground_truth": [ "2019-06-24 07:45:00", "2019-06-24 21:00:00" ], "eval_metric": "iou", "channel": "627", "ts_data_path": "ts_data/L2_T1_Shape_Identification_00087.csv", "meta": { "pattern": "step_down", "superlative": "largest", "direction": "down", "primary_amp": 0.17369935868553207, "local_std": 0.013895948694842565, "search_window": [ "2019-06-24 00:00:00", "2019-06-27 18:30:00" ], "source": "causal_rivers" } }, { "id": "L2_T1_Shape_Identification_00088", "level": 2, "level_name": "Pattern Recognition", "category": "Shape Identification", "subtask": "Shape Identification", "question": "Identify the time range of the highest upward spike in channel 647 within [2023-09-22 10:30:00 to 2023-09-28 09:00:00]. (Output format: [YYYY-MM-DD HH:MM:SS, YYYY-MM-DD HH:MM:SS])", "answer": "['2023-09-24 09:45:00', '2023-09-25 06:45:00']", "ground_truth": [ "2023-09-24 09:45:00", "2023-09-25 06:45:00" ], "eval_metric": "iou", "channel": "647", "ts_data_path": "ts_data/L2_T1_Shape_Identification_00088.csv", "meta": { "pattern": "spike", "superlative": "highest", "direction": "up", "primary_amp": 0.29231300217194056, "local_std": 0.0177909562638992, "search_window": [ "2023-09-22 10:30:00", "2023-09-28 09:00:00" ], "source": "causal_rivers" } }, { "id": "L2_T1_Shape_Identification_00090", "level": 2, "level_name": "Pattern Recognition", "category": "Shape Identification", "subtask": "Shape Identification", "question": "Identify the time range of the longest low plateau (bottom out) in channel 683 within [2022-10-24 04:30:00 to 2022-10-29 08:45:00]. (Output format: [YYYY-MM-DD HH:MM:SS, YYYY-MM-DD HH:MM:SS])", "answer": "['2022-10-26 05:45:00', '2022-10-27 00:00:00']", "ground_truth": [ "2022-10-26 05:45:00", "2022-10-27 00:00:00" ], "eval_metric": "iou", "channel": "683", "ts_data_path": "ts_data/L2_T1_Shape_Identification_00090.csv", "meta": { "pattern": "depression", "superlative": "longest", "direction": "down", "primary_amp": 0.11383394660926871, "local_std": 0.0037064492216456668, "search_window": [ "2022-10-24 04:30:00", "2022-10-29 08:45:00" ], "source": "causal_rivers" } }, { "id": "L2_T1_Shape_Identification_00091", "level": 2, "level_name": "Pattern Recognition", "category": "Shape Identification", "subtask": "Shape Identification", "question": "Identify the time range of the longest plateau (stable period) in channel 727 within [2023-11-02 06:00:00 to 2023-11-06 00:45:00]. (Output format: [YYYY-MM-DD HH:MM:SS, YYYY-MM-DD HH:MM:SS])", "answer": "['2023-11-02 22:30:00', '2023-11-03 11:45:00']", "ground_truth": [ "2023-11-02 22:30:00", "2023-11-03 11:45:00" ], "eval_metric": "iou", "channel": "727", "ts_data_path": "ts_data/L2_T1_Shape_Identification_00091.csv", "meta": { "pattern": "plateau", "superlative": "longest", "direction": "up", "primary_amp": 29.598474857240717, "local_std": 1.1860637509266188, "search_window": [ "2023-11-02 06:00:00", "2023-11-06 00:45:00" ], "source": "causal_rivers" } }, { "id": "L2_T1_Shape_Identification_00092", "level": 2, "level_name": "Pattern Recognition", "category": "Shape Identification", "subtask": "Shape Identification", "question": "Identify the time range of the highest upward spike in channel 728 within [2020-08-05 07:00:00 to 2020-08-14 06:30:00]. (Output format: [YYYY-MM-DD HH:MM:SS, YYYY-MM-DD HH:MM:SS])", "answer": "['2020-08-11 07:00:00', '2020-08-12 15:00:00']", "ground_truth": [ "2020-08-11 07:00:00", "2020-08-12 15:00:00" ], "eval_metric": "iou", "channel": "728", "ts_data_path": "ts_data/L2_T1_Shape_Identification_00092.csv", "meta": { "pattern": "spike", "superlative": "highest", "direction": "up", "primary_amp": 51.58108242060768, "local_std": 2.446256486286136, "search_window": [ "2020-08-05 07:00:00", "2020-08-14 06:30:00" ], "source": "causal_rivers" } }, { "id": "L2_T1_Shape_Identification_00093", "level": 2, "level_name": "Pattern Recognition", "category": "Shape Identification", "subtask": "Shape Identification", "question": "Identify the time range of the largest step descent in channel 729 within [2021-12-19 06:30:00 to 2021-12-27 18:00:00]. (Output format: [YYYY-MM-DD HH:MM:SS, YYYY-MM-DD HH:MM:SS])", "answer": "['2021-12-20 00:00:00', '2021-12-21 06:15:00']", "ground_truth": [ "2021-12-20 00:00:00", "2021-12-21 06:15:00" ], "eval_metric": "iou", "channel": "729", "ts_data_path": "ts_data/L2_T1_Shape_Identification_00093.csv", "meta": { "pattern": "step_down", "superlative": "largest", "direction": "down", "primary_amp": 21.230419033412566, "local_std": 1.3343217197924369, "search_window": [ "2021-12-19 06:30:00", "2021-12-27 18:00:00" ], "source": "causal_rivers" } }, { "id": "L2_T1_Shape_Identification_00094", "level": 2, "level_name": "Pattern Recognition", "category": "Shape Identification", "subtask": "Shape Identification", "question": "Identify the time range of the longest plateau (stable period) in channel 754 within [2022-11-09 12:30:00 to 2022-11-14 14:00:00]. (Output format: [YYYY-MM-DD HH:MM:SS, YYYY-MM-DD HH:MM:SS])", "answer": "['2022-11-09 14:00:00', '2022-11-10 08:00:00']", "ground_truth": [ "2022-11-09 14:00:00", "2022-11-10 08:00:00" ], "eval_metric": "iou", "channel": "754", "ts_data_path": "ts_data/L2_T1_Shape_Identification_00094.csv", "meta": { "pattern": "plateau", "superlative": "longest", "direction": "up", "primary_amp": 0.1404284609635551, "local_std": 0.0066716085989622, "search_window": [ "2022-11-09 12:30:00", "2022-11-14 14:00:00" ], "source": "causal_rivers" } }, { "id": "L2_T1_Shape_Identification_00095", "level": 2, "level_name": "Pattern Recognition", "category": "Shape Identification", "subtask": "Shape Identification", "question": "Identify the time range of the largest step descent in channel 762 within [2022-07-07 08:00:00 to 2022-07-15 08:45:00]. (Output format: [YYYY-MM-DD HH:MM:SS, YYYY-MM-DD HH:MM:SS])", "answer": "['2022-07-13 13:00:00', '2022-07-14 17:30:00']", "ground_truth": [ "2022-07-13 13:00:00", "2022-07-14 17:30:00" ], "eval_metric": "iou", "channel": "762", "ts_data_path": "ts_data/L2_T1_Shape_Identification_00095.csv", "meta": { "pattern": "step_down", "superlative": "largest", "direction": "down", "primary_amp": 0.05559673832468494, "local_std": 0.004447739065974795, "search_window": [ "2022-07-07 08:00:00", "2022-07-15 08:45:00" ], "source": "causal_rivers" } }, { "id": "L2_T1_Shape_Identification_00098", "level": 2, "level_name": "Pattern Recognition", "category": "Shape Identification", "subtask": "Shape Identification", "question": "Identify the time range of the longest low plateau (bottom out) in channel 865 within [2022-01-15 00:00:00 to 2022-01-24 09:30:00]. (Output format: [YYYY-MM-DD HH:MM:SS, YYYY-MM-DD HH:MM:SS])", "answer": "['2022-01-20 04:45:00', '2022-01-21 14:15:00']", "ground_truth": [ "2022-01-20 04:45:00", "2022-01-21 14:15:00" ], "eval_metric": "iou", "channel": "865", "ts_data_path": "ts_data/L2_T1_Shape_Identification_00098.csv", "meta": { "pattern": "depression", "superlative": "longest", "direction": "down", "primary_amp": 8.89547813194958, "local_std": 0.5930318754633054, "search_window": [ "2022-01-15 00:00:00", "2022-01-24 09:30:00" ], "source": "causal_rivers" } }, { "id": "L2_T1_Shape_Identification_00099", "level": 2, "level_name": "Pattern Recognition", "category": "Shape Identification", "subtask": "Shape Identification", "question": "Identify the time range of the largest step descent in channel 891 within [2021-06-11 04:45:00 to 2021-06-16 06:30:00]. (Output format: [YYYY-MM-DD HH:MM:SS, YYYY-MM-DD HH:MM:SS])", "answer": "['2021-06-14 23:15:00', '2021-06-15 17:15:00']", "ground_truth": [ "2021-06-14 23:15:00", "2021-06-15 17:15:00" ], "eval_metric": "iou", "channel": "891", "ts_data_path": "ts_data/L2_T1_Shape_Identification_00099.csv", "meta": { "pattern": "step_down", "superlative": "largest", "direction": "down", "primary_amp": 1.111934766493698, "local_std": 0.08895478131949584, "search_window": [ "2021-06-11 04:45:00", "2021-06-16 06:30:00" ], "source": "causal_rivers" } }, { "id": "L2_T1_Shape_Identification_00100", "level": 2, "level_name": "Pattern Recognition", "category": "Shape Identification", "subtask": "Shape Identification", "question": "Identify the time range of the largest step ascent in channel 894 within [2021-09-29 18:15:00 to 2021-10-03 03:30:00]. (Output format: [YYYY-MM-DD HH:MM:SS, YYYY-MM-DD HH:MM:SS])", "answer": "['2021-10-02 02:00:00', '2021-10-02 13:45:00']", "ground_truth": [ "2021-10-02 02:00:00", "2021-10-02 13:45:00" ], "eval_metric": "iou", "channel": "894", "ts_data_path": "ts_data/L2_T1_Shape_Identification_00100.csv", "meta": { "pattern": "step_up", "superlative": "largest", "direction": "up", "primary_amp": 0.4401408450704214, "local_std": 0.035211267605633714, "search_window": [ "2021-09-29 18:15:00", "2021-10-03 03:30:00" ], "source": "causal_rivers" } }, { "id": "L2_T1_Shape_Identification_00101", "level": 2, "level_name": "Pattern Recognition", "category": "Shape Identification", "subtask": "Shape Identification", "question": "Identify the time range of the longest plateau (stable period) in channel 895 within [2019-08-20 12:15:00 to 2019-08-27 02:45:00]. (Output format: [YYYY-MM-DD HH:MM:SS, YYYY-MM-DD HH:MM:SS])", "answer": "['2019-08-21 15:30:00', '2019-08-22 15:00:00']", "ground_truth": [ "2019-08-21 15:30:00", "2019-08-22 15:00:00" ], "eval_metric": "iou", "channel": "895", "ts_data_path": "ts_data/L2_T1_Shape_Identification_00101.csv", "meta": { "pattern": "plateau", "superlative": "longest", "direction": "up", "primary_amp": 0.6583853341349374, "local_std": 0.03409933283914005, "search_window": [ "2019-08-20 12:15:00", "2019-08-27 02:45:00" ], "source": "causal_rivers" } }, { "id": "L2_T1_Shape_Identification_00102", "level": 2, "level_name": "Pattern Recognition", "category": "Shape Identification", "subtask": "Shape Identification", "question": "Identify the time range of the largest step descent in channel 897 within [2019-10-16 00:00:00 to 2019-10-22 16:30:00]. (Output format: [YYYY-MM-DD HH:MM:SS, YYYY-MM-DD HH:MM:SS])", "answer": "['2019-10-16 15:15:00', '2019-10-17 15:00:00']", "ground_truth": [ "2019-10-16 15:15:00", "2019-10-17 15:00:00" ], "eval_metric": "iou", "channel": "897", "ts_data_path": "ts_data/L2_T1_Shape_Identification_00102.csv", "meta": { "pattern": "step_down", "superlative": "largest", "direction": "down", "primary_amp": 0.04633061527057079, "local_std": 0.003706449221645663, "search_window": [ "2019-10-16 00:00:00", "2019-10-22 16:30:00" ], "source": "causal_rivers" } }, { "id": "L2_T1_Shape_Identification_00104", "level": 2, "level_name": "Pattern Recognition", "category": "Shape Identification", "subtask": "Shape Identification", "question": "Identify the time range of the longest plateau (stable period) in channel HUFL within [2016-09-04 09:30:00 to 2016-09-06 16:00:00]. (Output format: [YYYY-MM-DD HH:MM:SS, YYYY-MM-DD HH:MM:SS])", "answer": "['2016-09-04 16:30:00', '2016-09-05 00:15:00']", "ground_truth": [ "2016-09-04 16:30:00", "2016-09-05 00:15:00" ], "eval_metric": "iou", "channel": "HUFL", "ts_data_path": "ts_data/L2_T1_Shape_Identification_00104.csv", "meta": { "pattern": "plateau", "superlative": "longest", "direction": "up", "primary_amp": 41.33617727733524, "local_std": 2.7557451518223495, "search_window": [ "2016-09-04 09:30:00", "2016-09-06 16:00:00" ], "source": "ettm1" } }, { "id": "L2_T1_Shape_Identification_00105", "level": 2, "level_name": "Pattern Recognition", "category": "Shape Identification", "subtask": "Shape Identification", "question": "Identify the time range of the longest plateau (stable period) in channel HULL within [2018-01-19 15:00:00 to 2018-01-25 14:15:00]. (Output format: [YYYY-MM-DD HH:MM:SS, YYYY-MM-DD HH:MM:SS])", "answer": "['2018-01-20 05:15:00', '2018-01-21 02:30:00']", "ground_truth": [ "2018-01-20 05:15:00", "2018-01-21 02:30:00" ], "eval_metric": "iou", "channel": "HULL", "ts_data_path": "ts_data/L2_T1_Shape_Identification_00105.csv", "meta": { "pattern": "plateau", "superlative": "longest", "direction": "up", "primary_amp": 14.555957986720074, "local_std": 0.9436619732448834, "search_window": [ "2018-01-19 15:00:00", "2018-01-25 14:15:00" ], "source": "ettm1" } }, { "id": "L2_T1_Shape_Identification_00106", "level": 2, "level_name": "Pattern Recognition", "category": "Shape Identification", "subtask": "Shape Identification", "question": "Identify the time range of the highest upward spike in channel MUFL within [2017-01-03 13:00:00 to 2017-01-11 06:15:00]. (Output format: [YYYY-MM-DD HH:MM:SS, YYYY-MM-DD HH:MM:SS])", "answer": "['2017-01-06 17:00:00', '2017-01-07 20:30:00']", "ground_truth": [ "2017-01-06 17:00:00", "2017-01-07 20:30:00" ], "eval_metric": "iou", "channel": "MUFL", "ts_data_path": "ts_data/L2_T1_Shape_Identification_00106.csv", "meta": { "pattern": "spike", "superlative": "highest", "direction": "up", "primary_amp": 31.783535348984465, "local_std": 1.7844699240331743, "search_window": [ "2017-01-03 13:00:00", "2017-01-11 06:15:00" ], "source": "ettm1" } }, { "id": "L2_T1_Shape_Identification_00107", "level": 2, "level_name": "Pattern Recognition", "category": "Shape Identification", "subtask": "Shape Identification", "question": "Identify the time range of the highest upward spike in channel MULL within [2017-02-07 09:00:00 to 2017-02-15 09:45:00]. (Output format: [YYYY-MM-DD HH:MM:SS, YYYY-MM-DD HH:MM:SS])", "answer": "['2017-02-09 00:00:00', '2017-02-10 04:30:00']", "ground_truth": [ "2017-02-09 00:00:00", "2017-02-10 04:30:00" ], "eval_metric": "iou", "channel": "MULL", "ts_data_path": "ts_data/L2_T1_Shape_Identification_00107.csv", "meta": { "pattern": "spike", "superlative": "highest", "direction": "up", "primary_amp": 12.133258084052805, "local_std": 0.6323202704393569, "search_window": [ "2017-02-07 09:00:00", "2017-02-15 09:45:00" ], "source": "ettm1" } }, { "id": "L2_T1_Shape_Identification_00108", "level": 2, "level_name": "Pattern Recognition", "category": "Shape Identification", "subtask": "Shape Identification", "question": "Identify the time range of the largest step descent in channel LUFL within [2017-10-20 13:30:00 to 2017-10-30 03:00:00]. (Output format: [YYYY-MM-DD HH:MM:SS, YYYY-MM-DD HH:MM:SS])", "answer": "['2017-10-21 01:45:00', '2017-10-22 11:45:00']", "ground_truth": [ "2017-10-21 01:45:00", "2017-10-22 11:45:00" ], "eval_metric": "iou", "channel": "LUFL", "ts_data_path": "ts_data/L2_T1_Shape_Identification_00108.csv", "meta": { "pattern": "step_down", "superlative": "largest", "direction": "down", "primary_amp": 7.202614119444439, "local_std": 0.5418828366154472, "search_window": [ "2017-10-20 13:30:00", "2017-10-30 03:00:00" ], "source": "ettm1" } }, { "id": "L2_T1_Shape_Identification_00109", "level": 2, "level_name": "Pattern Recognition", "category": "Shape Identification", "subtask": "Shape Identification", "question": "Identify the time range of the deepest deep valley in channel LULL within [2017-06-07 08:00:00 to 2017-06-14 00:45:00]. (Output format: [YYYY-MM-DD HH:MM:SS, YYYY-MM-DD HH:MM:SS])", "answer": "['2017-06-11 20:00:00', '2017-06-12 19:45:00']", "ground_truth": [ "2017-06-11 20:00:00", "2017-06-12 19:45:00" ], "eval_metric": "iou", "channel": "LULL", "ts_data_path": "ts_data/L2_T1_Shape_Identification_00109.csv", "meta": { "pattern": "valley", "superlative": "deepest", "direction": "down", "primary_amp": 2.6523384306743543, "local_std": 0.15789473648863073, "search_window": [ "2017-06-07 08:00:00", "2017-06-14 00:45:00" ], "source": "ettm1" } }, { "id": "L2_T1_Shape_Identification_00110", "level": 2, "level_name": "Pattern Recognition", "category": "Shape Identification", "subtask": "Shape Identification", "question": "Identify the time range of the largest step descent in channel OT within [2017-12-01 08:30:00 to 2017-12-10 16:45:00]. (Output format: [YYYY-MM-DD HH:MM:SS, YYYY-MM-DD HH:MM:SS])", "answer": "['2017-12-01 20:30:00', '2017-12-03 05:45:00']", "ground_truth": [ "2017-12-01 20:30:00", "2017-12-03 05:45:00" ], "eval_metric": "iou", "channel": "OT", "ts_data_path": "ts_data/L2_T1_Shape_Identification_00110.csv", "meta": { "pattern": "step_down", "superlative": "largest", "direction": "down", "primary_amp": 18.90701389623648, "local_std": 1.303928882833584, "search_window": [ "2017-12-01 08:30:00", "2017-12-10 16:45:00" ], "source": "ettm1" } }, { "id": "L2_T1_Shape_Identification_00111", "level": 2, "level_name": "Pattern Recognition", "category": "Shape Identification", "subtask": "Shape Identification", "question": "Identify the time range of the highest upward spike in channel 67 within [2023-05-15 07:30:00 to 2023-05-23 03:45:00]. (Output format: [YYYY-MM-DD HH:MM:SS, YYYY-MM-DD HH:MM:SS])", "answer": "['2023-05-19 02:30:00', '2023-05-20 06:30:00']", "ground_truth": [ "2023-05-19 02:30:00", "2023-05-20 06:30:00" ], "eval_metric": "iou", "channel": "67", "ts_data_path": "ts_data/L2_T1_Shape_Identification_00111.csv", "meta": { "pattern": "spike", "superlative": "highest", "direction": "up", "primary_amp": 0.10378320418111053, "local_std": 0.00667160859896218, "search_window": [ "2023-05-15 07:30:00", "2023-05-23 03:45:00" ], "source": "causal_rivers" } }, { "id": "L2_T1_Shape_Identification_00113", "level": 2, "level_name": "Pattern Recognition", "category": "Shape Identification", "subtask": "Shape Identification", "question": "Identify the time range of the longest plateau (stable period) in channel 99 within [2021-04-22 03:15:00 to 2021-04-27 01:45:00]. (Output format: [YYYY-MM-DD HH:MM:SS, YYYY-MM-DD HH:MM:SS])", "answer": "['2021-04-25 18:00:00', '2021-04-26 11:30:00']", "ground_truth": [ "2021-04-25 18:00:00", "2021-04-26 11:30:00" ], "eval_metric": "iou", "channel": "99", "ts_data_path": "ts_data/L2_T1_Shape_Identification_00113.csv", "meta": { "pattern": "plateau", "superlative": "longest", "direction": "up", "primary_amp": 0.056010644922164565, "local_std": 0.0029651593773165332, "search_window": [ "2021-04-22 03:15:00", "2021-04-27 01:45:00" ], "source": "causal_rivers" } }, { "id": "L2_T1_Shape_Identification_00114", "level": 2, "level_name": "Pattern Recognition", "category": "Shape Identification", "subtask": "Shape Identification", "question": "Identify the time range of the largest step ascent in channel 123 within [2020-11-18 20:45:00 to 2020-11-21 07:45:00]. (Output format: [YYYY-MM-DD HH:MM:SS, YYYY-MM-DD HH:MM:SS])", "answer": "['2020-11-19 14:15:00', '2020-11-19 22:45:00']", "ground_truth": [ "2020-11-19 14:15:00", "2020-11-19 22:45:00" ], "eval_metric": "iou", "channel": "123", "ts_data_path": "ts_data/L2_T1_Shape_Identification_00114.csv", "meta": { "pattern": "step_up", "superlative": "largest", "direction": "up", "primary_amp": 12.5, "local_std": 1.0, "search_window": [ "2020-11-18 20:45:00", "2020-11-21 07:45:00" ], "source": "causal_rivers" } }, { "id": "L2_T1_Shape_Identification_00115", "level": 2, "level_name": "Pattern Recognition", "category": "Shape Identification", "subtask": "Shape Identification", "question": "Identify the time range of the longest low plateau (bottom out) in channel 124 within [2022-05-11 02:15:00 to 2022-05-20 07:15:00]. (Output format: [YYYY-MM-DD HH:MM:SS, YYYY-MM-DD HH:MM:SS])", "answer": "['2022-05-11 13:45:00', '2022-05-12 22:30:00']", "ground_truth": [ "2022-05-11 13:45:00", "2022-05-12 22:30:00" ], "eval_metric": "iou", "channel": "124", "ts_data_path": "ts_data/L2_T1_Shape_Identification_00115.csv", "meta": { "pattern": "depression", "superlative": "longest", "direction": "down", "primary_amp": 0.09187414154866748, "local_std": 0.005189028910303934, "search_window": [ "2022-05-11 02:15:00", "2022-05-20 07:15:00" ], "source": "causal_rivers" } }, { "id": "L2_T1_Shape_Identification_00116", "level": 2, "level_name": "Pattern Recognition", "category": "Shape Identification", "subtask": "Shape Identification", "question": "Identify the time range of the longest plateau (stable period) in channel 146 within [2019-05-30 15:15:00 to 2019-06-04 01:15:00]. (Output format: [YYYY-MM-DD HH:MM:SS, YYYY-MM-DD HH:MM:SS])", "answer": "['2019-06-02 17:30:00', '2019-06-03 09:00:00']", "ground_truth": [ "2019-06-02 17:30:00", "2019-06-03 09:00:00" ], "eval_metric": "iou", "channel": "146", "ts_data_path": "ts_data/L2_T1_Shape_Identification_00116.csv", "meta": { "pattern": "plateau", "superlative": "longest", "direction": "up", "primary_amp": 0.389177168272795, "local_std": 0.025945144551519667, "search_window": [ "2019-05-30 15:15:00", "2019-06-04 01:15:00" ], "source": "causal_rivers" } }, { "id": "L2_T1_Shape_Identification_00117", "level": 2, "level_name": "Pattern Recognition", "category": "Shape Identification", "subtask": "Shape Identification", "question": "Identify the time range of the highest upward spike in channel 147 within [2020-10-18 15:15:00 to 2020-10-27 10:15:00]. (Output format: [YYYY-MM-DD HH:MM:SS, YYYY-MM-DD HH:MM:SS])", "answer": "['2020-10-23 17:15:00', '2020-10-25 00:30:00']", "ground_truth": [ "2020-10-23 17:15:00", "2020-10-25 00:30:00" ], "eval_metric": "iou", "channel": "147", "ts_data_path": "ts_data/L2_T1_Shape_Identification_00117.csv", "meta": { "pattern": "spike", "superlative": "highest", "direction": "up", "primary_amp": 0.07783543365455886, "local_std": 0.005189028910303923, "search_window": [ "2020-10-18 15:15:00", "2020-10-27 10:15:00" ], "source": "causal_rivers" } }, { "id": "L2_T1_Shape_Identification_00118", "level": 2, "level_name": "Pattern Recognition", "category": "Shape Identification", "subtask": "Shape Identification", "question": "Identify the time range of the highest upward spike in channel 151 within [2022-03-18 12:00:00 to 2022-03-22 00:15:00]. (Output format: [YYYY-MM-DD HH:MM:SS, YYYY-MM-DD HH:MM:SS])", "answer": "['2022-03-18 13:15:00', '2022-03-19 01:30:00']", "ground_truth": [ "2022-03-18 13:15:00", "2022-03-19 01:30:00" ], "eval_metric": "iou", "channel": "151", "ts_data_path": "ts_data/L2_T1_Shape_Identification_00118.csv", "meta": { "pattern": "spike", "superlative": "highest", "direction": "up", "primary_amp": 0.1579675652795628, "local_std": 0.0074128984432913336, "search_window": [ "2022-03-18 12:00:00", "2022-03-22 00:15:00" ], "source": "causal_rivers" } }, { "id": "L2_T1_Shape_Identification_00119", "level": 2, "level_name": "Pattern Recognition", "category": "Shape Identification", "subtask": "Shape Identification", "question": "Identify the time range of the largest step descent in channel 154 within [2021-04-14 09:00:00 to 2021-04-19 14:45:00]. (Output format: [YYYY-MM-DD HH:MM:SS, YYYY-MM-DD HH:MM:SS])", "answer": "['2021-04-14 16:00:00', '2021-04-15 10:30:00']", "ground_truth": [ "2021-04-14 16:00:00", "2021-04-15 10:30:00" ], "eval_metric": "iou", "channel": "154", "ts_data_path": "ts_data/L2_T1_Shape_Identification_00119.csv", "meta": { "pattern": "step_down", "superlative": "largest", "direction": "down", "primary_amp": 0.3706449221645661, "local_std": 0.029651593773165293, "search_window": [ "2021-04-14 09:00:00", "2021-04-19 14:45:00" ], "source": "causal_rivers" } }, { "id": "L2_T1_Shape_Identification_00120", "level": 2, "level_name": "Pattern Recognition", "category": "Shape Identification", "subtask": "Shape Identification", "question": "Identify the time range of the highest upward spike in channel 155 within [2020-08-22 23:00:00 to 2020-09-01 16:45:00]. (Output format: [YYYY-MM-DD HH:MM:SS, YYYY-MM-DD HH:MM:SS])", "answer": "['2020-08-26 19:30:00', '2020-08-28 06:15:00']", "ground_truth": [ "2020-08-26 19:30:00", "2020-08-28 06:15:00" ], "eval_metric": "iou", "channel": "155", "ts_data_path": "ts_data/L2_T1_Shape_Identification_00120.csv", "meta": { "pattern": "spike", "superlative": "highest", "direction": "up", "primary_amp": 0.13605556109938988, "local_std": 0.005930318754633062, "search_window": [ "2020-08-22 23:00:00", "2020-09-01 16:45:00" ], "source": "causal_rivers" } }, { "id": "L2_T1_Shape_Identification_00121", "level": 2, "level_name": "Pattern Recognition", "category": "Shape Identification", "subtask": "Shape Identification", "question": "Identify the time range of the longest plateau (stable period) in channel 166 within [2019-07-18 16:30:00 to 2019-07-28 10:45:00]. (Output format: [YYYY-MM-DD HH:MM:SS, YYYY-MM-DD HH:MM:SS])", "answer": "['2019-07-23 02:00:00', '2019-07-24 12:45:00']", "ground_truth": [ "2019-07-23 02:00:00", "2019-07-24 12:45:00" ], "eval_metric": "iou", "channel": "166", "ts_data_path": "ts_data/L2_T1_Shape_Identification_00121.csv", "meta": { "pattern": "plateau", "superlative": "longest", "direction": "up", "primary_amp": 133.4321719792439, "local_std": 8.895478131949593, "search_window": [ "2019-07-18 16:30:00", "2019-07-28 10:45:00" ], "source": "causal_rivers" } }, { "id": "L2_T1_Shape_Identification_00122", "level": 2, "level_name": "Pattern Recognition", "category": "Shape Identification", "subtask": "Shape Identification", "question": "Identify the time range of the longest low plateau (bottom out) in channel 169 within [2020-07-29 18:00:00 to 2020-08-08 13:45:00]. (Output format: [YYYY-MM-DD HH:MM:SS, YYYY-MM-DD HH:MM:SS])", "answer": "['2020-08-03 06:15:00', '2020-08-04 17:15:00']", "ground_truth": [ "2020-08-03 06:15:00", "2020-08-04 17:15:00" ], "eval_metric": "iou", "channel": "169", "ts_data_path": "ts_data/L2_T1_Shape_Identification_00122.csv", "meta": { "pattern": "depression", "superlative": "longest", "direction": "down", "primary_amp": 153.95760670176276, "local_std": 9.636767976278724, "search_window": [ "2020-07-29 18:00:00", "2020-08-08 13:45:00" ], "source": "causal_rivers" } }, { "id": "L2_T1_Shape_Identification_00123", "level": 2, "level_name": "Pattern Recognition", "category": "Shape Identification", "subtask": "Shape Identification", "question": "Identify the time range of the longest low plateau (bottom out) in channel 170 within [2019-07-06 11:15:00 to 2019-07-14 10:15:00]. (Output format: [YYYY-MM-DD HH:MM:SS, YYYY-MM-DD HH:MM:SS])", "answer": "['2019-07-12 20:00:00', '2019-07-14 00:15:00']", "ground_truth": [ "2019-07-12 20:00:00", "2019-07-14 00:15:00" ], "eval_metric": "iou", "channel": "170", "ts_data_path": "ts_data/L2_T1_Shape_Identification_00123.csv", "meta": { "pattern": "depression", "superlative": "longest", "direction": "down", "primary_amp": 27.107923080277317, "local_std": 1.4825796886582654, "search_window": [ "2019-07-06 11:15:00", "2019-07-14 10:15:00" ], "source": "causal_rivers" } }, { "id": "L2_T1_Shape_Identification_00124", "level": 2, "level_name": "Pattern Recognition", "category": "Shape Identification", "subtask": "Shape Identification", "question": "Identify the time range of the deepest deep valley in channel 172 within [2021-09-20 13:00:00 to 2021-09-30 10:45:00]. (Output format: [YYYY-MM-DD HH:MM:SS, YYYY-MM-DD HH:MM:SS])", "answer": "['2021-09-24 02:00:00', '2021-09-25 13:15:00']", "ground_truth": [ "2021-09-24 02:00:00", "2021-09-25 13:15:00" ], "eval_metric": "iou", "channel": "172", "ts_data_path": "ts_data/L2_T1_Shape_Identification_00124.csv", "meta": { "pattern": "valley", "superlative": "deepest", "direction": "down", "primary_amp": 155.67086730911785, "local_std": 10.378057820607857, "search_window": [ "2021-09-20 13:00:00", "2021-09-30 10:45:00" ], "source": "causal_rivers" } }, { "id": "L2_T1_Shape_Identification_00125", "level": 2, "level_name": "Pattern Recognition", "category": "Shape Identification", "subtask": "Shape Identification", "question": "Identify the time range of the highest upward spike in channel 173 within [2021-04-15 20:00:00 to 2021-04-22 22:15:00]. (Output format: [YYYY-MM-DD HH:MM:SS, YYYY-MM-DD HH:MM:SS])", "answer": "['2021-04-20 03:45:00', '2021-04-21 05:00:00']", "ground_truth": [ "2021-04-20 03:45:00", "2021-04-21 05:00:00" ], "eval_metric": "iou", "channel": "173", "ts_data_path": "ts_data/L2_T1_Shape_Identification_00125.csv", "meta": { "pattern": "spike", "superlative": "highest", "direction": "up", "primary_amp": 122.3128243143069, "local_std": 8.15418828762046, "search_window": [ "2021-04-15 20:00:00", "2021-04-22 22:15:00" ], "source": "causal_rivers" } }, { "id": "L2_T1_Shape_Identification_00126", "level": 2, "level_name": "Pattern Recognition", "category": "Shape Identification", "subtask": "Shape Identification", "question": "Identify the time range of the longest plateau (stable period) in channel 177 within [2019-09-17 07:00:00 to 2019-09-22 10:00:00]. (Output format: [YYYY-MM-DD HH:MM:SS, YYYY-MM-DD HH:MM:SS])", "answer": "['2019-09-19 14:15:00', '2019-09-20 08:15:00']", "ground_truth": [ "2019-09-19 14:15:00", "2019-09-20 08:15:00" ], "eval_metric": "iou", "channel": "177", "ts_data_path": "ts_data/L2_T1_Shape_Identification_00126.csv", "meta": { "pattern": "plateau", "superlative": "longest", "direction": "up", "primary_amp": 36.27311824400017, "local_std": 1.7790956263899227, "search_window": [ "2019-09-17 07:00:00", "2019-09-22 10:00:00" ], "source": "causal_rivers" } }, { "id": "L2_T1_Shape_Identification_00127", "level": 2, "level_name": "Pattern Recognition", "category": "Shape Identification", "subtask": "Shape Identification", "question": "Identify the time range of the largest step descent in channel 237 within [2019-09-28 02:15:00 to 2019-10-01 14:30:00]. (Output format: [YYYY-MM-DD HH:MM:SS, YYYY-MM-DD HH:MM:SS])", "answer": "['2019-09-30 14:30:00', '2019-10-01 02:45:00']", "ground_truth": [ "2019-09-30 14:30:00", "2019-10-01 02:45:00" ], "eval_metric": "iou", "channel": "237", "ts_data_path": "ts_data/L2_T1_Shape_Identification_00127.csv", "meta": { "pattern": "step_down", "superlative": "largest", "direction": "down", "primary_amp": 0.2409191994069681, "local_std": 0.019273535952557447, "search_window": [ "2019-09-28 02:15:00", "2019-10-01 14:30:00" ], "source": "causal_rivers" } }, { "id": "L2_T1_Shape_Identification_00128", "level": 2, "level_name": "Pattern Recognition", "category": "Shape Identification", "subtask": "Shape Identification", "question": "Identify the time range of the highest upward spike in channel 245 within [2023-08-19 15:00:00 to 2023-08-27 18:00:00]. (Output format: [YYYY-MM-DD HH:MM:SS, YYYY-MM-DD HH:MM:SS])", "answer": "['2023-08-24 07:45:00', '2023-08-25 12:45:00']", "ground_truth": [ "2023-08-24 07:45:00", "2023-08-25 12:45:00" ], "eval_metric": "iou", "channel": "245", "ts_data_path": "ts_data/L2_T1_Shape_Identification_00128.csv", "meta": { "pattern": "spike", "superlative": "highest", "direction": "up", "primary_amp": 0.38814428195970074, "local_std": 0.005189028910303934, "search_window": [ "2023-08-19 15:00:00", "2023-08-27 18:00:00" ], "source": "causal_rivers" } }, { "id": "L2_T1_Shape_Identification_00129", "level": 2, "level_name": "Pattern Recognition", "category": "Shape Identification", "subtask": "Shape Identification", "question": "Identify the time range of the longest low plateau (bottom out) in channel 312 within [2020-06-01 00:00:00 to 2020-06-10 19:45:00]. (Output format: [YYYY-MM-DD HH:MM:SS, YYYY-MM-DD HH:MM:SS])", "answer": "['2020-06-07 15:45:00', '2020-06-09 02:45:00']", "ground_truth": [ "2020-06-07 15:45:00", "2020-06-09 02:45:00" ], "eval_metric": "iou", "channel": "312", "ts_data_path": "ts_data/L2_T1_Shape_Identification_00129.csv", "meta": { "pattern": "depression", "superlative": "longest", "direction": "down", "primary_amp": 0.06334427337889965, "local_std": 0.0029651593773165233, "search_window": [ "2020-06-01 00:00:00", "2020-06-10 19:45:00" ], "source": "causal_rivers" } }, { "id": "L2_T1_Shape_Identification_00133", "level": 2, "level_name": "Pattern Recognition", "category": "Shape Identification", "subtask": "Shape Identification", "question": "Identify the time range of the largest step descent in channel 496 within [2023-08-01 03:30:00 to 2023-08-03 17:30:00]. (Output format: [YYYY-MM-DD HH:MM:SS, YYYY-MM-DD HH:MM:SS])", "answer": "['2023-08-02 15:15:00', '2023-08-03 00:15:00']", "ground_truth": [ "2023-08-02 15:15:00", "2023-08-03 00:15:00" ], "eval_metric": "iou", "channel": "496", "ts_data_path": "ts_data/L2_T1_Shape_Identification_00133.csv", "meta": { "pattern": "step_down", "superlative": "largest", "direction": "down", "primary_amp": 0.13327051720596234, "local_std": 0.0007412898443291333, "search_window": [ "2023-08-01 03:30:00", "2023-08-03 17:30:00" ], "source": "causal_rivers" } }, { "id": "L2_T1_Shape_Identification_00135", "level": 2, "level_name": "Pattern Recognition", "category": "Shape Identification", "subtask": "Shape Identification", "question": "Identify the time range of the longest low plateau (bottom out) in channel 578 within [2019-09-20 12:45:00 to 2019-09-25 09:30:00]. (Output format: [YYYY-MM-DD HH:MM:SS, YYYY-MM-DD HH:MM:SS])", "answer": "['2019-09-22 03:30:00', '2019-09-22 20:45:00']", "ground_truth": [ "2019-09-22 03:30:00", "2019-09-22 20:45:00" ], "eval_metric": "iou", "channel": "578", "ts_data_path": "ts_data/L2_T1_Shape_Identification_00135.csv", "meta": { "pattern": "depression", "superlative": "longest", "direction": "down", "primary_amp": 0.31341401512991574, "local_std": 0.020756115641215694, "search_window": [ "2019-09-20 12:45:00", "2019-09-25 09:30:00" ], "source": "causal_rivers" } }, { "id": "L2_T1_Shape_Identification_00136", "level": 2, "level_name": "Pattern Recognition", "category": "Shape Identification", "subtask": "Shape Identification", "question": "Identify the time range of the longest plateau (stable period) in channel 580 within [2019-05-05 05:30:00 to 2019-05-14 11:45:00]. (Output format: [YYYY-MM-DD HH:MM:SS, YYYY-MM-DD HH:MM:SS])", "answer": "['2019-05-08 14:45:00', '2019-05-09 23:45:00']", "ground_truth": [ "2019-05-08 14:45:00", "2019-05-09 23:45:00" ], "eval_metric": "iou", "channel": "580", "ts_data_path": "ts_data/L2_T1_Shape_Identification_00136.csv", "meta": { "pattern": "plateau", "superlative": "longest", "direction": "up", "primary_amp": 0.13439728637942377, "local_std": 0.0066716085989622, "search_window": [ "2019-05-05 05:30:00", "2019-05-14 11:45:00" ], "source": "causal_rivers" } }, { "id": "L2_T1_Shape_Identification_00137", "level": 2, "level_name": "Pattern Recognition", "category": "Shape Identification", "subtask": "Shape Identification", "question": "Identify the time range of the highest upward spike in channel 589 within [2023-10-15 16:00:00 to 2023-10-20 03:15:00]. (Output format: [YYYY-MM-DD HH:MM:SS, YYYY-MM-DD HH:MM:SS])", "answer": "['2023-10-16 12:30:00', '2023-10-17 04:15:00']", "ground_truth": [ "2023-10-16 12:30:00", "2023-10-17 04:15:00" ], "eval_metric": "iou", "channel": "589", "ts_data_path": "ts_data/L2_T1_Shape_Identification_00137.csv", "meta": { "pattern": "spike", "superlative": "highest", "direction": "up", "primary_amp": 26.01298463978486, "local_std": 1.4825796886582654, "search_window": [ "2023-10-15 16:00:00", "2023-10-20 03:15:00" ], "source": "causal_rivers" } }, { "id": "L2_T1_Shape_Identification_00138", "level": 2, "level_name": "Pattern Recognition", "category": "Shape Identification", "subtask": "Shape Identification", "question": "Identify the time range of the highest upward spike in channel 591 within [2019-07-25 00:00:00 to 2019-07-31 06:30:00]. (Output format: [YYYY-MM-DD HH:MM:SS, YYYY-MM-DD HH:MM:SS])", "answer": "['2019-07-27 00:45:00', '2019-07-27 23:00:00']", "ground_truth": [ "2019-07-27 00:45:00", "2019-07-27 23:00:00" ], "eval_metric": "iou", "channel": "591", "ts_data_path": "ts_data/L2_T1_Shape_Identification_00138.csv", "meta": { "pattern": "spike", "superlative": "highest", "direction": "up", "primary_amp": 51.14899925871025, "local_std": 3.409933283914017, "search_window": [ "2019-07-25 00:00:00", "2019-07-31 06:30:00" ], "source": "causal_rivers" } }, { "id": "L2_T1_Shape_Identification_00142", "level": 2, "level_name": "Pattern Recognition", "category": "Shape Identification", "subtask": "Shape Identification", "question": "Identify the time range of the longest low plateau (bottom out) in channel 627 within [2022-03-13 21:15:00 to 2022-03-23 20:15:00]. (Output format: [YYYY-MM-DD HH:MM:SS, YYYY-MM-DD HH:MM:SS])", "answer": "['2022-03-17 22:45:00', '2022-03-19 10:15:00']", "ground_truth": [ "2022-03-17 22:45:00", "2022-03-19 10:15:00" ], "eval_metric": "iou", "channel": "627", "ts_data_path": "ts_data/L2_T1_Shape_Identification_00142.csv", "meta": { "pattern": "depression", "superlative": "longest", "direction": "down", "primary_amp": 0.378057820607858, "local_std": 0.025203854707190533, "search_window": [ "2022-03-13 21:15:00", "2022-03-23 20:15:00" ], "source": "causal_rivers" } }, { "id": "L2_T1_Shape_Identification_00143", "level": 2, "level_name": "Pattern Recognition", "category": "Shape Identification", "subtask": "Shape Identification", "question": "Identify the time range of the largest step descent in channel 647 within [2023-07-20 00:00:00 to 2023-07-26 21:30:00]. (Output format: [YYYY-MM-DD HH:MM:SS, YYYY-MM-DD HH:MM:SS])", "answer": "['2023-07-23 07:30:00', '2023-07-24 08:00:00']", "ground_truth": [ "2023-07-23 07:30:00", "2023-07-24 08:00:00" ], "eval_metric": "iou", "channel": "647", "ts_data_path": "ts_data/L2_T1_Shape_Identification_00143.csv", "meta": { "pattern": "step_down", "superlative": "largest", "direction": "down", "primary_amp": 0.27798369162342473, "local_std": 0.02223869532987398, "search_window": [ "2023-07-20 00:00:00", "2023-07-26 21:30:00" ], "source": "causal_rivers" } }, { "id": "L2_T1_Shape_Identification_00145", "level": 2, "level_name": "Pattern Recognition", "category": "Shape Identification", "subtask": "Shape Identification", "question": "Identify the time range of the longest plateau (stable period) in channel 683 within [2023-11-30 07:30:00 to 2023-12-02 07:15:00]. (Output format: [YYYY-MM-DD HH:MM:SS, YYYY-MM-DD HH:MM:SS])", "answer": "['2023-12-01 07:45:00', '2023-12-01 14:30:00']", "ground_truth": [ "2023-12-01 07:45:00", "2023-12-01 14:30:00" ], "eval_metric": "iou", "channel": "683", "ts_data_path": "ts_data/L2_T1_Shape_Identification_00145.csv", "meta": { "pattern": "plateau", "superlative": "longest", "direction": "up", "primary_amp": 0.08576429299481159, "local_std": 0.0037064492216456668, "search_window": [ "2023-11-30 07:30:00", "2023-12-02 07:15:00" ], "source": "causal_rivers" } }, { "id": "L2_T1_Shape_Identification_00146", "level": 2, "level_name": "Pattern Recognition", "category": "Shape Identification", "subtask": "Shape Identification", "question": "Identify the time range of the largest step ascent in channel 727 within [2019-08-17 06:00:00 to 2019-08-21 13:30:00]. (Output format: [YYYY-MM-DD HH:MM:SS, YYYY-MM-DD HH:MM:SS])", "answer": "['2019-08-18 01:45:00', '2019-08-18 17:00:00']", "ground_truth": [ "2019-08-18 01:45:00", "2019-08-18 17:00:00" ], "eval_metric": "iou", "channel": "727", "ts_data_path": "ts_data/L2_T1_Shape_Identification_00146.csv", "meta": { "pattern": "step_up", "superlative": "largest", "direction": "up", "primary_amp": 25.646285959255852, "local_std": 1.7049666419570058, "search_window": [ "2019-08-17 06:00:00", "2019-08-21 13:30:00" ], "source": "causal_rivers" } }, { "id": "L2_T1_Shape_Identification_00147", "level": 2, "level_name": "Pattern Recognition", "category": "Shape Identification", "subtask": "Shape Identification", "question": "Identify the time range of the highest upward spike in channel 728 within [2021-12-09 00:00:00 to 2021-12-18 01:30:00]. (Output format: [YYYY-MM-DD HH:MM:SS, YYYY-MM-DD HH:MM:SS])", "answer": "['2021-12-13 05:15:00', '2021-12-14 13:30:00']", "ground_truth": [ "2021-12-13 05:15:00", "2021-12-14 13:30:00" ], "eval_metric": "iou", "channel": "728", "ts_data_path": "ts_data/L2_T1_Shape_Identification_00147.csv", "meta": { "pattern": "spike", "superlative": "highest", "direction": "up", "primary_amp": 50.926486686145054, "local_std": 3.3358042994810972, "search_window": [ "2021-12-09 00:00:00", "2021-12-18 01:30:00" ], "source": "causal_rivers" } }, { "id": "L2_T1_Shape_Identification_00148", "level": 2, "level_name": "Pattern Recognition", "category": "Shape Identification", "subtask": "Shape Identification", "question": "Identify the time range of the longest plateau (stable period) in channel 729 within [2020-08-23 00:00:00 to 2020-08-30 12:45:00]. (Output format: [YYYY-MM-DD HH:MM:SS, YYYY-MM-DD HH:MM:SS])", "answer": "['2020-08-28 07:45:00', '2020-08-29 10:30:00']", "ground_truth": [ "2020-08-28 07:45:00", "2020-08-29 10:30:00" ], "eval_metric": "iou", "channel": "729", "ts_data_path": "ts_data/L2_T1_Shape_Identification_00148.csv", "meta": { "pattern": "plateau", "superlative": "longest", "direction": "up", "primary_amp": 50.18304893332081, "local_std": 3.1134173461823593, "search_window": [ "2020-08-23 00:00:00", "2020-08-30 12:45:00" ], "source": "causal_rivers" } }, { "id": "L2_T1_Shape_Identification_00149", "level": 2, "level_name": "Pattern Recognition", "category": "Shape Identification", "subtask": "Shape Identification", "question": "Identify the time range of the longest plateau (stable period) in channel 754 within [2023-07-29 12:15:00 to 2023-08-01 16:45:00]. (Output format: [YYYY-MM-DD HH:MM:SS, YYYY-MM-DD HH:MM:SS])", "answer": "['2023-07-31 23:00:00', '2023-08-01 10:15:00']", "ground_truth": [ "2023-07-31 23:00:00", "2023-08-01 10:15:00" ], "eval_metric": "iou", "channel": "754", "ts_data_path": "ts_data/L2_T1_Shape_Identification_00149.csv", "meta": { "pattern": "plateau", "superlative": "longest", "direction": "up", "primary_amp": 0.07125114996510874, "local_std": 0.0044477390659748, "search_window": [ "2023-07-29 12:15:00", "2023-08-01 16:45:00" ], "source": "causal_rivers" } }, { "id": "L2_T1_Shape_Identification_00150", "level": 2, "level_name": "Pattern Recognition", "category": "Shape Identification", "subtask": "Shape Identification", "question": "Identify the time range of the largest step descent in channel 762 within [2020-04-05 15:00:00 to 2020-04-14 05:00:00]. (Output format: [YYYY-MM-DD HH:MM:SS, YYYY-MM-DD HH:MM:SS])", "answer": "['2020-04-12 12:30:00', '2020-04-13 19:00:00']", "ground_truth": [ "2020-04-12 12:30:00", "2020-04-13 19:00:00" ], "eval_metric": "iou", "channel": "762", "ts_data_path": "ts_data/L2_T1_Shape_Identification_00150.csv", "meta": { "pattern": "step_down", "superlative": "largest", "direction": "down", "primary_amp": 0.08770070085585287, "local_std": 0.0066716085989622, "search_window": [ "2020-04-05 15:00:00", "2020-04-14 05:00:00" ], "source": "causal_rivers" } }, { "id": "L2_T1_Shape_Identification_00151", "level": 2, "level_name": "Pattern Recognition", "category": "Shape Identification", "subtask": "Shape Identification", "question": "Identify the time range of the largest step ascent in channel 811 within [2022-08-23 15:45:00 to 2022-08-26 05:00:00]. (Output format: [YYYY-MM-DD HH:MM:SS, YYYY-MM-DD HH:MM:SS])", "answer": "['2022-08-25 15:30:00', '2022-08-26 00:15:00']", "ground_truth": [ "2022-08-25 15:30:00", "2022-08-26 00:15:00" ], "eval_metric": "iou", "channel": "811", "ts_data_path": "ts_data/L2_T1_Shape_Identification_00151.csv", "meta": { "pattern": "step_up", "superlative": "largest", "direction": "up", "primary_amp": 0.6986435977869787, "local_std": 0.0467012601927354, "search_window": [ "2022-08-23 15:45:00", "2022-08-26 05:00:00" ], "source": "causal_rivers" } }, { "id": "L2_T1_Shape_Identification_00152", "level": 2, "level_name": "Pattern Recognition", "category": "Shape Identification", "subtask": "Shape Identification", "question": "Identify the time range of the highest upward spike in channel 813 within [2022-07-26 16:00:00 to 2022-08-02 07:45:00]. (Output format: [YYYY-MM-DD HH:MM:SS, YYYY-MM-DD HH:MM:SS])", "answer": "['2022-07-30 08:00:00', '2022-07-31 07:45:00']", "ground_truth": [ "2022-07-30 08:00:00", "2022-07-31 07:45:00" ], "eval_metric": "iou", "channel": "813", "ts_data_path": "ts_data/L2_T1_Shape_Identification_00152.csv", "meta": { "pattern": "spike", "superlative": "highest", "direction": "up", "primary_amp": 0.3149706657709411, "local_std": 0.020756115641215694, "search_window": [ "2022-07-26 16:00:00", "2022-08-02 07:45:00" ], "source": "causal_rivers" } }, { "id": "L2_T1_Shape_Identification_00153", "level": 2, "level_name": "Pattern Recognition", "category": "Shape Identification", "subtask": "Shape Identification", "question": "Identify the time range of the highest upward spike in channel 865 within [2021-05-10 22:30:00 to 2021-05-15 12:00:00]. (Output format: [YYYY-MM-DD HH:MM:SS, YYYY-MM-DD HH:MM:SS])", "answer": "['2021-05-14 14:30:00', '2021-05-15 06:30:00']", "ground_truth": [ "2021-05-14 14:30:00", "2021-05-15 06:30:00" ], "eval_metric": "iou", "channel": "865", "ts_data_path": "ts_data/L2_T1_Shape_Identification_00153.csv", "meta": { "pattern": "spike", "superlative": "highest", "direction": "up", "primary_amp": 5.559673832468496, "local_std": 0.37064492216456635, "search_window": [ "2021-05-10 22:30:00", "2021-05-15 12:00:00" ], "source": "causal_rivers" } }, { "id": "L2_T1_Shape_Identification_00154", "level": 2, "level_name": "Pattern Recognition", "category": "Shape Identification", "subtask": "Shape Identification", "question": "Identify the time range of the deepest deep valley in channel 891 within [2021-09-15 21:00:00 to 2021-09-18 17:00:00]. (Output format: [YYYY-MM-DD HH:MM:SS, YYYY-MM-DD HH:MM:SS])", "answer": "['2021-09-16 05:15:00', '2021-09-16 15:00:00']", "ground_truth": [ "2021-09-16 05:15:00", "2021-09-16 15:00:00" ], "eval_metric": "iou", "channel": "891", "ts_data_path": "ts_data/L2_T1_Shape_Identification_00154.csv", "meta": { "pattern": "valley", "superlative": "deepest", "direction": "down", "primary_amp": 1.7790956263899202, "local_std": 0.11860637509266134, "search_window": [ "2021-09-15 21:00:00", "2021-09-18 17:00:00" ], "source": "causal_rivers" } }, { "id": "L2_T1_Shape_Identification_00156", "level": 2, "level_name": "Pattern Recognition", "category": "Shape Identification", "subtask": "Shape Identification", "question": "Identify the time range of the largest step descent in channel 895 within [2021-06-08 04:00:00 to 2021-06-17 08:15:00]. (Output format: [YYYY-MM-DD HH:MM:SS, YYYY-MM-DD HH:MM:SS])", "answer": "['2021-06-14 22:30:00', '2021-06-16 07:15:00']", "ground_truth": [ "2021-06-14 22:30:00", "2021-06-16 07:15:00" ], "eval_metric": "iou", "channel": "895", "ts_data_path": "ts_data/L2_T1_Shape_Identification_00156.csv", "meta": { "pattern": "step_down", "superlative": "largest", "direction": "down", "primary_amp": 0.7857763517659588, "local_std": 0.050741457635310114, "search_window": [ "2021-06-08 04:00:00", "2021-06-17 08:15:00" ], "source": "causal_rivers" } }, { "id": "L2_T1_Shape_Identification_00157", "level": 2, "level_name": "Pattern Recognition", "category": "Shape Identification", "subtask": "Shape Identification", "question": "Identify the time range of the largest step descent in channel 897 within [2019-01-21 01:00:00 to 2019-01-24 21:30:00]. (Output format: [YYYY-MM-DD HH:MM:SS, YYYY-MM-DD HH:MM:SS])", "answer": "['2019-01-23 13:15:00', '2019-01-24 02:45:00']", "ground_truth": [ "2019-01-23 13:15:00", "2019-01-24 02:45:00" ], "eval_metric": "iou", "channel": "897", "ts_data_path": "ts_data/L2_T1_Shape_Identification_00157.csv", "meta": { "pattern": "step_down", "superlative": "largest", "direction": "down", "primary_amp": 0.009266123054114167, "local_std": 0.0007412898443291333, "search_window": [ "2019-01-21 01:00:00", "2019-01-24 21:30:00" ], "source": "causal_rivers" } }, { "id": "L2_T1_Shape_Identification_00159", "level": 2, "level_name": "Pattern Recognition", "category": "Shape Identification", "subtask": "Shape Identification", "question": "Identify the time range of the deepest deep valley in channel HUFL within [2016-10-04 06:15:00 to 2016-10-12 08:00:00]. (Output format: [YYYY-MM-DD HH:MM:SS, YYYY-MM-DD HH:MM:SS])", "answer": "['2016-10-05 08:00:00', '2016-10-06 12:45:00']", "ground_truth": [ "2016-10-05 08:00:00", "2016-10-06 12:45:00" ], "eval_metric": "iou", "channel": "HUFL", "ts_data_path": "ts_data/L2_T1_Shape_Identification_00159.csv", "meta": { "pattern": "valley", "superlative": "deepest", "direction": "down", "primary_amp": 52.138627945183835, "local_std": 3.4759085296789225, "search_window": [ "2016-10-04 06:15:00", "2016-10-12 08:00:00" ], "source": "ettm1" } }, { "id": "L2_T1_Shape_Identification_00162", "level": 2, "level_name": "Pattern Recognition", "category": "Shape Identification", "subtask": "Shape Identification", "question": "Identify the time range of the largest step descent in channel MULL within [2016-08-20 20:00:00 to 2016-08-27 04:45:00]. (Output format: [YYYY-MM-DD HH:MM:SS, YYYY-MM-DD HH:MM:SS])", "answer": "['2016-08-20 20:30:00', '2016-08-21 19:00:00']", "ground_truth": [ "2016-08-20 20:30:00", "2016-08-21 19:00:00" ], "eval_metric": "iou", "channel": "MULL", "ts_data_path": "ts_data/L2_T1_Shape_Identification_00162.csv", "meta": { "pattern": "step_down", "superlative": "largest", "direction": "down", "primary_amp": 12.756111861962898, "local_std": 0.928836146312965, "search_window": [ "2016-08-20 20:00:00", "2016-08-27 04:45:00" ], "source": "ettm1" } }, { "id": "L2_T1_Shape_Identification_00163", "level": 2, "level_name": "Pattern Recognition", "category": "Shape Identification", "subtask": "Shape Identification", "question": "Identify the time range of the largest step ascent in channel LUFL within [2017-10-10 03:00:00 to 2017-10-13 14:00:00]. (Output format: [YYYY-MM-DD HH:MM:SS, YYYY-MM-DD HH:MM:SS])", "answer": "['2017-10-10 23:45:00', '2017-10-11 11:45:00']", "ground_truth": [ "2017-10-10 23:45:00", "2017-10-11 11:45:00" ], "eval_metric": "iou", "channel": "LUFL", "ts_data_path": "ts_data/L2_T1_Shape_Identification_00163.csv", "meta": { "pattern": "step_up", "superlative": "largest", "direction": "up", "primary_amp": 6.5997984825921385, "local_std": 0.47442557813504416, "search_window": [ "2017-10-10 03:00:00", "2017-10-13 14:00:00" ], "source": "ettm1" } }, { "id": "L2_T1_Shape_Identification_00164", "level": 2, "level_name": "Pattern Recognition", "category": "Shape Identification", "subtask": "Shape Identification", "question": "Identify the time range of the longest low plateau (bottom out) in channel LULL within [2017-09-12 09:00:00 to 2017-09-16 18:30:00]. (Output format: [YYYY-MM-DD HH:MM:SS, YYYY-MM-DD HH:MM:SS])", "answer": "['2017-09-15 00:45:00', '2017-09-15 16:15:00']", "ground_truth": [ "2017-09-15 00:45:00", "2017-09-15 16:15:00" ], "eval_metric": "iou", "channel": "LULL", "ts_data_path": "ts_data/L2_T1_Shape_Identification_00164.csv", "meta": { "pattern": "depression", "superlative": "longest", "direction": "down", "primary_amp": 3.0546939606318957, "local_std": 0.20311336503195004, "search_window": [ "2017-09-12 09:00:00", "2017-09-16 18:30:00" ], "source": "ettm1" } }, { "id": "L2_T3_Subsequence_Matching_00331", "level": 2, "level_name": "Pattern Recognition", "category": "Subsequence Matching", "subtask": "Subsequence Matching", "question": "Analyze the reference pattern in [2020-06-27 13:30:00 to 2020-06-28 04:15:00]. Find the time interval where channel 67 exhibits the most similar pattern within the search context [2020-06-29 03:00:00 to 2020-07-01 15:00:00]. (Output format: [YYYY-MM-DD HH:MM:SS, YYYY-MM-DD HH:MM:SS])", "answer": "['2020-06-30 21:00:00', '2020-07-01 11:45:00']", "ground_truth": [ "2020-06-30 21:00:00", "2020-07-01 11:45:00" ], "eval_metric": "iou", "channel": "67", "ts_data_path": "ts_data/L2_T3_Subsequence_Matching_00331.csv", "meta": { "prototype": "step pattern", "query_window": "[2020-06-27 13:30:00 to 2020-06-28 04:15:00]", "search_window": "[2020-06-29 03:00:00 to 2020-07-01 15:00:00]", "source": "causal_rivers" } }, { "id": "L2_T3_Subsequence_Matching_00332", "level": 2, "level_name": "Pattern Recognition", "category": "Subsequence Matching", "subtask": "Subsequence Matching", "question": "Analyze the reference pattern in [2023-09-07 15:00:00 to 2023-09-08 03:15:00]. Find the time interval where channel 71 exhibits the most similar pattern within the search context [2023-09-09 19:45:00 to 2023-09-16 01:00:00]. (Output format: [YYYY-MM-DD HH:MM:SS, YYYY-MM-DD HH:MM:SS])", "answer": "['2023-09-15 03:45:00', '2023-09-15 16:00:00']", "ground_truth": [ "2023-09-15 03:45:00", "2023-09-15 16:00:00" ], "eval_metric": "iou", "channel": "71", "ts_data_path": "ts_data/L2_T3_Subsequence_Matching_00332.csv", "meta": { "prototype": "bell curve", "query_window": "[2023-09-07 15:00:00 to 2023-09-08 03:15:00]", "search_window": "[2023-09-09 19:45:00 to 2023-09-16 01:00:00]", "source": "causal_rivers" } }, { "id": "L2_T3_Subsequence_Matching_00335", "level": 2, "level_name": "Pattern Recognition", "category": "Subsequence Matching", "subtask": "Subsequence Matching", "question": "Analyze the reference pattern in [2020-11-18 04:00:00 to 2020-11-18 18:45:00]. Find the time interval where channel 124 exhibits the most similar pattern within the search context [2020-11-20 09:00:00 to 2020-11-26 02:15:00]. (Output format: [YYYY-MM-DD HH:MM:SS, YYYY-MM-DD HH:MM:SS])", "answer": "['2020-11-23 23:45:00', '2020-11-24 14:30:00']", "ground_truth": [ "2020-11-23 23:45:00", "2020-11-24 14:30:00" ], "eval_metric": "iou", "channel": "124", "ts_data_path": "ts_data/L2_T3_Subsequence_Matching_00335.csv", "meta": { "prototype": "step pattern", "query_window": "[2020-11-18 04:00:00 to 2020-11-18 18:45:00]", "search_window": "[2020-11-20 09:00:00 to 2020-11-26 02:15:00]", "source": "causal_rivers" } }, { "id": "L2_T3_Subsequence_Matching_00336", "level": 2, "level_name": "Pattern Recognition", "category": "Subsequence Matching", "subtask": "Subsequence Matching", "question": "Analyze the reference pattern in [2021-08-09 13:30:00 to 2021-08-10 00:30:00]. Find the time interval where channel 146 exhibits the most similar pattern within the search context [2021-08-10 18:15:00 to 2021-08-13 04:45:00]. (Output format: [YYYY-MM-DD HH:MM:SS, YYYY-MM-DD HH:MM:SS])", "answer": "['2021-08-11 06:15:00', '2021-08-11 17:15:00']", "ground_truth": [ "2021-08-11 06:15:00", "2021-08-11 17:15:00" ], "eval_metric": "iou", "channel": "146", "ts_data_path": "ts_data/L2_T3_Subsequence_Matching_00336.csv", "meta": { "prototype": "double-peak pattern", "query_window": "[2021-08-09 13:30:00 to 2021-08-10 00:30:00]", "search_window": "[2021-08-10 18:15:00 to 2021-08-13 04:45:00]", "source": "causal_rivers" } }, { "id": "L2_T3_Subsequence_Matching_00338", "level": 2, "level_name": "Pattern Recognition", "category": "Subsequence Matching", "subtask": "Subsequence Matching", "question": "Analyze the reference pattern in [2020-09-17 15:00:00 to 2020-09-18 03:15:00]. Find the time interval where channel 151 exhibits the most similar pattern within the search context [2020-09-19 20:45:00 to 2020-09-26 05:15:00]. (Output format: [YYYY-MM-DD HH:MM:SS, YYYY-MM-DD HH:MM:SS])", "answer": "['2020-09-25 12:00:00', '2020-09-26 00:15:00']", "ground_truth": [ "2020-09-25 12:00:00", "2020-09-26 00:15:00" ], "eval_metric": "iou", "channel": "151", "ts_data_path": "ts_data/L2_T3_Subsequence_Matching_00338.csv", "meta": { "prototype": "bell curve", "query_window": "[2020-09-17 15:00:00 to 2020-09-18 03:15:00]", "search_window": "[2020-09-19 20:45:00 to 2020-09-26 05:15:00]", "source": "causal_rivers" } }, { "id": "L2_T3_Subsequence_Matching_00339", "level": 2, "level_name": "Pattern Recognition", "category": "Subsequence Matching", "subtask": "Subsequence Matching", "question": "Analyze the reference pattern in [2020-12-06 00:00:00 to 2020-12-06 11:00:00]. Find the time interval where channel 154 exhibits the most similar pattern within the search context [2020-12-08 08:15:00 to 2020-12-15 09:00:00]. (Output format: [YYYY-MM-DD HH:MM:SS, YYYY-MM-DD HH:MM:SS])", "answer": "['2020-12-13 19:00:00', '2020-12-14 06:00:00']", "ground_truth": [ "2020-12-13 19:00:00", "2020-12-14 06:00:00" ], "eval_metric": "iou", "channel": "154", "ts_data_path": "ts_data/L2_T3_Subsequence_Matching_00339.csv", "meta": { "prototype": "double-peak pattern", "query_window": "[2020-12-06 00:00:00 to 2020-12-06 11:00:00]", "search_window": "[2020-12-08 08:15:00 to 2020-12-15 09:00:00]", "source": "causal_rivers" } }, { "id": "L2_T3_Subsequence_Matching_00341", "level": 2, "level_name": "Pattern Recognition", "category": "Subsequence Matching", "subtask": "Subsequence Matching", "question": "Analyze the reference pattern in [2022-06-06 23:15:00 to 2022-06-07 10:15:00]. Find the time interval where channel 166 exhibits the most similar pattern within the search context [2022-06-08 06:45:00 to 2022-06-11 04:30:00]. (Output format: [YYYY-MM-DD HH:MM:SS, YYYY-MM-DD HH:MM:SS])", "answer": "['2022-06-08 20:30:00', '2022-06-09 07:30:00']", "ground_truth": [ "2022-06-08 20:30:00", "2022-06-09 07:30:00" ], "eval_metric": "iou", "channel": "166", "ts_data_path": "ts_data/L2_T3_Subsequence_Matching_00341.csv", "meta": { "prototype": "double-peak pattern", "query_window": "[2022-06-06 23:15:00 to 2022-06-07 10:15:00]", "search_window": "[2022-06-08 06:45:00 to 2022-06-11 04:30:00]", "source": "causal_rivers" } }, { "id": "L2_T3_Subsequence_Matching_00342", "level": 2, "level_name": "Pattern Recognition", "category": "Subsequence Matching", "subtask": "Subsequence Matching", "question": "Analyze the reference pattern in [2020-12-14 08:00:00 to 2020-12-14 20:15:00]. Find the time interval where channel 169 exhibits the most similar pattern within the search context [2020-12-16 04:30:00 to 2020-12-20 23:45:00]. (Output format: [YYYY-MM-DD HH:MM:SS, YYYY-MM-DD HH:MM:SS])", "answer": "['2020-12-19 09:30:00', '2020-12-19 21:45:00']", "ground_truth": [ "2020-12-19 09:30:00", "2020-12-19 21:45:00" ], "eval_metric": "iou", "channel": "169", "ts_data_path": "ts_data/L2_T3_Subsequence_Matching_00342.csv", "meta": { "prototype": "bell curve", "query_window": "[2020-12-14 08:00:00 to 2020-12-14 20:15:00]", "search_window": "[2020-12-16 04:30:00 to 2020-12-20 23:45:00]", "source": "causal_rivers" } }, { "id": "L2_T3_Subsequence_Matching_00345", "level": 2, "level_name": "Pattern Recognition", "category": "Subsequence Matching", "subtask": "Subsequence Matching", "question": "Analyze the reference pattern in [2023-07-13 13:30:00 to 2023-07-14 04:15:00]. Find the time interval where channel 173 exhibits the most similar pattern within the search context [2023-07-15 15:00:00 to 2023-07-20 18:15:00]. (Output format: [YYYY-MM-DD HH:MM:SS, YYYY-MM-DD HH:MM:SS])", "answer": "['2023-07-15 23:15:00', '2023-07-16 14:00:00']", "ground_truth": [ "2023-07-15 23:15:00", "2023-07-16 14:00:00" ], "eval_metric": "iou", "channel": "173", "ts_data_path": "ts_data/L2_T3_Subsequence_Matching_00345.csv", "meta": { "prototype": "step pattern", "query_window": "[2023-07-13 13:30:00 to 2023-07-14 04:15:00]", "search_window": "[2023-07-15 15:00:00 to 2023-07-20 18:15:00]", "source": "causal_rivers" } }, { "id": "L2_T3_Subsequence_Matching_00348", "level": 2, "level_name": "Pattern Recognition", "category": "Subsequence Matching", "subtask": "Subsequence Matching", "question": "Analyze the reference pattern in [2019-02-18 21:00:00 to 2019-02-19 08:00:00]. Find the time interval where channel 245 exhibits the most similar pattern within the search context [2019-02-20 18:45:00 to 2019-02-26 01:45:00]. (Output format: [YYYY-MM-DD HH:MM:SS, YYYY-MM-DD HH:MM:SS])", "answer": "['2019-02-25 02:00:00', '2019-02-25 13:00:00']", "ground_truth": [ "2019-02-25 02:00:00", "2019-02-25 13:00:00" ], "eval_metric": "iou", "channel": "245", "ts_data_path": "ts_data/L2_T3_Subsequence_Matching_00348.csv", "meta": { "prototype": "double-peak pattern", "query_window": "[2019-02-18 21:00:00 to 2019-02-19 08:00:00]", "search_window": "[2019-02-20 18:45:00 to 2019-02-26 01:45:00]", "source": "causal_rivers" } }, { "id": "L2_T3_Subsequence_Matching_00350", "level": 2, "level_name": "Pattern Recognition", "category": "Subsequence Matching", "subtask": "Subsequence Matching", "question": "Analyze the reference pattern in [2022-05-12 07:30:00 to 2022-05-12 22:15:00]. Find the time interval where channel 430 exhibits the most similar pattern within the search context [2022-05-13 21:00:00 to 2022-05-16 02:30:00]. (Output format: [YYYY-MM-DD HH:MM:SS, YYYY-MM-DD HH:MM:SS])", "answer": "['2022-05-14 07:15:00', '2022-05-14 22:00:00']", "ground_truth": [ "2022-05-14 07:15:00", "2022-05-14 22:00:00" ], "eval_metric": "iou", "channel": "430", "ts_data_path": "ts_data/L2_T3_Subsequence_Matching_00350.csv", "meta": { "prototype": "step pattern", "query_window": "[2022-05-12 07:30:00 to 2022-05-12 22:15:00]", "search_window": "[2022-05-13 21:00:00 to 2022-05-16 02:30:00]", "source": "causal_rivers" } }, { "id": "L2_T3_Subsequence_Matching_00351", "level": 2, "level_name": "Pattern Recognition", "category": "Subsequence Matching", "subtask": "Subsequence Matching", "question": "Analyze the reference pattern in [2020-07-07 11:15:00 to 2020-07-08 02:00:00]. Find the time interval where channel 441 exhibits the most similar pattern within the search context [2020-07-09 00:45:00 to 2020-07-12 03:00:00]. (Output format: [YYYY-MM-DD HH:MM:SS, YYYY-MM-DD HH:MM:SS])", "answer": "['2020-07-10 10:30:00', '2020-07-11 01:15:00']", "ground_truth": [ "2020-07-10 10:30:00", "2020-07-11 01:15:00" ], "eval_metric": "iou", "channel": "441", "ts_data_path": "ts_data/L2_T3_Subsequence_Matching_00351.csv", "meta": { "prototype": "step pattern", "query_window": "[2020-07-07 11:15:00 to 2020-07-08 02:00:00]", "search_window": "[2020-07-09 00:45:00 to 2020-07-12 03:00:00]", "source": "causal_rivers" } }, { "id": "L2_T3_Subsequence_Matching_00352", "level": 2, "level_name": "Pattern Recognition", "category": "Subsequence Matching", "subtask": "Subsequence Matching", "question": "Analyze the reference pattern in [2023-09-10 11:15:00 to 2023-09-10 22:15:00]. Find the time interval where channel 495 exhibits the most similar pattern within the search context [2023-09-12 11:15:00 to 2023-09-18 03:15:00]. (Output format: [YYYY-MM-DD HH:MM:SS, YYYY-MM-DD HH:MM:SS])", "answer": "['2023-09-17 00:00:00', '2023-09-17 11:00:00']", "ground_truth": [ "2023-09-17 00:00:00", "2023-09-17 11:00:00" ], "eval_metric": "iou", "channel": "495", "ts_data_path": "ts_data/L2_T3_Subsequence_Matching_00352.csv", "meta": { "prototype": "double-peak pattern", "query_window": "[2023-09-10 11:15:00 to 2023-09-10 22:15:00]", "search_window": "[2023-09-12 11:15:00 to 2023-09-18 03:15:00]", "source": "causal_rivers" } }, { "id": "L2_T3_Subsequence_Matching_00353", "level": 2, "level_name": "Pattern Recognition", "category": "Subsequence Matching", "subtask": "Subsequence Matching", "question": "Analyze the reference pattern in [2020-05-28 12:00:00 to 2020-05-28 19:15:00]. Find the time interval where channel 496 exhibits the most similar pattern within the search context [2020-05-29 15:00:00 to 2020-06-01 13:30:00]. (Output format: [YYYY-MM-DD HH:MM:SS, YYYY-MM-DD HH:MM:SS])", "answer": "['2020-05-30 16:00:00', '2020-05-30 23:15:00']", "ground_truth": [ "2020-05-30 16:00:00", "2020-05-30 23:15:00" ], "eval_metric": "iou", "channel": "496", "ts_data_path": "ts_data/L2_T3_Subsequence_Matching_00353.csv", "meta": { "prototype": "sharp spike", "query_window": "[2020-05-28 12:00:00 to 2020-05-28 19:15:00]", "search_window": "[2020-05-29 15:00:00 to 2020-06-01 13:30:00]", "source": "causal_rivers" } }, { "id": "L2_T3_Subsequence_Matching_00354", "level": 2, "level_name": "Pattern Recognition", "category": "Subsequence Matching", "subtask": "Subsequence Matching", "question": "Analyze the reference pattern in [2022-07-09 16:00:00 to 2022-07-09 23:15:00]. Find the time interval where channel 501 exhibits the most similar pattern within the search context [2022-07-11 08:45:00 to 2022-07-16 14:00:00]. (Output format: [YYYY-MM-DD HH:MM:SS, YYYY-MM-DD HH:MM:SS])", "answer": "['2022-07-13 15:15:00', '2022-07-13 22:30:00']", "ground_truth": [ "2022-07-13 15:15:00", "2022-07-13 22:30:00" ], "eval_metric": "iou", "channel": "501", "ts_data_path": "ts_data/L2_T3_Subsequence_Matching_00354.csv", "meta": { "prototype": "sharp spike", "query_window": "[2022-07-09 16:00:00 to 2022-07-09 23:15:00]", "search_window": "[2022-07-11 08:45:00 to 2022-07-16 14:00:00]", "source": "causal_rivers" } }, { "id": "L2_T3_Subsequence_Matching_00355", "level": 2, "level_name": "Pattern Recognition", "category": "Subsequence Matching", "subtask": "Subsequence Matching", "question": "Analyze the reference pattern in [2023-05-17 18:00:00 to 2023-05-18 06:15:00]. Find the time interval where channel 578 exhibits the most similar pattern within the search context [2023-05-20 00:30:00 to 2023-05-26 12:15:00]. (Output format: [YYYY-MM-DD HH:MM:SS, YYYY-MM-DD HH:MM:SS])", "answer": "['2023-05-24 10:45:00', '2023-05-24 23:00:00']", "ground_truth": [ "2023-05-24 10:45:00", "2023-05-24 23:00:00" ], "eval_metric": "iou", "channel": "578", "ts_data_path": "ts_data/L2_T3_Subsequence_Matching_00355.csv", "meta": { "prototype": "bell curve", "query_window": "[2023-05-17 18:00:00 to 2023-05-18 06:15:00]", "search_window": "[2023-05-20 00:30:00 to 2023-05-26 12:15:00]", "source": "causal_rivers" } }, { "id": "L2_T3_Subsequence_Matching_00358", "level": 2, "level_name": "Pattern Recognition", "category": "Subsequence Matching", "subtask": "Subsequence Matching", "question": "Analyze the reference pattern in [2023-12-06 16:00:00 to 2023-12-07 04:15:00]. Find the time interval where channel 591 exhibits the most similar pattern within the search context [2023-12-07 23:15:00 to 2023-12-10 11:45:00]. (Output format: [YYYY-MM-DD HH:MM:SS, YYYY-MM-DD HH:MM:SS])", "answer": "['2023-12-09 17:15:00', '2023-12-10 05:30:00']", "ground_truth": [ "2023-12-09 17:15:00", "2023-12-10 05:30:00" ], "eval_metric": "iou", "channel": "591", "ts_data_path": "ts_data/L2_T3_Subsequence_Matching_00358.csv", "meta": { "prototype": "bell curve", "query_window": "[2023-12-06 16:00:00 to 2023-12-07 04:15:00]", "search_window": "[2023-12-07 23:15:00 to 2023-12-10 11:45:00]", "source": "causal_rivers" } }, { "id": "L2_T3_Subsequence_Matching_00359", "level": 2, "level_name": "Pattern Recognition", "category": "Subsequence Matching", "subtask": "Subsequence Matching", "question": "Analyze the reference pattern in [2019-04-21 20:00:00 to 2019-04-22 10:45:00]. Find the time interval where channel 595 exhibits the most similar pattern within the search context [2019-04-23 15:00:00 to 2019-04-27 16:45:00]. (Output format: [YYYY-MM-DD HH:MM:SS, YYYY-MM-DD HH:MM:SS])", "answer": "['2019-04-24 21:00:00', '2019-04-25 11:45:00']", "ground_truth": [ "2019-04-24 21:00:00", "2019-04-25 11:45:00" ], "eval_metric": "iou", "channel": "595", "ts_data_path": "ts_data/L2_T3_Subsequence_Matching_00359.csv", "meta": { "prototype": "step pattern", "query_window": "[2019-04-21 20:00:00 to 2019-04-22 10:45:00]", "search_window": "[2019-04-23 15:00:00 to 2019-04-27 16:45:00]", "source": "causal_rivers" } }, { "id": "L2_T3_Subsequence_Matching_00360", "level": 2, "level_name": "Pattern Recognition", "category": "Subsequence Matching", "subtask": "Subsequence Matching", "question": "Analyze the reference pattern in [2019-07-17 12:00:00 to 2019-07-18 00:15:00]. Find the time interval where channel 625 exhibits the most similar pattern within the search context [2019-07-18 19:15:00 to 2019-07-20 00:30:00]. (Output format: [YYYY-MM-DD HH:MM:SS, YYYY-MM-DD HH:MM:SS])", "answer": "['2019-07-19 06:15:00', '2019-07-19 18:30:00']", "ground_truth": [ "2019-07-19 06:15:00", "2019-07-19 18:30:00" ], "eval_metric": "iou", "channel": "625", "ts_data_path": "ts_data/L2_T3_Subsequence_Matching_00360.csv", "meta": { "prototype": "bell curve", "query_window": "[2019-07-17 12:00:00 to 2019-07-18 00:15:00]", "search_window": "[2019-07-18 19:15:00 to 2019-07-20 00:30:00]", "source": "causal_rivers" } }, { "id": "L2_T3_Subsequence_Matching_00361", "level": 2, "level_name": "Pattern Recognition", "category": "Subsequence Matching", "subtask": "Subsequence Matching", "question": "Analyze the reference pattern in [2022-06-26 00:00:00 to 2022-06-26 07:15:00]. Find the time interval where channel 626 exhibits the most similar pattern within the search context [2022-06-27 07:30:00 to 2022-07-01 00:15:00]. (Output format: [YYYY-MM-DD HH:MM:SS, YYYY-MM-DD HH:MM:SS])", "answer": "['2022-06-27 19:30:00', '2022-06-28 02:45:00']", "ground_truth": [ "2022-06-27 19:30:00", "2022-06-28 02:45:00" ], "eval_metric": "iou", "channel": "626", "ts_data_path": "ts_data/L2_T3_Subsequence_Matching_00361.csv", "meta": { "prototype": "sharp spike", "query_window": "[2022-06-26 00:00:00 to 2022-06-26 07:15:00]", "search_window": "[2022-06-27 07:30:00 to 2022-07-01 00:15:00]", "source": "causal_rivers" } }, { "id": "L2_T3_Subsequence_Matching_00362", "level": 2, "level_name": "Pattern Recognition", "category": "Subsequence Matching", "subtask": "Subsequence Matching", "question": "Analyze the reference pattern in [2021-09-29 05:00:00 to 2021-09-29 12:15:00]. Find the time interval where channel 627 exhibits the most similar pattern within the search context [2021-09-30 05:15:00 to 2021-10-02 17:30:00]. (Output format: [YYYY-MM-DD HH:MM:SS, YYYY-MM-DD HH:MM:SS])", "answer": "['2021-10-01 13:30:00', '2021-10-01 20:45:00']", "ground_truth": [ "2021-10-01 13:30:00", "2021-10-01 20:45:00" ], "eval_metric": "iou", "channel": "627", "ts_data_path": "ts_data/L2_T3_Subsequence_Matching_00362.csv", "meta": { "prototype": "sharp spike", "query_window": "[2021-09-29 05:00:00 to 2021-09-29 12:15:00]", "search_window": "[2021-09-30 05:15:00 to 2021-10-02 17:30:00]", "source": "causal_rivers" } }, { "id": "L2_T3_Subsequence_Matching_00363", "level": 2, "level_name": "Pattern Recognition", "category": "Subsequence Matching", "subtask": "Subsequence Matching", "question": "Analyze the reference pattern in [2022-09-25 12:00:00 to 2022-09-26 02:45:00]. Find the time interval where channel 647 exhibits the most similar pattern within the search context [2022-09-27 19:30:00 to 2022-10-03 22:45:00]. (Output format: [YYYY-MM-DD HH:MM:SS, YYYY-MM-DD HH:MM:SS])", "answer": "['2022-10-01 08:15:00', '2022-10-01 23:00:00']", "ground_truth": [ "2022-10-01 08:15:00", "2022-10-01 23:00:00" ], "eval_metric": "iou", "channel": "647", "ts_data_path": "ts_data/L2_T3_Subsequence_Matching_00363.csv", "meta": { "prototype": "step pattern", "query_window": "[2022-09-25 12:00:00 to 2022-09-26 02:45:00]", "search_window": "[2022-09-27 19:30:00 to 2022-10-03 22:45:00]", "source": "causal_rivers" } }, { "id": "L2_T3_Subsequence_Matching_00364", "level": 2, "level_name": "Pattern Recognition", "category": "Subsequence Matching", "subtask": "Subsequence Matching", "question": "Analyze the reference pattern in [2020-11-22 16:30:00 to 2020-11-23 03:30:00]. Find the time interval where channel 680 exhibits the most similar pattern within the search context [2020-11-23 20:30:00 to 2020-11-24 21:15:00]. (Output format: [YYYY-MM-DD HH:MM:SS, YYYY-MM-DD HH:MM:SS])", "answer": "['2020-11-24 02:30:00', '2020-11-24 13:30:00']", "ground_truth": [ "2020-11-24 02:30:00", "2020-11-24 13:30:00" ], "eval_metric": "iou", "channel": "680", "ts_data_path": "ts_data/L2_T3_Subsequence_Matching_00364.csv", "meta": { "prototype": "double-peak pattern", "query_window": "[2020-11-22 16:30:00 to 2020-11-23 03:30:00]", "search_window": "[2020-11-23 20:30:00 to 2020-11-24 21:15:00]", "source": "causal_rivers" } }, { "id": "L2_T3_Subsequence_Matching_00366", "level": 2, "level_name": "Pattern Recognition", "category": "Subsequence Matching", "subtask": "Subsequence Matching", "question": "Analyze the reference pattern in [2019-11-28 23:45:00 to 2019-11-29 14:30:00]. Find the time interval where channel 727 exhibits the most similar pattern within the search context [2019-12-01 06:00:00 to 2019-12-07 04:00:00]. (Output format: [YYYY-MM-DD HH:MM:SS, YYYY-MM-DD HH:MM:SS])", "answer": "['2019-12-05 10:15:00', '2019-12-06 01:00:00']", "ground_truth": [ "2019-12-05 10:15:00", "2019-12-06 01:00:00" ], "eval_metric": "iou", "channel": "727", "ts_data_path": "ts_data/L2_T3_Subsequence_Matching_00366.csv", "meta": { "prototype": "step pattern", "query_window": "[2019-11-28 23:45:00 to 2019-11-29 14:30:00]", "search_window": "[2019-12-01 06:00:00 to 2019-12-07 04:00:00]", "source": "causal_rivers" } }, { "id": "L2_T3_Subsequence_Matching_00367", "level": 2, "level_name": "Pattern Recognition", "category": "Subsequence Matching", "subtask": "Subsequence Matching", "question": "Analyze the reference pattern in [2019-08-26 12:00:00 to 2019-08-27 00:15:00]. Find the time interval where channel 728 exhibits the most similar pattern within the search context [2019-08-29 00:00:00 to 2019-09-05 09:15:00]. (Output format: [YYYY-MM-DD HH:MM:SS, YYYY-MM-DD HH:MM:SS])", "answer": "['2019-09-01 20:15:00', '2019-09-02 08:30:00']", "ground_truth": [ "2019-09-01 20:15:00", "2019-09-02 08:30:00" ], "eval_metric": "iou", "channel": "728", "ts_data_path": "ts_data/L2_T3_Subsequence_Matching_00367.csv", "meta": { "prototype": "bell curve", "query_window": "[2019-08-26 12:00:00 to 2019-08-27 00:15:00]", "search_window": "[2019-08-29 00:00:00 to 2019-09-05 09:15:00]", "source": "causal_rivers" } }, { "id": "L2_T3_Subsequence_Matching_00368", "level": 2, "level_name": "Pattern Recognition", "category": "Subsequence Matching", "subtask": "Subsequence Matching", "question": "Analyze the reference pattern in [2019-12-24 17:00:00 to 2019-12-25 00:15:00]. Find the time interval where channel 729 exhibits the most similar pattern within the search context [2019-12-25 21:45:00 to 2019-12-29 04:00:00]. (Output format: [YYYY-MM-DD HH:MM:SS, YYYY-MM-DD HH:MM:SS])", "answer": "['2019-12-25 23:45:00', '2019-12-26 07:00:00']", "ground_truth": [ "2019-12-25 23:45:00", "2019-12-26 07:00:00" ], "eval_metric": "iou", "channel": "729", "ts_data_path": "ts_data/L2_T3_Subsequence_Matching_00368.csv", "meta": { "prototype": "sharp spike", "query_window": "[2019-12-24 17:00:00 to 2019-12-25 00:15:00]", "search_window": "[2019-12-25 21:45:00 to 2019-12-29 04:00:00]", "source": "causal_rivers" } }, { "id": "L2_T3_Subsequence_Matching_00369", "level": 2, "level_name": "Pattern Recognition", "category": "Subsequence Matching", "subtask": "Subsequence Matching", "question": "Analyze the reference pattern in [2020-07-20 09:00:00 to 2020-07-20 23:45:00]. Find the time interval where channel 754 exhibits the most similar pattern within the search context [2020-07-21 23:00:00 to 2020-07-25 04:00:00]. (Output format: [YYYY-MM-DD HH:MM:SS, YYYY-MM-DD HH:MM:SS])", "answer": "['2020-07-22 06:15:00', '2020-07-22 21:00:00']", "ground_truth": [ "2020-07-22 06:15:00", "2020-07-22 21:00:00" ], "eval_metric": "iou", "channel": "754", "ts_data_path": "ts_data/L2_T3_Subsequence_Matching_00369.csv", "meta": { "prototype": "step pattern", "query_window": "[2020-07-20 09:00:00 to 2020-07-20 23:45:00]", "search_window": "[2020-07-21 23:00:00 to 2020-07-25 04:00:00]", "source": "causal_rivers" } }, { "id": "L2_T3_Subsequence_Matching_00371", "level": 2, "level_name": "Pattern Recognition", "category": "Subsequence Matching", "subtask": "Subsequence Matching", "question": "Analyze the reference pattern in [2020-05-03 06:00:00 to 2020-05-03 13:15:00]. Find the time interval where channel 811 exhibits the most similar pattern within the search context [2020-05-04 20:30:00 to 2020-05-09 16:45:00]. (Output format: [YYYY-MM-DD HH:MM:SS, YYYY-MM-DD HH:MM:SS])", "answer": "['2020-05-08 17:00:00', '2020-05-09 00:15:00']", "ground_truth": [ "2020-05-08 17:00:00", "2020-05-09 00:15:00" ], "eval_metric": "iou", "channel": "811", "ts_data_path": "ts_data/L2_T3_Subsequence_Matching_00371.csv", "meta": { "prototype": "sharp spike", "query_window": "[2020-05-03 06:00:00 to 2020-05-03 13:15:00]", "search_window": "[2020-05-04 20:30:00 to 2020-05-09 16:45:00]", "source": "causal_rivers" } }, { "id": "L2_T3_Subsequence_Matching_00372", "level": 2, "level_name": "Pattern Recognition", "category": "Subsequence Matching", "subtask": "Subsequence Matching", "question": "Analyze the reference pattern in [2023-08-14 06:00:00 to 2023-08-14 20:45:00]. Find the time interval where channel 813 exhibits the most similar pattern within the search context [2023-08-16 20:30:00 to 2023-08-24 04:00:00]. (Output format: [YYYY-MM-DD HH:MM:SS, YYYY-MM-DD HH:MM:SS])", "answer": "['2023-08-22 19:30:00', '2023-08-23 10:15:00']", "ground_truth": [ "2023-08-22 19:30:00", "2023-08-23 10:15:00" ], "eval_metric": "iou", "channel": "813", "ts_data_path": "ts_data/L2_T3_Subsequence_Matching_00372.csv", "meta": { "prototype": "step pattern", "query_window": "[2023-08-14 06:00:00 to 2023-08-14 20:45:00]", "search_window": "[2023-08-16 20:30:00 to 2023-08-24 04:00:00]", "source": "causal_rivers" } }, { "id": "L2_T3_Subsequence_Matching_00376", "level": 2, "level_name": "Pattern Recognition", "category": "Subsequence Matching", "subtask": "Subsequence Matching", "question": "Analyze the reference pattern in [2019-04-24 00:30:00 to 2019-04-24 12:45:00]. Find the time interval where channel 895 exhibits the most similar pattern within the search context [2019-04-26 03:45:00 to 2019-05-02 02:00:00]. (Output format: [YYYY-MM-DD HH:MM:SS, YYYY-MM-DD HH:MM:SS])", "answer": "['2019-04-27 04:30:00', '2019-04-27 16:45:00']", "ground_truth": [ "2019-04-27 04:30:00", "2019-04-27 16:45:00" ], "eval_metric": "iou", "channel": "895", "ts_data_path": "ts_data/L2_T3_Subsequence_Matching_00376.csv", "meta": { "prototype": "bell curve", "query_window": "[2019-04-24 00:30:00 to 2019-04-24 12:45:00]", "search_window": "[2019-04-26 03:45:00 to 2019-05-02 02:00:00]", "source": "causal_rivers" } }, { "id": "L2_T3_Subsequence_Matching_00377", "level": 2, "level_name": "Pattern Recognition", "category": "Subsequence Matching", "subtask": "Subsequence Matching", "question": "Analyze the reference pattern in [2020-04-16 15:00:00 to 2020-04-17 02:00:00]. Find the time interval where channel 897 exhibits the most similar pattern within the search context [2020-04-18 16:45:00 to 2020-04-24 16:00:00]. (Output format: [YYYY-MM-DD HH:MM:SS, YYYY-MM-DD HH:MM:SS])", "answer": "['2020-04-21 11:15:00', '2020-04-21 22:15:00']", "ground_truth": [ "2020-04-21 11:15:00", "2020-04-21 22:15:00" ], "eval_metric": "iou", "channel": "897", "ts_data_path": "ts_data/L2_T3_Subsequence_Matching_00377.csv", "meta": { "prototype": "double-peak pattern", "query_window": "[2020-04-16 15:00:00 to 2020-04-17 02:00:00]", "search_window": "[2020-04-18 16:45:00 to 2020-04-24 16:00:00]", "source": "causal_rivers" } }, { "id": "L2_T3_Subsequence_Matching_00379", "level": 2, "level_name": "Pattern Recognition", "category": "Subsequence Matching", "subtask": "Subsequence Matching", "question": "Analyze the reference pattern in [2016-09-19 02:00:00 to 2016-09-19 09:15:00]. Find the time interval where channel HUFL exhibits the most similar pattern within the search context [2016-09-20 16:30:00 to 2016-09-25 13:15:00]. (Output format: [YYYY-MM-DD HH:MM:SS, YYYY-MM-DD HH:MM:SS])", "answer": "['2016-09-24 01:30:00', '2016-09-24 08:45:00']", "ground_truth": [ "2016-09-24 01:30:00", "2016-09-24 08:45:00" ], "eval_metric": "iou", "channel": "HUFL", "ts_data_path": "ts_data/L2_T3_Subsequence_Matching_00379.csv", "meta": { "prototype": "sharp spike", "query_window": "[2016-09-19 02:00:00 to 2016-09-19 09:15:00]", "search_window": "[2016-09-20 16:30:00 to 2016-09-25 13:15:00]", "source": "ettm1" } }, { "id": "L2_T3_Subsequence_Matching_00381", "level": 2, "level_name": "Pattern Recognition", "category": "Subsequence Matching", "subtask": "Subsequence Matching", "question": "Analyze the reference pattern in [2016-10-18 20:15:00 to 2016-10-19 03:30:00]. Find the time interval where channel MUFL exhibits the most similar pattern within the search context [2016-10-20 03:30:00 to 2016-10-23 18:45:00]. (Output format: [YYYY-MM-DD HH:MM:SS, YYYY-MM-DD HH:MM:SS])", "answer": "['2016-10-22 17:45:00', '2016-10-23 01:00:00']", "ground_truth": [ "2016-10-22 17:45:00", "2016-10-23 01:00:00" ], "eval_metric": "iou", "channel": "MUFL", "ts_data_path": "ts_data/L2_T3_Subsequence_Matching_00381.csv", "meta": { "prototype": "sharp spike", "query_window": "[2016-10-18 20:15:00 to 2016-10-19 03:30:00]", "search_window": "[2016-10-20 03:30:00 to 2016-10-23 18:45:00]", "source": "ettm1" } }, { "id": "L2_T3_Subsequence_Matching_00382", "level": 2, "level_name": "Pattern Recognition", "category": "Subsequence Matching", "subtask": "Subsequence Matching", "question": "Analyze the reference pattern in [2018-02-04 20:30:00 to 2018-02-05 08:45:00]. Find the time interval where channel MULL exhibits the most similar pattern within the search context [2018-02-07 08:30:00 to 2018-02-14 18:15:00]. (Output format: [YYYY-MM-DD HH:MM:SS, YYYY-MM-DD HH:MM:SS])", "answer": "['2018-02-11 10:15:00', '2018-02-11 22:30:00']", "ground_truth": [ "2018-02-11 10:15:00", "2018-02-11 22:30:00" ], "eval_metric": "iou", "channel": "MULL", "ts_data_path": "ts_data/L2_T3_Subsequence_Matching_00382.csv", "meta": { "prototype": "bell curve", "query_window": "[2018-02-04 20:30:00 to 2018-02-05 08:45:00]", "search_window": "[2018-02-07 08:30:00 to 2018-02-14 18:15:00]", "source": "ettm1" } }, { "id": "L2_T3_Subsequence_Matching_00383", "level": 2, "level_name": "Pattern Recognition", "category": "Subsequence Matching", "subtask": "Subsequence Matching", "question": "Analyze the reference pattern in [2017-06-12 15:00:00 to 2017-06-12 22:15:00]. Find the time interval where channel LUFL exhibits the most similar pattern within the search context [2017-06-13 22:00:00 to 2017-06-17 12:30:00]. (Output format: [YYYY-MM-DD HH:MM:SS, YYYY-MM-DD HH:MM:SS])", "answer": "['2017-06-14 11:30:00', '2017-06-14 18:45:00']", "ground_truth": [ "2017-06-14 11:30:00", "2017-06-14 18:45:00" ], "eval_metric": "iou", "channel": "LUFL", "ts_data_path": "ts_data/L2_T3_Subsequence_Matching_00383.csv", "meta": { "prototype": "sharp spike", "query_window": "[2017-06-12 15:00:00 to 2017-06-12 22:15:00]", "search_window": "[2017-06-13 22:00:00 to 2017-06-17 12:30:00]", "source": "ettm1" } }, { "id": "L2_T3_Subsequence_Matching_00384", "level": 2, "level_name": "Pattern Recognition", "category": "Subsequence Matching", "subtask": "Subsequence Matching", "question": "Analyze the reference pattern in [2017-06-25 15:00:00 to 2017-06-26 03:15:00]. Find the time interval where channel LULL exhibits the most similar pattern within the search context [2017-06-27 11:00:00 to 2017-07-02 04:30:00]. (Output format: [YYYY-MM-DD HH:MM:SS, YYYY-MM-DD HH:MM:SS])", "answer": "['2017-06-28 20:00:00', '2017-06-29 08:15:00']", "ground_truth": [ "2017-06-28 20:00:00", "2017-06-29 08:15:00" ], "eval_metric": "iou", "channel": "LULL", "ts_data_path": "ts_data/L2_T3_Subsequence_Matching_00384.csv", "meta": { "prototype": "bell curve", "query_window": "[2017-06-25 15:00:00 to 2017-06-26 03:15:00]", "search_window": "[2017-06-27 11:00:00 to 2017-07-02 04:30:00]", "source": "ettm1" } }, { "id": "L2_T3_Subsequence_Matching_00385", "level": 2, "level_name": "Pattern Recognition", "category": "Subsequence Matching", "subtask": "Subsequence Matching", "question": "Analyze the reference pattern in [2017-06-19 09:45:00 to 2017-06-19 17:00:00]. Find the time interval where channel OT exhibits the most similar pattern within the search context [2017-06-20 04:30:00 to 2017-06-21 16:00:00]. (Output format: [YYYY-MM-DD HH:MM:SS, YYYY-MM-DD HH:MM:SS])", "answer": "['2017-06-21 08:30:00', '2017-06-21 15:45:00']", "ground_truth": [ "2017-06-21 08:30:00", "2017-06-21 15:45:00" ], "eval_metric": "iou", "channel": "OT", "ts_data_path": "ts_data/L2_T3_Subsequence_Matching_00385.csv", "meta": { "prototype": "sharp spike", "query_window": "[2017-06-19 09:45:00 to 2017-06-19 17:00:00]", "search_window": "[2017-06-20 04:30:00 to 2017-06-21 16:00:00]", "source": "ettm1" } }, { "id": "L2_T3_Subsequence_Matching_00387", "level": 2, "level_name": "Pattern Recognition", "category": "Subsequence Matching", "subtask": "Subsequence Matching", "question": "Analyze the reference pattern in [2022-11-26 09:00:00 to 2022-11-26 21:15:00]. Find the time interval where channel 71 exhibits the most similar pattern within the search context [2022-11-27 16:15:00 to 2022-11-29 16:30:00]. (Output format: [YYYY-MM-DD HH:MM:SS, YYYY-MM-DD HH:MM:SS])", "answer": "['2022-11-28 15:30:00', '2022-11-29 03:45:00']", "ground_truth": [ "2022-11-28 15:30:00", "2022-11-29 03:45:00" ], "eval_metric": "iou", "channel": "71", "ts_data_path": "ts_data/L2_T3_Subsequence_Matching_00387.csv", "meta": { "prototype": "bell curve", "query_window": "[2022-11-26 09:00:00 to 2022-11-26 21:15:00]", "search_window": "[2022-11-27 16:15:00 to 2022-11-29 16:30:00]", "source": "causal_rivers" } }, { "id": "L2_T3_Subsequence_Matching_00388", "level": 2, "level_name": "Pattern Recognition", "category": "Subsequence Matching", "subtask": "Subsequence Matching", "question": "Analyze the reference pattern in [2023-01-20 10:00:00 to 2023-01-20 22:15:00]. Find the time interval where channel 99 exhibits the most similar pattern within the search context [2023-01-22 17:00:00 to 2023-01-29 07:00:00]. (Output format: [YYYY-MM-DD HH:MM:SS, YYYY-MM-DD HH:MM:SS])", "answer": "['2023-01-24 06:45:00', '2023-01-24 19:00:00']", "ground_truth": [ "2023-01-24 06:45:00", "2023-01-24 19:00:00" ], "eval_metric": "iou", "channel": "99", "ts_data_path": "ts_data/L2_T3_Subsequence_Matching_00388.csv", "meta": { "prototype": "bell curve", "query_window": "[2023-01-20 10:00:00 to 2023-01-20 22:15:00]", "search_window": "[2023-01-22 17:00:00 to 2023-01-29 07:00:00]", "source": "causal_rivers" } }, { "id": "L2_T3_Subsequence_Matching_00389", "level": 2, "level_name": "Pattern Recognition", "category": "Subsequence Matching", "subtask": "Subsequence Matching", "question": "Analyze the reference pattern in [2020-08-01 14:15:00 to 2020-08-02 05:00:00]. Find the time interval where channel 123 exhibits the most similar pattern within the search context [2020-08-03 21:30:00 to 2020-08-09 23:45:00]. (Output format: [YYYY-MM-DD HH:MM:SS, YYYY-MM-DD HH:MM:SS])", "answer": "['2020-08-08 05:30:00', '2020-08-08 20:15:00']", "ground_truth": [ "2020-08-08 05:30:00", "2020-08-08 20:15:00" ], "eval_metric": "iou", "channel": "123", "ts_data_path": "ts_data/L2_T3_Subsequence_Matching_00389.csv", "meta": { "prototype": "step pattern", "query_window": "[2020-08-01 14:15:00 to 2020-08-02 05:00:00]", "search_window": "[2020-08-03 21:30:00 to 2020-08-09 23:45:00]", "source": "causal_rivers" } }, { "id": "L2_T3_Subsequence_Matching_00391", "level": 2, "level_name": "Pattern Recognition", "category": "Subsequence Matching", "subtask": "Subsequence Matching", "question": "Analyze the reference pattern in [2023-05-22 13:30:00 to 2023-05-23 00:30:00]. Find the time interval where channel 146 exhibits the most similar pattern within the search context [2023-05-24 04:30:00 to 2023-05-28 08:45:00]. (Output format: [YYYY-MM-DD HH:MM:SS, YYYY-MM-DD HH:MM:SS])", "answer": "['2023-05-26 04:15:00', '2023-05-26 15:15:00']", "ground_truth": [ "2023-05-26 04:15:00", "2023-05-26 15:15:00" ], "eval_metric": "iou", "channel": "146", "ts_data_path": "ts_data/L2_T3_Subsequence_Matching_00391.csv", "meta": { "prototype": "double-peak pattern", "query_window": "[2023-05-22 13:30:00 to 2023-05-23 00:30:00]", "search_window": "[2023-05-24 04:30:00 to 2023-05-28 08:45:00]", "source": "causal_rivers" } }, { "id": "L2_T3_Subsequence_Matching_00393", "level": 2, "level_name": "Pattern Recognition", "category": "Subsequence Matching", "subtask": "Subsequence Matching", "question": "Analyze the reference pattern in [2020-07-15 21:00:00 to 2020-07-16 04:15:00]. Find the time interval where channel 151 exhibits the most similar pattern within the search context [2020-07-17 19:15:00 to 2020-07-23 22:30:00]. (Output format: [YYYY-MM-DD HH:MM:SS, YYYY-MM-DD HH:MM:SS])", "answer": "['2020-07-21 02:30:00', '2020-07-21 09:45:00']", "ground_truth": [ "2020-07-21 02:30:00", "2020-07-21 09:45:00" ], "eval_metric": "iou", "channel": "151", "ts_data_path": "ts_data/L2_T3_Subsequence_Matching_00393.csv", "meta": { "prototype": "sharp spike", "query_window": "[2020-07-15 21:00:00 to 2020-07-16 04:15:00]", "search_window": "[2020-07-17 19:15:00 to 2020-07-23 22:30:00]", "source": "causal_rivers" } }, { "id": "L2_T3_Subsequence_Matching_00394", "level": 2, "level_name": "Pattern Recognition", "category": "Subsequence Matching", "subtask": "Subsequence Matching", "question": "Analyze the reference pattern in [2022-08-11 08:00:00 to 2022-08-11 20:15:00]. Find the time interval where channel 154 exhibits the most similar pattern within the search context [2022-08-13 07:30:00 to 2022-08-18 14:45:00]. (Output format: [YYYY-MM-DD HH:MM:SS, YYYY-MM-DD HH:MM:SS])", "answer": "['2022-08-17 13:30:00', '2022-08-18 01:45:00']", "ground_truth": [ "2022-08-17 13:30:00", "2022-08-18 01:45:00" ], "eval_metric": "iou", "channel": "154", "ts_data_path": "ts_data/L2_T3_Subsequence_Matching_00394.csv", "meta": { "prototype": "bell curve", "query_window": "[2022-08-11 08:00:00 to 2022-08-11 20:15:00]", "search_window": "[2022-08-13 07:30:00 to 2022-08-18 14:45:00]", "source": "causal_rivers" } }, { "id": "L2_T3_Subsequence_Matching_00395", "level": 2, "level_name": "Pattern Recognition", "category": "Subsequence Matching", "subtask": "Subsequence Matching", "question": "Analyze the reference pattern in [2021-04-19 19:00:00 to 2021-04-20 06:00:00]. Find the time interval where channel 155 exhibits the most similar pattern within the search context [2021-04-20 23:00:00 to 2021-04-22 19:30:00]. (Output format: [YYYY-MM-DD HH:MM:SS, YYYY-MM-DD HH:MM:SS])", "answer": "['2021-04-22 06:00:00', '2021-04-22 17:00:00']", "ground_truth": [ "2021-04-22 06:00:00", "2021-04-22 17:00:00" ], "eval_metric": "iou", "channel": "155", "ts_data_path": "ts_data/L2_T3_Subsequence_Matching_00395.csv", "meta": { "prototype": "double-peak pattern", "query_window": "[2021-04-19 19:00:00 to 2021-04-20 06:00:00]", "search_window": "[2021-04-20 23:00:00 to 2021-04-22 19:30:00]", "source": "causal_rivers" } }, { "id": "L2_T3_Subsequence_Matching_00396", "level": 2, "level_name": "Pattern Recognition", "category": "Subsequence Matching", "subtask": "Subsequence Matching", "question": "Analyze the reference pattern in [2019-05-19 12:45:00 to 2019-05-19 23:45:00]. Find the time interval where channel 166 exhibits the most similar pattern within the search context [2019-05-20 16:45:00 to 2019-05-21 13:45:00]. (Output format: [YYYY-MM-DD HH:MM:SS, YYYY-MM-DD HH:MM:SS])", "answer": "['2019-05-20 19:15:00', '2019-05-21 06:15:00']", "ground_truth": [ "2019-05-20 19:15:00", "2019-05-21 06:15:00" ], "eval_metric": "iou", "channel": "166", "ts_data_path": "ts_data/L2_T3_Subsequence_Matching_00396.csv", "meta": { "prototype": "double-peak pattern", "query_window": "[2019-05-19 12:45:00 to 2019-05-19 23:45:00]", "search_window": "[2019-05-20 16:45:00 to 2019-05-21 13:45:00]", "source": "causal_rivers" } }, { "id": "L2_T3_Subsequence_Matching_00398", "level": 2, "level_name": "Pattern Recognition", "category": "Subsequence Matching", "subtask": "Subsequence Matching", "question": "Analyze the reference pattern in [2023-09-23 23:15:00 to 2023-09-24 14:00:00]. Find the time interval where channel 170 exhibits the most similar pattern within the search context [2023-09-26 08:30:00 to 2023-10-02 18:15:00]. (Output format: [YYYY-MM-DD HH:MM:SS, YYYY-MM-DD HH:MM:SS])", "answer": "['2023-09-27 04:45:00', '2023-09-27 19:30:00']", "ground_truth": [ "2023-09-27 04:45:00", "2023-09-27 19:30:00" ], "eval_metric": "iou", "channel": "170", "ts_data_path": "ts_data/L2_T3_Subsequence_Matching_00398.csv", "meta": { "prototype": "step pattern", "query_window": "[2023-09-23 23:15:00 to 2023-09-24 14:00:00]", "search_window": "[2023-09-26 08:30:00 to 2023-10-02 18:15:00]", "source": "causal_rivers" } }, { "id": "L2_T3_Subsequence_Matching_00402", "level": 2, "level_name": "Pattern Recognition", "category": "Subsequence Matching", "subtask": "Subsequence Matching", "question": "Analyze the reference pattern in [2020-06-08 08:00:00 to 2020-06-08 22:45:00]. Find the time interval where channel 237 exhibits the most similar pattern within the search context [2020-06-10 01:00:00 to 2020-06-13 18:45:00]. (Output format: [YYYY-MM-DD HH:MM:SS, YYYY-MM-DD HH:MM:SS])", "answer": "['2020-06-12 03:45:00', '2020-06-12 18:30:00']", "ground_truth": [ "2020-06-12 03:45:00", "2020-06-12 18:30:00" ], "eval_metric": "iou", "channel": "237", "ts_data_path": "ts_data/L2_T3_Subsequence_Matching_00402.csv", "meta": { "prototype": "step pattern", "query_window": "[2020-06-08 08:00:00 to 2020-06-08 22:45:00]", "search_window": "[2020-06-10 01:00:00 to 2020-06-13 18:45:00]", "source": "causal_rivers" } }, { "id": "L2_T3_Subsequence_Matching_00404", "level": 2, "level_name": "Pattern Recognition", "category": "Subsequence Matching", "subtask": "Subsequence Matching", "question": "Analyze the reference pattern in [2020-06-26 22:30:00 to 2020-06-27 05:45:00]. Find the time interval where channel 312 exhibits the most similar pattern within the search context [2020-06-27 17:15:00 to 2020-06-29 03:30:00]. (Output format: [YYYY-MM-DD HH:MM:SS, YYYY-MM-DD HH:MM:SS])", "answer": "['2020-06-28 06:30:00', '2020-06-28 13:45:00']", "ground_truth": [ "2020-06-28 06:30:00", "2020-06-28 13:45:00" ], "eval_metric": "iou", "channel": "312", "ts_data_path": "ts_data/L2_T3_Subsequence_Matching_00404.csv", "meta": { "prototype": "sharp spike", "query_window": "[2020-06-26 22:30:00 to 2020-06-27 05:45:00]", "search_window": "[2020-06-27 17:15:00 to 2020-06-29 03:30:00]", "source": "causal_rivers" } }, { "id": "L2_T3_Subsequence_Matching_00407", "level": 2, "level_name": "Pattern Recognition", "category": "Subsequence Matching", "subtask": "Subsequence Matching", "question": "Analyze the reference pattern in [2021-08-08 19:30:00 to 2021-08-09 10:15:00]. Find the time interval where channel 495 exhibits the most similar pattern within the search context [2021-08-10 11:15:00 to 2021-08-13 23:00:00]. (Output format: [YYYY-MM-DD HH:MM:SS, YYYY-MM-DD HH:MM:SS])", "answer": "['2021-08-11 22:30:00', '2021-08-12 13:15:00']", "ground_truth": [ "2021-08-11 22:30:00", "2021-08-12 13:15:00" ], "eval_metric": "iou", "channel": "495", "ts_data_path": "ts_data/L2_T3_Subsequence_Matching_00407.csv", "meta": { "prototype": "step pattern", "query_window": "[2021-08-08 19:30:00 to 2021-08-09 10:15:00]", "search_window": "[2021-08-10 11:15:00 to 2021-08-13 23:00:00]", "source": "causal_rivers" } }, { "id": "L2_T3_Subsequence_Matching_00408", "level": 2, "level_name": "Pattern Recognition", "category": "Subsequence Matching", "subtask": "Subsequence Matching", "question": "Analyze the reference pattern in [2023-03-07 13:30:00 to 2023-03-08 01:45:00]. Find the time interval where channel 496 exhibits the most similar pattern within the search context [2023-03-08 20:45:00 to 2023-03-10 04:00:00]. (Output format: [YYYY-MM-DD HH:MM:SS, YYYY-MM-DD HH:MM:SS])", "answer": "['2023-03-08 22:00:00', '2023-03-09 10:15:00']", "ground_truth": [ "2023-03-08 22:00:00", "2023-03-09 10:15:00" ], "eval_metric": "iou", "channel": "496", "ts_data_path": "ts_data/L2_T3_Subsequence_Matching_00408.csv", "meta": { "prototype": "bell curve", "query_window": "[2023-03-07 13:30:00 to 2023-03-08 01:45:00]", "search_window": "[2023-03-08 20:45:00 to 2023-03-10 04:00:00]", "source": "causal_rivers" } }, { "id": "L2_T3_Subsequence_Matching_00409", "level": 2, "level_name": "Pattern Recognition", "category": "Subsequence Matching", "subtask": "Subsequence Matching", "question": "Analyze the reference pattern in [2022-10-11 05:15:00 to 2022-10-11 16:15:00]. Find the time interval where channel 501 exhibits the most similar pattern within the search context [2022-10-12 09:15:00 to 2022-10-14 15:00:00]. (Output format: [YYYY-MM-DD HH:MM:SS, YYYY-MM-DD HH:MM:SS])", "answer": "['2022-10-12 17:00:00', '2022-10-13 04:00:00']", "ground_truth": [ "2022-10-12 17:00:00", "2022-10-13 04:00:00" ], "eval_metric": "iou", "channel": "501", "ts_data_path": "ts_data/L2_T3_Subsequence_Matching_00409.csv", "meta": { "prototype": "double-peak pattern", "query_window": "[2022-10-11 05:15:00 to 2022-10-11 16:15:00]", "search_window": "[2022-10-12 09:15:00 to 2022-10-14 15:00:00]", "source": "causal_rivers" } }, { "id": "L2_T3_Subsequence_Matching_00410", "level": 2, "level_name": "Pattern Recognition", "category": "Subsequence Matching", "subtask": "Subsequence Matching", "question": "Analyze the reference pattern in [2020-11-17 07:00:00 to 2020-11-17 18:00:00]. Find the time interval where channel 578 exhibits the most similar pattern within the search context [2020-11-19 15:30:00 to 2020-11-26 18:00:00]. (Output format: [YYYY-MM-DD HH:MM:SS, YYYY-MM-DD HH:MM:SS])", "answer": "['2020-11-23 10:45:00', '2020-11-23 21:45:00']", "ground_truth": [ "2020-11-23 10:45:00", "2020-11-23 21:45:00" ], "eval_metric": "iou", "channel": "578", "ts_data_path": "ts_data/L2_T3_Subsequence_Matching_00410.csv", "meta": { "prototype": "double-peak pattern", "query_window": "[2020-11-17 07:00:00 to 2020-11-17 18:00:00]", "search_window": "[2020-11-19 15:30:00 to 2020-11-26 18:00:00]", "source": "causal_rivers" } }, { "id": "L2_T3_Subsequence_Matching_00413", "level": 2, "level_name": "Pattern Recognition", "category": "Subsequence Matching", "subtask": "Subsequence Matching", "question": "Analyze the reference pattern in [2023-10-06 19:00:00 to 2023-10-07 02:15:00]. Find the time interval where channel 591 exhibits the most similar pattern within the search context [2023-10-08 13:30:00 to 2023-10-14 02:30:00]. (Output format: [YYYY-MM-DD HH:MM:SS, YYYY-MM-DD HH:MM:SS])", "answer": "['2023-10-11 06:30:00', '2023-10-11 13:45:00']", "ground_truth": [ "2023-10-11 06:30:00", "2023-10-11 13:45:00" ], "eval_metric": "iou", "channel": "591", "ts_data_path": "ts_data/L2_T3_Subsequence_Matching_00413.csv", "meta": { "prototype": "sharp spike", "query_window": "[2023-10-06 19:00:00 to 2023-10-07 02:15:00]", "search_window": "[2023-10-08 13:30:00 to 2023-10-14 02:30:00]", "source": "causal_rivers" } }, { "id": "L2_T3_Subsequence_Matching_00414", "level": 2, "level_name": "Pattern Recognition", "category": "Subsequence Matching", "subtask": "Subsequence Matching", "question": "Analyze the reference pattern in [2022-09-04 09:45:00 to 2022-09-04 22:00:00]. Find the time interval where channel 595 exhibits the most similar pattern within the search context [2022-09-06 06:30:00 to 2022-09-11 03:00:00]. (Output format: [YYYY-MM-DD HH:MM:SS, YYYY-MM-DD HH:MM:SS])", "answer": "['2022-09-10 06:45:00', '2022-09-10 19:00:00']", "ground_truth": [ "2022-09-10 06:45:00", "2022-09-10 19:00:00" ], "eval_metric": "iou", "channel": "595", "ts_data_path": "ts_data/L2_T3_Subsequence_Matching_00414.csv", "meta": { "prototype": "bell curve", "query_window": "[2022-09-04 09:45:00 to 2022-09-04 22:00:00]", "search_window": "[2022-09-06 06:30:00 to 2022-09-11 03:00:00]", "source": "causal_rivers" } }, { "id": "L2_T3_Subsequence_Matching_00415", "level": 2, "level_name": "Pattern Recognition", "category": "Subsequence Matching", "subtask": "Subsequence Matching", "question": "Analyze the reference pattern in [2023-06-28 16:30:00 to 2023-06-28 23:45:00]. Find the time interval where channel 625 exhibits the most similar pattern within the search context [2023-06-29 22:30:00 to 2023-07-03 09:45:00]. (Output format: [YYYY-MM-DD HH:MM:SS, YYYY-MM-DD HH:MM:SS])", "answer": "['2023-06-30 22:30:00', '2023-07-01 05:45:00']", "ground_truth": [ "2023-06-30 22:30:00", "2023-07-01 05:45:00" ], "eval_metric": "iou", "channel": "625", "ts_data_path": "ts_data/L2_T3_Subsequence_Matching_00415.csv", "meta": { "prototype": "sharp spike", "query_window": "[2023-06-28 16:30:00 to 2023-06-28 23:45:00]", "search_window": "[2023-06-29 22:30:00 to 2023-07-03 09:45:00]", "source": "causal_rivers" } }, { "id": "L2_T3_Subsequence_Matching_00416", "level": 2, "level_name": "Pattern Recognition", "category": "Subsequence Matching", "subtask": "Subsequence Matching", "question": "Analyze the reference pattern in [2019-06-21 06:00:00 to 2019-06-21 18:15:00]. Find the time interval where channel 626 exhibits the most similar pattern within the search context [2019-06-23 00:30:00 to 2019-06-27 11:45:00]. (Output format: [YYYY-MM-DD HH:MM:SS, YYYY-MM-DD HH:MM:SS])", "answer": "['2019-06-26 03:30:00', '2019-06-26 15:45:00']", "ground_truth": [ "2019-06-26 03:30:00", "2019-06-26 15:45:00" ], "eval_metric": "iou", "channel": "626", "ts_data_path": "ts_data/L2_T3_Subsequence_Matching_00416.csv", "meta": { "prototype": "bell curve", "query_window": "[2019-06-21 06:00:00 to 2019-06-21 18:15:00]", "search_window": "[2019-06-23 00:30:00 to 2019-06-27 11:45:00]", "source": "causal_rivers" } }, { "id": "L2_T3_Subsequence_Matching_00417", "level": 2, "level_name": "Pattern Recognition", "category": "Subsequence Matching", "subtask": "Subsequence Matching", "question": "Analyze the reference pattern in [2019-09-18 18:00:00 to 2019-09-19 08:45:00]. Find the time interval where channel 627 exhibits the most similar pattern within the search context [2019-09-20 07:30:00 to 2019-09-21 16:45:00]. (Output format: [YYYY-MM-DD HH:MM:SS, YYYY-MM-DD HH:MM:SS])", "answer": "['2019-09-20 23:45:00', '2019-09-21 14:30:00']", "ground_truth": [ "2019-09-20 23:45:00", "2019-09-21 14:30:00" ], "eval_metric": "iou", "channel": "627", "ts_data_path": "ts_data/L2_T3_Subsequence_Matching_00417.csv", "meta": { "prototype": "step pattern", "query_window": "[2019-09-18 18:00:00 to 2019-09-19 08:45:00]", "search_window": "[2019-09-20 07:30:00 to 2019-09-21 16:45:00]", "source": "causal_rivers" } }, { "id": "L2_T3_Subsequence_Matching_00418", "level": 2, "level_name": "Pattern Recognition", "category": "Subsequence Matching", "subtask": "Subsequence Matching", "question": "Analyze the reference pattern in [2019-12-28 07:00:00 to 2019-12-28 14:15:00]. Find the time interval where channel 647 exhibits the most similar pattern within the search context [2019-12-29 19:30:00 to 2020-01-03 07:45:00]. (Output format: [YYYY-MM-DD HH:MM:SS, YYYY-MM-DD HH:MM:SS])", "answer": "['2020-01-02 15:15:00', '2020-01-02 22:30:00']", "ground_truth": [ "2020-01-02 15:15:00", "2020-01-02 22:30:00" ], "eval_metric": "iou", "channel": "647", "ts_data_path": "ts_data/L2_T3_Subsequence_Matching_00418.csv", "meta": { "prototype": "sharp spike", "query_window": "[2019-12-28 07:00:00 to 2019-12-28 14:15:00]", "search_window": "[2019-12-29 19:30:00 to 2020-01-03 07:45:00]", "source": "causal_rivers" } }, { "id": "L2_T3_Subsequence_Matching_00419", "level": 2, "level_name": "Pattern Recognition", "category": "Subsequence Matching", "subtask": "Subsequence Matching", "question": "Analyze the reference pattern in [2019-08-03 10:45:00 to 2019-08-04 01:30:00]. Find the time interval where channel 680 exhibits the most similar pattern within the search context [2019-08-05 21:00:00 to 2019-08-12 11:30:00]. (Output format: [YYYY-MM-DD HH:MM:SS, YYYY-MM-DD HH:MM:SS])", "answer": "['2019-08-11 15:15:00', '2019-08-12 06:00:00']", "ground_truth": [ "2019-08-11 15:15:00", "2019-08-12 06:00:00" ], "eval_metric": "iou", "channel": "680", "ts_data_path": "ts_data/L2_T3_Subsequence_Matching_00419.csv", "meta": { "prototype": "step pattern", "query_window": "[2019-08-03 10:45:00 to 2019-08-04 01:30:00]", "search_window": "[2019-08-05 21:00:00 to 2019-08-12 11:30:00]", "source": "causal_rivers" } }, { "id": "L2_T3_Subsequence_Matching_00420", "level": 2, "level_name": "Pattern Recognition", "category": "Subsequence Matching", "subtask": "Subsequence Matching", "question": "Analyze the reference pattern in [2021-02-28 21:00:00 to 2021-03-01 09:15:00]. Find the time interval where channel 683 exhibits the most similar pattern within the search context [2021-03-03 06:00:00 to 2021-03-10 04:15:00]. (Output format: [YYYY-MM-DD HH:MM:SS, YYYY-MM-DD HH:MM:SS])", "answer": "['2021-03-06 06:30:00', '2021-03-06 18:45:00']", "ground_truth": [ "2021-03-06 06:30:00", "2021-03-06 18:45:00" ], "eval_metric": "iou", "channel": "683", "ts_data_path": "ts_data/L2_T3_Subsequence_Matching_00420.csv", "meta": { "prototype": "bell curve", "query_window": "[2021-02-28 21:00:00 to 2021-03-01 09:15:00]", "search_window": "[2021-03-03 06:00:00 to 2021-03-10 04:15:00]", "source": "causal_rivers" } }, { "id": "L2_T3_Subsequence_Matching_00421", "level": 2, "level_name": "Pattern Recognition", "category": "Subsequence Matching", "subtask": "Subsequence Matching", "question": "Analyze the reference pattern in [2023-10-13 18:30:00 to 2023-10-14 09:15:00]. Find the time interval where channel 727 exhibits the most similar pattern within the search context [2023-10-15 08:00:00 to 2023-10-18 01:00:00]. (Output format: [YYYY-MM-DD HH:MM:SS, YYYY-MM-DD HH:MM:SS])", "answer": "['2023-10-16 02:15:00', '2023-10-16 17:00:00']", "ground_truth": [ "2023-10-16 02:15:00", "2023-10-16 17:00:00" ], "eval_metric": "iou", "channel": "727", "ts_data_path": "ts_data/L2_T3_Subsequence_Matching_00421.csv", "meta": { "prototype": "step pattern", "query_window": "[2023-10-13 18:30:00 to 2023-10-14 09:15:00]", "search_window": "[2023-10-15 08:00:00 to 2023-10-18 01:00:00]", "source": "causal_rivers" } }, { "id": "L2_T3_Subsequence_Matching_00422", "level": 2, "level_name": "Pattern Recognition", "category": "Subsequence Matching", "subtask": "Subsequence Matching", "question": "Analyze the reference pattern in [2021-12-07 15:45:00 to 2021-12-08 02:45:00]. Find the time interval where channel 728 exhibits the most similar pattern within the search context [2021-12-09 19:15:00 to 2021-12-16 01:15:00]. (Output format: [YYYY-MM-DD HH:MM:SS, YYYY-MM-DD HH:MM:SS])", "answer": "['2021-12-13 07:30:00', '2021-12-13 18:30:00']", "ground_truth": [ "2021-12-13 07:30:00", "2021-12-13 18:30:00" ], "eval_metric": "iou", "channel": "728", "ts_data_path": "ts_data/L2_T3_Subsequence_Matching_00422.csv", "meta": { "prototype": "double-peak pattern", "query_window": "[2021-12-07 15:45:00 to 2021-12-08 02:45:00]", "search_window": "[2021-12-09 19:15:00 to 2021-12-16 01:15:00]", "source": "causal_rivers" } }, { "id": "L2_T3_Subsequence_Matching_00423", "level": 2, "level_name": "Pattern Recognition", "category": "Subsequence Matching", "subtask": "Subsequence Matching", "question": "Analyze the reference pattern in [2021-10-28 06:00:00 to 2021-10-28 20:45:00]. Find the time interval where channel 729 exhibits the most similar pattern within the search context [2021-10-30 00:30:00 to 2021-11-03 00:15:00]. (Output format: [YYYY-MM-DD HH:MM:SS, YYYY-MM-DD HH:MM:SS])", "answer": "['2021-10-30 23:45:00', '2021-10-31 14:30:00']", "ground_truth": [ "2021-10-30 23:45:00", "2021-10-31 14:30:00" ], "eval_metric": "iou", "channel": "729", "ts_data_path": "ts_data/L2_T3_Subsequence_Matching_00423.csv", "meta": { "prototype": "step pattern", "query_window": "[2021-10-28 06:00:00 to 2021-10-28 20:45:00]", "search_window": "[2021-10-30 00:30:00 to 2021-11-03 00:15:00]", "source": "causal_rivers" } }, { "id": "L2_T3_Subsequence_Matching_00425", "level": 2, "level_name": "Pattern Recognition", "category": "Subsequence Matching", "subtask": "Subsequence Matching", "question": "Analyze the reference pattern in [2021-06-08 04:00:00 to 2021-06-08 11:15:00]. Find the time interval where channel 762 exhibits the most similar pattern within the search context [2021-06-10 07:30:00 to 2021-06-17 08:00:00]. (Output format: [YYYY-MM-DD HH:MM:SS, YYYY-MM-DD HH:MM:SS])", "answer": "['2021-06-10 13:15:00', '2021-06-10 20:30:00']", "ground_truth": [ "2021-06-10 13:15:00", "2021-06-10 20:30:00" ], "eval_metric": "iou", "channel": "762", "ts_data_path": "ts_data/L2_T3_Subsequence_Matching_00425.csv", "meta": { "prototype": "sharp spike", "query_window": "[2021-06-08 04:00:00 to 2021-06-08 11:15:00]", "search_window": "[2021-06-10 07:30:00 to 2021-06-17 08:00:00]", "source": "causal_rivers" } }, { "id": "L2_T3_Subsequence_Matching_00426", "level": 2, "level_name": "Pattern Recognition", "category": "Subsequence Matching", "subtask": "Subsequence Matching", "question": "Analyze the reference pattern in [2021-10-06 14:45:00 to 2021-10-07 01:45:00]. Find the time interval where channel 811 exhibits the most similar pattern within the search context [2021-10-08 03:45:00 to 2021-10-12 00:00:00]. (Output format: [YYYY-MM-DD HH:MM:SS, YYYY-MM-DD HH:MM:SS])", "answer": "['2021-10-08 20:00:00', '2021-10-09 07:00:00']", "ground_truth": [ "2021-10-08 20:00:00", "2021-10-09 07:00:00" ], "eval_metric": "iou", "channel": "811", "ts_data_path": "ts_data/L2_T3_Subsequence_Matching_00426.csv", "meta": { "prototype": "double-peak pattern", "query_window": "[2021-10-06 14:45:00 to 2021-10-07 01:45:00]", "search_window": "[2021-10-08 03:45:00 to 2021-10-12 00:00:00]", "source": "causal_rivers" } }, { "id": "L2_T3_Subsequence_Matching_00427", "level": 2, "level_name": "Pattern Recognition", "category": "Subsequence Matching", "subtask": "Subsequence Matching", "question": "Analyze the reference pattern in [2020-03-16 22:30:00 to 2020-03-17 13:15:00]. Find the time interval where channel 813 exhibits the most similar pattern within the search context [2020-03-19 07:00:00 to 2020-03-25 14:15:00]. (Output format: [YYYY-MM-DD HH:MM:SS, YYYY-MM-DD HH:MM:SS])", "answer": "['2020-03-20 13:15:00', '2020-03-21 04:00:00']", "ground_truth": [ "2020-03-20 13:15:00", "2020-03-21 04:00:00" ], "eval_metric": "iou", "channel": "813", "ts_data_path": "ts_data/L2_T3_Subsequence_Matching_00427.csv", "meta": { "prototype": "step pattern", "query_window": "[2020-03-16 22:30:00 to 2020-03-17 13:15:00]", "search_window": "[2020-03-19 07:00:00 to 2020-03-25 14:15:00]", "source": "causal_rivers" } }, { "id": "L2_T3_Subsequence_Matching_00428", "level": 2, "level_name": "Pattern Recognition", "category": "Subsequence Matching", "subtask": "Subsequence Matching", "question": "Analyze the reference pattern in [2019-10-22 19:30:00 to 2019-10-23 07:45:00]. Find the time interval where channel 865 exhibits the most similar pattern within the search context [2019-10-24 10:30:00 to 2019-10-28 08:00:00]. (Output format: [YYYY-MM-DD HH:MM:SS, YYYY-MM-DD HH:MM:SS])", "answer": "['2019-10-27 11:00:00', '2019-10-27 23:15:00']", "ground_truth": [ "2019-10-27 11:00:00", "2019-10-27 23:15:00" ], "eval_metric": "iou", "channel": "865", "ts_data_path": "ts_data/L2_T3_Subsequence_Matching_00428.csv", "meta": { "prototype": "bell curve", "query_window": "[2019-10-22 19:30:00 to 2019-10-23 07:45:00]", "search_window": "[2019-10-24 10:30:00 to 2019-10-28 08:00:00]", "source": "causal_rivers" } }, { "id": "L2_T3_Subsequence_Matching_00430", "level": 2, "level_name": "Pattern Recognition", "category": "Subsequence Matching", "subtask": "Subsequence Matching", "question": "Analyze the reference pattern in [2019-10-07 01:00:00 to 2019-10-07 15:45:00]. Find the time interval where channel 894 exhibits the most similar pattern within the search context [2019-10-09 13:15:00 to 2019-10-16 11:15:00]. (Output format: [YYYY-MM-DD HH:MM:SS, YYYY-MM-DD HH:MM:SS])", "answer": "['2019-10-09 22:00:00', '2019-10-10 12:45:00']", "ground_truth": [ "2019-10-09 22:00:00", "2019-10-10 12:45:00" ], "eval_metric": "iou", "channel": "894", "ts_data_path": "ts_data/L2_T3_Subsequence_Matching_00430.csv", "meta": { "prototype": "step pattern", "query_window": "[2019-10-07 01:00:00 to 2019-10-07 15:45:00]", "search_window": "[2019-10-09 13:15:00 to 2019-10-16 11:15:00]", "source": "causal_rivers" } }, { "id": "L2_T3_Subsequence_Matching_00432", "level": 2, "level_name": "Pattern Recognition", "category": "Subsequence Matching", "subtask": "Subsequence Matching", "question": "Analyze the reference pattern in [2020-08-13 04:15:00 to 2020-08-13 15:15:00]. Find the time interval where channel 897 exhibits the most similar pattern within the search context [2020-08-14 08:15:00 to 2020-08-15 23:15:00]. (Output format: [YYYY-MM-DD HH:MM:SS, YYYY-MM-DD HH:MM:SS])", "answer": "['2020-08-14 09:45:00', '2020-08-14 20:45:00']", "ground_truth": [ "2020-08-14 09:45:00", "2020-08-14 20:45:00" ], "eval_metric": "iou", "channel": "897", "ts_data_path": "ts_data/L2_T3_Subsequence_Matching_00432.csv", "meta": { "prototype": "double-peak pattern", "query_window": "[2020-08-13 04:15:00 to 2020-08-13 15:15:00]", "search_window": "[2020-08-14 08:15:00 to 2020-08-15 23:15:00]", "source": "causal_rivers" } }, { "id": "L2_T3_Subsequence_Matching_00433", "level": 2, "level_name": "Pattern Recognition", "category": "Subsequence Matching", "subtask": "Subsequence Matching", "question": "Analyze the reference pattern in [2022-11-27 18:45:00 to 2022-11-28 09:30:00]. Find the time interval where channel 933 exhibits the most similar pattern within the search context [2022-11-30 04:30:00 to 2022-12-06 16:15:00]. (Output format: [YYYY-MM-DD HH:MM:SS, YYYY-MM-DD HH:MM:SS])", "answer": "['2022-12-02 00:30:00', '2022-12-02 15:15:00']", "ground_truth": [ "2022-12-02 00:30:00", "2022-12-02 15:15:00" ], "eval_metric": "iou", "channel": "933", "ts_data_path": "ts_data/L2_T3_Subsequence_Matching_00433.csv", "meta": { "prototype": "step pattern", "query_window": "[2022-11-27 18:45:00 to 2022-11-28 09:30:00]", "search_window": "[2022-11-30 04:30:00 to 2022-12-06 16:15:00]", "source": "causal_rivers" } }, { "id": "L2_T3_Subsequence_Matching_00434", "level": 2, "level_name": "Pattern Recognition", "category": "Subsequence Matching", "subtask": "Subsequence Matching", "question": "Analyze the reference pattern in [2016-07-06 06:00:00 to 2016-07-06 17:00:00]. Find the time interval where channel HUFL exhibits the most similar pattern within the search context [2016-07-07 11:15:00 to 2016-07-10 00:15:00]. (Output format: [YYYY-MM-DD HH:MM:SS, YYYY-MM-DD HH:MM:SS])", "answer": "['2016-07-08 01:00:00', '2016-07-08 12:00:00']", "ground_truth": [ "2016-07-08 01:00:00", "2016-07-08 12:00:00" ], "eval_metric": "iou", "channel": "HUFL", "ts_data_path": "ts_data/L2_T3_Subsequence_Matching_00434.csv", "meta": { "prototype": "double-peak pattern", "query_window": "[2016-07-06 06:00:00 to 2016-07-06 17:00:00]", "search_window": "[2016-07-07 11:15:00 to 2016-07-10 00:15:00]", "source": "ettm1" } }, { "id": "L2_T3_Subsequence_Matching_00435", "level": 2, "level_name": "Pattern Recognition", "category": "Subsequence Matching", "subtask": "Subsequence Matching", "question": "Analyze the reference pattern in [2018-02-18 18:45:00 to 2018-02-19 09:30:00]. Find the time interval where channel HULL exhibits the most similar pattern within the search context [2018-02-20 21:00:00 to 2018-02-26 03:00:00]. (Output format: [YYYY-MM-DD HH:MM:SS, YYYY-MM-DD HH:MM:SS])", "answer": "['2018-02-21 06:00:00', '2018-02-21 20:45:00']", "ground_truth": [ "2018-02-21 06:00:00", "2018-02-21 20:45:00" ], "eval_metric": "iou", "channel": "HULL", "ts_data_path": "ts_data/L2_T3_Subsequence_Matching_00435.csv", "meta": { "prototype": "step pattern", "query_window": "[2018-02-18 18:45:00 to 2018-02-19 09:30:00]", "search_window": "[2018-02-20 21:00:00 to 2018-02-26 03:00:00]", "source": "ettm1" } }, { "id": "L2_T3_Subsequence_Matching_00436", "level": 2, "level_name": "Pattern Recognition", "category": "Subsequence Matching", "subtask": "Subsequence Matching", "question": "Analyze the reference pattern in [2016-07-23 11:30:00 to 2016-07-23 22:30:00]. Find the time interval where channel MUFL exhibits the most similar pattern within the search context [2016-07-24 19:30:00 to 2016-07-27 19:45:00]. (Output format: [YYYY-MM-DD HH:MM:SS, YYYY-MM-DD HH:MM:SS])", "answer": "['2016-07-26 10:30:00', '2016-07-26 21:30:00']", "ground_truth": [ "2016-07-26 10:30:00", "2016-07-26 21:30:00" ], "eval_metric": "iou", "channel": "MUFL", "ts_data_path": "ts_data/L2_T3_Subsequence_Matching_00436.csv", "meta": { "prototype": "double-peak pattern", "query_window": "[2016-07-23 11:30:00 to 2016-07-23 22:30:00]", "search_window": "[2016-07-24 19:30:00 to 2016-07-27 19:45:00]", "source": "ettm1" } }, { "id": "L2_T3_Subsequence_Matching_00438", "level": 2, "level_name": "Pattern Recognition", "category": "Subsequence Matching", "subtask": "Subsequence Matching", "question": "Analyze the reference pattern in [2017-10-29 14:30:00 to 2017-10-30 01:30:00]. Find the time interval where channel LUFL exhibits the most similar pattern within the search context [2017-10-31 18:30:00 to 2017-11-07 03:00:00]. (Output format: [YYYY-MM-DD HH:MM:SS, YYYY-MM-DD HH:MM:SS])", "answer": "['2017-11-04 01:15:00', '2017-11-04 12:15:00']", "ground_truth": [ "2017-11-04 01:15:00", "2017-11-04 12:15:00" ], "eval_metric": "iou", "channel": "LUFL", "ts_data_path": "ts_data/L2_T3_Subsequence_Matching_00438.csv", "meta": { "prototype": "double-peak pattern", "query_window": "[2017-10-29 14:30:00 to 2017-10-30 01:30:00]", "search_window": "[2017-10-31 18:30:00 to 2017-11-07 03:00:00]", "source": "ettm1" } }, { "id": "L2_T3_Subsequence_Matching_00439", "level": 2, "level_name": "Pattern Recognition", "category": "Subsequence Matching", "subtask": "Subsequence Matching", "question": "Analyze the reference pattern in [2017-05-05 04:00:00 to 2017-05-05 18:45:00]. Find the time interval where channel LULL exhibits the most similar pattern within the search context [2017-05-07 14:00:00 to 2017-05-14 03:15:00]. (Output format: [YYYY-MM-DD HH:MM:SS, YYYY-MM-DD HH:MM:SS])", "answer": "['2017-05-07 18:30:00', '2017-05-08 09:15:00']", "ground_truth": [ "2017-05-07 18:30:00", "2017-05-08 09:15:00" ], "eval_metric": "iou", "channel": "LULL", "ts_data_path": "ts_data/L2_T3_Subsequence_Matching_00439.csv", "meta": { "prototype": "step pattern", "query_window": "[2017-05-05 04:00:00 to 2017-05-05 18:45:00]", "search_window": "[2017-05-07 14:00:00 to 2017-05-14 03:15:00]", "source": "ettm1" } }, { "id": "L2_T3_Subsequence_Matching_00441", "level": 2, "level_name": "Pattern Recognition", "category": "Subsequence Matching", "subtask": "Subsequence Matching", "question": "Analyze the reference pattern in [2019-04-02 16:30:00 to 2019-04-03 03:30:00]. Find the time interval where channel 67 exhibits the most similar pattern within the search context [2019-04-04 20:30:00 to 2019-04-11 05:00:00]. (Output format: [YYYY-MM-DD HH:MM:SS, YYYY-MM-DD HH:MM:SS])", "answer": "['2019-04-10 03:00:00', '2019-04-10 14:00:00']", "ground_truth": [ "2019-04-10 03:00:00", "2019-04-10 14:00:00" ], "eval_metric": "iou", "channel": "67", "ts_data_path": "ts_data/L2_T3_Subsequence_Matching_00441.csv", "meta": { "prototype": "double-peak pattern", "query_window": "[2019-04-02 16:30:00 to 2019-04-03 03:30:00]", "search_window": "[2019-04-04 20:30:00 to 2019-04-11 05:00:00]", "source": "causal_rivers" } }, { "id": "L2_T3_Subsequence_Matching_00442", "level": 2, "level_name": "Pattern Recognition", "category": "Subsequence Matching", "subtask": "Subsequence Matching", "question": "Analyze the reference pattern in [2022-05-31 19:00:00 to 2022-06-01 07:15:00]. Find the time interval where channel 71 exhibits the most similar pattern within the search context [2022-06-02 02:15:00 to 2022-06-03 04:15:00]. (Output format: [YYYY-MM-DD HH:MM:SS, YYYY-MM-DD HH:MM:SS])", "answer": "['2022-06-02 14:30:00', '2022-06-03 02:45:00']", "ground_truth": [ "2022-06-02 14:30:00", "2022-06-03 02:45:00" ], "eval_metric": "iou", "channel": "71", "ts_data_path": "ts_data/L2_T3_Subsequence_Matching_00442.csv", "meta": { "prototype": "bell curve", "query_window": "[2022-05-31 19:00:00 to 2022-06-01 07:15:00]", "search_window": "[2022-06-02 02:15:00 to 2022-06-03 04:15:00]", "source": "causal_rivers" } }, { "id": "L2_T3_Subsequence_Matching_00443", "level": 2, "level_name": "Pattern Recognition", "category": "Subsequence Matching", "subtask": "Subsequence Matching", "question": "Analyze the reference pattern in [2023-12-03 09:30:00 to 2023-12-03 20:30:00]. Find the time interval where channel 99 exhibits the most similar pattern within the search context [2023-12-05 09:00:00 to 2023-12-10 23:15:00]. (Output format: [YYYY-MM-DD HH:MM:SS, YYYY-MM-DD HH:MM:SS])", "answer": "['2023-12-07 11:30:00', '2023-12-07 22:30:00']", "ground_truth": [ "2023-12-07 11:30:00", "2023-12-07 22:30:00" ], "eval_metric": "iou", "channel": "99", "ts_data_path": "ts_data/L2_T3_Subsequence_Matching_00443.csv", "meta": { "prototype": "double-peak pattern", "query_window": "[2023-12-03 09:30:00 to 2023-12-03 20:30:00]", "search_window": "[2023-12-05 09:00:00 to 2023-12-10 23:15:00]", "source": "causal_rivers" } }, { "id": "L2_T3_Subsequence_Matching_00444", "level": 2, "level_name": "Pattern Recognition", "category": "Subsequence Matching", "subtask": "Subsequence Matching", "question": "Analyze the reference pattern in [2020-08-03 04:45:00 to 2020-08-03 12:00:00]. Find the time interval where channel 123 exhibits the most similar pattern within the search context [2020-08-04 06:30:00 to 2020-08-07 00:15:00]. (Output format: [YYYY-MM-DD HH:MM:SS, YYYY-MM-DD HH:MM:SS])", "answer": "['2020-08-04 07:30:00', '2020-08-04 14:45:00']", "ground_truth": [ "2020-08-04 07:30:00", "2020-08-04 14:45:00" ], "eval_metric": "iou", "channel": "123", "ts_data_path": "ts_data/L2_T3_Subsequence_Matching_00444.csv", "meta": { "prototype": "sharp spike", "query_window": "[2020-08-03 04:45:00 to 2020-08-03 12:00:00]", "search_window": "[2020-08-04 06:30:00 to 2020-08-07 00:15:00]", "source": "causal_rivers" } }, { "id": "L2_T3_Subsequence_Matching_00446", "level": 2, "level_name": "Pattern Recognition", "category": "Subsequence Matching", "subtask": "Subsequence Matching", "question": "Analyze the reference pattern in [2019-11-21 20:15:00 to 2019-11-22 07:15:00]. Find the time interval where channel 146 exhibits the most similar pattern within the search context [2019-11-23 02:45:00 to 2019-11-25 20:15:00]. (Output format: [YYYY-MM-DD HH:MM:SS, YYYY-MM-DD HH:MM:SS])", "answer": "['2019-11-24 11:45:00', '2019-11-24 22:45:00']", "ground_truth": [ "2019-11-24 11:45:00", "2019-11-24 22:45:00" ], "eval_metric": "iou", "channel": "146", "ts_data_path": "ts_data/L2_T3_Subsequence_Matching_00446.csv", "meta": { "prototype": "double-peak pattern", "query_window": "[2019-11-21 20:15:00 to 2019-11-22 07:15:00]", "search_window": "[2019-11-23 02:45:00 to 2019-11-25 20:15:00]", "source": "causal_rivers" } }, { "id": "L2_T3_Subsequence_Matching_00447", "level": 2, "level_name": "Pattern Recognition", "category": "Subsequence Matching", "subtask": "Subsequence Matching", "question": "Analyze the reference pattern in [2023-05-20 06:45:00 to 2023-05-20 19:00:00]. Find the time interval where channel 147 exhibits the most similar pattern within the search context [2023-05-22 02:00:00 to 2023-05-26 17:00:00]. (Output format: [YYYY-MM-DD HH:MM:SS, YYYY-MM-DD HH:MM:SS])", "answer": "['2023-05-26 03:45:00', '2023-05-26 16:00:00']", "ground_truth": [ "2023-05-26 03:45:00", "2023-05-26 16:00:00" ], "eval_metric": "iou", "channel": "147", "ts_data_path": "ts_data/L2_T3_Subsequence_Matching_00447.csv", "meta": { "prototype": "bell curve", "query_window": "[2023-05-20 06:45:00 to 2023-05-20 19:00:00]", "search_window": "[2023-05-22 02:00:00 to 2023-05-26 17:00:00]", "source": "causal_rivers" } }, { "id": "L2_T3_Subsequence_Matching_00449", "level": 2, "level_name": "Pattern Recognition", "category": "Subsequence Matching", "subtask": "Subsequence Matching", "question": "Analyze the reference pattern in [2020-06-08 02:15:00 to 2020-06-08 09:30:00]. Find the time interval where channel 154 exhibits the most similar pattern within the search context [2020-06-09 09:00:00 to 2020-06-12 23:00:00]. (Output format: [YYYY-MM-DD HH:MM:SS, YYYY-MM-DD HH:MM:SS])", "answer": "['2020-06-11 10:30:00', '2020-06-11 17:45:00']", "ground_truth": [ "2020-06-11 10:30:00", "2020-06-11 17:45:00" ], "eval_metric": "iou", "channel": "154", "ts_data_path": "ts_data/L2_T3_Subsequence_Matching_00449.csv", "meta": { "prototype": "sharp spike", "query_window": "[2020-06-08 02:15:00 to 2020-06-08 09:30:00]", "search_window": "[2020-06-09 09:00:00 to 2020-06-12 23:00:00]", "source": "causal_rivers" } }, { "id": "L2_T3_Subsequence_Matching_00450", "level": 2, "level_name": "Pattern Recognition", "category": "Subsequence Matching", "subtask": "Subsequence Matching", "question": "Analyze the reference pattern in [2019-07-16 04:30:00 to 2019-07-16 11:45:00]. Find the time interval where channel 155 exhibits the most similar pattern within the search context [2019-07-18 00:30:00 to 2019-07-23 19:45:00]. (Output format: [YYYY-MM-DD HH:MM:SS, YYYY-MM-DD HH:MM:SS])", "answer": "['2019-07-18 17:15:00', '2019-07-19 00:30:00']", "ground_truth": [ "2019-07-18 17:15:00", "2019-07-19 00:30:00" ], "eval_metric": "iou", "channel": "155", "ts_data_path": "ts_data/L2_T3_Subsequence_Matching_00450.csv", "meta": { "prototype": "sharp spike", "query_window": "[2019-07-16 04:30:00 to 2019-07-16 11:45:00]", "search_window": "[2019-07-18 00:30:00 to 2019-07-23 19:45:00]", "source": "causal_rivers" } }, { "id": "L2_T3_Subsequence_Matching_00451", "level": 2, "level_name": "Pattern Recognition", "category": "Subsequence Matching", "subtask": "Subsequence Matching", "question": "Analyze the reference pattern in [2022-11-01 00:00:00 to 2022-11-01 11:00:00]. Find the time interval where channel 166 exhibits the most similar pattern within the search context [2022-11-02 04:00:00 to 2022-11-03 02:45:00]. (Output format: [YYYY-MM-DD HH:MM:SS, YYYY-MM-DD HH:MM:SS])", "answer": "['2022-11-02 15:15:00', '2022-11-03 02:15:00']", "ground_truth": [ "2022-11-02 15:15:00", "2022-11-03 02:15:00" ], "eval_metric": "iou", "channel": "166", "ts_data_path": "ts_data/L2_T3_Subsequence_Matching_00451.csv", "meta": { "prototype": "double-peak pattern", "query_window": "[2022-11-01 00:00:00 to 2022-11-01 11:00:00]", "search_window": "[2022-11-02 04:00:00 to 2022-11-03 02:45:00]", "source": "causal_rivers" } }, { "id": "L2_T3_Subsequence_Matching_00452", "level": 2, "level_name": "Pattern Recognition", "category": "Subsequence Matching", "subtask": "Subsequence Matching", "question": "Analyze the reference pattern in [2019-12-13 12:00:00 to 2019-12-13 23:00:00]. Find the time interval where channel 169 exhibits the most similar pattern within the search context [2019-12-14 16:00:00 to 2019-12-16 10:45:00]. (Output format: [YYYY-MM-DD HH:MM:SS, YYYY-MM-DD HH:MM:SS])", "answer": "['2019-12-15 12:15:00', '2019-12-15 23:15:00']", "ground_truth": [ "2019-12-15 12:15:00", "2019-12-15 23:15:00" ], "eval_metric": "iou", "channel": "169", "ts_data_path": "ts_data/L2_T3_Subsequence_Matching_00452.csv", "meta": { "prototype": "double-peak pattern", "query_window": "[2019-12-13 12:00:00 to 2019-12-13 23:00:00]", "search_window": "[2019-12-14 16:00:00 to 2019-12-16 10:45:00]", "source": "causal_rivers" } }, { "id": "L2_T3_Subsequence_Matching_00453", "level": 2, "level_name": "Pattern Recognition", "category": "Subsequence Matching", "subtask": "Subsequence Matching", "question": "Analyze the reference pattern in [2022-06-02 14:15:00 to 2022-06-03 05:00:00]. Find the time interval where channel 170 exhibits the most similar pattern within the search context [2022-06-04 14:00:00 to 2022-06-09 09:45:00]. (Output format: [YYYY-MM-DD HH:MM:SS, YYYY-MM-DD HH:MM:SS])", "answer": "['2022-06-06 11:30:00', '2022-06-07 02:15:00']", "ground_truth": [ "2022-06-06 11:30:00", "2022-06-07 02:15:00" ], "eval_metric": "iou", "channel": "170", "ts_data_path": "ts_data/L2_T3_Subsequence_Matching_00453.csv", "meta": { "prototype": "step pattern", "query_window": "[2022-06-02 14:15:00 to 2022-06-03 05:00:00]", "search_window": "[2022-06-04 14:00:00 to 2022-06-09 09:45:00]", "source": "causal_rivers" } }, { "id": "L2_T3_Subsequence_Matching_00456", "level": 2, "level_name": "Pattern Recognition", "category": "Subsequence Matching", "subtask": "Subsequence Matching", "question": "Analyze the reference pattern in [2022-03-28 23:00:00 to 2022-03-29 11:15:00]. Find the time interval where channel 177 exhibits the most similar pattern within the search context [2022-03-30 16:30:00 to 2022-04-04 00:45:00]. (Output format: [YYYY-MM-DD HH:MM:SS, YYYY-MM-DD HH:MM:SS])", "answer": "['2022-04-03 02:45:00', '2022-04-03 15:00:00']", "ground_truth": [ "2022-04-03 02:45:00", "2022-04-03 15:00:00" ], "eval_metric": "iou", "channel": "177", "ts_data_path": "ts_data/L2_T3_Subsequence_Matching_00456.csv", "meta": { "prototype": "bell curve", "query_window": "[2022-03-28 23:00:00 to 2022-03-29 11:15:00]", "search_window": "[2022-03-30 16:30:00 to 2022-04-04 00:45:00]", "source": "causal_rivers" } }, { "id": "L2_T3_Subsequence_Matching_00457", "level": 2, "level_name": "Pattern Recognition", "category": "Subsequence Matching", "subtask": "Subsequence Matching", "question": "Analyze the reference pattern in [2023-06-04 00:00:00 to 2023-06-04 14:45:00]. Find the time interval where channel 237 exhibits the most similar pattern within the search context [2023-06-05 13:30:00 to 2023-06-07 12:45:00]. (Output format: [YYYY-MM-DD HH:MM:SS, YYYY-MM-DD HH:MM:SS])", "answer": "['2023-06-06 07:45:00', '2023-06-06 22:30:00']", "ground_truth": [ "2023-06-06 07:45:00", "2023-06-06 22:30:00" ], "eval_metric": "iou", "channel": "237", "ts_data_path": "ts_data/L2_T3_Subsequence_Matching_00457.csv", "meta": { "prototype": "step pattern", "query_window": "[2023-06-04 00:00:00 to 2023-06-04 14:45:00]", "search_window": "[2023-06-05 13:30:00 to 2023-06-07 12:45:00]", "source": "causal_rivers" } }, { "id": "L2_T3_Subsequence_Matching_00459", "level": 2, "level_name": "Pattern Recognition", "category": "Subsequence Matching", "subtask": "Subsequence Matching", "question": "Analyze the reference pattern in [2019-04-11 00:00:00 to 2019-04-11 11:00:00]. Find the time interval where channel 312 exhibits the most similar pattern within the search context [2019-04-12 04:00:00 to 2019-04-13 12:45:00]. (Output format: [YYYY-MM-DD HH:MM:SS, YYYY-MM-DD HH:MM:SS])", "answer": "['2019-04-12 16:00:00', '2019-04-13 03:00:00']", "ground_truth": [ "2019-04-12 16:00:00", "2019-04-13 03:00:00" ], "eval_metric": "iou", "channel": "312", "ts_data_path": "ts_data/L2_T3_Subsequence_Matching_00459.csv", "meta": { "prototype": "double-peak pattern", "query_window": "[2019-04-11 00:00:00 to 2019-04-11 11:00:00]", "search_window": "[2019-04-12 04:00:00 to 2019-04-13 12:45:00]", "source": "causal_rivers" } }, { "id": "L2_T3_Subsequence_Matching_00460", "level": 2, "level_name": "Pattern Recognition", "category": "Subsequence Matching", "subtask": "Subsequence Matching", "question": "Analyze the reference pattern in [2023-07-10 06:30:00 to 2023-07-10 18:45:00]. Find the time interval where channel 430 exhibits the most similar pattern within the search context [2023-07-12 13:15:00 to 2023-07-19 02:30:00]. (Output format: [YYYY-MM-DD HH:MM:SS, YYYY-MM-DD HH:MM:SS])", "answer": "['2023-07-12 19:45:00', '2023-07-13 08:00:00']", "ground_truth": [ "2023-07-12 19:45:00", "2023-07-13 08:00:00" ], "eval_metric": "iou", "channel": "430", "ts_data_path": "ts_data/L2_T3_Subsequence_Matching_00460.csv", "meta": { "prototype": "bell curve", "query_window": "[2023-07-10 06:30:00 to 2023-07-10 18:45:00]", "search_window": "[2023-07-12 13:15:00 to 2023-07-19 02:30:00]", "source": "causal_rivers" } }, { "id": "L2_T3_Subsequence_Matching_00462", "level": 2, "level_name": "Pattern Recognition", "category": "Subsequence Matching", "subtask": "Subsequence Matching", "question": "Analyze the reference pattern in [2019-05-15 00:00:00 to 2019-05-15 11:00:00]. Find the time interval where channel 496 exhibits the most similar pattern within the search context [2019-05-16 11:15:00 to 2019-05-20 00:00:00]. (Output format: [YYYY-MM-DD HH:MM:SS, YYYY-MM-DD HH:MM:SS])", "answer": "['2019-05-19 13:00:00', '2019-05-20 00:00:00']", "ground_truth": [ "2019-05-19 13:00:00", "2019-05-20 00:00:00" ], "eval_metric": "iou", "channel": "496", "ts_data_path": "ts_data/L2_T3_Subsequence_Matching_00462.csv", "meta": { "prototype": "double-peak pattern", "query_window": "[2019-05-15 00:00:00 to 2019-05-15 11:00:00]", "search_window": "[2019-05-16 11:15:00 to 2019-05-20 00:00:00]", "source": "causal_rivers" } }, { "id": "L2_T3_Subsequence_Matching_00465", "level": 2, "level_name": "Pattern Recognition", "category": "Subsequence Matching", "subtask": "Subsequence Matching", "question": "Analyze the reference pattern in [2022-05-08 15:30:00 to 2022-05-09 06:15:00]. Find the time interval where channel 580 exhibits the most similar pattern within the search context [2022-05-11 01:00:00 to 2022-05-17 12:30:00]. (Output format: [YYYY-MM-DD HH:MM:SS, YYYY-MM-DD HH:MM:SS])", "answer": "['2022-05-14 16:00:00', '2022-05-15 06:45:00']", "ground_truth": [ "2022-05-14 16:00:00", "2022-05-15 06:45:00" ], "eval_metric": "iou", "channel": "580", "ts_data_path": "ts_data/L2_T3_Subsequence_Matching_00465.csv", "meta": { "prototype": "step pattern", "query_window": "[2022-05-08 15:30:00 to 2022-05-09 06:15:00]", "search_window": "[2022-05-11 01:00:00 to 2022-05-17 12:30:00]", "source": "causal_rivers" } }, { "id": "L2_T3_Subsequence_Matching_00467", "level": 2, "level_name": "Pattern Recognition", "category": "Subsequence Matching", "subtask": "Subsequence Matching", "question": "Analyze the reference pattern in [2021-07-03 05:00:00 to 2021-07-03 17:15:00]. Find the time interval where channel 595 exhibits the most similar pattern within the search context [2021-07-04 12:15:00 to 2021-07-07 02:00:00]. (Output format: [YYYY-MM-DD HH:MM:SS, YYYY-MM-DD HH:MM:SS])", "answer": "['2021-07-05 21:45:00', '2021-07-06 10:00:00']", "ground_truth": [ "2021-07-05 21:45:00", "2021-07-06 10:00:00" ], "eval_metric": "iou", "channel": "595", "ts_data_path": "ts_data/L2_T3_Subsequence_Matching_00467.csv", "meta": { "prototype": "bell curve", "query_window": "[2021-07-03 05:00:00 to 2021-07-03 17:15:00]", "search_window": "[2021-07-04 12:15:00 to 2021-07-07 02:00:00]", "source": "causal_rivers" } }, { "id": "L2_T3_Subsequence_Matching_00468", "level": 2, "level_name": "Pattern Recognition", "category": "Subsequence Matching", "subtask": "Subsequence Matching", "question": "Analyze the reference pattern in [2022-06-14 15:45:00 to 2022-06-15 04:00:00]. Find the time interval where channel 625 exhibits the most similar pattern within the search context [2022-06-15 23:00:00 to 2022-06-17 07:30:00]. (Output format: [YYYY-MM-DD HH:MM:SS, YYYY-MM-DD HH:MM:SS])", "answer": "['2022-06-16 01:00:00', '2022-06-16 13:15:00']", "ground_truth": [ "2022-06-16 01:00:00", "2022-06-16 13:15:00" ], "eval_metric": "iou", "channel": "625", "ts_data_path": "ts_data/L2_T3_Subsequence_Matching_00468.csv", "meta": { "prototype": "bell curve", "query_window": "[2022-06-14 15:45:00 to 2022-06-15 04:00:00]", "search_window": "[2022-06-15 23:00:00 to 2022-06-17 07:30:00]", "source": "causal_rivers" } }, { "id": "L2_T3_Subsequence_Matching_00469", "level": 2, "level_name": "Pattern Recognition", "category": "Subsequence Matching", "subtask": "Subsequence Matching", "question": "Analyze the reference pattern in [2021-01-26 12:00:00 to 2021-01-27 00:15:00]. Find the time interval where channel 626 exhibits the most similar pattern within the search context [2021-01-27 19:15:00 to 2021-01-30 00:45:00]. (Output format: [YYYY-MM-DD HH:MM:SS, YYYY-MM-DD HH:MM:SS])", "answer": "['2021-01-28 17:45:00', '2021-01-29 06:00:00']", "ground_truth": [ "2021-01-28 17:45:00", "2021-01-29 06:00:00" ], "eval_metric": "iou", "channel": "626", "ts_data_path": "ts_data/L2_T3_Subsequence_Matching_00469.csv", "meta": { "prototype": "bell curve", "query_window": "[2021-01-26 12:00:00 to 2021-01-27 00:15:00]", "search_window": "[2021-01-27 19:15:00 to 2021-01-30 00:45:00]", "source": "causal_rivers" } }, { "id": "L2_T3_Subsequence_Matching_00470", "level": 2, "level_name": "Pattern Recognition", "category": "Subsequence Matching", "subtask": "Subsequence Matching", "question": "Analyze the reference pattern in [2019-12-03 21:00:00 to 2019-12-04 08:00:00]. Find the time interval where channel 627 exhibits the most similar pattern within the search context [2019-12-06 01:30:00 to 2019-12-12 11:15:00]. (Output format: [YYYY-MM-DD HH:MM:SS, YYYY-MM-DD HH:MM:SS])", "answer": "['2019-12-11 01:15:00', '2019-12-11 12:15:00']", "ground_truth": [ "2019-12-11 01:15:00", "2019-12-11 12:15:00" ], "eval_metric": "iou", "channel": "627", "ts_data_path": "ts_data/L2_T3_Subsequence_Matching_00470.csv", "meta": { "prototype": "double-peak pattern", "query_window": "[2019-12-03 21:00:00 to 2019-12-04 08:00:00]", "search_window": "[2019-12-06 01:30:00 to 2019-12-12 11:15:00]", "source": "causal_rivers" } }, { "id": "L2_T3_Subsequence_Matching_00472", "level": 2, "level_name": "Pattern Recognition", "category": "Subsequence Matching", "subtask": "Subsequence Matching", "question": "Analyze the reference pattern in [2019-02-21 18:00:00 to 2019-02-22 06:15:00]. Find the time interval where channel 680 exhibits the most similar pattern within the search context [2019-02-23 01:15:00 to 2019-02-24 04:00:00]. (Output format: [YYYY-MM-DD HH:MM:SS, YYYY-MM-DD HH:MM:SS])", "answer": "['2019-02-23 09:00:00', '2019-02-23 21:15:00']", "ground_truth": [ "2019-02-23 09:00:00", "2019-02-23 21:15:00" ], "eval_metric": "iou", "channel": "680", "ts_data_path": "ts_data/L2_T3_Subsequence_Matching_00472.csv", "meta": { "prototype": "bell curve", "query_window": "[2019-02-21 18:00:00 to 2019-02-22 06:15:00]", "search_window": "[2019-02-23 01:15:00 to 2019-02-24 04:00:00]", "source": "causal_rivers" } }, { "id": "L2_T3_Subsequence_Matching_00475", "level": 2, "level_name": "Pattern Recognition", "category": "Subsequence Matching", "subtask": "Subsequence Matching", "question": "Analyze the reference pattern in [2023-06-07 12:00:00 to 2023-06-08 02:45:00]. Find the time interval where channel 728 exhibits the most similar pattern within the search context [2023-06-09 01:30:00 to 2023-06-10 05:00:00]. (Output format: [YYYY-MM-DD HH:MM:SS, YYYY-MM-DD HH:MM:SS])", "answer": "['2023-06-09 09:15:00', '2023-06-10 00:00:00']", "ground_truth": [ "2023-06-09 09:15:00", "2023-06-10 00:00:00" ], "eval_metric": "iou", "channel": "728", "ts_data_path": "ts_data/L2_T3_Subsequence_Matching_00475.csv", "meta": { "prototype": "step pattern", "query_window": "[2023-06-07 12:00:00 to 2023-06-08 02:45:00]", "search_window": "[2023-06-09 01:30:00 to 2023-06-10 05:00:00]", "source": "causal_rivers" } }, { "id": "L2_T3_Subsequence_Matching_00478", "level": 2, "level_name": "Pattern Recognition", "category": "Subsequence Matching", "subtask": "Subsequence Matching", "question": "Analyze the reference pattern in [2023-05-13 04:30:00 to 2023-05-13 11:45:00]. Find the time interval where channel 762 exhibits the most similar pattern within the search context [2023-05-15 07:30:00 to 2023-05-22 06:15:00]. (Output format: [YYYY-MM-DD HH:MM:SS, YYYY-MM-DD HH:MM:SS])", "answer": "['2023-05-21 04:45:00', '2023-05-21 12:00:00']", "ground_truth": [ "2023-05-21 04:45:00", "2023-05-21 12:00:00" ], "eval_metric": "iou", "channel": "762", "ts_data_path": "ts_data/L2_T3_Subsequence_Matching_00478.csv", "meta": { "prototype": "sharp spike", "query_window": "[2023-05-13 04:30:00 to 2023-05-13 11:45:00]", "search_window": "[2023-05-15 07:30:00 to 2023-05-22 06:15:00]", "source": "causal_rivers" } }, { "id": "L2_T3_Subsequence_Matching_00479", "level": 2, "level_name": "Pattern Recognition", "category": "Subsequence Matching", "subtask": "Subsequence Matching", "question": "Analyze the reference pattern in [2020-01-20 02:00:00 to 2020-01-20 13:00:00]. Find the time interval where channel 811 exhibits the most similar pattern within the search context [2020-01-21 06:00:00 to 2020-01-22 09:00:00]. (Output format: [YYYY-MM-DD HH:MM:SS, YYYY-MM-DD HH:MM:SS])", "answer": "['2020-01-21 07:45:00', '2020-01-21 18:45:00']", "ground_truth": [ "2020-01-21 07:45:00", "2020-01-21 18:45:00" ], "eval_metric": "iou", "channel": "811", "ts_data_path": "ts_data/L2_T3_Subsequence_Matching_00479.csv", "meta": { "prototype": "double-peak pattern", "query_window": "[2020-01-20 02:00:00 to 2020-01-20 13:00:00]", "search_window": "[2020-01-21 06:00:00 to 2020-01-22 09:00:00]", "source": "causal_rivers" } }, { "id": "L2_T3_Subsequence_Matching_00480", "level": 2, "level_name": "Pattern Recognition", "category": "Subsequence Matching", "subtask": "Subsequence Matching", "question": "Analyze the reference pattern in [2022-07-28 21:00:00 to 2022-07-29 09:15:00]. Find the time interval where channel 813 exhibits the most similar pattern within the search context [2022-07-30 22:00:00 to 2022-08-05 11:45:00]. (Output format: [YYYY-MM-DD HH:MM:SS, YYYY-MM-DD HH:MM:SS])", "answer": "['2022-07-31 20:00:00', '2022-08-01 08:15:00']", "ground_truth": [ "2022-07-31 20:00:00", "2022-08-01 08:15:00" ], "eval_metric": "iou", "channel": "813", "ts_data_path": "ts_data/L2_T3_Subsequence_Matching_00480.csv", "meta": { "prototype": "bell curve", "query_window": "[2022-07-28 21:00:00 to 2022-07-29 09:15:00]", "search_window": "[2022-07-30 22:00:00 to 2022-08-05 11:45:00]", "source": "causal_rivers" } }, { "id": "L2_T3_Subsequence_Matching_00481", "level": 2, "level_name": "Pattern Recognition", "category": "Subsequence Matching", "subtask": "Subsequence Matching", "question": "Analyze the reference pattern in [2021-09-20 22:30:00 to 2021-09-21 10:45:00]. Find the time interval where channel 865 exhibits the most similar pattern within the search context [2021-09-23 03:30:00 to 2021-09-29 09:15:00]. (Output format: [YYYY-MM-DD HH:MM:SS, YYYY-MM-DD HH:MM:SS])", "answer": "['2021-09-24 00:00:00', '2021-09-24 12:15:00']", "ground_truth": [ "2021-09-24 00:00:00", "2021-09-24 12:15:00" ], "eval_metric": "iou", "channel": "865", "ts_data_path": "ts_data/L2_T3_Subsequence_Matching_00481.csv", "meta": { "prototype": "bell curve", "query_window": "[2021-09-20 22:30:00 to 2021-09-21 10:45:00]", "search_window": "[2021-09-23 03:30:00 to 2021-09-29 09:15:00]", "source": "causal_rivers" } }, { "id": "L2_T3_Subsequence_Matching_00482", "level": 2, "level_name": "Pattern Recognition", "category": "Subsequence Matching", "subtask": "Subsequence Matching", "question": "Analyze the reference pattern in [2019-08-22 18:30:00 to 2019-08-23 06:45:00]. Find the time interval where channel 891 exhibits the most similar pattern within the search context [2019-08-24 07:30:00 to 2019-08-27 21:00:00]. (Output format: [YYYY-MM-DD HH:MM:SS, YYYY-MM-DD HH:MM:SS])", "answer": "['2019-08-26 09:00:00', '2019-08-26 21:15:00']", "ground_truth": [ "2019-08-26 09:00:00", "2019-08-26 21:15:00" ], "eval_metric": "iou", "channel": "891", "ts_data_path": "ts_data/L2_T3_Subsequence_Matching_00482.csv", "meta": { "prototype": "bell curve", "query_window": "[2019-08-22 18:30:00 to 2019-08-23 06:45:00]", "search_window": "[2019-08-24 07:30:00 to 2019-08-27 21:00:00]", "source": "causal_rivers" } }, { "id": "L2_T3_Subsequence_Matching_00483", "level": 2, "level_name": "Pattern Recognition", "category": "Subsequence Matching", "subtask": "Subsequence Matching", "question": "Analyze the reference pattern in [2022-11-28 22:45:00 to 2022-11-29 09:45:00]. Find the time interval where channel 894 exhibits the most similar pattern within the search context [2022-12-01 02:45:00 to 2022-12-07 10:30:00]. (Output format: [YYYY-MM-DD HH:MM:SS, YYYY-MM-DD HH:MM:SS])", "answer": "['2022-12-02 22:00:00', '2022-12-03 09:00:00']", "ground_truth": [ "2022-12-02 22:00:00", "2022-12-03 09:00:00" ], "eval_metric": "iou", "channel": "894", "ts_data_path": "ts_data/L2_T3_Subsequence_Matching_00483.csv", "meta": { "prototype": "double-peak pattern", "query_window": "[2022-11-28 22:45:00 to 2022-11-29 09:45:00]", "search_window": "[2022-12-01 02:45:00 to 2022-12-07 10:30:00]", "source": "causal_rivers" } }, { "id": "L2_T3_Subsequence_Matching_00485", "level": 2, "level_name": "Pattern Recognition", "category": "Subsequence Matching", "subtask": "Subsequence Matching", "question": "Analyze the reference pattern in [2019-01-23 12:00:00 to 2019-01-23 23:00:00]. Find the time interval where channel 897 exhibits the most similar pattern within the search context [2019-01-24 16:00:00 to 2019-01-26 00:45:00]. (Output format: [YYYY-MM-DD HH:MM:SS, YYYY-MM-DD HH:MM:SS])", "answer": "['2019-01-25 04:15:00', '2019-01-25 15:15:00']", "ground_truth": [ "2019-01-25 04:15:00", "2019-01-25 15:15:00" ], "eval_metric": "iou", "channel": "897", "ts_data_path": "ts_data/L2_T3_Subsequence_Matching_00485.csv", "meta": { "prototype": "double-peak pattern", "query_window": "[2019-01-23 12:00:00 to 2019-01-23 23:00:00]", "search_window": "[2019-01-24 16:00:00 to 2019-01-26 00:45:00]", "source": "causal_rivers" } }, { "id": "L2_T3_Subsequence_Matching_00486", "level": 2, "level_name": "Pattern Recognition", "category": "Subsequence Matching", "subtask": "Subsequence Matching", "question": "Analyze the reference pattern in [2019-07-30 18:00:00 to 2019-07-31 08:45:00]. Find the time interval where channel 933 exhibits the most similar pattern within the search context [2019-08-01 07:30:00 to 2019-08-03 12:30:00]. (Output format: [YYYY-MM-DD HH:MM:SS, YYYY-MM-DD HH:MM:SS])", "answer": "['2019-08-02 10:15:00', '2019-08-03 01:00:00']", "ground_truth": [ "2019-08-02 10:15:00", "2019-08-03 01:00:00" ], "eval_metric": "iou", "channel": "933", "ts_data_path": "ts_data/L2_T3_Subsequence_Matching_00486.csv", "meta": { "prototype": "step pattern", "query_window": "[2019-07-30 18:00:00 to 2019-07-31 08:45:00]", "search_window": "[2019-08-01 07:30:00 to 2019-08-03 12:30:00]", "source": "causal_rivers" } }, { "id": "L2_T3_Subsequence_Matching_00487", "level": 2, "level_name": "Pattern Recognition", "category": "Subsequence Matching", "subtask": "Subsequence Matching", "question": "Analyze the reference pattern in [2016-08-19 17:45:00 to 2016-08-20 06:00:00]. Find the time interval where channel HUFL exhibits the most similar pattern within the search context [2016-08-22 06:00:00 to 2016-08-29 17:15:00]. (Output format: [YYYY-MM-DD HH:MM:SS, YYYY-MM-DD HH:MM:SS])", "answer": "['2016-08-22 16:45:00', '2016-08-23 05:00:00']", "ground_truth": [ "2016-08-22 16:45:00", "2016-08-23 05:00:00" ], "eval_metric": "iou", "channel": "HUFL", "ts_data_path": "ts_data/L2_T3_Subsequence_Matching_00487.csv", "meta": { "prototype": "bell curve", "query_window": "[2016-08-19 17:45:00 to 2016-08-20 06:00:00]", "search_window": "[2016-08-22 06:00:00 to 2016-08-29 17:15:00]", "source": "ettm1" } }, { "id": "L2_T3_Subsequence_Matching_00488", "level": 2, "level_name": "Pattern Recognition", "category": "Subsequence Matching", "subtask": "Subsequence Matching", "question": "Analyze the reference pattern in [2018-02-20 06:00:00 to 2018-02-20 13:15:00]. Find the time interval where channel HULL exhibits the most similar pattern within the search context [2018-02-21 13:00:00 to 2018-02-25 04:00:00]. (Output format: [YYYY-MM-DD HH:MM:SS, YYYY-MM-DD HH:MM:SS])", "answer": "['2018-02-22 01:45:00', '2018-02-22 09:00:00']", "ground_truth": [ "2018-02-22 01:45:00", "2018-02-22 09:00:00" ], "eval_metric": "iou", "channel": "HULL", "ts_data_path": "ts_data/L2_T3_Subsequence_Matching_00488.csv", "meta": { "prototype": "sharp spike", "query_window": "[2018-02-20 06:00:00 to 2018-02-20 13:15:00]", "search_window": "[2018-02-21 13:00:00 to 2018-02-25 04:00:00]", "source": "ettm1" } }, { "id": "L2_T3_Subsequence_Matching_00489", "level": 2, "level_name": "Pattern Recognition", "category": "Subsequence Matching", "subtask": "Subsequence Matching", "question": "Analyze the reference pattern in [2016-08-22 13:30:00 to 2016-08-23 01:45:00]. Find the time interval where channel MUFL exhibits the most similar pattern within the search context [2016-08-24 21:30:00 to 2016-08-31 14:45:00]. (Output format: [YYYY-MM-DD HH:MM:SS, YYYY-MM-DD HH:MM:SS])", "answer": "['2016-08-26 03:30:00', '2016-08-26 15:45:00']", "ground_truth": [ "2016-08-26 03:30:00", "2016-08-26 15:45:00" ], "eval_metric": "iou", "channel": "MUFL", "ts_data_path": "ts_data/L2_T3_Subsequence_Matching_00489.csv", "meta": { "prototype": "bell curve", "query_window": "[2016-08-22 13:30:00 to 2016-08-23 01:45:00]", "search_window": "[2016-08-24 21:30:00 to 2016-08-31 14:45:00]", "source": "ettm1" } }, { "id": "L2_T3_Subsequence_Matching_00490", "level": 2, "level_name": "Pattern Recognition", "category": "Subsequence Matching", "subtask": "Subsequence Matching", "question": "Analyze the reference pattern in [2017-01-25 19:15:00 to 2017-01-26 02:30:00]. Find the time interval where channel MULL exhibits the most similar pattern within the search context [2017-01-27 02:30:00 to 2017-01-30 18:15:00]. (Output format: [YYYY-MM-DD HH:MM:SS, YYYY-MM-DD HH:MM:SS])", "answer": "['2017-01-29 17:15:00', '2017-01-30 00:30:00']", "ground_truth": [ "2017-01-29 17:15:00", "2017-01-30 00:30:00" ], "eval_metric": "iou", "channel": "MULL", "ts_data_path": "ts_data/L2_T3_Subsequence_Matching_00490.csv", "meta": { "prototype": "sharp spike", "query_window": "[2017-01-25 19:15:00 to 2017-01-26 02:30:00]", "search_window": "[2017-01-27 02:30:00 to 2017-01-30 18:15:00]", "source": "ettm1" } }, { "id": "L2_T3_Subsequence_Matching_00493", "level": 2, "level_name": "Pattern Recognition", "category": "Subsequence Matching", "subtask": "Subsequence Matching", "question": "Analyze the reference pattern in [2018-05-24 06:00:00 to 2018-05-24 18:15:00]. Find the time interval where channel OT exhibits the most similar pattern within the search context [2018-05-25 13:15:00 to 2018-05-27 23:30:00]. (Output format: [YYYY-MM-DD HH:MM:SS, YYYY-MM-DD HH:MM:SS])", "answer": "['2018-05-26 23:45:00', '2018-05-27 12:00:00']", "ground_truth": [ "2018-05-26 23:45:00", "2018-05-27 12:00:00" ], "eval_metric": "iou", "channel": "OT", "ts_data_path": "ts_data/L2_T3_Subsequence_Matching_00493.csv", "meta": { "prototype": "bell curve", "query_window": "[2018-05-24 06:00:00 to 2018-05-24 18:15:00]", "search_window": "[2018-05-25 13:15:00 to 2018-05-27 23:30:00]", "source": "ettm1" } }, { "id": "L3_T3_Causal_Anomaly_00386", "level": 3, "level_name": "Semantic Reasoning", "category": "Causal Anomaly", "subtask": "Causal Anomaly", "question": "Given that channel 67_up_2022 is the upstream source of channel 67_down_2022, identify the time period in 2022 where 67_down_2022 shows a significant causal anomaly, such as an flat line during high activity. (Output format: [YYYY-MM-DD HH:MM:SS, YYYY-MM-DD HH:MM:SS])", "answer": "[2022-02-17 21:00:00, 2022-02-20 22:15:00]", "ground_truth": [ "2022-02-17 21:00:00", "2022-02-20 22:15:00" ], "eval_metric": "iou", "channel": "67_down_2022", "ts_data_path": "ts_data/L3_T3_Causal_Anomaly_00386.csv", "meta": { "pair_upstream": "67_up_2022", "pair_downstream": "67_down_2022", "upstream_channel_raw": "67", "downstream_channel_raw": "67_synth", "break_kind": "flat_line", "year": 2022, "source": "causal_rivers" } }, { "id": "L3_T3_Causal_Anomaly_00387", "level": 3, "level_name": "Semantic Reasoning", "category": "Causal Anomaly", "subtask": "Causal Anomaly", "question": "Given that channel 71_up_2019 is the upstream source of channel 71_down_2019, identify the time period in 2019 where 71_down_2019 shows a significant causal anomaly, such as an inverse trend against the source. (Output format: [YYYY-MM-DD HH:MM:SS, YYYY-MM-DD HH:MM:SS])", "answer": "[2019-04-13 05:30:00, 2019-04-22 18:45:00]", "ground_truth": [ "2019-04-13 05:30:00", "2019-04-22 18:45:00" ], "eval_metric": "iou", "channel": "71_down_2019", "ts_data_path": "ts_data/L3_T3_Causal_Anomaly_00387.csv", "meta": { "pair_upstream": "71_up_2019", "pair_downstream": "71_down_2019", "upstream_channel_raw": "71", "downstream_channel_raw": "71_synth", "break_kind": "inverse", "year": 2019, "source": "causal_rivers" } }, { "id": "L3_T3_Causal_Anomaly_00388", "level": 3, "level_name": "Semantic Reasoning", "category": "Causal Anomaly", "subtask": "Causal Anomaly", "question": "Given that channel 99_up_2022 is the upstream source of channel 99_down_2022, identify the time period in 2022 where 99_down_2022 shows a significant causal anomaly, such as an flat line during high activity. (Output format: [YYYY-MM-DD HH:MM:SS, YYYY-MM-DD HH:MM:SS])", "answer": "[2022-04-06 07:45:00, 2022-04-12 13:15:00]", "ground_truth": [ "2022-04-06 07:45:00", "2022-04-12 13:15:00" ], "eval_metric": "iou", "channel": "99_down_2022", "ts_data_path": "ts_data/L3_T3_Causal_Anomaly_00388.csv", "meta": { "pair_upstream": "99_up_2022", "pair_downstream": "99_down_2022", "upstream_channel_raw": "99", "downstream_channel_raw": "99_synth", "break_kind": "flat_line", "year": 2022, "source": "causal_rivers" } }, { "id": "L3_T3_Causal_Anomaly_00389", "level": 3, "level_name": "Semantic Reasoning", "category": "Causal Anomaly", "subtask": "Causal Anomaly", "question": "Given that channel 123_up_2021 is the upstream source of channel 123_down_2021, identify the time period in 2021 where 123_down_2021 shows a significant causal anomaly, such as an inverse trend against the source. (Output format: [YYYY-MM-DD HH:MM:SS, YYYY-MM-DD HH:MM:SS])", "answer": "[2021-12-22 21:15:00, 2021-12-28 09:15:00]", "ground_truth": [ "2021-12-22 21:15:00", "2021-12-28 09:15:00" ], "eval_metric": "iou", "channel": "123_down_2021", "ts_data_path": "ts_data/L3_T3_Causal_Anomaly_00389.csv", "meta": { "pair_upstream": "123_up_2021", "pair_downstream": "123_down_2021", "upstream_channel_raw": "123", "downstream_channel_raw": "123_synth", "break_kind": "inverse", "year": 2021, "source": "causal_rivers" } }, { "id": "L3_T3_Causal_Anomaly_00390", "level": 3, "level_name": "Semantic Reasoning", "category": "Causal Anomaly", "subtask": "Causal Anomaly", "question": "Given that channel 124_up_2022 is the upstream source of channel 124_down_2022, identify the time period in 2022 where 124_down_2022 shows a significant causal anomaly, such as an inverse trend against the source. (Output format: [YYYY-MM-DD HH:MM:SS, YYYY-MM-DD HH:MM:SS])", "answer": "[2022-10-29 15:45:00, 2022-11-02 13:15:00]", "ground_truth": [ "2022-10-29 15:45:00", "2022-11-02 13:15:00" ], "eval_metric": "iou", "channel": "124_down_2022", "ts_data_path": "ts_data/L3_T3_Causal_Anomaly_00390.csv", "meta": { "pair_upstream": "124_up_2022", "pair_downstream": "124_down_2022", "upstream_channel_raw": "124", "downstream_channel_raw": "124_synth", "break_kind": "inverse", "year": 2022, "source": "causal_rivers" } }, { "id": "L3_T3_Causal_Anomaly_00391", "level": 3, "level_name": "Semantic Reasoning", "category": "Causal Anomaly", "subtask": "Causal Anomaly", "question": "Given that channel 146_up_2020 is the upstream source of channel 146_down_2020, identify the time period in 2020 where 146_down_2020 shows a significant causal anomaly, such as an inverse trend against the source. (Output format: [YYYY-MM-DD HH:MM:SS, YYYY-MM-DD HH:MM:SS])", "answer": "[2020-05-04 23:00:00, 2020-05-09 05:15:00]", "ground_truth": [ "2020-05-04 23:00:00", "2020-05-09 05:15:00" ], "eval_metric": "iou", "channel": "146_down_2020", "ts_data_path": "ts_data/L3_T3_Causal_Anomaly_00391.csv", "meta": { "pair_upstream": "146_up_2020", "pair_downstream": "146_down_2020", "upstream_channel_raw": "146", "downstream_channel_raw": "146_synth", "break_kind": "inverse", "year": 2020, "source": "causal_rivers" } }, { "id": "L3_T3_Causal_Anomaly_00393", "level": 3, "level_name": "Semantic Reasoning", "category": "Causal Anomaly", "subtask": "Causal Anomaly", "question": "Given that channel 151_up_2023 is the upstream source of channel 151_down_2023, identify the time period in 2023 where 151_down_2023 shows a significant causal anomaly, such as an flat line during high activity. (Output format: [YYYY-MM-DD HH:MM:SS, YYYY-MM-DD HH:MM:SS])", "answer": "[2023-05-02 02:00:00, 2023-05-08 06:45:00]", "ground_truth": [ "2023-05-02 02:00:00", "2023-05-08 06:45:00" ], "eval_metric": "iou", "channel": "151_down_2023", "ts_data_path": "ts_data/L3_T3_Causal_Anomaly_00393.csv", "meta": { "pair_upstream": "151_up_2023", "pair_downstream": "151_down_2023", "upstream_channel_raw": "151", "downstream_channel_raw": "151_synth", "break_kind": "flat_line", "year": 2023, "source": "causal_rivers" } }, { "id": "L3_T3_Causal_Anomaly_00395", "level": 3, "level_name": "Semantic Reasoning", "category": "Causal Anomaly", "subtask": "Causal Anomaly", "question": "Given that channel 155_up_2020 is the upstream source of channel 155_down_2020, identify the time period in 2020 where 155_down_2020 shows a significant causal anomaly, such as an flat line during high activity. (Output format: [YYYY-MM-DD HH:MM:SS, YYYY-MM-DD HH:MM:SS])", "answer": "[2020-04-10 14:45:00, 2020-04-15 20:30:00]", "ground_truth": [ "2020-04-10 14:45:00", "2020-04-15 20:30:00" ], "eval_metric": "iou", "channel": "155_down_2020", "ts_data_path": "ts_data/L3_T3_Causal_Anomaly_00395.csv", "meta": { "pair_upstream": "155_up_2020", "pair_downstream": "155_down_2020", "upstream_channel_raw": "155", "downstream_channel_raw": "155_synth", "break_kind": "flat_line", "year": 2020, "source": "causal_rivers" } }, { "id": "L3_T3_Causal_Anomaly_00396", "level": 3, "level_name": "Semantic Reasoning", "category": "Causal Anomaly", "subtask": "Causal Anomaly", "question": "Given that channel 166_up_2021 is the upstream source of channel 166_down_2021, identify the time period in 2021 where 166_down_2021 shows a significant causal anomaly, such as an inverse trend against the source. (Output format: [YYYY-MM-DD HH:MM:SS, YYYY-MM-DD HH:MM:SS])", "answer": "[2021-01-28 09:00:00, 2021-02-03 07:00:00]", "ground_truth": [ "2021-01-28 09:00:00", "2021-02-03 07:00:00" ], "eval_metric": "iou", "channel": "166_down_2021", "ts_data_path": "ts_data/L3_T3_Causal_Anomaly_00396.csv", "meta": { "pair_upstream": "166_up_2021", "pair_downstream": "166_down_2021", "upstream_channel_raw": "166", "downstream_channel_raw": "166_synth", "break_kind": "inverse", "year": 2021, "source": "causal_rivers" } }, { "id": "L3_T3_Causal_Anomaly_00397", "level": 3, "level_name": "Semantic Reasoning", "category": "Causal Anomaly", "subtask": "Causal Anomaly", "question": "Given that channel 169_up_2022 is the upstream source of channel 169_down_2022, identify the time period in 2022 where 169_down_2022 shows a significant causal anomaly, such as an inverse trend against the source. (Output format: [YYYY-MM-DD HH:MM:SS, YYYY-MM-DD HH:MM:SS])", "answer": "[2022-01-26 18:30:00, 2022-02-04 00:00:00]", "ground_truth": [ "2022-01-26 18:30:00", "2022-02-04 00:00:00" ], "eval_metric": "iou", "channel": "169_down_2022", "ts_data_path": "ts_data/L3_T3_Causal_Anomaly_00397.csv", "meta": { "pair_upstream": "169_up_2022", "pair_downstream": "169_down_2022", "upstream_channel_raw": "169", "downstream_channel_raw": "169_synth", "break_kind": "inverse", "year": 2022, "source": "causal_rivers" } }, { "id": "L3_T3_Causal_Anomaly_00399", "level": 3, "level_name": "Semantic Reasoning", "category": "Causal Anomaly", "subtask": "Causal Anomaly", "question": "Given that channel 172_up_2019 is the upstream source of channel 172_down_2019, identify the time period in 2019 where 172_down_2019 shows a significant causal anomaly, such as an flat line during high activity. (Output format: [YYYY-MM-DD HH:MM:SS, YYYY-MM-DD HH:MM:SS])", "answer": "[2019-08-01 06:15:00, 2019-08-10 08:00:00]", "ground_truth": [ "2019-08-01 06:15:00", "2019-08-10 08:00:00" ], "eval_metric": "iou", "channel": "172_down_2019", "ts_data_path": "ts_data/L3_T3_Causal_Anomaly_00399.csv", "meta": { "pair_upstream": "172_up_2019", "pair_downstream": "172_down_2019", "upstream_channel_raw": "172", "downstream_channel_raw": "172_synth", "break_kind": "flat_line", "year": 2019, "source": "causal_rivers" } }, { "id": "L3_T3_Causal_Anomaly_00401", "level": 3, "level_name": "Semantic Reasoning", "category": "Causal Anomaly", "subtask": "Causal Anomaly", "question": "Given that channel 177_up_2019 is the upstream source of channel 177_down_2019, identify the time period in 2019 where 177_down_2019 shows a significant causal anomaly, such as an inverse trend against the source. (Output format: [YYYY-MM-DD HH:MM:SS, YYYY-MM-DD HH:MM:SS])", "answer": "[2019-04-01 08:15:00, 2019-04-05 16:15:00]", "ground_truth": [ "2019-04-01 08:15:00", "2019-04-05 16:15:00" ], "eval_metric": "iou", "channel": "177_down_2019", "ts_data_path": "ts_data/L3_T3_Causal_Anomaly_00401.csv", "meta": { "pair_upstream": "177_up_2019", "pair_downstream": "177_down_2019", "upstream_channel_raw": "177", "downstream_channel_raw": "177_synth", "break_kind": "inverse", "year": 2019, "source": "causal_rivers" } }, { "id": "L3_T3_Causal_Anomaly_00403", "level": 3, "level_name": "Semantic Reasoning", "category": "Causal Anomaly", "subtask": "Causal Anomaly", "question": "Given that channel 245_up_2023 is the upstream source of channel 245_down_2023, identify the time period in 2023 where 245_down_2023 shows a significant causal anomaly, such as an inverse trend against the source. (Output format: [YYYY-MM-DD HH:MM:SS, YYYY-MM-DD HH:MM:SS])", "answer": "[2023-03-21 01:45:00, 2023-03-27 03:45:00]", "ground_truth": [ "2023-03-21 01:45:00", "2023-03-27 03:45:00" ], "eval_metric": "iou", "channel": "245_down_2023", "ts_data_path": "ts_data/L3_T3_Causal_Anomaly_00403.csv", "meta": { "pair_upstream": "245_up_2023", "pair_downstream": "245_down_2023", "upstream_channel_raw": "245", "downstream_channel_raw": "245_synth", "break_kind": "inverse", "year": 2023, "source": "causal_rivers" } }, { "id": "L3_T3_Causal_Anomaly_00404", "level": 3, "level_name": "Semantic Reasoning", "category": "Causal Anomaly", "subtask": "Causal Anomaly", "question": "Given that channel 312_up_2022 is the upstream source of channel 312_down_2022, identify the time period in 2022 where 312_down_2022 shows a significant causal anomaly, such as an inverse trend against the source. (Output format: [YYYY-MM-DD HH:MM:SS, YYYY-MM-DD HH:MM:SS])", "answer": "[2022-04-23 13:15:00, 2022-05-01 02:00:00]", "ground_truth": [ "2022-04-23 13:15:00", "2022-05-01 02:00:00" ], "eval_metric": "iou", "channel": "312_down_2022", "ts_data_path": "ts_data/L3_T3_Causal_Anomaly_00404.csv", "meta": { "pair_upstream": "312_up_2022", "pair_downstream": "312_down_2022", "upstream_channel_raw": "312", "downstream_channel_raw": "312_synth", "break_kind": "inverse", "year": 2022, "source": "causal_rivers" } }, { "id": "L3_T3_Causal_Anomaly_00405", "level": 3, "level_name": "Semantic Reasoning", "category": "Causal Anomaly", "subtask": "Causal Anomaly", "question": "Given that channel 430_up_2020 is the upstream source of channel 430_down_2020, identify the time period in 2020 where 430_down_2020 shows a significant causal anomaly, such as an inverse trend against the source. (Output format: [YYYY-MM-DD HH:MM:SS, YYYY-MM-DD HH:MM:SS])", "answer": "[2020-01-21 02:30:00, 2020-01-25 20:30:00]", "ground_truth": [ "2020-01-21 02:30:00", "2020-01-25 20:30:00" ], "eval_metric": "iou", "channel": "430_down_2020", "ts_data_path": "ts_data/L3_T3_Causal_Anomaly_00405.csv", "meta": { "pair_upstream": "430_up_2020", "pair_downstream": "430_down_2020", "upstream_channel_raw": "430", "downstream_channel_raw": "430_synth", "break_kind": "inverse", "year": 2020, "source": "causal_rivers" } }, { "id": "L3_T3_Causal_Anomaly_00407", "level": 3, "level_name": "Semantic Reasoning", "category": "Causal Anomaly", "subtask": "Causal Anomaly", "question": "Given that channel 495_up_2019 is the upstream source of channel 495_down_2019, identify the time period in 2019 where 495_down_2019 shows a significant causal anomaly, such as an flat line during high activity. (Output format: [YYYY-MM-DD HH:MM:SS, YYYY-MM-DD HH:MM:SS])", "answer": "[2019-04-01 13:45:00, 2019-04-10 09:15:00]", "ground_truth": [ "2019-04-01 13:45:00", "2019-04-10 09:15:00" ], "eval_metric": "iou", "channel": "495_down_2019", "ts_data_path": "ts_data/L3_T3_Causal_Anomaly_00407.csv", "meta": { "pair_upstream": "495_up_2019", "pair_downstream": "495_down_2019", "upstream_channel_raw": "495", "downstream_channel_raw": "495_synth", "break_kind": "flat_line", "year": 2019, "source": "causal_rivers" } }, { "id": "L3_T3_Causal_Anomaly_00408", "level": 3, "level_name": "Semantic Reasoning", "category": "Causal Anomaly", "subtask": "Causal Anomaly", "question": "Given that channel 496_up_2020 is the upstream source of channel 496_down_2020, identify the time period in 2020 where 496_down_2020 shows a significant causal anomaly, such as an flat line during high activity. (Output format: [YYYY-MM-DD HH:MM:SS, YYYY-MM-DD HH:MM:SS])", "answer": "[2020-09-05 16:00:00, 2020-09-12 09:00:00]", "ground_truth": [ "2020-09-05 16:00:00", "2020-09-12 09:00:00" ], "eval_metric": "iou", "channel": "496_down_2020", "ts_data_path": "ts_data/L3_T3_Causal_Anomaly_00408.csv", "meta": { "pair_upstream": "496_up_2020", "pair_downstream": "496_down_2020", "upstream_channel_raw": "496", "downstream_channel_raw": "496_synth", "break_kind": "flat_line", "year": 2020, "source": "causal_rivers" } }, { "id": "L3_T3_Causal_Anomaly_00409", "level": 3, "level_name": "Semantic Reasoning", "category": "Causal Anomaly", "subtask": "Causal Anomaly", "question": "Given that channel 501_up_2020 is the upstream source of channel 501_down_2020, identify the time period in 2020 where 501_down_2020 shows a significant causal anomaly, such as an flat line during high activity. (Output format: [YYYY-MM-DD HH:MM:SS, YYYY-MM-DD HH:MM:SS])", "answer": "[2020-05-02 20:45:00, 2020-05-09 09:30:00]", "ground_truth": [ "2020-05-02 20:45:00", "2020-05-09 09:30:00" ], "eval_metric": "iou", "channel": "501_down_2020", "ts_data_path": "ts_data/L3_T3_Causal_Anomaly_00409.csv", "meta": { "pair_upstream": "501_up_2020", "pair_downstream": "501_down_2020", "upstream_channel_raw": "501", "downstream_channel_raw": "501_synth", "break_kind": "flat_line", "year": 2020, "source": "causal_rivers" } }, { "id": "L3_T3_Causal_Anomaly_00410", "level": 3, "level_name": "Semantic Reasoning", "category": "Causal Anomaly", "subtask": "Causal Anomaly", "question": "Given that channel 578_up_2019 is the upstream source of channel 578_down_2019, identify the time period in 2019 where 578_down_2019 shows a significant causal anomaly, such as an flat line during high activity. (Output format: [YYYY-MM-DD HH:MM:SS, YYYY-MM-DD HH:MM:SS])", "answer": "[2019-06-24 15:45:00, 2019-07-03 17:15:00]", "ground_truth": [ "2019-06-24 15:45:00", "2019-07-03 17:15:00" ], "eval_metric": "iou", "channel": "578_down_2019", "ts_data_path": "ts_data/L3_T3_Causal_Anomaly_00410.csv", "meta": { "pair_upstream": "578_up_2019", "pair_downstream": "578_down_2019", "upstream_channel_raw": "578", "downstream_channel_raw": "578_synth", "break_kind": "flat_line", "year": 2019, "source": "causal_rivers" } }, { "id": "L3_T3_Causal_Anomaly_00411", "level": 3, "level_name": "Semantic Reasoning", "category": "Causal Anomaly", "subtask": "Causal Anomaly", "question": "Given that channel 580_up_2023 is the upstream source of channel 580_down_2023, identify the time period in 2023 where 580_down_2023 shows a significant causal anomaly, such as an flat line during high activity. (Output format: [YYYY-MM-DD HH:MM:SS, YYYY-MM-DD HH:MM:SS])", "answer": "[2023-03-18 17:45:00, 2023-03-27 21:15:00]", "ground_truth": [ "2023-03-18 17:45:00", "2023-03-27 21:15:00" ], "eval_metric": "iou", "channel": "580_down_2023", "ts_data_path": "ts_data/L3_T3_Causal_Anomaly_00411.csv", "meta": { "pair_upstream": "580_up_2023", "pair_downstream": "580_down_2023", "upstream_channel_raw": "580", "downstream_channel_raw": "580_synth", "break_kind": "flat_line", "year": 2023, "source": "causal_rivers" } }, { "id": "L3_T3_Causal_Anomaly_00413", "level": 3, "level_name": "Semantic Reasoning", "category": "Causal Anomaly", "subtask": "Causal Anomaly", "question": "Given that channel 591_up_2022 is the upstream source of channel 591_down_2022, identify the time period in 2022 where 591_down_2022 shows a significant causal anomaly, such as an inverse trend against the source. (Output format: [YYYY-MM-DD HH:MM:SS, YYYY-MM-DD HH:MM:SS])", "answer": "[2022-08-08 08:30:00, 2022-08-17 18:15:00]", "ground_truth": [ "2022-08-08 08:30:00", "2022-08-17 18:15:00" ], "eval_metric": "iou", "channel": "591_down_2022", "ts_data_path": "ts_data/L3_T3_Causal_Anomaly_00413.csv", "meta": { "pair_upstream": "591_up_2022", "pair_downstream": "591_down_2022", "upstream_channel_raw": "591", "downstream_channel_raw": "591_synth", "break_kind": "inverse", "year": 2022, "source": "causal_rivers" } }, { "id": "L3_T3_Causal_Anomaly_00414", "level": 3, "level_name": "Semantic Reasoning", "category": "Causal Anomaly", "subtask": "Causal Anomaly", "question": "Given that channel 595_up_2023 is the upstream source of channel 595_down_2023, identify the time period in 2023 where 595_down_2023 shows a significant causal anomaly, such as an inverse trend against the source. (Output format: [YYYY-MM-DD HH:MM:SS, YYYY-MM-DD HH:MM:SS])", "answer": "[2023-06-05 04:30:00, 2023-06-09 23:00:00]", "ground_truth": [ "2023-06-05 04:30:00", "2023-06-09 23:00:00" ], "eval_metric": "iou", "channel": "595_down_2023", "ts_data_path": "ts_data/L3_T3_Causal_Anomaly_00414.csv", "meta": { "pair_upstream": "595_up_2023", "pair_downstream": "595_down_2023", "upstream_channel_raw": "595", "downstream_channel_raw": "595_synth", "break_kind": "inverse", "year": 2023, "source": "causal_rivers" } }, { "id": "L3_T3_Causal_Anomaly_00415", "level": 3, "level_name": "Semantic Reasoning", "category": "Causal Anomaly", "subtask": "Causal Anomaly", "question": "Given that channel 625_up_2021 is the upstream source of channel 625_down_2021, identify the time period in 2021 where 625_down_2021 shows a significant causal anomaly, such as an flat line during high activity. (Output format: [YYYY-MM-DD HH:MM:SS, YYYY-MM-DD HH:MM:SS])", "answer": "[2021-10-09 14:45:00, 2021-10-13 03:00:00]", "ground_truth": [ "2021-10-09 14:45:00", "2021-10-13 03:00:00" ], "eval_metric": "iou", "channel": "625_down_2021", "ts_data_path": "ts_data/L3_T3_Causal_Anomaly_00415.csv", "meta": { "pair_upstream": "625_up_2021", "pair_downstream": "625_down_2021", "upstream_channel_raw": "625", "downstream_channel_raw": "625_synth", "break_kind": "flat_line", "year": 2021, "source": "causal_rivers" } }, { "id": "L3_T3_Causal_Anomaly_00416", "level": 3, "level_name": "Semantic Reasoning", "category": "Causal Anomaly", "subtask": "Causal Anomaly", "question": "Given that channel 626_up_2020 is the upstream source of channel 626_down_2020, identify the time period in 2020 where 626_down_2020 shows a significant causal anomaly, such as an inverse trend against the source. (Output format: [YYYY-MM-DD HH:MM:SS, YYYY-MM-DD HH:MM:SS])", "answer": "[2020-03-17 04:00:00, 2020-03-26 12:45:00]", "ground_truth": [ "2020-03-17 04:00:00", "2020-03-26 12:45:00" ], "eval_metric": "iou", "channel": "626_down_2020", "ts_data_path": "ts_data/L3_T3_Causal_Anomaly_00416.csv", "meta": { "pair_upstream": "626_up_2020", "pair_downstream": "626_down_2020", "upstream_channel_raw": "626", "downstream_channel_raw": "626_synth", "break_kind": "inverse", "year": 2020, "source": "causal_rivers" } }, { "id": "L3_T3_Causal_Anomaly_00417", "level": 3, "level_name": "Semantic Reasoning", "category": "Causal Anomaly", "subtask": "Causal Anomaly", "question": "Given that channel 627_up_2023 is the upstream source of channel 627_down_2023, identify the time period in 2023 where 627_down_2023 shows a significant causal anomaly, such as an flat line during high activity. (Output format: [YYYY-MM-DD HH:MM:SS, YYYY-MM-DD HH:MM:SS])", "answer": "[2023-04-12 12:45:00, 2023-04-21 22:15:00]", "ground_truth": [ "2023-04-12 12:45:00", "2023-04-21 22:15:00" ], "eval_metric": "iou", "channel": "627_down_2023", "ts_data_path": "ts_data/L3_T3_Causal_Anomaly_00417.csv", "meta": { "pair_upstream": "627_up_2023", "pair_downstream": "627_down_2023", "upstream_channel_raw": "627", "downstream_channel_raw": "627_synth", "break_kind": "flat_line", "year": 2023, "source": "causal_rivers" } }, { "id": "L3_T3_Causal_Anomaly_00419", "level": 3, "level_name": "Semantic Reasoning", "category": "Causal Anomaly", "subtask": "Causal Anomaly", "question": "Given that channel 680_up_2019 is the upstream source of channel 680_down_2019, identify the time period in 2019 where 680_down_2019 shows a significant causal anomaly, such as an inverse trend against the source. (Output format: [YYYY-MM-DD HH:MM:SS, YYYY-MM-DD HH:MM:SS])", "answer": "[2019-05-19 15:00:00, 2019-05-23 01:45:00]", "ground_truth": [ "2019-05-19 15:00:00", "2019-05-23 01:45:00" ], "eval_metric": "iou", "channel": "680_down_2019", "ts_data_path": "ts_data/L3_T3_Causal_Anomaly_00419.csv", "meta": { "pair_upstream": "680_up_2019", "pair_downstream": "680_down_2019", "upstream_channel_raw": "680", "downstream_channel_raw": "680_synth", "break_kind": "inverse", "year": 2019, "source": "causal_rivers" } }, { "id": "L3_T3_Causal_Anomaly_00420", "level": 3, "level_name": "Semantic Reasoning", "category": "Causal Anomaly", "subtask": "Causal Anomaly", "question": "Given that channel 683_up_2022 is the upstream source of channel 683_down_2022, identify the time period in 2022 where 683_down_2022 shows a significant causal anomaly, such as an inverse trend against the source. (Output format: [YYYY-MM-DD HH:MM:SS, YYYY-MM-DD HH:MM:SS])", "answer": "[2022-05-15 13:30:00, 2022-05-19 13:15:00]", "ground_truth": [ "2022-05-15 13:30:00", "2022-05-19 13:15:00" ], "eval_metric": "iou", "channel": "683_down_2022", "ts_data_path": "ts_data/L3_T3_Causal_Anomaly_00420.csv", "meta": { "pair_upstream": "683_up_2022", "pair_downstream": "683_down_2022", "upstream_channel_raw": "683", "downstream_channel_raw": "683_synth", "break_kind": "inverse", "year": 2022, "source": "causal_rivers" } }, { "id": "L3_T3_Causal_Anomaly_00421", "level": 3, "level_name": "Semantic Reasoning", "category": "Causal Anomaly", "subtask": "Causal Anomaly", "question": "Given that channel 727_up_2023 is the upstream source of channel 727_down_2023, identify the time period in 2023 where 727_down_2023 shows a significant causal anomaly, such as an flat line during high activity. (Output format: [YYYY-MM-DD HH:MM:SS, YYYY-MM-DD HH:MM:SS])", "answer": "[2023-08-06 01:30:00, 2023-08-10 13:15:00]", "ground_truth": [ "2023-08-06 01:30:00", "2023-08-10 13:15:00" ], "eval_metric": "iou", "channel": "727_down_2023", "ts_data_path": "ts_data/L3_T3_Causal_Anomaly_00421.csv", "meta": { "pair_upstream": "727_up_2023", "pair_downstream": "727_down_2023", "upstream_channel_raw": "727", "downstream_channel_raw": "727_synth", "break_kind": "flat_line", "year": 2023, "source": "causal_rivers" } }, { "id": "L3_T3_Causal_Anomaly_00422", "level": 3, "level_name": "Semantic Reasoning", "category": "Causal Anomaly", "subtask": "Causal Anomaly", "question": "Given that channel 728_up_2022 is the upstream source of channel 728_down_2022, identify the time period in 2022 where 728_down_2022 shows a significant causal anomaly, such as an inverse trend against the source. (Output format: [YYYY-MM-DD HH:MM:SS, YYYY-MM-DD HH:MM:SS])", "answer": "[2022-01-05 15:00:00, 2022-01-08 16:30:00]", "ground_truth": [ "2022-01-05 15:00:00", "2022-01-08 16:30:00" ], "eval_metric": "iou", "channel": "728_down_2022", "ts_data_path": "ts_data/L3_T3_Causal_Anomaly_00422.csv", "meta": { "pair_upstream": "728_up_2022", "pair_downstream": "728_down_2022", "upstream_channel_raw": "728", "downstream_channel_raw": "728_synth", "break_kind": "inverse", "year": 2022, "source": "causal_rivers" } }, { "id": "L3_T3_Causal_Anomaly_00423", "level": 3, "level_name": "Semantic Reasoning", "category": "Causal Anomaly", "subtask": "Causal Anomaly", "question": "Given that channel 729_up_2019 is the upstream source of channel 729_down_2019, identify the time period in 2019 where 729_down_2019 shows a significant causal anomaly, such as an inverse trend against the source. (Output format: [YYYY-MM-DD HH:MM:SS, YYYY-MM-DD HH:MM:SS])", "answer": "[2019-04-28 12:45:00, 2019-05-04 18:15:00]", "ground_truth": [ "2019-04-28 12:45:00", "2019-05-04 18:15:00" ], "eval_metric": "iou", "channel": "729_down_2019", "ts_data_path": "ts_data/L3_T3_Causal_Anomaly_00423.csv", "meta": { "pair_upstream": "729_up_2019", "pair_downstream": "729_down_2019", "upstream_channel_raw": "729", "downstream_channel_raw": "729_synth", "break_kind": "inverse", "year": 2019, "source": "causal_rivers" } }, { "id": "L3_T3_Causal_Anomaly_00424", "level": 3, "level_name": "Semantic Reasoning", "category": "Causal Anomaly", "subtask": "Causal Anomaly", "question": "Given that channel 754_up_2022 is the upstream source of channel 754_down_2022, identify the time period in 2022 where 754_down_2022 shows a significant causal anomaly, such as an flat line during high activity. (Output format: [YYYY-MM-DD HH:MM:SS, YYYY-MM-DD HH:MM:SS])", "answer": "[2022-02-02 05:15:00, 2022-02-09 23:15:00]", "ground_truth": [ "2022-02-02 05:15:00", "2022-02-09 23:15:00" ], "eval_metric": "iou", "channel": "754_down_2022", "ts_data_path": "ts_data/L3_T3_Causal_Anomaly_00424.csv", "meta": { "pair_upstream": "754_up_2022", "pair_downstream": "754_down_2022", "upstream_channel_raw": "754", "downstream_channel_raw": "754_synth", "break_kind": "flat_line", "year": 2022, "source": "causal_rivers" } }, { "id": "L3_T3_Causal_Anomaly_00427", "level": 3, "level_name": "Semantic Reasoning", "category": "Causal Anomaly", "subtask": "Causal Anomaly", "question": "Given that channel 813_up_2023 is the upstream source of channel 813_down_2023, identify the time period in 2023 where 813_down_2023 shows a significant causal anomaly, such as an flat line during high activity. (Output format: [YYYY-MM-DD HH:MM:SS, YYYY-MM-DD HH:MM:SS])", "answer": "[2023-07-03 08:00:00, 2023-07-07 09:15:00]", "ground_truth": [ "2023-07-03 08:00:00", "2023-07-07 09:15:00" ], "eval_metric": "iou", "channel": "813_down_2023", "ts_data_path": "ts_data/L3_T3_Causal_Anomaly_00427.csv", "meta": { "pair_upstream": "813_up_2023", "pair_downstream": "813_down_2023", "upstream_channel_raw": "813", "downstream_channel_raw": "813_synth", "break_kind": "flat_line", "year": 2023, "source": "causal_rivers" } }, { "id": "L3_T3_Causal_Anomaly_00428", "level": 3, "level_name": "Semantic Reasoning", "category": "Causal Anomaly", "subtask": "Causal Anomaly", "question": "Given that channel 865_up_2020 is the upstream source of channel 865_down_2020, identify the time period in 2020 where 865_down_2020 shows a significant causal anomaly, such as an inverse trend against the source. (Output format: [YYYY-MM-DD HH:MM:SS, YYYY-MM-DD HH:MM:SS])", "answer": "[2020-06-03 19:45:00, 2020-06-11 08:30:00]", "ground_truth": [ "2020-06-03 19:45:00", "2020-06-11 08:30:00" ], "eval_metric": "iou", "channel": "865_down_2020", "ts_data_path": "ts_data/L3_T3_Causal_Anomaly_00428.csv", "meta": { "pair_upstream": "865_up_2020", "pair_downstream": "865_down_2020", "upstream_channel_raw": "865", "downstream_channel_raw": "865_synth", "break_kind": "inverse", "year": 2020, "source": "causal_rivers" } }, { "id": "L3_T3_Causal_Anomaly_00429", "level": 3, "level_name": "Semantic Reasoning", "category": "Causal Anomaly", "subtask": "Causal Anomaly", "question": "Given that channel 891_up_2019 is the upstream source of channel 891_down_2019, identify the time period in 2019 where 891_down_2019 shows a significant causal anomaly, such as an flat line during high activity. (Output format: [YYYY-MM-DD HH:MM:SS, YYYY-MM-DD HH:MM:SS])", "answer": "[2019-08-31 02:30:00, 2019-09-03 22:00:00]", "ground_truth": [ "2019-08-31 02:30:00", "2019-09-03 22:00:00" ], "eval_metric": "iou", "channel": "891_down_2019", "ts_data_path": "ts_data/L3_T3_Causal_Anomaly_00429.csv", "meta": { "pair_upstream": "891_up_2019", "pair_downstream": "891_down_2019", "upstream_channel_raw": "891", "downstream_channel_raw": "891_synth", "break_kind": "flat_line", "year": 2019, "source": "causal_rivers" } }, { "id": "L3_T3_Causal_Anomaly_00432", "level": 3, "level_name": "Semantic Reasoning", "category": "Causal Anomaly", "subtask": "Causal Anomaly", "question": "Given that channel 897_up_2020 is the upstream source of channel 897_down_2020, identify the time period in 2020 where 897_down_2020 shows a significant causal anomaly, such as an flat line during high activity. (Output format: [YYYY-MM-DD HH:MM:SS, YYYY-MM-DD HH:MM:SS])", "answer": "[2020-11-11 17:45:00, 2020-11-20 18:00:00]", "ground_truth": [ "2020-11-11 17:45:00", "2020-11-20 18:00:00" ], "eval_metric": "iou", "channel": "897_down_2020", "ts_data_path": "ts_data/L3_T3_Causal_Anomaly_00432.csv", "meta": { "pair_upstream": "897_up_2020", "pair_downstream": "897_down_2020", "upstream_channel_raw": "897", "downstream_channel_raw": "897_synth", "break_kind": "flat_line", "year": 2020, "source": "causal_rivers" } }, { "id": "L3_T3_Causal_Anomaly_00433", "level": 3, "level_name": "Semantic Reasoning", "category": "Causal Anomaly", "subtask": "Causal Anomaly", "question": "Given that channel 933_up_2019 is the upstream source of channel 933_down_2019, identify the time period in 2019 where 933_down_2019 shows a significant causal anomaly, such as an inverse trend against the source. (Output format: [YYYY-MM-DD HH:MM:SS, YYYY-MM-DD HH:MM:SS])", "answer": "[2019-04-05 19:30:00, 2019-04-11 00:45:00]", "ground_truth": [ "2019-04-05 19:30:00", "2019-04-11 00:45:00" ], "eval_metric": "iou", "channel": "933_down_2019", "ts_data_path": "ts_data/L3_T3_Causal_Anomaly_00433.csv", "meta": { "pair_upstream": "933_up_2019", "pair_downstream": "933_down_2019", "upstream_channel_raw": "933", "downstream_channel_raw": "933_synth", "break_kind": "inverse", "year": 2019, "source": "causal_rivers" } }, { "id": "L3_T3_Causal_Anomaly_00435", "level": 3, "level_name": "Semantic Reasoning", "category": "Causal Anomaly", "subtask": "Causal Anomaly", "question": "Given that channel 71_up_2021 is the upstream source of channel 71_down_2021, identify the time period in 2021 where 71_down_2021 shows a significant causal anomaly, such as an flat line during high activity. (Output format: [YYYY-MM-DD HH:MM:SS, YYYY-MM-DD HH:MM:SS])", "answer": "[2021-12-22 21:15:00, 2021-12-26 17:30:00]", "ground_truth": [ "2021-12-22 21:15:00", "2021-12-26 17:30:00" ], "eval_metric": "iou", "channel": "71_down_2021", "ts_data_path": "ts_data/L3_T3_Causal_Anomaly_00435.csv", "meta": { "pair_upstream": "71_up_2021", "pair_downstream": "71_down_2021", "upstream_channel_raw": "71", "downstream_channel_raw": "71_synth", "break_kind": "flat_line", "year": 2021, "source": "causal_rivers" } }, { "id": "L3_T3_Causal_Anomaly_00437", "level": 3, "level_name": "Semantic Reasoning", "category": "Causal Anomaly", "subtask": "Causal Anomaly", "question": "Given that channel 123_up_2021 is the upstream source of channel 123_down_2021, identify the time period in 2021 where 123_down_2021 shows a significant causal anomaly, such as an flat line during high activity. (Output format: [YYYY-MM-DD HH:MM:SS, YYYY-MM-DD HH:MM:SS])", "answer": "[2021-12-13 01:30:00, 2021-12-18 08:15:00]", "ground_truth": [ "2021-12-13 01:30:00", "2021-12-18 08:15:00" ], "eval_metric": "iou", "channel": "123_down_2021", "ts_data_path": "ts_data/L3_T3_Causal_Anomaly_00437.csv", "meta": { "pair_upstream": "123_up_2021", "pair_downstream": "123_down_2021", "upstream_channel_raw": "123", "downstream_channel_raw": "123_synth", "break_kind": "flat_line", "year": 2021, "source": "causal_rivers" } }, { "id": "L3_T3_Causal_Anomaly_00440", "level": 3, "level_name": "Semantic Reasoning", "category": "Causal Anomaly", "subtask": "Causal Anomaly", "question": "Given that channel 147_up_2023 is the upstream source of channel 147_down_2023, identify the time period in 2023 where 147_down_2023 shows a significant causal anomaly, such as an flat line during high activity. (Output format: [YYYY-MM-DD HH:MM:SS, YYYY-MM-DD HH:MM:SS])", "answer": "[2023-06-14 14:45:00, 2023-06-24 11:00:00]", "ground_truth": [ "2023-06-14 14:45:00", "2023-06-24 11:00:00" ], "eval_metric": "iou", "channel": "147_down_2023", "ts_data_path": "ts_data/L3_T3_Causal_Anomaly_00440.csv", "meta": { "pair_upstream": "147_up_2023", "pair_downstream": "147_down_2023", "upstream_channel_raw": "147", "downstream_channel_raw": "147_synth", "break_kind": "flat_line", "year": 2023, "source": "causal_rivers" } }, { "id": "L3_T3_Causal_Anomaly_00443", "level": 3, "level_name": "Semantic Reasoning", "category": "Causal Anomaly", "subtask": "Causal Anomaly", "question": "Given that channel 155_up_2023 is the upstream source of channel 155_down_2023, identify the time period in 2023 where 155_down_2023 shows a significant causal anomaly, such as an flat line during high activity. (Output format: [YYYY-MM-DD HH:MM:SS, YYYY-MM-DD HH:MM:SS])", "answer": "[2023-03-17 09:15:00, 2023-03-21 12:30:00]", "ground_truth": [ "2023-03-17 09:15:00", "2023-03-21 12:30:00" ], "eval_metric": "iou", "channel": "155_down_2023", "ts_data_path": "ts_data/L3_T3_Causal_Anomaly_00443.csv", "meta": { "pair_upstream": "155_up_2023", "pair_downstream": "155_down_2023", "upstream_channel_raw": "155", "downstream_channel_raw": "155_synth", "break_kind": "flat_line", "year": 2023, "source": "causal_rivers" } }, { "id": "L3_T3_Causal_Anomaly_00444", "level": 3, "level_name": "Semantic Reasoning", "category": "Causal Anomaly", "subtask": "Causal Anomaly", "question": "Given that channel 166_up_2021 is the upstream source of channel 166_down_2021, identify the time period in 2021 where 166_down_2021 shows a significant causal anomaly, such as an inverse trend against the source. (Output format: [YYYY-MM-DD HH:MM:SS, YYYY-MM-DD HH:MM:SS])", "answer": "[2021-10-18 14:15:00, 2021-10-25 03:15:00]", "ground_truth": [ "2021-10-18 14:15:00", "2021-10-25 03:15:00" ], "eval_metric": "iou", "channel": "166_down_2021", "ts_data_path": "ts_data/L3_T3_Causal_Anomaly_00444.csv", "meta": { "pair_upstream": "166_up_2021", "pair_downstream": "166_down_2021", "upstream_channel_raw": "166", "downstream_channel_raw": "166_synth", "break_kind": "inverse", "year": 2021, "source": "causal_rivers" } }, { "id": "L3_T3_Causal_Anomaly_00447", "level": 3, "level_name": "Semantic Reasoning", "category": "Causal Anomaly", "subtask": "Causal Anomaly", "question": "Given that channel 172_up_2020 is the upstream source of channel 172_down_2020, identify the time period in 2020 where 172_down_2020 shows a significant causal anomaly, such as an flat line during high activity. (Output format: [YYYY-MM-DD HH:MM:SS, YYYY-MM-DD HH:MM:SS])", "answer": "[2020-05-07 07:00:00, 2020-05-14 18:30:00]", "ground_truth": [ "2020-05-07 07:00:00", "2020-05-14 18:30:00" ], "eval_metric": "iou", "channel": "172_down_2020", "ts_data_path": "ts_data/L3_T3_Causal_Anomaly_00447.csv", "meta": { "pair_upstream": "172_up_2020", "pair_downstream": "172_down_2020", "upstream_channel_raw": "172", "downstream_channel_raw": "172_synth", "break_kind": "flat_line", "year": 2020, "source": "causal_rivers" } }, { "id": "L3_T3_Causal_Anomaly_00448", "level": 3, "level_name": "Semantic Reasoning", "category": "Causal Anomaly", "subtask": "Causal Anomaly", "question": "Given that channel 173_up_2022 is the upstream source of channel 173_down_2022, identify the time period in 2022 where 173_down_2022 shows a significant causal anomaly, such as an inverse trend against the source. (Output format: [YYYY-MM-DD HH:MM:SS, YYYY-MM-DD HH:MM:SS])", "answer": "[2022-10-24 21:15:00, 2022-11-01 13:45:00]", "ground_truth": [ "2022-10-24 21:15:00", "2022-11-01 13:45:00" ], "eval_metric": "iou", "channel": "173_down_2022", "ts_data_path": "ts_data/L3_T3_Causal_Anomaly_00448.csv", "meta": { "pair_upstream": "173_up_2022", "pair_downstream": "173_down_2022", "upstream_channel_raw": "173", "downstream_channel_raw": "173_synth", "break_kind": "inverse", "year": 2022, "source": "causal_rivers" } }, { "id": "L3_T3_Causal_Anomaly_00449", "level": 3, "level_name": "Semantic Reasoning", "category": "Causal Anomaly", "subtask": "Causal Anomaly", "question": "Given that channel 177_up_2020 is the upstream source of channel 177_down_2020, identify the time period in 2020 where 177_down_2020 shows a significant causal anomaly, such as an inverse trend against the source. (Output format: [YYYY-MM-DD HH:MM:SS, YYYY-MM-DD HH:MM:SS])", "answer": "[2020-02-17 17:30:00, 2020-02-26 17:30:00]", "ground_truth": [ "2020-02-17 17:30:00", "2020-02-26 17:30:00" ], "eval_metric": "iou", "channel": "177_down_2020", "ts_data_path": "ts_data/L3_T3_Causal_Anomaly_00449.csv", "meta": { "pair_upstream": "177_up_2020", "pair_downstream": "177_down_2020", "upstream_channel_raw": "177", "downstream_channel_raw": "177_synth", "break_kind": "inverse", "year": 2020, "source": "causal_rivers" } }, { "id": "L3_T3_Causal_Anomaly_00450", "level": 3, "level_name": "Semantic Reasoning", "category": "Causal Anomaly", "subtask": "Causal Anomaly", "question": "Given that channel 237_up_2021 is the upstream source of channel 237_down_2021, identify the time period in 2021 where 237_down_2021 shows a significant causal anomaly, such as an flat line during high activity. (Output format: [YYYY-MM-DD HH:MM:SS, YYYY-MM-DD HH:MM:SS])", "answer": "[2021-03-06 05:30:00, 2021-03-09 17:15:00]", "ground_truth": [ "2021-03-06 05:30:00", "2021-03-09 17:15:00" ], "eval_metric": "iou", "channel": "237_down_2021", "ts_data_path": "ts_data/L3_T3_Causal_Anomaly_00450.csv", "meta": { "pair_upstream": "237_up_2021", "pair_downstream": "237_down_2021", "upstream_channel_raw": "237", "downstream_channel_raw": "237_synth", "break_kind": "flat_line", "year": 2021, "source": "causal_rivers" } }, { "id": "L3_T3_Causal_Anomaly_00453", "level": 3, "level_name": "Semantic Reasoning", "category": "Causal Anomaly", "subtask": "Causal Anomaly", "question": "Given that channel 430_up_2021 is the upstream source of channel 430_down_2021, identify the time period in 2021 where 430_down_2021 shows a significant causal anomaly, such as an inverse trend against the source. (Output format: [YYYY-MM-DD HH:MM:SS, YYYY-MM-DD HH:MM:SS])", "answer": "[2021-09-12 14:15:00, 2021-09-16 16:45:00]", "ground_truth": [ "2021-09-12 14:15:00", "2021-09-16 16:45:00" ], "eval_metric": "iou", "channel": "430_down_2021", "ts_data_path": "ts_data/L3_T3_Causal_Anomaly_00453.csv", "meta": { "pair_upstream": "430_up_2021", "pair_downstream": "430_down_2021", "upstream_channel_raw": "430", "downstream_channel_raw": "430_synth", "break_kind": "inverse", "year": 2021, "source": "causal_rivers" } }, { "id": "L3_T3_Causal_Anomaly_00454", "level": 3, "level_name": "Semantic Reasoning", "category": "Causal Anomaly", "subtask": "Causal Anomaly", "question": "Given that channel 441_up_2021 is the upstream source of channel 441_down_2021, identify the time period in 2021 where 441_down_2021 shows a significant causal anomaly, such as an inverse trend against the source. (Output format: [YYYY-MM-DD HH:MM:SS, YYYY-MM-DD HH:MM:SS])", "answer": "[2021-07-17 05:00:00, 2021-07-21 04:00:00]", "ground_truth": [ "2021-07-17 05:00:00", "2021-07-21 04:00:00" ], "eval_metric": "iou", "channel": "441_down_2021", "ts_data_path": "ts_data/L3_T3_Causal_Anomaly_00454.csv", "meta": { "pair_upstream": "441_up_2021", "pair_downstream": "441_down_2021", "upstream_channel_raw": "441", "downstream_channel_raw": "441_synth", "break_kind": "inverse", "year": 2021, "source": "causal_rivers" } }, { "id": "L3_T3_Causal_Anomaly_00455", "level": 3, "level_name": "Semantic Reasoning", "category": "Causal Anomaly", "subtask": "Causal Anomaly", "question": "Given that channel 495_up_2019 is the upstream source of channel 495_down_2019, identify the time period in 2019 where 495_down_2019 shows a significant causal anomaly, such as an inverse trend against the source. (Output format: [YYYY-MM-DD HH:MM:SS, YYYY-MM-DD HH:MM:SS])", "answer": "[2019-10-01 16:30:00, 2019-10-08 23:45:00]", "ground_truth": [ "2019-10-01 16:30:00", "2019-10-08 23:45:00" ], "eval_metric": "iou", "channel": "495_down_2019", "ts_data_path": "ts_data/L3_T3_Causal_Anomaly_00455.csv", "meta": { "pair_upstream": "495_up_2019", "pair_downstream": "495_down_2019", "upstream_channel_raw": "495", "downstream_channel_raw": "495_synth", "break_kind": "inverse", "year": 2019, "source": "causal_rivers" } }, { "id": "L3_T3_Causal_Anomaly_00457", "level": 3, "level_name": "Semantic Reasoning", "category": "Causal Anomaly", "subtask": "Causal Anomaly", "question": "Given that channel 501_up_2021 is the upstream source of channel 501_down_2021, identify the time period in 2021 where 501_down_2021 shows a significant causal anomaly, such as an inverse trend against the source. (Output format: [YYYY-MM-DD HH:MM:SS, YYYY-MM-DD HH:MM:SS])", "answer": "[2021-01-18 11:45:00, 2021-01-22 21:15:00]", "ground_truth": [ "2021-01-18 11:45:00", "2021-01-22 21:15:00" ], "eval_metric": "iou", "channel": "501_down_2021", "ts_data_path": "ts_data/L3_T3_Causal_Anomaly_00457.csv", "meta": { "pair_upstream": "501_up_2021", "pair_downstream": "501_down_2021", "upstream_channel_raw": "501", "downstream_channel_raw": "501_synth", "break_kind": "inverse", "year": 2021, "source": "causal_rivers" } }, { "id": "L3_T3_Causal_Anomaly_00459", "level": 3, "level_name": "Semantic Reasoning", "category": "Causal Anomaly", "subtask": "Causal Anomaly", "question": "Given that channel 580_up_2021 is the upstream source of channel 580_down_2021, identify the time period in 2021 where 580_down_2021 shows a significant causal anomaly, such as an inverse trend against the source. (Output format: [YYYY-MM-DD HH:MM:SS, YYYY-MM-DD HH:MM:SS])", "answer": "[2021-05-18 15:00:00, 2021-05-25 15:00:00]", "ground_truth": [ "2021-05-18 15:00:00", "2021-05-25 15:00:00" ], "eval_metric": "iou", "channel": "580_down_2021", "ts_data_path": "ts_data/L3_T3_Causal_Anomaly_00459.csv", "meta": { "pair_upstream": "580_up_2021", "pair_downstream": "580_down_2021", "upstream_channel_raw": "580", "downstream_channel_raw": "580_synth", "break_kind": "inverse", "year": 2021, "source": "causal_rivers" } }, { "id": "L3_T3_Causal_Anomaly_00460", "level": 3, "level_name": "Semantic Reasoning", "category": "Causal Anomaly", "subtask": "Causal Anomaly", "question": "Given that channel 589_up_2021 is the upstream source of channel 589_down_2021, identify the time period in 2021 where 589_down_2021 shows a significant causal anomaly, such as an inverse trend against the source. (Output format: [YYYY-MM-DD HH:MM:SS, YYYY-MM-DD HH:MM:SS])", "answer": "[2021-07-13 22:00:00, 2021-07-22 01:15:00]", "ground_truth": [ "2021-07-13 22:00:00", "2021-07-22 01:15:00" ], "eval_metric": "iou", "channel": "589_down_2021", "ts_data_path": "ts_data/L3_T3_Causal_Anomaly_00460.csv", "meta": { "pair_upstream": "589_up_2021", "pair_downstream": "589_down_2021", "upstream_channel_raw": "589", "downstream_channel_raw": "589_synth", "break_kind": "inverse", "year": 2021, "source": "causal_rivers" } }, { "id": "L3_T3_Causal_Anomaly_00461", "level": 3, "level_name": "Semantic Reasoning", "category": "Causal Anomaly", "subtask": "Causal Anomaly", "question": "Given that channel 591_up_2020 is the upstream source of channel 591_down_2020, identify the time period in 2020 where 591_down_2020 shows a significant causal anomaly, such as an flat line during high activity. (Output format: [YYYY-MM-DD HH:MM:SS, YYYY-MM-DD HH:MM:SS])", "answer": "[2020-04-22 20:30:00, 2020-04-27 14:45:00]", "ground_truth": [ "2020-04-22 20:30:00", "2020-04-27 14:45:00" ], "eval_metric": "iou", "channel": "591_down_2020", "ts_data_path": "ts_data/L3_T3_Causal_Anomaly_00461.csv", "meta": { "pair_upstream": "591_up_2020", "pair_downstream": "591_down_2020", "upstream_channel_raw": "591", "downstream_channel_raw": "591_synth", "break_kind": "flat_line", "year": 2020, "source": "causal_rivers" } }, { "id": "L3_T3_Causal_Anomaly_00463", "level": 3, "level_name": "Semantic Reasoning", "category": "Causal Anomaly", "subtask": "Causal Anomaly", "question": "Given that channel 625_up_2021 is the upstream source of channel 625_down_2021, identify the time period in 2021 where 625_down_2021 shows a significant causal anomaly, such as an inverse trend against the source. (Output format: [YYYY-MM-DD HH:MM:SS, YYYY-MM-DD HH:MM:SS])", "answer": "[2021-05-11 08:00:00, 2021-05-19 17:45:00]", "ground_truth": [ "2021-05-11 08:00:00", "2021-05-19 17:45:00" ], "eval_metric": "iou", "channel": "625_down_2021", "ts_data_path": "ts_data/L3_T3_Causal_Anomaly_00463.csv", "meta": { "pair_upstream": "625_up_2021", "pair_downstream": "625_down_2021", "upstream_channel_raw": "625", "downstream_channel_raw": "625_synth", "break_kind": "inverse", "year": 2021, "source": "causal_rivers" } }, { "id": "L3_T3_Causal_Anomaly_00465", "level": 3, "level_name": "Semantic Reasoning", "category": "Causal Anomaly", "subtask": "Causal Anomaly", "question": "Given that channel 627_up_2021 is the upstream source of channel 627_down_2021, identify the time period in 2021 where 627_down_2021 shows a significant causal anomaly, such as an flat line during high activity. (Output format: [YYYY-MM-DD HH:MM:SS, YYYY-MM-DD HH:MM:SS])", "answer": "[2021-11-20 08:15:00, 2021-11-24 11:00:00]", "ground_truth": [ "2021-11-20 08:15:00", "2021-11-24 11:00:00" ], "eval_metric": "iou", "channel": "627_down_2021", "ts_data_path": "ts_data/L3_T3_Causal_Anomaly_00465.csv", "meta": { "pair_upstream": "627_up_2021", "pair_downstream": "627_down_2021", "upstream_channel_raw": "627", "downstream_channel_raw": "627_synth", "break_kind": "flat_line", "year": 2021, "source": "causal_rivers" } }, { "id": "L3_T3_Causal_Anomaly_00466", "level": 3, "level_name": "Semantic Reasoning", "category": "Causal Anomaly", "subtask": "Causal Anomaly", "question": "Given that channel 647_up_2023 is the upstream source of channel 647_down_2023, identify the time period in 2023 where 647_down_2023 shows a significant causal anomaly, such as an flat line during high activity. (Output format: [YYYY-MM-DD HH:MM:SS, YYYY-MM-DD HH:MM:SS])", "answer": "[2023-04-20 15:00:00, 2023-04-26 08:00:00]", "ground_truth": [ "2023-04-20 15:00:00", "2023-04-26 08:00:00" ], "eval_metric": "iou", "channel": "647_down_2023", "ts_data_path": "ts_data/L3_T3_Causal_Anomaly_00466.csv", "meta": { "pair_upstream": "647_up_2023", "pair_downstream": "647_down_2023", "upstream_channel_raw": "647", "downstream_channel_raw": "647_synth", "break_kind": "flat_line", "year": 2023, "source": "causal_rivers" } }, { "id": "L3_T3_Causal_Anomaly_00469", "level": 3, "level_name": "Semantic Reasoning", "category": "Causal Anomaly", "subtask": "Causal Anomaly", "question": "Given that channel 727_up_2021 is the upstream source of channel 727_down_2021, identify the time period in 2021 where 727_down_2021 shows a significant causal anomaly, such as an flat line during high activity. (Output format: [YYYY-MM-DD HH:MM:SS, YYYY-MM-DD HH:MM:SS])", "answer": "[2021-03-28 03:15:00, 2021-04-05 05:45:00]", "ground_truth": [ "2021-03-28 03:15:00", "2021-04-05 05:45:00" ], "eval_metric": "iou", "channel": "727_down_2021", "ts_data_path": "ts_data/L3_T3_Causal_Anomaly_00469.csv", "meta": { "pair_upstream": "727_up_2021", "pair_downstream": "727_down_2021", "upstream_channel_raw": "727", "downstream_channel_raw": "727_synth", "break_kind": "flat_line", "year": 2021, "source": "causal_rivers" } }, { "id": "L3_T3_Causal_Anomaly_00470", "level": 3, "level_name": "Semantic Reasoning", "category": "Causal Anomaly", "subtask": "Causal Anomaly", "question": "Given that channel 728_up_2020 is the upstream source of channel 728_down_2020, identify the time period in 2020 where 728_down_2020 shows a significant causal anomaly, such as an flat line during high activity. (Output format: [YYYY-MM-DD HH:MM:SS, YYYY-MM-DD HH:MM:SS])", "answer": "[2020-07-12 21:30:00, 2020-07-18 02:45:00]", "ground_truth": [ "2020-07-12 21:30:00", "2020-07-18 02:45:00" ], "eval_metric": "iou", "channel": "728_down_2020", "ts_data_path": "ts_data/L3_T3_Causal_Anomaly_00470.csv", "meta": { "pair_upstream": "728_up_2020", "pair_downstream": "728_down_2020", "upstream_channel_raw": "728", "downstream_channel_raw": "728_synth", "break_kind": "flat_line", "year": 2020, "source": "causal_rivers" } }, { "id": "L3_T3_Causal_Anomaly_00472", "level": 3, "level_name": "Semantic Reasoning", "category": "Causal Anomaly", "subtask": "Causal Anomaly", "question": "Given that channel 754_up_2023 is the upstream source of channel 754_down_2023, identify the time period in 2023 where 754_down_2023 shows a significant causal anomaly, such as an inverse trend against the source. (Output format: [YYYY-MM-DD HH:MM:SS, YYYY-MM-DD HH:MM:SS])", "answer": "[2023-01-14 18:30:00, 2023-01-19 19:45:00]", "ground_truth": [ "2023-01-14 18:30:00", "2023-01-19 19:45:00" ], "eval_metric": "iou", "channel": "754_down_2023", "ts_data_path": "ts_data/L3_T3_Causal_Anomaly_00472.csv", "meta": { "pair_upstream": "754_up_2023", "pair_downstream": "754_down_2023", "upstream_channel_raw": "754", "downstream_channel_raw": "754_synth", "break_kind": "inverse", "year": 2023, "source": "causal_rivers" } }, { "id": "L3_T3_Causal_Anomaly_00473", "level": 3, "level_name": "Semantic Reasoning", "category": "Causal Anomaly", "subtask": "Causal Anomaly", "question": "Given that channel 762_up_2022 is the upstream source of channel 762_down_2022, identify the time period in 2022 where 762_down_2022 shows a significant causal anomaly, such as an inverse trend against the source. (Output format: [YYYY-MM-DD HH:MM:SS, YYYY-MM-DD HH:MM:SS])", "answer": "[2022-04-27 02:30:00, 2022-05-03 11:45:00]", "ground_truth": [ "2022-04-27 02:30:00", "2022-05-03 11:45:00" ], "eval_metric": "iou", "channel": "762_down_2022", "ts_data_path": "ts_data/L3_T3_Causal_Anomaly_00473.csv", "meta": { "pair_upstream": "762_up_2022", "pair_downstream": "762_down_2022", "upstream_channel_raw": "762", "downstream_channel_raw": "762_synth", "break_kind": "inverse", "year": 2022, "source": "causal_rivers" } }, { "id": "L3_T3_Causal_Anomaly_00474", "level": 3, "level_name": "Semantic Reasoning", "category": "Causal Anomaly", "subtask": "Causal Anomaly", "question": "Given that channel 811_up_2019 is the upstream source of channel 811_down_2019, identify the time period in 2019 where 811_down_2019 shows a significant causal anomaly, such as an inverse trend against the source. (Output format: [YYYY-MM-DD HH:MM:SS, YYYY-MM-DD HH:MM:SS])", "answer": "[2019-09-01 09:15:00, 2019-09-07 12:30:00]", "ground_truth": [ "2019-09-01 09:15:00", "2019-09-07 12:30:00" ], "eval_metric": "iou", "channel": "811_down_2019", "ts_data_path": "ts_data/L3_T3_Causal_Anomaly_00474.csv", "meta": { "pair_upstream": "811_up_2019", "pair_downstream": "811_down_2019", "upstream_channel_raw": "811", "downstream_channel_raw": "811_synth", "break_kind": "inverse", "year": 2019, "source": "causal_rivers" } }, { "id": "L3_T3_Causal_Anomaly_00476", "level": 3, "level_name": "Semantic Reasoning", "category": "Causal Anomaly", "subtask": "Causal Anomaly", "question": "Given that channel 865_up_2021 is the upstream source of channel 865_down_2021, identify the time period in 2021 where 865_down_2021 shows a significant causal anomaly, such as an flat line during high activity. (Output format: [YYYY-MM-DD HH:MM:SS, YYYY-MM-DD HH:MM:SS])", "answer": "[2021-09-05 11:00:00, 2021-09-08 21:45:00]", "ground_truth": [ "2021-09-05 11:00:00", "2021-09-08 21:45:00" ], "eval_metric": "iou", "channel": "865_down_2021", "ts_data_path": "ts_data/L3_T3_Causal_Anomaly_00476.csv", "meta": { "pair_upstream": "865_up_2021", "pair_downstream": "865_down_2021", "upstream_channel_raw": "865", "downstream_channel_raw": "865_synth", "break_kind": "flat_line", "year": 2021, "source": "causal_rivers" } }, { "id": "L3_T3_Causal_Anomaly_00478", "level": 3, "level_name": "Semantic Reasoning", "category": "Causal Anomaly", "subtask": "Causal Anomaly", "question": "Given that channel 894_up_2020 is the upstream source of channel 894_down_2020, identify the time period in 2020 where 894_down_2020 shows a significant causal anomaly, such as an flat line during high activity. (Output format: [YYYY-MM-DD HH:MM:SS, YYYY-MM-DD HH:MM:SS])", "answer": "[2020-02-18 15:30:00, 2020-02-22 07:15:00]", "ground_truth": [ "2020-02-18 15:30:00", "2020-02-22 07:15:00" ], "eval_metric": "iou", "channel": "894_down_2020", "ts_data_path": "ts_data/L3_T3_Causal_Anomaly_00478.csv", "meta": { "pair_upstream": "894_up_2020", "pair_downstream": "894_down_2020", "upstream_channel_raw": "894", "downstream_channel_raw": "894_synth", "break_kind": "flat_line", "year": 2020, "source": "causal_rivers" } }, { "id": "L3_T3_Causal_Anomaly_00479", "level": 3, "level_name": "Semantic Reasoning", "category": "Causal Anomaly", "subtask": "Causal Anomaly", "question": "Given that channel 895_up_2022 is the upstream source of channel 895_down_2022, identify the time period in 2022 where 895_down_2022 shows a significant causal anomaly, such as an inverse trend against the source. (Output format: [YYYY-MM-DD HH:MM:SS, YYYY-MM-DD HH:MM:SS])", "answer": "[2022-05-06 14:45:00, 2022-05-14 00:15:00]", "ground_truth": [ "2022-05-06 14:45:00", "2022-05-14 00:15:00" ], "eval_metric": "iou", "channel": "895_down_2022", "ts_data_path": "ts_data/L3_T3_Causal_Anomaly_00479.csv", "meta": { "pair_upstream": "895_up_2022", "pair_downstream": "895_down_2022", "upstream_channel_raw": "895", "downstream_channel_raw": "895_synth", "break_kind": "inverse", "year": 2022, "source": "causal_rivers" } }, { "id": "L3_T3_Causal_Anomaly_00480", "level": 3, "level_name": "Semantic Reasoning", "category": "Causal Anomaly", "subtask": "Causal Anomaly", "question": "Given that channel 897_up_2022 is the upstream source of channel 897_down_2022, identify the time period in 2022 where 897_down_2022 shows a significant causal anomaly, such as an inverse trend against the source. (Output format: [YYYY-MM-DD HH:MM:SS, YYYY-MM-DD HH:MM:SS])", "answer": "[2022-10-26 00:00:00, 2022-11-02 08:45:00]", "ground_truth": [ "2022-10-26 00:00:00", "2022-11-02 08:45:00" ], "eval_metric": "iou", "channel": "897_down_2022", "ts_data_path": "ts_data/L3_T3_Causal_Anomaly_00480.csv", "meta": { "pair_upstream": "897_up_2022", "pair_downstream": "897_down_2022", "upstream_channel_raw": "897", "downstream_channel_raw": "897_synth", "break_kind": "inverse", "year": 2022, "source": "causal_rivers" } }, { "id": "L3_T3_Causal_Anomaly_00481", "level": 3, "level_name": "Semantic Reasoning", "category": "Causal Anomaly", "subtask": "Causal Anomaly", "question": "Given that channel 933_up_2023 is the upstream source of channel 933_down_2023, identify the time period in 2023 where 933_down_2023 shows a significant causal anomaly, such as an flat line during high activity. (Output format: [YYYY-MM-DD HH:MM:SS, YYYY-MM-DD HH:MM:SS])", "answer": "[2023-02-13 21:00:00, 2023-02-20 09:30:00]", "ground_truth": [ "2023-02-13 21:00:00", "2023-02-20 09:30:00" ], "eval_metric": "iou", "channel": "933_down_2023", "ts_data_path": "ts_data/L3_T3_Causal_Anomaly_00481.csv", "meta": { "pair_upstream": "933_up_2023", "pair_downstream": "933_down_2023", "upstream_channel_raw": "933", "downstream_channel_raw": "933_synth", "break_kind": "flat_line", "year": 2023, "source": "causal_rivers" } }, { "id": "L3_T3_Causal_Anomaly_00486", "level": 3, "level_name": "Semantic Reasoning", "category": "Causal Anomaly", "subtask": "Causal Anomaly", "question": "Given that channel 124_up_2021 is the upstream source of channel 124_down_2021, identify the time period in 2021 where 124_down_2021 shows a significant causal anomaly, such as an flat line during high activity. (Output format: [YYYY-MM-DD HH:MM:SS, YYYY-MM-DD HH:MM:SS])", "answer": "[2021-10-13 08:00:00, 2021-10-19 16:45:00]", "ground_truth": [ "2021-10-13 08:00:00", "2021-10-19 16:45:00" ], "eval_metric": "iou", "channel": "124_down_2021", "ts_data_path": "ts_data/L3_T3_Causal_Anomaly_00486.csv", "meta": { "pair_upstream": "124_up_2021", "pair_downstream": "124_down_2021", "upstream_channel_raw": "124", "downstream_channel_raw": "124_synth", "break_kind": "flat_line", "year": 2021, "source": "causal_rivers" } }, { "id": "L3_T3_Causal_Anomaly_00487", "level": 3, "level_name": "Semantic Reasoning", "category": "Causal Anomaly", "subtask": "Causal Anomaly", "question": "Given that channel 146_up_2023 is the upstream source of channel 146_down_2023, identify the time period in 2023 where 146_down_2023 shows a significant causal anomaly, such as an inverse trend against the source. (Output format: [YYYY-MM-DD HH:MM:SS, YYYY-MM-DD HH:MM:SS])", "answer": "[2023-03-01 17:00:00, 2023-03-11 14:45:00]", "ground_truth": [ "2023-03-01 17:00:00", "2023-03-11 14:45:00" ], "eval_metric": "iou", "channel": "146_down_2023", "ts_data_path": "ts_data/L3_T3_Causal_Anomaly_00487.csv", "meta": { "pair_upstream": "146_up_2023", "pair_downstream": "146_down_2023", "upstream_channel_raw": "146", "downstream_channel_raw": "146_synth", "break_kind": "inverse", "year": 2023, "source": "causal_rivers" } }, { "id": "L3_T3_Causal_Anomaly_00489", "level": 3, "level_name": "Semantic Reasoning", "category": "Causal Anomaly", "subtask": "Causal Anomaly", "question": "Given that channel 151_up_2019 is the upstream source of channel 151_down_2019, identify the time period in 2019 where 151_down_2019 shows a significant causal anomaly, such as an inverse trend against the source. (Output format: [YYYY-MM-DD HH:MM:SS, YYYY-MM-DD HH:MM:SS])", "answer": "[2019-11-03 22:30:00, 2019-11-09 07:15:00]", "ground_truth": [ "2019-11-03 22:30:00", "2019-11-09 07:15:00" ], "eval_metric": "iou", "channel": "151_down_2019", "ts_data_path": "ts_data/L3_T3_Causal_Anomaly_00489.csv", "meta": { "pair_upstream": "151_up_2019", "pair_downstream": "151_down_2019", "upstream_channel_raw": "151", "downstream_channel_raw": "151_synth", "break_kind": "inverse", "year": 2019, "source": "causal_rivers" } }, { "id": "L3_T3_Causal_Anomaly_00492", "level": 3, "level_name": "Semantic Reasoning", "category": "Causal Anomaly", "subtask": "Causal Anomaly", "question": "Given that channel 166_up_2022 is the upstream source of channel 166_down_2022, identify the time period in 2022 where 166_down_2022 shows a significant causal anomaly, such as an flat line during high activity. (Output format: [YYYY-MM-DD HH:MM:SS, YYYY-MM-DD HH:MM:SS])", "answer": "[2022-07-13 10:30:00, 2022-07-16 11:00:00]", "ground_truth": [ "2022-07-13 10:30:00", "2022-07-16 11:00:00" ], "eval_metric": "iou", "channel": "166_down_2022", "ts_data_path": "ts_data/L3_T3_Causal_Anomaly_00492.csv", "meta": { "pair_upstream": "166_up_2022", "pair_downstream": "166_down_2022", "upstream_channel_raw": "166", "downstream_channel_raw": "166_synth", "break_kind": "flat_line", "year": 2022, "source": "causal_rivers" } }, { "id": "L3_T3_Causal_Anomaly_00493", "level": 3, "level_name": "Semantic Reasoning", "category": "Causal Anomaly", "subtask": "Causal Anomaly", "question": "Given that channel 169_up_2020 is the upstream source of channel 169_down_2020, identify the time period in 2020 where 169_down_2020 shows a significant causal anomaly, such as an inverse trend against the source. (Output format: [YYYY-MM-DD HH:MM:SS, YYYY-MM-DD HH:MM:SS])", "answer": "[2020-03-04 00:45:00, 2020-03-13 10:00:00]", "ground_truth": [ "2020-03-04 00:45:00", "2020-03-13 10:00:00" ], "eval_metric": "iou", "channel": "169_down_2020", "ts_data_path": "ts_data/L3_T3_Causal_Anomaly_00493.csv", "meta": { "pair_upstream": "169_up_2020", "pair_downstream": "169_down_2020", "upstream_channel_raw": "169", "downstream_channel_raw": "169_synth", "break_kind": "inverse", "year": 2020, "source": "causal_rivers" } }, { "id": "L3_T3_Causal_Anomaly_00494", "level": 3, "level_name": "Semantic Reasoning", "category": "Causal Anomaly", "subtask": "Causal Anomaly", "question": "Given that channel 170_up_2020 is the upstream source of channel 170_down_2020, identify the time period in 2020 where 170_down_2020 shows a significant causal anomaly, such as an inverse trend against the source. (Output format: [YYYY-MM-DD HH:MM:SS, YYYY-MM-DD HH:MM:SS])", "answer": "[2020-06-24 01:15:00, 2020-07-02 20:00:00]", "ground_truth": [ "2020-06-24 01:15:00", "2020-07-02 20:00:00" ], "eval_metric": "iou", "channel": "170_down_2020", "ts_data_path": "ts_data/L3_T3_Causal_Anomaly_00494.csv", "meta": { "pair_upstream": "170_up_2020", "pair_downstream": "170_down_2020", "upstream_channel_raw": "170", "downstream_channel_raw": "170_synth", "break_kind": "inverse", "year": 2020, "source": "causal_rivers" } }, { "id": "L3_T3_Causal_Anomaly_00495", "level": 3, "level_name": "Semantic Reasoning", "category": "Causal Anomaly", "subtask": "Causal Anomaly", "question": "Given that channel 172_up_2022 is the upstream source of channel 172_down_2022, identify the time period in 2022 where 172_down_2022 shows a significant causal anomaly, such as an inverse trend against the source. (Output format: [YYYY-MM-DD HH:MM:SS, YYYY-MM-DD HH:MM:SS])", "answer": "[2022-12-24 14:30:00, 2022-12-30 20:30:00]", "ground_truth": [ "2022-12-24 14:30:00", "2022-12-30 20:30:00" ], "eval_metric": "iou", "channel": "172_down_2022", "ts_data_path": "ts_data/L3_T3_Causal_Anomaly_00495.csv", "meta": { "pair_upstream": "172_up_2022", "pair_downstream": "172_down_2022", "upstream_channel_raw": "172", "downstream_channel_raw": "172_synth", "break_kind": "inverse", "year": 2022, "source": "causal_rivers" } }, { "id": "L3_T3_Causal_Anomaly_00496", "level": 3, "level_name": "Semantic Reasoning", "category": "Causal Anomaly", "subtask": "Causal Anomaly", "question": "Given that channel 173_up_2019 is the upstream source of channel 173_down_2019, identify the time period in 2019 where 173_down_2019 shows a significant causal anomaly, such as an flat line during high activity. (Output format: [YYYY-MM-DD HH:MM:SS, YYYY-MM-DD HH:MM:SS])", "answer": "[2019-08-30 13:15:00, 2019-09-07 04:00:00]", "ground_truth": [ "2019-08-30 13:15:00", "2019-09-07 04:00:00" ], "eval_metric": "iou", "channel": "173_down_2019", "ts_data_path": "ts_data/L3_T3_Causal_Anomaly_00496.csv", "meta": { "pair_upstream": "173_up_2019", "pair_downstream": "173_down_2019", "upstream_channel_raw": "173", "downstream_channel_raw": "173_synth", "break_kind": "flat_line", "year": 2019, "source": "causal_rivers" } }, { "id": "L3_T3_Causal_Anomaly_00499", "level": 3, "level_name": "Semantic Reasoning", "category": "Causal Anomaly", "subtask": "Causal Anomaly", "question": "Given that channel 245_up_2020 is the upstream source of channel 245_down_2020, identify the time period in 2020 where 245_down_2020 shows a significant causal anomaly, such as an flat line during high activity. (Output format: [YYYY-MM-DD HH:MM:SS, YYYY-MM-DD HH:MM:SS])", "answer": "[2020-06-03 16:45:00, 2020-06-11 23:30:00]", "ground_truth": [ "2020-06-03 16:45:00", "2020-06-11 23:30:00" ], "eval_metric": "iou", "channel": "245_down_2020", "ts_data_path": "ts_data/L3_T3_Causal_Anomaly_00499.csv", "meta": { "pair_upstream": "245_up_2020", "pair_downstream": "245_down_2020", "upstream_channel_raw": "245", "downstream_channel_raw": "245_synth", "break_kind": "flat_line", "year": 2020, "source": "causal_rivers" } }, { "id": "L3_T3_Causal_Anomaly_00500", "level": 3, "level_name": "Semantic Reasoning", "category": "Causal Anomaly", "subtask": "Causal Anomaly", "question": "Given that channel 312_up_2023 is the upstream source of channel 312_down_2023, identify the time period in 2023 where 312_down_2023 shows a significant causal anomaly, such as an inverse trend against the source. (Output format: [YYYY-MM-DD HH:MM:SS, YYYY-MM-DD HH:MM:SS])", "answer": "[2023-07-20 10:00:00, 2023-07-23 22:15:00]", "ground_truth": [ "2023-07-20 10:00:00", "2023-07-23 22:15:00" ], "eval_metric": "iou", "channel": "312_down_2023", "ts_data_path": "ts_data/L3_T3_Causal_Anomaly_00500.csv", "meta": { "pair_upstream": "312_up_2023", "pair_downstream": "312_down_2023", "upstream_channel_raw": "312", "downstream_channel_raw": "312_synth", "break_kind": "inverse", "year": 2023, "source": "causal_rivers" } }, { "id": "L3_T3_Causal_Anomaly_00501", "level": 3, "level_name": "Semantic Reasoning", "category": "Causal Anomaly", "subtask": "Causal Anomaly", "question": "Given that channel 430_up_2019 is the upstream source of channel 430_down_2019, identify the time period in 2019 where 430_down_2019 shows a significant causal anomaly, such as an inverse trend against the source. (Output format: [YYYY-MM-DD HH:MM:SS, YYYY-MM-DD HH:MM:SS])", "answer": "[2019-10-11 20:15:00, 2019-10-20 10:15:00]", "ground_truth": [ "2019-10-11 20:15:00", "2019-10-20 10:15:00" ], "eval_metric": "iou", "channel": "430_down_2019", "ts_data_path": "ts_data/L3_T3_Causal_Anomaly_00501.csv", "meta": { "pair_upstream": "430_up_2019", "pair_downstream": "430_down_2019", "upstream_channel_raw": "430", "downstream_channel_raw": "430_synth", "break_kind": "inverse", "year": 2019, "source": "causal_rivers" } }, { "id": "L3_T3_Causal_Anomaly_00502", "level": 3, "level_name": "Semantic Reasoning", "category": "Causal Anomaly", "subtask": "Causal Anomaly", "question": "Given that channel 441_up_2023 is the upstream source of channel 441_down_2023, identify the time period in 2023 where 441_down_2023 shows a significant causal anomaly, such as an inverse trend against the source. (Output format: [YYYY-MM-DD HH:MM:SS, YYYY-MM-DD HH:MM:SS])", "answer": "[2023-03-21 11:30:00, 2023-03-26 11:30:00]", "ground_truth": [ "2023-03-21 11:30:00", "2023-03-26 11:30:00" ], "eval_metric": "iou", "channel": "441_down_2023", "ts_data_path": "ts_data/L3_T3_Causal_Anomaly_00502.csv", "meta": { "pair_upstream": "441_up_2023", "pair_downstream": "441_down_2023", "upstream_channel_raw": "441", "downstream_channel_raw": "441_synth", "break_kind": "inverse", "year": 2023, "source": "causal_rivers" } }, { "id": "L3_T3_Causal_Anomaly_00503", "level": 3, "level_name": "Semantic Reasoning", "category": "Causal Anomaly", "subtask": "Causal Anomaly", "question": "Given that channel 495_up_2021 is the upstream source of channel 495_down_2021, identify the time period in 2021 where 495_down_2021 shows a significant causal anomaly, such as an inverse trend against the source. (Output format: [YYYY-MM-DD HH:MM:SS, YYYY-MM-DD HH:MM:SS])", "answer": "[2021-04-25 21:30:00, 2021-04-29 21:00:00]", "ground_truth": [ "2021-04-25 21:30:00", "2021-04-29 21:00:00" ], "eval_metric": "iou", "channel": "495_down_2021", "ts_data_path": "ts_data/L3_T3_Causal_Anomaly_00503.csv", "meta": { "pair_upstream": "495_up_2021", "pair_downstream": "495_down_2021", "upstream_channel_raw": "495", "downstream_channel_raw": "495_synth", "break_kind": "inverse", "year": 2021, "source": "causal_rivers" } }, { "id": "L3_T3_Causal_Anomaly_00504", "level": 3, "level_name": "Semantic Reasoning", "category": "Causal Anomaly", "subtask": "Causal Anomaly", "question": "Given that channel 496_up_2019 is the upstream source of channel 496_down_2019, identify the time period in 2019 where 496_down_2019 shows a significant causal anomaly, such as an flat line during high activity. (Output format: [YYYY-MM-DD HH:MM:SS, YYYY-MM-DD HH:MM:SS])", "answer": "[2019-04-29 20:30:00, 2019-05-03 19:30:00]", "ground_truth": [ "2019-04-29 20:30:00", "2019-05-03 19:30:00" ], "eval_metric": "iou", "channel": "496_down_2019", "ts_data_path": "ts_data/L3_T3_Causal_Anomaly_00504.csv", "meta": { "pair_upstream": "496_up_2019", "pair_downstream": "496_down_2019", "upstream_channel_raw": "496", "downstream_channel_raw": "496_synth", "break_kind": "flat_line", "year": 2019, "source": "causal_rivers" } }, { "id": "L3_T3_Causal_Anomaly_00505", "level": 3, "level_name": "Semantic Reasoning", "category": "Causal Anomaly", "subtask": "Causal Anomaly", "question": "Given that channel 501_up_2019 is the upstream source of channel 501_down_2019, identify the time period in 2019 where 501_down_2019 shows a significant causal anomaly, such as an flat line during high activity. (Output format: [YYYY-MM-DD HH:MM:SS, YYYY-MM-DD HH:MM:SS])", "answer": "[2019-04-23 00:45:00, 2019-05-02 07:30:00]", "ground_truth": [ "2019-04-23 00:45:00", "2019-05-02 07:30:00" ], "eval_metric": "iou", "channel": "501_down_2019", "ts_data_path": "ts_data/L3_T3_Causal_Anomaly_00505.csv", "meta": { "pair_upstream": "501_up_2019", "pair_downstream": "501_down_2019", "upstream_channel_raw": "501", "downstream_channel_raw": "501_synth", "break_kind": "flat_line", "year": 2019, "source": "causal_rivers" } }, { "id": "L3_T3_Causal_Anomaly_00506", "level": 3, "level_name": "Semantic Reasoning", "category": "Causal Anomaly", "subtask": "Causal Anomaly", "question": "Given that channel 578_up_2023 is the upstream source of channel 578_down_2023, identify the time period in 2023 where 578_down_2023 shows a significant causal anomaly, such as an flat line during high activity. (Output format: [YYYY-MM-DD HH:MM:SS, YYYY-MM-DD HH:MM:SS])", "answer": "[2023-10-07 01:45:00, 2023-10-14 17:15:00]", "ground_truth": [ "2023-10-07 01:45:00", "2023-10-14 17:15:00" ], "eval_metric": "iou", "channel": "578_down_2023", "ts_data_path": "ts_data/L3_T3_Causal_Anomaly_00506.csv", "meta": { "pair_upstream": "578_up_2023", "pair_downstream": "578_down_2023", "upstream_channel_raw": "578", "downstream_channel_raw": "578_synth", "break_kind": "flat_line", "year": 2023, "source": "causal_rivers" } }, { "id": "L3_T3_Causal_Anomaly_00509", "level": 3, "level_name": "Semantic Reasoning", "category": "Causal Anomaly", "subtask": "Causal Anomaly", "question": "Given that channel 591_up_2020 is the upstream source of channel 591_down_2020, identify the time period in 2020 where 591_down_2020 shows a significant causal anomaly, such as an flat line during high activity. (Output format: [YYYY-MM-DD HH:MM:SS, YYYY-MM-DD HH:MM:SS])", "answer": "[2020-05-21 01:15:00, 2020-05-27 19:45:00]", "ground_truth": [ "2020-05-21 01:15:00", "2020-05-27 19:45:00" ], "eval_metric": "iou", "channel": "591_down_2020", "ts_data_path": "ts_data/L3_T3_Causal_Anomaly_00509.csv", "meta": { "pair_upstream": "591_up_2020", "pair_downstream": "591_down_2020", "upstream_channel_raw": "591", "downstream_channel_raw": "591_synth", "break_kind": "flat_line", "year": 2020, "source": "causal_rivers" } }, { "id": "L3_T3_Causal_Anomaly_00511", "level": 3, "level_name": "Semantic Reasoning", "category": "Causal Anomaly", "subtask": "Causal Anomaly", "question": "Given that channel 625_up_2020 is the upstream source of channel 625_down_2020, identify the time period in 2020 where 625_down_2020 shows a significant causal anomaly, such as an flat line during high activity. (Output format: [YYYY-MM-DD HH:MM:SS, YYYY-MM-DD HH:MM:SS])", "answer": "[2020-10-10 18:30:00, 2020-10-17 09:30:00]", "ground_truth": [ "2020-10-10 18:30:00", "2020-10-17 09:30:00" ], "eval_metric": "iou", "channel": "625_down_2020", "ts_data_path": "ts_data/L3_T3_Causal_Anomaly_00511.csv", "meta": { "pair_upstream": "625_up_2020", "pair_downstream": "625_down_2020", "upstream_channel_raw": "625", "downstream_channel_raw": "625_synth", "break_kind": "flat_line", "year": 2020, "source": "causal_rivers" } }, { "id": "L3_T3_Causal_Anomaly_00512", "level": 3, "level_name": "Semantic Reasoning", "category": "Causal Anomaly", "subtask": "Causal Anomaly", "question": "Given that channel 626_up_2023 is the upstream source of channel 626_down_2023, identify the time period in 2023 where 626_down_2023 shows a significant causal anomaly, such as an inverse trend against the source. (Output format: [YYYY-MM-DD HH:MM:SS, YYYY-MM-DD HH:MM:SS])", "answer": "[2023-04-24 14:15:00, 2023-04-30 11:45:00]", "ground_truth": [ "2023-04-24 14:15:00", "2023-04-30 11:45:00" ], "eval_metric": "iou", "channel": "626_down_2023", "ts_data_path": "ts_data/L3_T3_Causal_Anomaly_00512.csv", "meta": { "pair_upstream": "626_up_2023", "pair_downstream": "626_down_2023", "upstream_channel_raw": "626", "downstream_channel_raw": "626_synth", "break_kind": "inverse", "year": 2023, "source": "causal_rivers" } }, { "id": "L3_T3_Causal_Anomaly_00513", "level": 3, "level_name": "Semantic Reasoning", "category": "Causal Anomaly", "subtask": "Causal Anomaly", "question": "Given that channel 627_up_2019 is the upstream source of channel 627_down_2019, identify the time period in 2019 where 627_down_2019 shows a significant causal anomaly, such as an inverse trend against the source. (Output format: [YYYY-MM-DD HH:MM:SS, YYYY-MM-DD HH:MM:SS])", "answer": "[2019-01-31 05:15:00, 2019-02-06 14:15:00]", "ground_truth": [ "2019-01-31 05:15:00", "2019-02-06 14:15:00" ], "eval_metric": "iou", "channel": "627_down_2019", "ts_data_path": "ts_data/L3_T3_Causal_Anomaly_00513.csv", "meta": { "pair_upstream": "627_up_2019", "pair_downstream": "627_down_2019", "upstream_channel_raw": "627", "downstream_channel_raw": "627_synth", "break_kind": "inverse", "year": 2019, "source": "causal_rivers" } }, { "id": "L3_T3_Causal_Anomaly_00514", "level": 3, "level_name": "Semantic Reasoning", "category": "Causal Anomaly", "subtask": "Causal Anomaly", "question": "Given that channel 647_up_2021 is the upstream source of channel 647_down_2021, identify the time period in 2021 where 647_down_2021 shows a significant causal anomaly, such as an flat line during high activity. (Output format: [YYYY-MM-DD HH:MM:SS, YYYY-MM-DD HH:MM:SS])", "answer": "[2021-08-03 19:15:00, 2021-08-08 19:30:00]", "ground_truth": [ "2021-08-03 19:15:00", "2021-08-08 19:30:00" ], "eval_metric": "iou", "channel": "647_down_2021", "ts_data_path": "ts_data/L3_T3_Causal_Anomaly_00514.csv", "meta": { "pair_upstream": "647_up_2021", "pair_downstream": "647_down_2021", "upstream_channel_raw": "647", "downstream_channel_raw": "647_synth", "break_kind": "flat_line", "year": 2021, "source": "causal_rivers" } }, { "id": "L3_T3_Causal_Anomaly_00516", "level": 3, "level_name": "Semantic Reasoning", "category": "Causal Anomaly", "subtask": "Causal Anomaly", "question": "Given that channel 683_up_2019 is the upstream source of channel 683_down_2019, identify the time period in 2019 where 683_down_2019 shows a significant causal anomaly, such as an flat line during high activity. (Output format: [YYYY-MM-DD HH:MM:SS, YYYY-MM-DD HH:MM:SS])", "answer": "[2019-05-23 06:45:00, 2019-05-27 01:00:00]", "ground_truth": [ "2019-05-23 06:45:00", "2019-05-27 01:00:00" ], "eval_metric": "iou", "channel": "683_down_2019", "ts_data_path": "ts_data/L3_T3_Causal_Anomaly_00516.csv", "meta": { "pair_upstream": "683_up_2019", "pair_downstream": "683_down_2019", "upstream_channel_raw": "683", "downstream_channel_raw": "683_synth", "break_kind": "flat_line", "year": 2019, "source": "causal_rivers" } }, { "id": "L3_T3_Causal_Anomaly_00517", "level": 3, "level_name": "Semantic Reasoning", "category": "Causal Anomaly", "subtask": "Causal Anomaly", "question": "Given that channel 727_up_2019 is the upstream source of channel 727_down_2019, identify the time period in 2019 where 727_down_2019 shows a significant causal anomaly, such as an flat line during high activity. (Output format: [YYYY-MM-DD HH:MM:SS, YYYY-MM-DD HH:MM:SS])", "answer": "[2019-12-09 17:45:00, 2019-12-13 09:45:00]", "ground_truth": [ "2019-12-09 17:45:00", "2019-12-13 09:45:00" ], "eval_metric": "iou", "channel": "727_down_2019", "ts_data_path": "ts_data/L3_T3_Causal_Anomaly_00517.csv", "meta": { "pair_upstream": "727_up_2019", "pair_downstream": "727_down_2019", "upstream_channel_raw": "727", "downstream_channel_raw": "727_synth", "break_kind": "flat_line", "year": 2019, "source": "causal_rivers" } }, { "id": "L3_T3_Causal_Anomaly_00519", "level": 3, "level_name": "Semantic Reasoning", "category": "Causal Anomaly", "subtask": "Causal Anomaly", "question": "Given that channel 729_up_2021 is the upstream source of channel 729_down_2021, identify the time period in 2021 where 729_down_2021 shows a significant causal anomaly, such as an inverse trend against the source. (Output format: [YYYY-MM-DD HH:MM:SS, YYYY-MM-DD HH:MM:SS])", "answer": "[2021-12-22 05:00:00, 2021-12-26 07:30:00]", "ground_truth": [ "2021-12-22 05:00:00", "2021-12-26 07:30:00" ], "eval_metric": "iou", "channel": "729_down_2021", "ts_data_path": "ts_data/L3_T3_Causal_Anomaly_00519.csv", "meta": { "pair_upstream": "729_up_2021", "pair_downstream": "729_down_2021", "upstream_channel_raw": "729", "downstream_channel_raw": "729_synth", "break_kind": "inverse", "year": 2021, "source": "causal_rivers" } }, { "id": "L3_T3_Causal_Anomaly_00520", "level": 3, "level_name": "Semantic Reasoning", "category": "Causal Anomaly", "subtask": "Causal Anomaly", "question": "Given that channel 754_up_2022 is the upstream source of channel 754_down_2022, identify the time period in 2022 where 754_down_2022 shows a significant causal anomaly, such as an flat line during high activity. (Output format: [YYYY-MM-DD HH:MM:SS, YYYY-MM-DD HH:MM:SS])", "answer": "[2022-10-04 02:15:00, 2022-10-09 18:30:00]", "ground_truth": [ "2022-10-04 02:15:00", "2022-10-09 18:30:00" ], "eval_metric": "iou", "channel": "754_down_2022", "ts_data_path": "ts_data/L3_T3_Causal_Anomaly_00520.csv", "meta": { "pair_upstream": "754_up_2022", "pair_downstream": "754_down_2022", "upstream_channel_raw": "754", "downstream_channel_raw": "754_synth", "break_kind": "flat_line", "year": 2022, "source": "causal_rivers" } }, { "id": "L3_T3_Causal_Anomaly_00521", "level": 3, "level_name": "Semantic Reasoning", "category": "Causal Anomaly", "subtask": "Causal Anomaly", "question": "Given that channel 762_up_2019 is the upstream source of channel 762_down_2019, identify the time period in 2019 where 762_down_2019 shows a significant causal anomaly, such as an inverse trend against the source. (Output format: [YYYY-MM-DD HH:MM:SS, YYYY-MM-DD HH:MM:SS])", "answer": "[2019-05-01 03:45:00, 2019-05-08 03:15:00]", "ground_truth": [ "2019-05-01 03:45:00", "2019-05-08 03:15:00" ], "eval_metric": "iou", "channel": "762_down_2019", "ts_data_path": "ts_data/L3_T3_Causal_Anomaly_00521.csv", "meta": { "pair_upstream": "762_up_2019", "pair_downstream": "762_down_2019", "upstream_channel_raw": "762", "downstream_channel_raw": "762_synth", "break_kind": "inverse", "year": 2019, "source": "causal_rivers" } }, { "id": "L3_T3_Causal_Anomaly_00523", "level": 3, "level_name": "Semantic Reasoning", "category": "Causal Anomaly", "subtask": "Causal Anomaly", "question": "Given that channel 813_up_2023 is the upstream source of channel 813_down_2023, identify the time period in 2023 where 813_down_2023 shows a significant causal anomaly, such as an flat line during high activity. (Output format: [YYYY-MM-DD HH:MM:SS, YYYY-MM-DD HH:MM:SS])", "answer": "[2023-01-24 22:15:00, 2023-02-02 03:45:00]", "ground_truth": [ "2023-01-24 22:15:00", "2023-02-02 03:45:00" ], "eval_metric": "iou", "channel": "813_down_2023", "ts_data_path": "ts_data/L3_T3_Causal_Anomaly_00523.csv", "meta": { "pair_upstream": "813_up_2023", "pair_downstream": "813_down_2023", "upstream_channel_raw": "813", "downstream_channel_raw": "813_synth", "break_kind": "flat_line", "year": 2023, "source": "causal_rivers" } }, { "id": "L3_T3_Causal_Anomaly_00525", "level": 3, "level_name": "Semantic Reasoning", "category": "Causal Anomaly", "subtask": "Causal Anomaly", "question": "Given that channel 891_up_2020 is the upstream source of channel 891_down_2020, identify the time period in 2020 where 891_down_2020 shows a significant causal anomaly, such as an flat line during high activity. (Output format: [YYYY-MM-DD HH:MM:SS, YYYY-MM-DD HH:MM:SS])", "answer": "[2020-03-21 04:15:00, 2020-03-30 00:00:00]", "ground_truth": [ "2020-03-21 04:15:00", "2020-03-30 00:00:00" ], "eval_metric": "iou", "channel": "891_down_2020", "ts_data_path": "ts_data/L3_T3_Causal_Anomaly_00525.csv", "meta": { "pair_upstream": "891_up_2020", "pair_downstream": "891_down_2020", "upstream_channel_raw": "891", "downstream_channel_raw": "891_synth", "break_kind": "flat_line", "year": 2020, "source": "causal_rivers" } }, { "id": "L3_T3_Causal_Anomaly_00528", "level": 3, "level_name": "Semantic Reasoning", "category": "Causal Anomaly", "subtask": "Causal Anomaly", "question": "Given that channel 897_up_2022 is the upstream source of channel 897_down_2022, identify the time period in 2022 where 897_down_2022 shows a significant causal anomaly, such as an flat line during high activity. (Output format: [YYYY-MM-DD HH:MM:SS, YYYY-MM-DD HH:MM:SS])", "answer": "[2022-02-23 22:15:00, 2022-03-03 23:45:00]", "ground_truth": [ "2022-02-23 22:15:00", "2022-03-03 23:45:00" ], "eval_metric": "iou", "channel": "897_down_2022", "ts_data_path": "ts_data/L3_T3_Causal_Anomaly_00528.csv", "meta": { "pair_upstream": "897_up_2022", "pair_downstream": "897_down_2022", "upstream_channel_raw": "897", "downstream_channel_raw": "897_synth", "break_kind": "flat_line", "year": 2022, "source": "causal_rivers" } }, { "id": "L3_T3_Causal_Anomaly_00529", "level": 3, "level_name": "Semantic Reasoning", "category": "Causal Anomaly", "subtask": "Causal Anomaly", "question": "Given that channel 933_up_2020 is the upstream source of channel 933_down_2020, identify the time period in 2020 where 933_down_2020 shows a significant causal anomaly, such as an inverse trend against the source. (Output format: [YYYY-MM-DD HH:MM:SS, YYYY-MM-DD HH:MM:SS])", "answer": "[2020-05-08 00:45:00, 2020-05-15 02:30:00]", "ground_truth": [ "2020-05-08 00:45:00", "2020-05-15 02:30:00" ], "eval_metric": "iou", "channel": "933_down_2020", "ts_data_path": "ts_data/L3_T3_Causal_Anomaly_00529.csv", "meta": { "pair_upstream": "933_up_2020", "pair_downstream": "933_down_2020", "upstream_channel_raw": "933", "downstream_channel_raw": "933_synth", "break_kind": "inverse", "year": 2020, "source": "causal_rivers" } }, { "id": "L3_T1_Composite_Trend_00001", "level": 3, "level_name": "Semantic Reasoning", "category": "Composite Trend", "subtask": "Composite Trend", "question": "Among days in channel 67 during 2023 that exhibit the trend pattern 'steady stable, then rapid fall, then slow fall', identify the top-5 days where the slow fall segment is the slowest. (Output format: a ranked list of dates, e.g., ['YYYY-MM-DD', ...])", "answer": "['2023-07-31', '2023-11-08', '2023-03-29', '2023-09-23', '2023-05-06']", "ground_truth": [ "2023-07-31", "2023-11-08", "2023-03-29", "2023-09-23", "2023-05-06" ], "eval_metric": "set_f1", "channel": "67", "ts_data_path": "ts_data/L3_T1_Composite_Trend_00001.csv", "meta": { "segments": [ { "kind": "stable", "adj": "steady" }, { "kind": "fall", "adj": "rapid" }, { "kind": "fall", "adj": "slow" } ], "pattern_phrase": "steady stable, then rapid fall, then slow fall", "rank_target_idx": 2, "rank_kind": "fall", "rank_adj": "slow", "rank_axis": "slope", "rank_direction": "ASC", "rank_extreme": "slowest", "top_segments_meta": [ { "adj": "steady", "kind": "stable", "label": "steady stable", "start_idx": 0, "end_idx": 42 }, { "adj": "rapid", "kind": "fall", "label": "rapid fall", "start_idx": 42, "end_idx": 55 }, { "adj": "slow", "kind": "fall", "label": "slow fall", "start_idx": 55, "end_idx": 98 } ], "points_per_day": 96, "pattern": "steady stable, then rapid fall, then slow fall", "year": 2023, "top_k": [ "2023-07-31", "2023-11-08", "2023-03-29", "2023-09-23", "2023-05-06" ], "all_injections": [ { "rank": 1, "intensity": 1.5, "date": "2023-07-31" }, { "rank": 2, "intensity": 1.530612244897959, "date": "2023-11-08" }, { "rank": 3, "intensity": 1.5612244897959184, "date": "2023-03-29" }, { "rank": 4, "intensity": 1.5918367346938775, "date": "2023-09-23" }, { "rank": 5, "intensity": 1.6224489795918366, "date": "2023-05-06" }, { "rank": 6, "intensity": 1.653061224489796, "date": "2023-10-22" }, { "rank": 7, "intensity": 1.683673469387755, "date": "2023-01-29" }, { "rank": 8, "intensity": 1.7142857142857142, "date": "2023-12-30" }, { "rank": 9, "intensity": 1.7448979591836735, "date": "2023-06-17" }, { "rank": 10, "intensity": 1.7755102040816326, "date": "2023-09-03" }, { "rank": 11, "intensity": 1.806122448979592, "date": "2023-12-17" }, { "rank": 12, "intensity": 1.836734693877551, "date": "2023-09-30" }, { "rank": 13, "intensity": 1.8673469387755102, "date": "2023-01-05" }, { "rank": 14, "intensity": 1.8979591836734695, "date": "2023-07-28" }, { "rank": 15, "intensity": 1.9285714285714286, "date": "2023-07-13" }, { "rank": 16, "intensity": 1.9591836734693877, "date": "2023-10-28" }, { "rank": 17, "intensity": 1.989795918367347, "date": "2023-09-20" }, { "rank": 18, "intensity": 2.020408163265306, "date": "2023-10-08" }, { "rank": 19, "intensity": 2.0510204081632653, "date": "2023-01-12" }, { "rank": 20, "intensity": 2.0816326530612246, "date": "2023-11-25" }, { "rank": 21, "intensity": 2.112244897959184, "date": "2023-12-21" }, { "rank": 22, "intensity": 2.142857142857143, "date": "2023-06-26" }, { "rank": 23, "intensity": 2.173469387755102, "date": "2023-05-23" }, { "rank": 24, "intensity": 2.204081632653061, "date": "2023-06-21" }, { "rank": 25, "intensity": 2.2346938775510203, "date": "2023-10-14" }, { "rank": 26, "intensity": 2.2653061224489797, "date": "2023-06-07" }, { "rank": 27, "intensity": 2.295918367346939, "date": "2023-03-03" }, { "rank": 28, "intensity": 2.326530612244898, "date": "2023-10-12" }, { "rank": 29, "intensity": 2.357142857142857, "date": "2023-04-25" }, { "rank": 30, "intensity": 2.387755102040816, "date": "2023-02-24" }, { "rank": 31, "intensity": 2.4183673469387754, "date": "2023-03-13" }, { "rank": 32, "intensity": 2.4489795918367347, "date": "2023-01-01" }, { "rank": 33, "intensity": 2.479591836734694, "date": "2023-02-16" }, { "rank": 34, "intensity": 2.510204081632653, "date": "2023-02-03" }, { "rank": 35, "intensity": 2.5408163265306123, "date": "2023-05-03" }, { "rank": 36, "intensity": 2.571428571428571, "date": "2023-06-24" }, { "rank": 37, "intensity": 2.6020408163265305, "date": "2023-02-11" }, { "rank": 38, "intensity": 2.63265306122449, "date": "2023-12-04" }, { "rank": 39, "intensity": 2.663265306122449, "date": "2023-01-08" }, { "rank": 40, "intensity": 2.693877551020408, "date": "2023-11-23" }, { "rank": 41, "intensity": 2.7244897959183674, "date": "2023-01-18" }, { "rank": 42, "intensity": 2.7551020408163263, "date": "2023-12-25" }, { "rank": 43, "intensity": 2.7857142857142856, "date": "2023-02-21" }, { "rank": 44, "intensity": 2.816326530612245, "date": "2023-07-10" }, { "rank": 45, "intensity": 2.8469387755102042, "date": "2023-06-19" }, { "rank": 46, "intensity": 2.877551020408163, "date": "2023-09-09" }, { "rank": 47, "intensity": 2.9081632653061225, "date": "2023-08-28" }, { "rank": 48, "intensity": 2.9387755102040813, "date": "2023-03-08" }, { "rank": 49, "intensity": 2.9693877551020407, "date": "2023-01-16" }, { "rank": 50, "intensity": 3.0, "date": "2023-04-16" } ], "source": "causal_rivers" } }, { "id": "L3_T1_Composite_Trend_00002", "level": 3, "level_name": "Semantic Reasoning", "category": "Composite Trend", "subtask": "Composite Trend", "question": "Among days in channel 71 during 2022 that exhibit the trend pattern 'rapid fall, then rapid rise, then steady stable', identify the top-5 days where the rapid rise segment is the fastest. (Output format: a ranked list of dates, e.g., ['YYYY-MM-DD', ...])", "answer": "['2022-02-02', '2022-08-06', '2022-12-30', '2022-07-25', '2022-03-01']", "ground_truth": [ "2022-02-02", "2022-08-06", "2022-12-30", "2022-07-25", "2022-03-01" ], "eval_metric": "set_f1", "channel": "71", "ts_data_path": "ts_data/L3_T1_Composite_Trend_00002.csv", "meta": { "segments": [ { "kind": "fall", "adj": "rapid" }, { "kind": "rise", "adj": "rapid" }, { "kind": "stable", "adj": "steady" } ], "pattern_phrase": "rapid fall, then rapid rise, then steady stable", "rank_target_idx": 1, "rank_kind": "rise", "rank_adj": "rapid", "rank_axis": "slope", "rank_direction": "DESC", "rank_extreme": "fastest", "top_segments_meta": [ { "adj": "rapid", "kind": "fall", "label": "rapid fall", "start_idx": 0, "end_idx": 16 }, { "adj": "rapid", "kind": "rise", "label": "rapid rise", "start_idx": 16, "end_idx": 33 }, { "adj": "steady", "kind": "stable", "label": "steady stable", "start_idx": 33, "end_idx": 98 } ], "points_per_day": 96, "pattern": "rapid fall, then rapid rise, then steady stable", "year": 2022, "top_k": [ "2022-02-02", "2022-08-06", "2022-12-30", "2022-07-25", "2022-03-01" ], "all_injections": [ { "rank": 1, "intensity": 3.0, "date": "2022-02-02" }, { "rank": 2, "intensity": 2.9693877551020407, "date": "2022-08-06" }, { "rank": 3, "intensity": 2.938775510204082, "date": "2022-12-30" }, { "rank": 4, "intensity": 2.9081632653061225, "date": "2022-07-25" }, { "rank": 5, "intensity": 2.877551020408163, "date": "2022-03-01" }, { "rank": 6, "intensity": 2.8469387755102042, "date": "2022-06-04" }, { "rank": 7, "intensity": 2.816326530612245, "date": "2022-03-12" }, { "rank": 8, "intensity": 2.7857142857142856, "date": "2022-08-14" }, { "rank": 9, "intensity": 2.7551020408163267, "date": "2022-06-17" }, { "rank": 10, "intensity": 2.7244897959183674, "date": "2022-10-22" }, { "rank": 11, "intensity": 2.693877551020408, "date": "2022-04-28" }, { "rank": 12, "intensity": 2.663265306122449, "date": "2022-03-06" }, { "rank": 13, "intensity": 2.63265306122449, "date": "2022-03-29" }, { "rank": 14, "intensity": 2.6020408163265305, "date": "2022-04-20" }, { "rank": 15, "intensity": 2.5714285714285716, "date": "2022-02-25" }, { "rank": 16, "intensity": 2.5408163265306123, "date": "2022-07-06" }, { "rank": 17, "intensity": 2.510204081632653, "date": "2022-05-27" }, { "rank": 18, "intensity": 2.479591836734694, "date": "2022-12-08" }, { "rank": 19, "intensity": 2.4489795918367347, "date": "2022-04-25" }, { "rank": 20, "intensity": 2.4183673469387754, "date": "2022-12-17" }, { "rank": 21, "intensity": 2.387755102040816, "date": "2022-10-19" }, { "rank": 22, "intensity": 2.357142857142857, "date": "2022-08-23" }, { "rank": 23, "intensity": 2.326530612244898, "date": "2022-08-10" }, { "rank": 24, "intensity": 2.295918367346939, "date": "2022-07-28" }, { "rank": 25, "intensity": 2.2653061224489797, "date": "2022-01-21" }, { "rank": 26, "intensity": 2.2346938775510203, "date": "2022-06-29" }, { "rank": 27, "intensity": 2.204081632653061, "date": "2022-01-10" }, { "rank": 28, "intensity": 2.173469387755102, "date": "2022-02-20" }, { "rank": 29, "intensity": 2.142857142857143, "date": "2022-02-07" }, { "rank": 30, "intensity": 2.112244897959184, "date": "2022-01-02" }, { "rank": 31, "intensity": 2.0816326530612246, "date": "2022-01-15" }, { "rank": 32, "intensity": 2.0510204081632653, "date": "2022-02-05" }, { "rank": 33, "intensity": 2.020408163265306, "date": "2022-05-22" }, { "rank": 34, "intensity": 1.989795918367347, "date": "2022-10-17" }, { "rank": 35, "intensity": 1.9591836734693877, "date": "2022-02-10" }, { "rank": 36, "intensity": 1.9285714285714286, "date": "2022-06-08" }, { "rank": 37, "intensity": 1.8979591836734695, "date": "2022-09-02" }, { "rank": 38, "intensity": 1.8673469387755102, "date": "2022-11-19" }, { "rank": 39, "intensity": 1.836734693877551, "date": "2022-12-04" }, { "rank": 40, "intensity": 1.806122448979592, "date": "2022-09-19" }, { "rank": 41, "intensity": 1.7755102040816326, "date": "2022-05-29" }, { "rank": 42, "intensity": 1.7448979591836735, "date": "2022-12-01" }, { "rank": 43, "intensity": 1.7142857142857144, "date": "2022-09-25" }, { "rank": 44, "intensity": 1.683673469387755, "date": "2022-06-10" }, { "rank": 45, "intensity": 1.653061224489796, "date": "2022-01-28" }, { "rank": 46, "intensity": 1.6224489795918369, "date": "2022-09-21" }, { "rank": 47, "intensity": 1.5918367346938775, "date": "2022-12-20" }, { "rank": 48, "intensity": 1.5612244897959184, "date": "2022-12-14" }, { "rank": 49, "intensity": 1.5306122448979593, "date": "2022-08-20" }, { "rank": 50, "intensity": 1.5, "date": "2022-01-05" } ], "source": "causal_rivers" } }, { "id": "L3_T1_Composite_Trend_00003", "level": 3, "level_name": "Semantic Reasoning", "category": "Composite Trend", "subtask": "Composite Trend", "question": "Among days in channel 99 during 2020 that exhibit the trend pattern 'slow rise, then rapid rise, then fluctuating stable', identify the top-5 days where the fluctuating stable segment is the most fluctuating. (Output format: a ranked list of dates, e.g., ['YYYY-MM-DD', ...])", "answer": "['2020-03-30', '2020-04-06', '2020-01-31', '2020-12-03', '2020-11-11']", "ground_truth": [ "2020-03-30", "2020-04-06", "2020-01-31", "2020-12-03", "2020-11-11" ], "eval_metric": "set_f1", "channel": "99", "ts_data_path": "ts_data/L3_T1_Composite_Trend_00003.csv", "meta": { "segments": [ { "kind": "rise", "adj": "slow" }, { "kind": "rise", "adj": "rapid" }, { "kind": "stable", "adj": "fluctuating" } ], "pattern_phrase": "slow rise, then rapid rise, then fluctuating stable", "rank_target_idx": 2, "rank_kind": "stable", "rank_adj": "fluctuating", "rank_axis": "std", "rank_direction": "DESC", "rank_extreme": "most fluctuating", "top_segments_meta": [ { "adj": "slow", "kind": "rise", "label": "slow rise", "start_idx": 0, "end_idx": 51 }, { "adj": "rapid", "kind": "rise", "label": "rapid rise", "start_idx": 51, "end_idx": 65 }, { "adj": "fluctuating", "kind": "stable", "label": "fluctuating stable", "start_idx": 65, "end_idx": 98 } ], "points_per_day": 96, "pattern": "slow rise, then rapid rise, then fluctuating stable", "year": 2020, "top_k": [ "2020-03-30", "2020-04-06", "2020-01-31", "2020-12-03", "2020-11-11" ], "all_injections": [ { "rank": 1, "intensity": 3.0, "date": "2020-03-30" }, { "rank": 2, "intensity": 2.9693877551020407, "date": "2020-04-06" }, { "rank": 3, "intensity": 2.938775510204082, "date": "2020-01-31" }, { "rank": 4, "intensity": 2.9081632653061225, "date": "2020-12-03" }, { "rank": 5, "intensity": 2.877551020408163, "date": "2020-11-11" }, { "rank": 6, "intensity": 2.8469387755102042, "date": "2020-05-04" }, { "rank": 7, "intensity": 2.816326530612245, "date": "2020-02-04" }, { "rank": 8, "intensity": 2.7857142857142856, "date": "2020-06-01" }, { "rank": 9, "intensity": 2.7551020408163267, "date": "2020-06-05" }, { "rank": 10, "intensity": 2.7244897959183674, "date": "2020-06-18" }, { "rank": 11, "intensity": 2.693877551020408, "date": "2020-05-28" }, { "rank": 12, "intensity": 2.663265306122449, "date": "2020-06-13" }, { "rank": 13, "intensity": 2.63265306122449, "date": "2020-01-05" }, { "rank": 14, "intensity": 2.6020408163265305, "date": "2020-07-13" }, { "rank": 15, "intensity": 2.5714285714285716, "date": "2020-06-10" }, { "rank": 16, "intensity": 2.5408163265306123, "date": "2020-10-25" }, { "rank": 17, "intensity": 2.510204081632653, "date": "2020-07-08" }, { "rank": 18, "intensity": 2.479591836734694, "date": "2020-07-21" }, { "rank": 19, "intensity": 2.4489795918367347, "date": "2020-12-24" }, { "rank": 20, "intensity": 2.4183673469387754, "date": "2020-10-18" }, { "rank": 21, "intensity": 2.387755102040816, "date": "2020-04-20" }, { "rank": 22, "intensity": 2.357142857142857, "date": "2020-05-21" }, { "rank": 23, "intensity": 2.326530612244898, "date": "2020-03-02" }, { "rank": 24, "intensity": 2.295918367346939, "date": "2020-10-05" }, { "rank": 25, "intensity": 2.2653061224489797, "date": "2020-11-18" }, { "rank": 26, "intensity": 2.2346938775510203, "date": "2020-06-16" }, { "rank": 27, "intensity": 2.204081632653061, "date": "2020-11-23" }, { "rank": 28, "intensity": 2.173469387755102, "date": "2020-04-24" }, { "rank": 29, "intensity": 2.142857142857143, "date": "2020-11-04" }, { "rank": 30, "intensity": 2.112244897959184, "date": "2020-01-29" }, { "rank": 31, "intensity": 2.0816326530612246, "date": "2020-07-01" }, { "rank": 32, "intensity": 2.0510204081632653, "date": "2020-02-21" }, { "rank": 33, "intensity": 2.020408163265306, "date": "2020-02-08" }, { "rank": 34, "intensity": 1.989795918367347, "date": "2020-01-12" }, { "rank": 35, "intensity": 1.9591836734693877, "date": "2020-02-16" }, { "rank": 36, "intensity": 1.9285714285714286, "date": "2020-09-08" }, { "rank": 37, "intensity": 1.8979591836734695, "date": "2020-09-10" }, { "rank": 38, "intensity": 1.8673469387755102, "date": "2020-02-26" }, { "rank": 39, "intensity": 1.836734693877551, "date": "2020-02-10" }, { "rank": 40, "intensity": 1.806122448979592, "date": "2020-07-28" }, { "rank": 41, "intensity": 1.7755102040816326, "date": "2020-07-10" }, { "rank": 42, "intensity": 1.7448979591836735, "date": "2020-07-18" }, { "rank": 43, "intensity": 1.7142857142857144, "date": "2020-01-24" }, { "rank": 44, "intensity": 1.683673469387755, "date": "2020-06-07" }, { "rank": 45, "intensity": 1.653061224489796, "date": "2020-10-02" }, { "rank": 46, "intensity": 1.6224489795918369, "date": "2020-02-28" }, { "rank": 47, "intensity": 1.5918367346938775, "date": "2020-07-04" }, { "rank": 48, "intensity": 1.5612244897959184, "date": "2020-03-19" }, { "rank": 49, "intensity": 1.5306122448979593, "date": "2020-07-30" }, { "rank": 50, "intensity": 1.5, "date": "2020-03-16" } ], "source": "causal_rivers" } }, { "id": "L3_T1_Composite_Trend_00004", "level": 3, "level_name": "Semantic Reasoning", "category": "Composite Trend", "subtask": "Composite Trend", "question": "Among days in channel 123 during 2021 that exhibit the trend pattern 'rapid rise, then fall', identify the top-5 days where the rapid rise segment is the fastest. (Output format: a ranked list of dates, e.g., ['YYYY-MM-DD', ...])", "answer": "['2021-04-30', '2021-09-02', '2021-01-17', '2021-02-18', '2021-02-15']", "ground_truth": [ "2021-04-30", "2021-09-02", "2021-01-17", "2021-02-18", "2021-02-15" ], "eval_metric": "set_f1", "channel": "123", "ts_data_path": "ts_data/L3_T1_Composite_Trend_00004.csv", "meta": { "segments": [ { "kind": "rise", "adj": "rapid" }, { "kind": "fall", "adj": "none" } ], "pattern_phrase": "rapid rise, then fall", "rank_target_idx": 0, "rank_kind": "rise", "rank_adj": "rapid", "rank_axis": "slope", "rank_direction": "DESC", "rank_extreme": "fastest", "top_segments_meta": [ { "adj": "rapid", "kind": "rise", "label": "rapid rise", "start_idx": 0, "end_idx": 31 }, { "adj": "none", "kind": "fall", "label": "none fall", "start_idx": 31, "end_idx": 98 } ], "points_per_day": 96, "pattern": "rapid rise, then fall", "year": 2021, "top_k": [ "2021-04-30", "2021-09-02", "2021-01-17", "2021-02-18", "2021-02-15" ], "all_injections": [ { "rank": 1, "intensity": 3.0, "date": "2021-04-30" }, { "rank": 2, "intensity": 2.9693877551020407, "date": "2021-09-02" }, { "rank": 3, "intensity": 2.938775510204082, "date": "2021-01-17" }, { "rank": 4, "intensity": 2.9081632653061225, "date": "2021-02-18" }, { "rank": 5, "intensity": 2.877551020408163, "date": "2021-02-15" }, { "rank": 6, "intensity": 2.8469387755102042, "date": "2021-12-11" }, { "rank": 7, "intensity": 2.816326530612245, "date": "2021-12-20" }, { "rank": 8, "intensity": 2.7857142857142856, "date": "2021-02-06" }, { "rank": 9, "intensity": 2.7551020408163267, "date": "2021-07-18" }, { "rank": 10, "intensity": 2.7244897959183674, "date": "2021-08-13" }, { "rank": 11, "intensity": 2.693877551020408, "date": "2021-07-07" }, { "rank": 12, "intensity": 2.663265306122449, "date": "2021-03-02" }, { "rank": 13, "intensity": 2.63265306122449, "date": "2021-09-19" }, { "rank": 14, "intensity": 2.6020408163265305, "date": "2021-10-01" }, { "rank": 15, "intensity": 2.5714285714285716, "date": "2021-04-21" }, { "rank": 16, "intensity": 2.5408163265306123, "date": "2021-01-26" }, { "rank": 17, "intensity": 2.510204081632653, "date": "2021-03-06" }, { "rank": 18, "intensity": 2.479591836734694, "date": "2021-02-26" }, { "rank": 19, "intensity": 2.4489795918367347, "date": "2021-07-12" }, { "rank": 20, "intensity": 2.4183673469387754, "date": "2021-02-24" }, { "rank": 21, "intensity": 2.387755102040816, "date": "2021-06-04" }, { "rank": 22, "intensity": 2.357142857142857, "date": "2021-08-28" }, { "rank": 23, "intensity": 2.326530612244898, "date": "2021-07-23" }, { "rank": 24, "intensity": 2.295918367346939, "date": "2021-03-28" }, { "rank": 25, "intensity": 2.2653061224489797, "date": "2021-02-20" }, { "rank": 26, "intensity": 2.2346938775510203, "date": "2021-10-26" }, { "rank": 27, "intensity": 2.204081632653061, "date": "2021-11-27" }, { "rank": 28, "intensity": 2.173469387755102, "date": "2021-04-28" }, { "rank": 29, "intensity": 2.142857142857143, "date": "2021-12-28" }, { "rank": 30, "intensity": 2.112244897959184, "date": "2021-04-14" }, { "rank": 31, "intensity": 2.0816326530612246, "date": "2021-06-13" }, { "rank": 32, "intensity": 2.0510204081632653, "date": "2021-07-09" }, { "rank": 33, "intensity": 2.020408163265306, "date": "2021-03-25" }, { "rank": 34, "intensity": 1.989795918367347, "date": "2021-04-09" }, { "rank": 35, "intensity": 1.9591836734693877, "date": "2021-10-22" }, { "rank": 36, "intensity": 1.9285714285714286, "date": "2021-08-22" }, { "rank": 37, "intensity": 1.8979591836734695, "date": "2021-11-16" }, { "rank": 38, "intensity": 1.8673469387755102, "date": "2021-03-19" }, { "rank": 39, "intensity": 1.836734693877551, "date": "2021-01-24" }, { "rank": 40, "intensity": 1.806122448979592, "date": "2021-06-29" }, { "rank": 41, "intensity": 1.7755102040816326, "date": "2021-04-02" }, { "rank": 42, "intensity": 1.7448979591836735, "date": "2021-07-01" }, { "rank": 43, "intensity": 1.7142857142857144, "date": "2021-05-19" }, { "rank": 44, "intensity": 1.683673469387755, "date": "2021-09-12" }, { "rank": 45, "intensity": 1.653061224489796, "date": "2021-10-17" }, { "rank": 46, "intensity": 1.6224489795918369, "date": "2021-01-06" }, { "rank": 47, "intensity": 1.5918367346938775, "date": "2021-09-22" }, { "rank": 48, "intensity": 1.5612244897959184, "date": "2021-01-28" }, { "rank": 49, "intensity": 1.5306122448979593, "date": "2021-06-15" }, { "rank": 50, "intensity": 1.5, "date": "2021-09-28" } ], "source": "causal_rivers" } }, { "id": "L3_T1_Composite_Trend_00005", "level": 3, "level_name": "Semantic Reasoning", "category": "Composite Trend", "subtask": "Composite Trend", "question": "Among days in channel 124 during 2021 that exhibit the trend pattern 'rapid fall, then steady stable, then fall', identify the top-5 days where the rapid fall segment is the fastest. (Output format: a ranked list of dates, e.g., ['YYYY-MM-DD', ...])", "answer": "['2021-05-11', '2021-05-21', '2021-06-01', '2021-12-12', '2021-08-25']", "ground_truth": [ "2021-05-11", "2021-05-21", "2021-06-01", "2021-12-12", "2021-08-25" ], "eval_metric": "set_f1", "channel": "124", "ts_data_path": "ts_data/L3_T1_Composite_Trend_00005.csv", "meta": { "segments": [ { "kind": "fall", "adj": "rapid" }, { "kind": "stable", "adj": "steady" }, { "kind": "fall", "adj": "none" } ], "pattern_phrase": "rapid fall, then steady stable, then fall", "rank_target_idx": 0, "rank_kind": "fall", "rank_adj": "rapid", "rank_axis": "slope", "rank_direction": "DESC", "rank_extreme": "fastest", "top_segments_meta": [ { "adj": "rapid", "kind": "fall", "label": "rapid fall", "start_idx": 0, "end_idx": 16 }, { "adj": "steady", "kind": "stable", "label": "steady stable", "start_idx": 16, "end_idx": 70 }, { "adj": "none", "kind": "fall", "label": "none fall", "start_idx": 70, "end_idx": 98 } ], "points_per_day": 96, "pattern": "rapid fall, then steady stable, then fall", "year": 2021, "top_k": [ "2021-05-11", "2021-05-21", "2021-06-01", "2021-12-12", "2021-08-25" ], "all_injections": [ { "rank": 1, "intensity": 3.0, "date": "2021-05-11" }, { "rank": 2, "intensity": 2.9693877551020407, "date": "2021-05-21" }, { "rank": 3, "intensity": 2.938775510204082, "date": "2021-06-01" }, { "rank": 4, "intensity": 2.9081632653061225, "date": "2021-12-12" }, { "rank": 5, "intensity": 2.877551020408163, "date": "2021-08-25" }, { "rank": 6, "intensity": 2.8469387755102042, "date": "2021-06-14" }, { "rank": 7, "intensity": 2.816326530612245, "date": "2021-08-07" }, { "rank": 8, "intensity": 2.7857142857142856, "date": "2021-08-16" }, { "rank": 9, "intensity": 2.7551020408163267, "date": "2021-07-22" }, { "rank": 10, "intensity": 2.7244897959183674, "date": "2021-07-24" }, { "rank": 11, "intensity": 2.693877551020408, "date": "2021-07-26" }, { "rank": 12, "intensity": 2.663265306122449, "date": "2021-06-23" }, { "rank": 13, "intensity": 2.63265306122449, "date": "2021-04-05" }, { "rank": 14, "intensity": 2.6020408163265305, "date": "2021-06-28" }, { "rank": 15, "intensity": 2.5714285714285716, "date": "2021-03-17" }, { "rank": 16, "intensity": 2.5408163265306123, "date": "2021-10-11" }, { "rank": 17, "intensity": 2.510204081632653, "date": "2021-06-26" }, { "rank": 18, "intensity": 2.479591836734694, "date": "2021-03-20" }, { "rank": 19, "intensity": 2.4489795918367347, "date": "2021-10-31" }, { "rank": 20, "intensity": 2.4183673469387754, "date": "2021-09-23" }, { "rank": 21, "intensity": 2.387755102040816, "date": "2021-03-02" }, { "rank": 22, "intensity": 2.357142857142857, "date": "2021-10-06" }, { "rank": 23, "intensity": 2.326530612244898, "date": "2021-10-08" }, { "rank": 24, "intensity": 2.295918367346939, "date": "2021-02-15" }, { "rank": 25, "intensity": 2.2653061224489797, "date": "2021-05-18" }, { "rank": 26, "intensity": 2.2346938775510203, "date": "2021-05-29" }, { "rank": 27, "intensity": 2.204081632653061, "date": "2021-09-30" }, { "rank": 28, "intensity": 2.173469387755102, "date": "2021-05-08" }, { "rank": 29, "intensity": 2.142857142857143, "date": "2021-02-23" }, { "rank": 30, "intensity": 2.112244897959184, "date": "2021-08-10" }, { "rank": 31, "intensity": 2.0816326530612246, "date": "2021-06-19" }, { "rank": 32, "intensity": 2.0510204081632653, "date": "2021-02-11" }, { "rank": 33, "intensity": 2.020408163265306, "date": "2021-09-10" }, { "rank": 34, "intensity": 1.989795918367347, "date": "2021-12-09" }, { "rank": 35, "intensity": 1.9591836734693877, "date": "2021-05-16" }, { "rank": 36, "intensity": 1.9285714285714286, "date": "2021-04-07" }, { "rank": 37, "intensity": 1.8979591836734695, "date": "2021-01-30" }, { "rank": 38, "intensity": 1.8673469387755102, "date": "2021-03-26" }, { "rank": 39, "intensity": 1.836734693877551, "date": "2021-10-28" }, { "rank": 40, "intensity": 1.806122448979592, "date": "2021-07-12" }, { "rank": 41, "intensity": 1.7755102040816326, "date": "2021-09-16" }, { "rank": 42, "intensity": 1.7448979591836735, "date": "2021-04-16" }, { "rank": 43, "intensity": 1.7142857142857144, "date": "2021-05-02" }, { "rank": 44, "intensity": 1.683673469387755, "date": "2021-02-13" }, { "rank": 45, "intensity": 1.653061224489796, "date": "2021-06-30" }, { "rank": 46, "intensity": 1.6224489795918369, "date": "2021-01-18" }, { "rank": 47, "intensity": 1.5918367346938775, "date": "2021-10-20" }, { "rank": 48, "intensity": 1.5612244897959184, "date": "2021-08-13" }, { "rank": 49, "intensity": 1.5306122448979593, "date": "2021-12-16" }, { "rank": 50, "intensity": 1.5, "date": "2021-03-31" } ], "source": "causal_rivers" } }, { "id": "L3_T1_Composite_Trend_00006", "level": 3, "level_name": "Semantic Reasoning", "category": "Composite Trend", "subtask": "Composite Trend", "question": "Among days in channel 146 during 2021 that exhibit the trend pattern 'slow fall, then fall, then rapid fall, then rapid rise', identify the top-5 days where the rapid fall segment is the fastest. (Output format: a ranked list of dates, e.g., ['YYYY-MM-DD', ...])", "answer": "['2021-01-02', '2021-08-13', '2021-08-01', '2021-08-22', '2021-12-24']", "ground_truth": [ "2021-01-02", "2021-08-13", "2021-08-01", "2021-08-22", "2021-12-24" ], "eval_metric": "set_f1", "channel": "146", "ts_data_path": "ts_data/L3_T1_Composite_Trend_00006.csv", "meta": { "segments": [ { "kind": "fall", "adj": "slow" }, { "kind": "fall", "adj": "none" }, { "kind": "fall", "adj": "rapid" }, { "kind": "rise", "adj": "rapid" } ], "pattern_phrase": "slow fall, then fall, then rapid fall, then rapid rise", "rank_target_idx": 2, "rank_kind": "fall", "rank_adj": "rapid", "rank_axis": "slope", "rank_direction": "DESC", "rank_extreme": "fastest", "top_segments_meta": [ { "adj": "slow", "kind": "fall", "label": "slow fall", "start_idx": 0, "end_idx": 48 }, { "adj": "none", "kind": "fall", "label": "none fall", "start_idx": 48, "end_idx": 72 }, { "adj": "rapid", "kind": "fall", "label": "rapid fall", "start_idx": 72, "end_idx": 86 }, { "adj": "rapid", "kind": "rise", "label": "rapid rise", "start_idx": 86, "end_idx": 98 } ], "points_per_day": 96, "pattern": "slow fall, then fall, then rapid fall, then rapid rise", "year": 2021, "top_k": [ "2021-01-02", "2021-08-13", "2021-08-01", "2021-08-22", "2021-12-24" ], "all_injections": [ { "rank": 1, "intensity": 3.0, "date": "2021-01-02" }, { "rank": 2, "intensity": 2.9693877551020407, "date": "2021-08-13" }, { "rank": 3, "intensity": 2.938775510204082, "date": "2021-08-01" }, { "rank": 4, "intensity": 2.9081632653061225, "date": "2021-08-22" }, { "rank": 5, "intensity": 2.877551020408163, "date": "2021-12-24" }, { "rank": 6, "intensity": 2.8469387755102042, "date": "2021-01-04" }, { "rank": 7, "intensity": 2.816326530612245, "date": "2021-06-21" }, { "rank": 8, "intensity": 2.7857142857142856, "date": "2021-02-19" }, { "rank": 9, "intensity": 2.7551020408163267, "date": "2021-12-17" }, { "rank": 10, "intensity": 2.7244897959183674, "date": "2021-07-12" }, { "rank": 11, "intensity": 2.693877551020408, "date": "2021-06-05" }, { "rank": 12, "intensity": 2.663265306122449, "date": "2021-03-20" }, { "rank": 13, "intensity": 2.63265306122449, "date": "2021-07-10" }, { "rank": 14, "intensity": 2.6020408163265305, "date": "2021-10-29" }, { "rank": 15, "intensity": 2.5714285714285716, "date": "2021-11-14" }, { "rank": 16, "intensity": 2.5408163265306123, "date": "2021-08-31" }, { "rank": 17, "intensity": 2.510204081632653, "date": "2021-10-10" }, { "rank": 18, "intensity": 2.479591836734694, "date": "2021-12-28" }, { "rank": 19, "intensity": 2.4489795918367347, "date": "2021-02-26" }, { "rank": 20, "intensity": 2.4183673469387754, "date": "2021-12-14" }, { "rank": 21, "intensity": 2.387755102040816, "date": "2021-07-03" }, { "rank": 22, "intensity": 2.357142857142857, "date": "2021-06-28" }, { "rank": 23, "intensity": 2.326530612244898, "date": "2021-10-21" }, { "rank": 24, "intensity": 2.295918367346939, "date": "2021-01-13" }, { "rank": 25, "intensity": 2.2653061224489797, "date": "2021-02-04" }, { "rank": 26, "intensity": 2.2346938775510203, "date": "2021-12-04" }, { "rank": 27, "intensity": 2.204081632653061, "date": "2021-03-17" }, { "rank": 28, "intensity": 2.173469387755102, "date": "2021-05-24" }, { "rank": 29, "intensity": 2.142857142857143, "date": "2021-01-09" }, { "rank": 30, "intensity": 2.112244897959184, "date": "2021-11-03" }, { "rank": 31, "intensity": 2.0816326530612246, "date": "2021-09-11" }, { "rank": 32, "intensity": 2.0510204081632653, "date": "2021-01-26" }, { "rank": 33, "intensity": 2.020408163265306, "date": "2021-05-20" }, { "rank": 34, "intensity": 1.989795918367347, "date": "2021-01-07" }, { "rank": 35, "intensity": 1.9591836734693877, "date": "2021-12-01" }, { "rank": 36, "intensity": 1.9285714285714286, "date": "2021-04-16" }, { "rank": 37, "intensity": 1.8979591836734695, "date": "2021-05-12" }, { "rank": 38, "intensity": 1.8673469387755102, "date": "2021-07-25" }, { "rank": 39, "intensity": 1.836734693877551, "date": "2021-07-18" }, { "rank": 40, "intensity": 1.806122448979592, "date": "2021-02-08" }, { "rank": 41, "intensity": 1.7755102040816326, "date": "2021-04-09" }, { "rank": 42, "intensity": 1.7448979591836735, "date": "2021-11-18" }, { "rank": 43, "intensity": 1.7142857142857144, "date": "2021-04-30" }, { "rank": 44, "intensity": 1.683673469387755, "date": "2021-06-02" }, { "rank": 45, "intensity": 1.653061224489796, "date": "2021-12-11" }, { "rank": 46, "intensity": 1.6224489795918369, "date": "2021-08-10" }, { "rank": 47, "intensity": 1.5918367346938775, "date": "2021-04-13" }, { "rank": 48, "intensity": 1.5612244897959184, "date": "2021-05-18" }, { "rank": 49, "intensity": 1.5306122448979593, "date": "2021-03-12" }, { "rank": 50, "intensity": 1.5, "date": "2021-07-05" } ], "source": "causal_rivers" } }, { "id": "L3_T1_Composite_Trend_00007", "level": 3, "level_name": "Semantic Reasoning", "category": "Composite Trend", "subtask": "Composite Trend", "question": "Among days in channel 147 during 2022 that exhibit the trend pattern 'steady stable, then fluctuating stable', identify the top-5 days where the fluctuating stable segment is the most fluctuating. (Output format: a ranked list of dates, e.g., ['YYYY-MM-DD', ...])", "answer": "['2022-11-27', '2022-10-18', '2022-06-13', '2022-04-08', '2022-03-01']", "ground_truth": [ "2022-11-27", "2022-10-18", "2022-06-13", "2022-04-08", "2022-03-01" ], "eval_metric": "set_f1", "channel": "147", "ts_data_path": "ts_data/L3_T1_Composite_Trend_00007.csv", "meta": { "segments": [ { "kind": "stable", "adj": "steady" }, { "kind": "stable", "adj": "fluctuating" } ], "pattern_phrase": "steady stable, then fluctuating stable", "rank_target_idx": 1, "rank_kind": "stable", "rank_adj": "fluctuating", "rank_axis": "std", "rank_direction": "DESC", "rank_extreme": "most fluctuating", "top_segments_meta": [ { "adj": "steady", "kind": "stable", "label": "steady stable", "start_idx": 0, "end_idx": 63 }, { "adj": "fluctuating", "kind": "stable", "label": "fluctuating stable", "start_idx": 63, "end_idx": 98 } ], "points_per_day": 96, "pattern": "steady stable, then fluctuating stable", "year": 2022, "top_k": [ "2022-11-27", "2022-10-18", "2022-06-13", "2022-04-08", "2022-03-01" ], "all_injections": [ { "rank": 1, "intensity": 3.0, "date": "2022-11-27" }, { "rank": 2, "intensity": 2.9693877551020407, "date": "2022-10-18" }, { "rank": 3, "intensity": 2.938775510204082, "date": "2022-06-13" }, { "rank": 4, "intensity": 2.9081632653061225, "date": "2022-04-08" }, { "rank": 5, "intensity": 2.877551020408163, "date": "2022-03-01" }, { "rank": 6, "intensity": 2.8469387755102042, "date": "2022-09-25" }, { "rank": 7, "intensity": 2.816326530612245, "date": "2022-12-19" }, { "rank": 8, "intensity": 2.7857142857142856, "date": "2022-10-07" }, { "rank": 9, "intensity": 2.7551020408163267, "date": "2022-04-28" }, { "rank": 10, "intensity": 2.7244897959183674, "date": "2022-11-08" }, { "rank": 11, "intensity": 2.693877551020408, "date": "2022-05-24" }, { "rank": 12, "intensity": 2.663265306122449, "date": "2022-02-04" }, { "rank": 13, "intensity": 2.63265306122449, "date": "2022-06-24" }, { "rank": 14, "intensity": 2.6020408163265305, "date": "2022-03-25" }, { "rank": 15, "intensity": 2.5714285714285716, "date": "2022-06-09" }, { "rank": 16, "intensity": 2.5408163265306123, "date": "2022-01-30" }, { "rank": 17, "intensity": 2.510204081632653, "date": "2022-11-10" }, { "rank": 18, "intensity": 2.479591836734694, "date": "2022-08-11" }, { "rank": 19, "intensity": 2.4489795918367347, "date": "2022-09-23" }, { "rank": 20, "intensity": 2.4183673469387754, "date": "2022-02-12" }, { "rank": 21, "intensity": 2.387755102040816, "date": "2022-10-02" }, { "rank": 22, "intensity": 2.357142857142857, "date": "2022-01-23" }, { "rank": 23, "intensity": 2.326530612244898, "date": "2022-05-07" }, { "rank": 24, "intensity": 2.295918367346939, "date": "2022-06-19" }, { "rank": 25, "intensity": 2.2653061224489797, "date": "2022-08-03" }, { "rank": 26, "intensity": 2.2346938775510203, "date": "2022-09-02" }, { "rank": 27, "intensity": 2.204081632653061, "date": "2022-04-06" }, { "rank": 28, "intensity": 2.173469387755102, "date": "2022-03-16" }, { "rank": 29, "intensity": 2.142857142857143, "date": "2022-04-20" }, { "rank": 30, "intensity": 2.112244897959184, "date": "2022-06-15" }, { "rank": 31, "intensity": 2.0816326530612246, "date": "2022-05-09" }, { "rank": 32, "intensity": 2.0510204081632653, "date": "2022-03-11" }, { "rank": 33, "intensity": 2.020408163265306, "date": "2022-10-22" }, { "rank": 34, "intensity": 1.989795918367347, "date": "2022-01-05" }, { "rank": 35, "intensity": 1.9591836734693877, "date": "2022-04-17" }, { "rank": 36, "intensity": 1.9285714285714286, "date": "2022-07-22" }, { "rank": 37, "intensity": 1.8979591836734695, "date": "2022-09-15" }, { "rank": 38, "intensity": 1.8673469387755102, "date": "2022-09-20" }, { "rank": 39, "intensity": 1.836734693877551, "date": "2022-10-25" }, { "rank": 40, "intensity": 1.806122448979592, "date": "2022-08-19" }, { "rank": 41, "intensity": 1.7755102040816326, "date": "2022-12-10" }, { "rank": 42, "intensity": 1.7448979591836735, "date": "2022-06-27" }, { "rank": 43, "intensity": 1.7142857142857144, "date": "2022-04-13" }, { "rank": 44, "intensity": 1.683673469387755, "date": "2022-05-31" }, { "rank": 45, "intensity": 1.653061224489796, "date": "2022-07-09" }, { "rank": 46, "intensity": 1.6224489795918369, "date": "2022-11-02" }, { "rank": 47, "intensity": 1.5918367346938775, "date": "2022-07-20" }, { "rank": 48, "intensity": 1.5612244897959184, "date": "2022-11-06" }, { "rank": 49, "intensity": 1.5306122448979593, "date": "2022-09-09" }, { "rank": 50, "intensity": 1.5, "date": "2022-01-10" } ], "source": "causal_rivers" } }, { "id": "L3_T1_Composite_Trend_00008", "level": 3, "level_name": "Semantic Reasoning", "category": "Composite Trend", "subtask": "Composite Trend", "question": "Among days in channel 151 during 2023 that exhibit the trend pattern 'slow rise, then rapid fall, then slow rise', identify the top-5 days where the rapid fall segment is the fastest. (Output format: a ranked list of dates, e.g., ['YYYY-MM-DD', ...])", "answer": "['2023-11-25', '2023-07-11', '2023-08-20', '2023-10-28', '2023-03-15']", "ground_truth": [ "2023-11-25", "2023-07-11", "2023-08-20", "2023-10-28", "2023-03-15" ], "eval_metric": "set_f1", "channel": "151", "ts_data_path": "ts_data/L3_T1_Composite_Trend_00008.csv", "meta": { "segments": [ { "kind": "rise", "adj": "slow" }, { "kind": "fall", "adj": "rapid" }, { "kind": "rise", "adj": "slow" } ], "pattern_phrase": "slow rise, then rapid fall, then slow rise", "rank_target_idx": 1, "rank_kind": "fall", "rank_adj": "rapid", "rank_axis": "slope", "rank_direction": "DESC", "rank_extreme": "fastest", "top_segments_meta": [ { "adj": "slow", "kind": "rise", "label": "slow rise", "start_idx": 0, "end_idx": 42 }, { "adj": "rapid", "kind": "fall", "label": "rapid fall", "start_idx": 42, "end_idx": 56 }, { "adj": "slow", "kind": "rise", "label": "slow rise", "start_idx": 56, "end_idx": 98 } ], "points_per_day": 96, "pattern": "slow rise, then rapid fall, then slow rise", "year": 2023, "top_k": [ "2023-11-25", "2023-07-11", "2023-08-20", "2023-10-28", "2023-03-15" ], "all_injections": [ { "rank": 1, "intensity": 3.0, "date": "2023-11-25" }, { "rank": 2, "intensity": 2.9693877551020407, "date": "2023-07-11" }, { "rank": 3, "intensity": 2.938775510204082, "date": "2023-08-20" }, { "rank": 4, "intensity": 2.9081632653061225, "date": "2023-10-28" }, { "rank": 5, "intensity": 2.877551020408163, "date": "2023-03-15" }, { "rank": 6, "intensity": 2.8469387755102042, "date": "2023-09-21" }, { "rank": 7, "intensity": 2.816326530612245, "date": "2023-02-13" }, { "rank": 8, "intensity": 2.7857142857142856, "date": "2023-02-05" }, { "rank": 9, "intensity": 2.7551020408163267, "date": "2023-08-09" }, { "rank": 10, "intensity": 2.7244897959183674, "date": "2023-11-08" }, { "rank": 11, "intensity": 2.693877551020408, "date": "2023-03-25" }, { "rank": 12, "intensity": 2.663265306122449, "date": "2023-05-16" }, { "rank": 13, "intensity": 2.63265306122449, "date": "2023-03-07" }, { "rank": 14, "intensity": 2.6020408163265305, "date": "2023-09-18" }, { "rank": 15, "intensity": 2.5714285714285716, "date": "2023-03-05" }, { "rank": 16, "intensity": 2.5408163265306123, "date": "2023-09-11" }, { "rank": 17, "intensity": 2.510204081632653, "date": "2023-09-04" }, { "rank": 18, "intensity": 2.479591836734694, "date": "2023-08-17" }, { "rank": 19, "intensity": 2.4489795918367347, "date": "2023-01-23" }, { "rank": 20, "intensity": 2.4183673469387754, "date": "2023-01-11" }, { "rank": 21, "intensity": 2.387755102040816, "date": "2023-07-21" }, { "rank": 22, "intensity": 2.357142857142857, "date": "2023-05-13" }, { "rank": 23, "intensity": 2.326530612244898, "date": "2023-01-27" }, { "rank": 24, "intensity": 2.295918367346939, "date": "2023-12-20" }, { "rank": 25, "intensity": 2.2653061224489797, "date": "2023-06-06" }, { "rank": 26, "intensity": 2.2346938775510203, "date": "2023-06-30" }, { "rank": 27, "intensity": 2.204081632653061, "date": "2023-05-02" }, { "rank": 28, "intensity": 2.173469387755102, "date": "2023-11-11" }, { "rank": 29, "intensity": 2.142857142857143, "date": "2023-02-07" }, { "rank": 30, "intensity": 2.112244897959184, "date": "2023-06-03" }, { "rank": 31, "intensity": 2.0816326530612246, "date": "2023-08-28" }, { "rank": 32, "intensity": 2.0510204081632653, "date": "2023-12-07" }, { "rank": 33, "intensity": 2.020408163265306, "date": "2023-10-07" }, { "rank": 34, "intensity": 1.989795918367347, "date": "2023-11-03" }, { "rank": 35, "intensity": 1.9591836734693877, "date": "2023-09-01" }, { "rank": 36, "intensity": 1.9285714285714286, "date": "2023-07-30" }, { "rank": 37, "intensity": 1.8979591836734695, "date": "2023-10-17" }, { "rank": 38, "intensity": 1.8673469387755102, "date": "2023-06-13" }, { "rank": 39, "intensity": 1.836734693877551, "date": "2023-12-09" }, { "rank": 40, "intensity": 1.806122448979592, "date": "2023-04-26" }, { "rank": 41, "intensity": 1.7755102040816326, "date": "2023-10-23" }, { "rank": 42, "intensity": 1.7448979591836735, "date": "2023-09-24" }, { "rank": 43, "intensity": 1.7142857142857144, "date": "2023-04-19" }, { "rank": 44, "intensity": 1.683673469387755, "date": "2023-03-30" }, { "rank": 45, "intensity": 1.653061224489796, "date": "2023-04-11" }, { "rank": 46, "intensity": 1.6224489795918369, "date": "2023-05-10" }, { "rank": 47, "intensity": 1.5918367346938775, "date": "2023-01-15" }, { "rank": 48, "intensity": 1.5612244897959184, "date": "2023-08-03" }, { "rank": 49, "intensity": 1.5306122448979593, "date": "2023-03-19" }, { "rank": 50, "intensity": 1.5, "date": "2023-12-26" } ], "source": "causal_rivers" } }, { "id": "L3_T1_Composite_Trend_00009", "level": 3, "level_name": "Semantic Reasoning", "category": "Composite Trend", "subtask": "Composite Trend", "question": "Among days in channel 154 during 2019 that exhibit the trend pattern 'fall, then fluctuating stable', identify the top-5 days where the fluctuating stable segment is the most fluctuating. (Output format: a ranked list of dates, e.g., ['YYYY-MM-DD', ...])", "answer": "['2019-06-22', '2019-03-24', '2019-08-23', '2019-01-24', '2019-12-03']", "ground_truth": [ "2019-06-22", "2019-03-24", "2019-08-23", "2019-01-24", "2019-12-03" ], "eval_metric": "set_f1", "channel": "154", "ts_data_path": "ts_data/L3_T1_Composite_Trend_00009.csv", "meta": { "segments": [ { "kind": "fall", "adj": "none" }, { "kind": "stable", "adj": "fluctuating" } ], "pattern_phrase": "fall, then fluctuating stable", "rank_target_idx": 1, "rank_kind": "stable", "rank_adj": "fluctuating", "rank_axis": "std", "rank_direction": "DESC", "rank_extreme": "most fluctuating", "top_segments_meta": [ { "adj": "none", "kind": "fall", "label": "none fall", "start_idx": 0, "end_idx": 44 }, { "adj": "fluctuating", "kind": "stable", "label": "fluctuating stable", "start_idx": 44, "end_idx": 98 } ], "points_per_day": 96, "pattern": "fall, then fluctuating stable", "year": 2019, "top_k": [ "2019-06-22", "2019-03-24", "2019-08-23", "2019-01-24", "2019-12-03" ], "all_injections": [ { "rank": 1, "intensity": 3.0, "date": "2019-06-22" }, { "rank": 2, "intensity": 2.9693877551020407, "date": "2019-03-24" }, { "rank": 3, "intensity": 2.938775510204082, "date": "2019-08-23" }, { "rank": 4, "intensity": 2.9081632653061225, "date": "2019-01-24" }, { "rank": 5, "intensity": 2.877551020408163, "date": "2019-12-03" }, { "rank": 6, "intensity": 2.8469387755102042, "date": "2019-02-24" }, { "rank": 7, "intensity": 2.816326530612245, "date": "2019-12-18" }, { "rank": 8, "intensity": 2.7857142857142856, "date": "2019-11-11" }, { "rank": 9, "intensity": 2.7551020408163267, "date": "2019-01-18" }, { "rank": 10, "intensity": 2.7244897959183674, "date": "2019-01-06" }, { "rank": 11, "intensity": 2.693877551020408, "date": "2019-07-05" }, { "rank": 12, "intensity": 2.663265306122449, "date": "2019-06-25" }, { "rank": 13, "intensity": 2.63265306122449, "date": "2019-01-04" }, { "rank": 14, "intensity": 2.6020408163265305, "date": "2019-04-21" }, { "rank": 15, "intensity": 2.5714285714285716, "date": "2019-07-20" }, { "rank": 16, "intensity": 2.5408163265306123, "date": "2019-08-17" }, { "rank": 17, "intensity": 2.510204081632653, "date": "2019-10-14" }, { "rank": 18, "intensity": 2.479591836734694, "date": "2019-12-24" }, { "rank": 19, "intensity": 2.4489795918367347, "date": "2019-01-22" }, { "rank": 20, "intensity": 2.4183673469387754, "date": "2019-11-20" }, { "rank": 21, "intensity": 2.387755102040816, "date": "2019-12-01" }, { "rank": 22, "intensity": 2.357142857142857, "date": "2019-09-07" }, { "rank": 23, "intensity": 2.326530612244898, "date": "2019-06-07" }, { "rank": 24, "intensity": 2.295918367346939, "date": "2019-09-12" }, { "rank": 25, "intensity": 2.2653061224489797, "date": "2019-11-28" }, { "rank": 26, "intensity": 2.2346938775510203, "date": "2019-07-03" }, { "rank": 27, "intensity": 2.204081632653061, "date": "2019-05-29" }, { "rank": 28, "intensity": 2.173469387755102, "date": "2019-02-12" }, { "rank": 29, "intensity": 2.142857142857143, "date": "2019-01-14" }, { "rank": 30, "intensity": 2.112244897959184, "date": "2019-10-20" }, { "rank": 31, "intensity": 2.0816326530612246, "date": "2019-02-15" }, { "rank": 32, "intensity": 2.0510204081632653, "date": "2019-08-30" }, { "rank": 33, "intensity": 2.020408163265306, "date": "2019-01-02" }, { "rank": 34, "intensity": 1.989795918367347, "date": "2019-10-31" }, { "rank": 35, "intensity": 1.9591836734693877, "date": "2019-07-18" }, { "rank": 36, "intensity": 1.9285714285714286, "date": "2019-07-29" }, { "rank": 37, "intensity": 1.8979591836734695, "date": "2019-12-15" }, { "rank": 38, "intensity": 1.8673469387755102, "date": "2019-03-26" }, { "rank": 39, "intensity": 1.836734693877551, "date": "2019-07-26" }, { "rank": 40, "intensity": 1.806122448979592, "date": "2019-08-27" }, { "rank": 41, "intensity": 1.7755102040816326, "date": "2019-05-04" }, { "rank": 42, "intensity": 1.7448979591836735, "date": "2019-10-04" }, { "rank": 43, "intensity": 1.7142857142857144, "date": "2019-03-05" }, { "rank": 44, "intensity": 1.683673469387755, "date": "2019-10-07" }, { "rank": 45, "intensity": 1.653061224489796, "date": "2019-08-11" }, { "rank": 46, "intensity": 1.6224489795918369, "date": "2019-08-14" }, { "rank": 47, "intensity": 1.5918367346938775, "date": "2019-09-03" }, { "rank": 48, "intensity": 1.5612244897959184, "date": "2019-11-17" }, { "rank": 49, "intensity": 1.5306122448979593, "date": "2019-09-24" }, { "rank": 50, "intensity": 1.5, "date": "2019-07-16" } ], "source": "causal_rivers" } }, { "id": "L3_T1_Composite_Trend_00010", "level": 3, "level_name": "Semantic Reasoning", "category": "Composite Trend", "subtask": "Composite Trend", "question": "Among days in channel 155 during 2021 that exhibit the trend pattern 'fluctuating stable, then fall', identify the top-5 days where the fluctuating stable segment is the most fluctuating. (Output format: a ranked list of dates, e.g., ['YYYY-MM-DD', ...])", "answer": "['2021-01-05', '2021-02-01', '2021-05-12', '2021-05-10', '2021-06-12']", "ground_truth": [ "2021-01-05", "2021-02-01", "2021-05-12", "2021-05-10", "2021-06-12" ], "eval_metric": "set_f1", "channel": "155", "ts_data_path": "ts_data/L3_T1_Composite_Trend_00010.csv", "meta": { "segments": [ { "kind": "stable", "adj": "fluctuating" }, { "kind": "fall", "adj": "none" } ], "pattern_phrase": "fluctuating stable, then fall", "rank_target_idx": 0, "rank_kind": "stable", "rank_adj": "fluctuating", "rank_axis": "std", "rank_direction": "DESC", "rank_extreme": "most fluctuating", "top_segments_meta": [ { "adj": "fluctuating", "kind": "stable", "label": "fluctuating stable", "start_idx": 0, "end_idx": 51 }, { "adj": "none", "kind": "fall", "label": "none fall", "start_idx": 51, "end_idx": 98 } ], "points_per_day": 96, "pattern": "fluctuating stable, then fall", "year": 2021, "top_k": [ "2021-01-05", "2021-02-01", "2021-05-12", "2021-05-10", "2021-06-12" ], "all_injections": [ { "rank": 1, "intensity": 3.0, "date": "2021-01-05" }, { "rank": 2, "intensity": 2.9693877551020407, "date": "2021-02-01" }, { "rank": 3, "intensity": 2.938775510204082, "date": "2021-05-12" }, { "rank": 4, "intensity": 2.9081632653061225, "date": "2021-05-10" }, { "rank": 5, "intensity": 2.877551020408163, "date": "2021-06-12" }, { "rank": 6, "intensity": 2.8469387755102042, "date": "2021-09-07" }, { "rank": 7, "intensity": 2.816326530612245, "date": "2021-11-19" }, { "rank": 8, "intensity": 2.7857142857142856, "date": "2021-12-23" }, { "rank": 9, "intensity": 2.7551020408163267, "date": "2021-10-03" }, { "rank": 10, "intensity": 2.7244897959183674, "date": "2021-12-20" }, { "rank": 11, "intensity": 2.693877551020408, "date": "2021-11-10" }, { "rank": 12, "intensity": 2.663265306122449, "date": "2021-08-02" }, { "rank": 13, "intensity": 2.63265306122449, "date": "2021-07-29" }, { "rank": 14, "intensity": 2.6020408163265305, "date": "2021-10-15" }, { "rank": 15, "intensity": 2.5714285714285716, "date": "2021-07-21" }, { "rank": 16, "intensity": 2.5408163265306123, "date": "2021-01-26" }, { "rank": 17, "intensity": 2.510204081632653, "date": "2021-07-01" }, { "rank": 18, "intensity": 2.479591836734694, "date": "2021-06-20" }, { "rank": 19, "intensity": 2.4489795918367347, "date": "2021-08-17" }, { "rank": 20, "intensity": 2.4183673469387754, "date": "2021-01-19" }, { "rank": 21, "intensity": 2.387755102040816, "date": "2021-05-15" }, { "rank": 22, "intensity": 2.357142857142857, "date": "2021-03-24" }, { "rank": 23, "intensity": 2.326530612244898, "date": "2021-01-23" }, { "rank": 24, "intensity": 2.295918367346939, "date": "2021-03-15" }, { "rank": 25, "intensity": 2.2653061224489797, "date": "2021-03-26" }, { "rank": 26, "intensity": 2.2346938775510203, "date": "2021-05-02" }, { "rank": 27, "intensity": 2.204081632653061, "date": "2021-06-03" }, { "rank": 28, "intensity": 2.173469387755102, "date": "2021-03-29" }, { "rank": 29, "intensity": 2.142857142857143, "date": "2021-08-24" }, { "rank": 30, "intensity": 2.112244897959184, "date": "2021-08-13" }, { "rank": 31, "intensity": 2.0816326530612246, "date": "2021-07-27" }, { "rank": 32, "intensity": 2.0510204081632653, "date": "2021-12-25" }, { "rank": 33, "intensity": 2.020408163265306, "date": "2021-09-23" }, { "rank": 34, "intensity": 1.989795918367347, "date": "2021-06-14" }, { "rank": 35, "intensity": 1.9591836734693877, "date": "2021-09-27" }, { "rank": 36, "intensity": 1.9285714285714286, "date": "2021-02-21" }, { "rank": 37, "intensity": 1.8979591836734695, "date": "2021-11-24" }, { "rank": 38, "intensity": 1.8673469387755102, "date": "2021-11-07" }, { "rank": 39, "intensity": 1.836734693877551, "date": "2021-10-11" }, { "rank": 40, "intensity": 1.806122448979592, "date": "2021-12-06" }, { "rank": 41, "intensity": 1.7755102040816326, "date": "2021-02-06" }, { "rank": 42, "intensity": 1.7448979591836735, "date": "2021-09-18" }, { "rank": 43, "intensity": 1.7142857142857144, "date": "2021-11-26" }, { "rank": 44, "intensity": 1.683673469387755, "date": "2021-10-18" }, { "rank": 45, "intensity": 1.653061224489796, "date": "2021-09-15" }, { "rank": 46, "intensity": 1.6224489795918369, "date": "2021-05-27" }, { "rank": 47, "intensity": 1.5918367346938775, "date": "2021-03-11" }, { "rank": 48, "intensity": 1.5612244897959184, "date": "2021-10-27" }, { "rank": 49, "intensity": 1.5306122448979593, "date": "2021-03-05" }, { "rank": 50, "intensity": 1.5, "date": "2021-07-31" } ], "source": "causal_rivers" } }, { "id": "L3_T1_Composite_Trend_00011", "level": 3, "level_name": "Semantic Reasoning", "category": "Composite Trend", "subtask": "Composite Trend", "question": "Among days in channel 166 during 2022 that exhibit the trend pattern 'slow fall, then steady stable', identify the top-5 days where the slow fall segment is the slowest. (Output format: a ranked list of dates, e.g., ['YYYY-MM-DD', ...])", "answer": "['2022-09-26', '2022-03-20', '2022-11-07', '2022-11-18', '2022-10-31']", "ground_truth": [ "2022-09-26", "2022-03-20", "2022-11-07", "2022-11-18", "2022-10-31" ], "eval_metric": "set_f1", "channel": "166", "ts_data_path": "ts_data/L3_T1_Composite_Trend_00011.csv", "meta": { "segments": [ { "kind": "fall", "adj": "slow" }, { "kind": "stable", "adj": "steady" } ], "pattern_phrase": "slow fall, then steady stable", "rank_target_idx": 0, "rank_kind": "fall", "rank_adj": "slow", "rank_axis": "slope", "rank_direction": "ASC", "rank_extreme": "slowest", "top_segments_meta": [ { "adj": "slow", "kind": "fall", "label": "slow fall", "start_idx": 0, "end_idx": 43 }, { "adj": "steady", "kind": "stable", "label": "steady stable", "start_idx": 43, "end_idx": 98 } ], "points_per_day": 96, "pattern": "slow fall, then steady stable", "year": 2022, "top_k": [ "2022-09-26", "2022-03-20", "2022-11-07", "2022-11-18", "2022-10-31" ], "all_injections": [ { "rank": 1, "intensity": 1.5, "date": "2022-09-26" }, { "rank": 2, "intensity": 1.530612244897959, "date": "2022-03-20" }, { "rank": 3, "intensity": 1.5612244897959184, "date": "2022-11-07" }, { "rank": 4, "intensity": 1.5918367346938775, "date": "2022-11-18" }, { "rank": 5, "intensity": 1.6224489795918366, "date": "2022-10-31" }, { "rank": 6, "intensity": 1.653061224489796, "date": "2022-05-01" }, { "rank": 7, "intensity": 1.683673469387755, "date": "2022-08-10" }, { "rank": 8, "intensity": 1.7142857142857142, "date": "2022-12-26" }, { "rank": 9, "intensity": 1.7448979591836735, "date": "2022-09-28" }, { "rank": 10, "intensity": 1.7755102040816326, "date": "2022-08-28" }, { "rank": 11, "intensity": 1.806122448979592, "date": "2022-12-20" }, { "rank": 12, "intensity": 1.836734693877551, "date": "2022-10-06" }, { "rank": 13, "intensity": 1.8673469387755102, "date": "2022-08-05" }, { "rank": 14, "intensity": 1.8979591836734695, "date": "2022-05-30" }, { "rank": 15, "intensity": 1.9285714285714286, "date": "2022-03-14" }, { "rank": 16, "intensity": 1.9591836734693877, "date": "2022-01-13" }, { "rank": 17, "intensity": 1.989795918367347, "date": "2022-06-25" }, { "rank": 18, "intensity": 2.020408163265306, "date": "2022-06-12" }, { "rank": 19, "intensity": 2.0510204081632653, "date": "2022-11-22" }, { "rank": 20, "intensity": 2.0816326530612246, "date": "2022-01-24" }, { "rank": 21, "intensity": 2.112244897959184, "date": "2022-02-09" }, { "rank": 22, "intensity": 2.142857142857143, "date": "2022-10-16" }, { "rank": 23, "intensity": 2.173469387755102, "date": "2022-12-23" }, { "rank": 24, "intensity": 2.204081632653061, "date": "2022-03-31" }, { "rank": 25, "intensity": 2.2346938775510203, "date": "2022-10-12" }, { "rank": 26, "intensity": 2.2653061224489797, "date": "2022-07-12" }, { "rank": 27, "intensity": 2.295918367346939, "date": "2022-01-20" }, { "rank": 28, "intensity": 2.326530612244898, "date": "2022-02-28" }, { "rank": 29, "intensity": 2.357142857142857, "date": "2022-09-18" }, { "rank": 30, "intensity": 2.387755102040816, "date": "2022-02-16" }, { "rank": 31, "intensity": 2.4183673469387754, "date": "2022-10-19" }, { "rank": 32, "intensity": 2.4489795918367347, "date": "2022-12-02" }, { "rank": 33, "intensity": 2.479591836734694, "date": "2022-04-10" }, { "rank": 34, "intensity": 2.510204081632653, "date": "2022-05-11" }, { "rank": 35, "intensity": 2.5408163265306123, "date": "2022-04-24" }, { "rank": 36, "intensity": 2.571428571428571, "date": "2022-06-06" }, { "rank": 37, "intensity": 2.6020408163265305, "date": "2022-07-18" }, { "rank": 38, "intensity": 2.63265306122449, "date": "2022-01-09" }, { "rank": 39, "intensity": 2.663265306122449, "date": "2022-03-08" }, { "rank": 40, "intensity": 2.693877551020408, "date": "2022-02-19" }, { "rank": 41, "intensity": 2.7244897959183674, "date": "2022-03-23" }, { "rank": 42, "intensity": 2.7551020408163263, "date": "2022-05-25" }, { "rank": 43, "intensity": 2.7857142857142856, "date": "2022-03-25" }, { "rank": 44, "intensity": 2.816326530612245, "date": "2022-06-01" }, { "rank": 45, "intensity": 2.8469387755102042, "date": "2022-07-07" }, { "rank": 46, "intensity": 2.877551020408163, "date": "2022-06-20" }, { "rank": 47, "intensity": 2.9081632653061225, "date": "2022-06-29" }, { "rank": 48, "intensity": 2.9387755102040813, "date": "2022-02-06" }, { "rank": 49, "intensity": 2.9693877551020407, "date": "2022-10-23" }, { "rank": 50, "intensity": 3.0, "date": "2022-06-14" } ], "source": "causal_rivers" } }, { "id": "L3_T1_Composite_Trend_00012", "level": 3, "level_name": "Semantic Reasoning", "category": "Composite Trend", "subtask": "Composite Trend", "question": "Among days in channel 169 during 2020 that exhibit the trend pattern 'slow fall, then rapid rise, then slow rise', identify the top-5 days where the slow rise segment is the slowest. (Output format: a ranked list of dates, e.g., ['YYYY-MM-DD', ...])", "answer": "['2020-09-08', '2020-11-20', '2020-08-02', '2020-10-24', '2020-12-23']", "ground_truth": [ "2020-09-08", "2020-11-20", "2020-08-02", "2020-10-24", "2020-12-23" ], "eval_metric": "set_f1", "channel": "169", "ts_data_path": "ts_data/L3_T1_Composite_Trend_00012.csv", "meta": { "segments": [ { "kind": "fall", "adj": "slow" }, { "kind": "rise", "adj": "rapid" }, { "kind": "rise", "adj": "slow" } ], "pattern_phrase": "slow fall, then rapid rise, then slow rise", "rank_target_idx": 2, "rank_kind": "rise", "rank_adj": "slow", "rank_axis": "slope", "rank_direction": "ASC", "rank_extreme": "slowest", "top_segments_meta": [ { "adj": "slow", "kind": "fall", "label": "slow fall", "start_idx": 0, "end_idx": 45 }, { "adj": "rapid", "kind": "rise", "label": "rapid rise", "start_idx": 45, "end_idx": 57 }, { "adj": "slow", "kind": "rise", "label": "slow rise", "start_idx": 57, "end_idx": 98 } ], "points_per_day": 96, "pattern": "slow fall, then rapid rise, then slow rise", "year": 2020, "top_k": [ "2020-09-08", "2020-11-20", "2020-08-02", "2020-10-24", "2020-12-23" ], "all_injections": [ { "rank": 1, "intensity": 1.5, "date": "2020-09-08" }, { "rank": 2, "intensity": 1.530612244897959, "date": "2020-11-20" }, { "rank": 3, "intensity": 1.5612244897959184, "date": "2020-08-02" }, { "rank": 4, "intensity": 1.5918367346938775, "date": "2020-10-24" }, { "rank": 5, "intensity": 1.6224489795918366, "date": "2020-12-23" }, { "rank": 6, "intensity": 1.653061224489796, "date": "2020-03-12" }, { "rank": 7, "intensity": 1.683673469387755, "date": "2020-02-07" }, { "rank": 8, "intensity": 1.7142857142857142, "date": "2020-01-10" }, { "rank": 9, "intensity": 1.7448979591836735, "date": "2020-01-15" }, { "rank": 10, "intensity": 1.7755102040816326, "date": "2020-12-26" }, { "rank": 11, "intensity": 1.806122448979592, "date": "2020-01-19" }, { "rank": 12, "intensity": 1.836734693877551, "date": "2020-04-04" }, { "rank": 13, "intensity": 1.8673469387755102, "date": "2020-12-29" }, { "rank": 14, "intensity": 1.8979591836734695, "date": "2020-10-08" }, { "rank": 15, "intensity": 1.9285714285714286, "date": "2020-04-25" }, { "rank": 16, "intensity": 1.9591836734693877, "date": "2020-05-30" }, { "rank": 17, "intensity": 1.989795918367347, "date": "2020-08-08" }, { "rank": 18, "intensity": 2.020408163265306, "date": "2020-07-29" }, { "rank": 19, "intensity": 2.0510204081632653, "date": "2020-06-07" }, { "rank": 20, "intensity": 2.0816326530612246, "date": "2020-10-28" }, { "rank": 21, "intensity": 2.112244897959184, "date": "2020-09-02" }, { "rank": 22, "intensity": 2.142857142857143, "date": "2020-08-31" }, { "rank": 23, "intensity": 2.173469387755102, "date": "2020-05-24" }, { "rank": 24, "intensity": 2.204081632653061, "date": "2020-02-04" }, { "rank": 25, "intensity": 2.2346938775510203, "date": "2020-07-26" }, { "rank": 26, "intensity": 2.2653061224489797, "date": "2020-08-12" }, { "rank": 27, "intensity": 2.295918367346939, "date": "2020-12-21" }, { "rank": 28, "intensity": 2.326530612244898, "date": "2020-11-02" }, { "rank": 29, "intensity": 2.357142857142857, "date": "2020-03-16" }, { "rank": 30, "intensity": 2.387755102040816, "date": "2020-04-22" }, { "rank": 31, "intensity": 2.4183673469387754, "date": "2020-06-17" }, { "rank": 32, "intensity": 2.4489795918367347, "date": "2020-10-05" }, { "rank": 33, "intensity": 2.479591836734694, "date": "2020-03-06" }, { "rank": 34, "intensity": 2.510204081632653, "date": "2020-01-24" }, { "rank": 35, "intensity": 2.5408163265306123, "date": "2020-11-04" }, { "rank": 36, "intensity": 2.571428571428571, "date": "2020-07-17" }, { "rank": 37, "intensity": 2.6020408163265305, "date": "2020-01-17" }, { "rank": 38, "intensity": 2.63265306122449, "date": "2020-05-28" }, { "rank": 39, "intensity": 2.663265306122449, "date": "2020-03-18" }, { "rank": 40, "intensity": 2.693877551020408, "date": "2020-03-02" }, { "rank": 41, "intensity": 2.7244897959183674, "date": "2020-04-16" }, { "rank": 42, "intensity": 2.7551020408163263, "date": "2020-06-25" }, { "rank": 43, "intensity": 2.7857142857142856, "date": "2020-07-22" }, { "rank": 44, "intensity": 2.816326530612245, "date": "2020-03-14" }, { "rank": 45, "intensity": 2.8469387755102042, "date": "2020-05-16" }, { "rank": 46, "intensity": 2.877551020408163, "date": "2020-02-09" }, { "rank": 47, "intensity": 2.9081632653061225, "date": "2020-08-19" }, { "rank": 48, "intensity": 2.9387755102040813, "date": "2020-03-26" }, { "rank": 49, "intensity": 2.9693877551020407, "date": "2020-10-13" }, { "rank": 50, "intensity": 3.0, "date": "2020-11-16" } ], "source": "causal_rivers" } }, { "id": "L3_T1_Composite_Trend_00013", "level": 3, "level_name": "Semantic Reasoning", "category": "Composite Trend", "subtask": "Composite Trend", "question": "Among days in channel 170 during 2022 that exhibit the trend pattern 'rapid rise, then rapid fall, then rapid rise', identify the top-5 days where the rapid fall segment is the fastest. (Output format: a ranked list of dates, e.g., ['YYYY-MM-DD', ...])", "answer": "['2022-11-23', '2022-03-11', '2022-09-29', '2022-08-02', '2022-11-14']", "ground_truth": [ "2022-11-23", "2022-03-11", "2022-09-29", "2022-08-02", "2022-11-14" ], "eval_metric": "set_f1", "channel": "170", "ts_data_path": "ts_data/L3_T1_Composite_Trend_00013.csv", "meta": { "segments": [ { "kind": "rise", "adj": "rapid" }, { "kind": "fall", "adj": "rapid" }, { "kind": "rise", "adj": "rapid" } ], "pattern_phrase": "rapid rise, then rapid fall, then rapid rise", "rank_target_idx": 1, "rank_kind": "fall", "rank_adj": "rapid", "rank_axis": "slope", "rank_direction": "DESC", "rank_extreme": "fastest", "top_segments_meta": [ { "adj": "rapid", "kind": "rise", "label": "rapid rise", "start_idx": 0, "end_idx": 35 }, { "adj": "rapid", "kind": "fall", "label": "rapid fall", "start_idx": 35, "end_idx": 66 }, { "adj": "rapid", "kind": "rise", "label": "rapid rise", "start_idx": 66, "end_idx": 98 } ], "points_per_day": 96, "pattern": "rapid rise, then rapid fall, then rapid rise", "year": 2022, "top_k": [ "2022-11-23", "2022-03-11", "2022-09-29", "2022-08-02", "2022-11-14" ], "all_injections": [ { "rank": 1, "intensity": 3.0, "date": "2022-11-23" }, { "rank": 2, "intensity": 2.9693877551020407, "date": "2022-03-11" }, { "rank": 3, "intensity": 2.938775510204082, "date": "2022-09-29" }, { "rank": 4, "intensity": 2.9081632653061225, "date": "2022-08-02" }, { "rank": 5, "intensity": 2.877551020408163, "date": "2022-11-14" }, { "rank": 6, "intensity": 2.8469387755102042, "date": "2022-03-02" }, { "rank": 7, "intensity": 2.816326530612245, "date": "2022-08-14" }, { "rank": 8, "intensity": 2.7857142857142856, "date": "2022-06-05" }, { "rank": 9, "intensity": 2.7551020408163267, "date": "2022-08-24" }, { "rank": 10, "intensity": 2.7244897959183674, "date": "2022-01-12" }, { "rank": 11, "intensity": 2.693877551020408, "date": "2022-05-26" }, { "rank": 12, "intensity": 2.663265306122449, "date": "2022-08-16" }, { "rank": 13, "intensity": 2.63265306122449, "date": "2022-12-08" }, { "rank": 14, "intensity": 2.6020408163265305, "date": "2022-05-28" }, { "rank": 15, "intensity": 2.5714285714285716, "date": "2022-12-13" }, { "rank": 16, "intensity": 2.5408163265306123, "date": "2022-10-24" }, { "rank": 17, "intensity": 2.510204081632653, "date": "2022-03-27" }, { "rank": 18, "intensity": 2.479591836734694, "date": "2022-06-11" }, { "rank": 19, "intensity": 2.4489795918367347, "date": "2022-05-08" }, { "rank": 20, "intensity": 2.4183673469387754, "date": "2022-10-09" }, { "rank": 21, "intensity": 2.387755102040816, "date": "2022-02-06" }, { "rank": 22, "intensity": 2.357142857142857, "date": "2022-12-18" }, { "rank": 23, "intensity": 2.326530612244898, "date": "2022-09-05" }, { "rank": 24, "intensity": 2.295918367346939, "date": "2022-05-22" }, { "rank": 25, "intensity": 2.2653061224489797, "date": "2022-07-20" }, { "rank": 26, "intensity": 2.2346938775510203, "date": "2022-01-29" }, { "rank": 27, "intensity": 2.204081632653061, "date": "2022-06-16" }, { "rank": 28, "intensity": 2.173469387755102, "date": "2022-02-13" }, { "rank": 29, "intensity": 2.142857142857143, "date": "2022-10-03" }, { "rank": 30, "intensity": 2.112244897959184, "date": "2022-07-16" }, { "rank": 31, "intensity": 2.0816326530612246, "date": "2022-07-04" }, { "rank": 32, "intensity": 2.0510204081632653, "date": "2022-03-24" }, { "rank": 33, "intensity": 2.020408163265306, "date": "2022-10-07" }, { "rank": 34, "intensity": 1.989795918367347, "date": "2022-12-23" }, { "rank": 35, "intensity": 1.9591836734693877, "date": "2022-01-03" }, { "rank": 36, "intensity": 1.9285714285714286, "date": "2022-08-04" }, { "rank": 37, "intensity": 1.8979591836734695, "date": "2022-09-03" }, { "rank": 38, "intensity": 1.8673469387755102, "date": "2022-11-21" }, { "rank": 39, "intensity": 1.836734693877551, "date": "2022-08-20" }, { "rank": 40, "intensity": 1.806122448979592, "date": "2022-04-08" }, { "rank": 41, "intensity": 1.7755102040816326, "date": "2022-10-05" }, { "rank": 42, "intensity": 1.7448979591836735, "date": "2022-07-18" }, { "rank": 43, "intensity": 1.7142857142857144, "date": "2022-03-16" }, { "rank": 44, "intensity": 1.683673469387755, "date": "2022-09-18" }, { "rank": 45, "intensity": 1.653061224489796, "date": "2022-08-27" }, { "rank": 46, "intensity": 1.6224489795918369, "date": "2022-09-10" }, { "rank": 47, "intensity": 1.5918367346938775, "date": "2022-04-14" }, { "rank": 48, "intensity": 1.5612244897959184, "date": "2022-07-24" }, { "rank": 49, "intensity": 1.5306122448979593, "date": "2022-12-21" }, { "rank": 50, "intensity": 1.5, "date": "2022-06-19" } ], "source": "causal_rivers" } }, { "id": "L3_T1_Composite_Trend_00014", "level": 3, "level_name": "Semantic Reasoning", "category": "Composite Trend", "subtask": "Composite Trend", "question": "Among days in channel 172 during 2021 that exhibit the trend pattern 'fluctuating stable, then slow rise, then fall, then slow fall', identify the top-5 days where the slow rise segment is the slowest. (Output format: a ranked list of dates, e.g., ['YYYY-MM-DD', ...])", "answer": "['2021-10-22', '2021-12-10', '2021-02-25', '2021-07-10', '2021-09-11']", "ground_truth": [ "2021-10-22", "2021-12-10", "2021-02-25", "2021-07-10", "2021-09-11" ], "eval_metric": "set_f1", "channel": "172", "ts_data_path": "ts_data/L3_T1_Composite_Trend_00014.csv", "meta": { "segments": [ { "kind": "stable", "adj": "fluctuating" }, { "kind": "rise", "adj": "slow" }, { "kind": "fall", "adj": "none" }, { "kind": "fall", "adj": "slow" } ], "pattern_phrase": "fluctuating stable, then slow rise, then fall, then slow fall", "rank_target_idx": 1, "rank_kind": "rise", "rank_adj": "slow", "rank_axis": "slope", "rank_direction": "ASC", "rank_extreme": "slowest", "top_segments_meta": [ { "adj": "fluctuating", "kind": "stable", "label": "fluctuating stable", "start_idx": 0, "end_idx": 20 }, { "adj": "slow", "kind": "rise", "label": "slow rise", "start_idx": 20, "end_idx": 52 }, { "adj": "none", "kind": "fall", "label": "none fall", "start_idx": 52, "end_idx": 70 }, { "adj": "slow", "kind": "fall", "label": "slow fall", "start_idx": 70, "end_idx": 98 } ], "points_per_day": 96, "pattern": "fluctuating stable, then slow rise, then fall, then slow fall", "year": 2021, "top_k": [ "2021-10-22", "2021-12-10", "2021-02-25", "2021-07-10", "2021-09-11" ], "all_injections": [ { "rank": 1, "intensity": 1.5, "date": "2021-10-22" }, { "rank": 2, "intensity": 1.530612244897959, "date": "2021-12-10" }, { "rank": 3, "intensity": 1.5612244897959184, "date": "2021-02-25" }, { "rank": 4, "intensity": 1.5918367346938775, "date": "2021-07-10" }, { "rank": 5, "intensity": 1.6224489795918366, "date": "2021-09-11" }, { "rank": 6, "intensity": 1.653061224489796, "date": "2021-03-20" }, { "rank": 7, "intensity": 1.683673469387755, "date": "2021-05-23" }, { "rank": 8, "intensity": 1.7142857142857142, "date": "2021-05-30" }, { "rank": 9, "intensity": 1.7448979591836735, "date": "2021-08-23" }, { "rank": 10, "intensity": 1.7755102040816326, "date": "2021-12-29" }, { "rank": 11, "intensity": 1.806122448979592, "date": "2021-05-18" }, { "rank": 12, "intensity": 1.836734693877551, "date": "2021-07-31" }, { "rank": 13, "intensity": 1.8673469387755102, "date": "2021-12-16" }, { "rank": 14, "intensity": 1.8979591836734695, "date": "2021-09-26" }, { "rank": 15, "intensity": 1.9285714285714286, "date": "2021-11-13" }, { "rank": 16, "intensity": 1.9591836734693877, "date": "2021-02-18" }, { "rank": 17, "intensity": 1.989795918367347, "date": "2021-10-02" }, { "rank": 18, "intensity": 2.020408163265306, "date": "2021-11-21" }, { "rank": 19, "intensity": 2.0510204081632653, "date": "2021-11-11" }, { "rank": 20, "intensity": 2.0816326530612246, "date": "2021-03-04" }, { "rank": 21, "intensity": 2.112244897959184, "date": "2021-12-03" }, { "rank": 22, "intensity": 2.142857142857143, "date": "2021-09-16" }, { "rank": 23, "intensity": 2.173469387755102, "date": "2021-05-05" }, { "rank": 24, "intensity": 2.204081632653061, "date": "2021-01-23" }, { "rank": 25, "intensity": 2.2346938775510203, "date": "2021-02-04" }, { "rank": 26, "intensity": 2.2653061224489797, "date": "2021-12-25" }, { "rank": 27, "intensity": 2.295918367346939, "date": "2021-09-04" }, { "rank": 28, "intensity": 2.326530612244898, "date": "2021-06-29" }, { "rank": 29, "intensity": 2.357142857142857, "date": "2021-08-18" }, { "rank": 30, "intensity": 2.387755102040816, "date": "2021-12-20" }, { "rank": 31, "intensity": 2.4183673469387754, "date": "2021-03-25" }, { "rank": 32, "intensity": 2.4489795918367347, "date": "2021-11-02" }, { "rank": 33, "intensity": 2.479591836734694, "date": "2021-05-20" }, { "rank": 34, "intensity": 2.510204081632653, "date": "2021-06-19" }, { "rank": 35, "intensity": 2.5408163265306123, "date": "2021-10-15" }, { "rank": 36, "intensity": 2.571428571428571, "date": "2021-08-26" }, { "rank": 37, "intensity": 2.6020408163265305, "date": "2021-02-20" }, { "rank": 38, "intensity": 2.63265306122449, "date": "2021-01-15" }, { "rank": 39, "intensity": 2.663265306122449, "date": "2021-08-11" }, { "rank": 40, "intensity": 2.693877551020408, "date": "2021-10-05" }, { "rank": 41, "intensity": 2.7244897959183674, "date": "2021-09-06" }, { "rank": 42, "intensity": 2.7551020408163263, "date": "2021-03-13" }, { "rank": 43, "intensity": 2.7857142857142856, "date": "2021-11-06" }, { "rank": 44, "intensity": 2.816326530612245, "date": "2021-01-31" }, { "rank": 45, "intensity": 2.8469387755102042, "date": "2021-05-07" }, { "rank": 46, "intensity": 2.877551020408163, "date": "2021-04-26" }, { "rank": 47, "intensity": 2.9081632653061225, "date": "2021-01-26" }, { "rank": 48, "intensity": 2.9387755102040813, "date": "2021-04-13" }, { "rank": 49, "intensity": 2.9693877551020407, "date": "2021-03-31" }, { "rank": 50, "intensity": 3.0, "date": "2021-11-16" } ], "source": "causal_rivers" } }, { "id": "L3_T1_Composite_Trend_00015", "level": 3, "level_name": "Semantic Reasoning", "category": "Composite Trend", "subtask": "Composite Trend", "question": "Among days in channel 173 during 2020 that exhibit the trend pattern 'fall, then rapid fall, then fluctuating stable', identify the top-5 days where the rapid fall segment is the fastest. (Output format: a ranked list of dates, e.g., ['YYYY-MM-DD', ...])", "answer": "['2020-11-17', '2020-10-20', '2020-11-19', '2020-03-15', '2020-06-26']", "ground_truth": [ "2020-11-17", "2020-10-20", "2020-11-19", "2020-03-15", "2020-06-26" ], "eval_metric": "set_f1", "channel": "173", "ts_data_path": "ts_data/L3_T1_Composite_Trend_00015.csv", "meta": { "segments": [ { "kind": "fall", "adj": "none" }, { "kind": "fall", "adj": "rapid" }, { "kind": "stable", "adj": "fluctuating" } ], "pattern_phrase": "fall, then rapid fall, then fluctuating stable", "rank_target_idx": 1, "rank_kind": "fall", "rank_adj": "rapid", "rank_axis": "slope", "rank_direction": "DESC", "rank_extreme": "fastest", "top_segments_meta": [ { "adj": "none", "kind": "fall", "label": "none fall", "start_idx": 0, "end_idx": 35 }, { "adj": "rapid", "kind": "fall", "label": "rapid fall", "start_idx": 35, "end_idx": 53 }, { "adj": "fluctuating", "kind": "stable", "label": "fluctuating stable", "start_idx": 53, "end_idx": 98 } ], "points_per_day": 96, "pattern": "fall, then rapid fall, then fluctuating stable", "year": 2020, "top_k": [ "2020-11-17", "2020-10-20", "2020-11-19", "2020-03-15", "2020-06-26" ], "all_injections": [ { "rank": 1, "intensity": 3.0, "date": "2020-11-17" }, { "rank": 2, "intensity": 2.9693877551020407, "date": "2020-10-20" }, { "rank": 3, "intensity": 2.938775510204082, "date": "2020-11-19" }, { "rank": 4, "intensity": 2.9081632653061225, "date": "2020-03-15" }, { "rank": 5, "intensity": 2.877551020408163, "date": "2020-06-26" }, { "rank": 6, "intensity": 2.8469387755102042, "date": "2020-03-19" }, { "rank": 7, "intensity": 2.816326530612245, "date": "2020-06-11" }, { "rank": 8, "intensity": 2.7857142857142856, "date": "2020-11-01" }, { "rank": 9, "intensity": 2.7551020408163267, "date": "2020-10-12" }, { "rank": 10, "intensity": 2.7244897959183674, "date": "2020-11-30" }, { "rank": 11, "intensity": 2.693877551020408, "date": "2020-02-10" }, { "rank": 12, "intensity": 2.663265306122449, "date": "2020-11-12" }, { "rank": 13, "intensity": 2.63265306122449, "date": "2020-10-27" }, { "rank": 14, "intensity": 2.6020408163265305, "date": "2020-04-20" }, { "rank": 15, "intensity": 2.5714285714285716, "date": "2020-04-13" }, { "rank": 16, "intensity": 2.5408163265306123, "date": "2020-09-06" }, { "rank": 17, "intensity": 2.510204081632653, "date": "2020-02-18" }, { "rank": 18, "intensity": 2.479591836734694, "date": "2020-09-10" }, { "rank": 19, "intensity": 2.4489795918367347, "date": "2020-04-15" }, { "rank": 20, "intensity": 2.4183673469387754, "date": "2020-03-21" }, { "rank": 21, "intensity": 2.387755102040816, "date": "2020-09-29" }, { "rank": 22, "intensity": 2.357142857142857, "date": "2020-07-20" }, { "rank": 23, "intensity": 2.326530612244898, "date": "2020-02-27" }, { "rank": 24, "intensity": 2.295918367346939, "date": "2020-04-01" }, { "rank": 25, "intensity": 2.2653061224489797, "date": "2020-01-15" }, { "rank": 26, "intensity": 2.2346938775510203, "date": "2020-10-01" }, { "rank": 27, "intensity": 2.204081632653061, "date": "2020-03-10" }, { "rank": 28, "intensity": 2.173469387755102, "date": "2020-01-29" }, { "rank": 29, "intensity": 2.142857142857143, "date": "2020-05-20" }, { "rank": 30, "intensity": 2.112244897959184, "date": "2020-10-29" }, { "rank": 31, "intensity": 2.0816326530612246, "date": "2020-07-08" }, { "rank": 32, "intensity": 2.0510204081632653, "date": "2020-08-20" }, { "rank": 33, "intensity": 2.020408163265306, "date": "2020-09-18" }, { "rank": 34, "intensity": 1.989795918367347, "date": "2020-11-08" }, { "rank": 35, "intensity": 1.9591836734693877, "date": "2020-12-28" }, { "rank": 36, "intensity": 1.9285714285714286, "date": "2020-12-20" }, { "rank": 37, "intensity": 1.8979591836734695, "date": "2020-11-05" }, { "rank": 38, "intensity": 1.8673469387755102, "date": "2020-05-09" }, { "rank": 39, "intensity": 1.836734693877551, "date": "2020-09-01" }, { "rank": 40, "intensity": 1.806122448979592, "date": "2020-09-21" }, { "rank": 41, "intensity": 1.7755102040816326, "date": "2020-05-12" }, { "rank": 42, "intensity": 1.7448979591836735, "date": "2020-05-01" }, { "rank": 43, "intensity": 1.7142857142857144, "date": "2020-08-03" }, { "rank": 44, "intensity": 1.683673469387755, "date": "2020-01-23" }, { "rank": 45, "intensity": 1.653061224489796, "date": "2020-01-12" }, { "rank": 46, "intensity": 1.6224489795918369, "date": "2020-10-04" }, { "rank": 47, "intensity": 1.5918367346938775, "date": "2020-09-25" }, { "rank": 48, "intensity": 1.5612244897959184, "date": "2020-05-26" }, { "rank": 49, "intensity": 1.5306122448979593, "date": "2020-06-07" }, { "rank": 50, "intensity": 1.5, "date": "2020-08-08" } ], "source": "causal_rivers" } }, { "id": "L3_T1_Composite_Trend_00016", "level": 3, "level_name": "Semantic Reasoning", "category": "Composite Trend", "subtask": "Composite Trend", "question": "Among days in channel 177 during 2021 that exhibit the trend pattern 'rapid rise, then steady stable', identify the top-5 days where the rapid rise segment is the fastest. (Output format: a ranked list of dates, e.g., ['YYYY-MM-DD', ...])", "answer": "['2021-08-07', '2021-07-26', '2021-10-19', '2021-01-25', '2021-04-27']", "ground_truth": [ "2021-08-07", "2021-07-26", "2021-10-19", "2021-01-25", "2021-04-27" ], "eval_metric": "set_f1", "channel": "177", "ts_data_path": "ts_data/L3_T1_Composite_Trend_00016.csv", "meta": { "segments": [ { "kind": "rise", "adj": "rapid" }, { "kind": "stable", "adj": "steady" } ], "pattern_phrase": "rapid rise, then steady stable", "rank_target_idx": 0, "rank_kind": "rise", "rank_adj": "rapid", "rank_axis": "slope", "rank_direction": "DESC", "rank_extreme": "fastest", "top_segments_meta": [ { "adj": "rapid", "kind": "rise", "label": "rapid rise", "start_idx": 0, "end_idx": 17 }, { "adj": "steady", "kind": "stable", "label": "steady stable", "start_idx": 17, "end_idx": 98 } ], "points_per_day": 96, "pattern": "rapid rise, then steady stable", "year": 2021, "top_k": [ "2021-08-07", "2021-07-26", "2021-10-19", "2021-01-25", "2021-04-27" ], "all_injections": [ { "rank": 1, "intensity": 3.0, "date": "2021-08-07" }, { "rank": 2, "intensity": 2.9693877551020407, "date": "2021-07-26" }, { "rank": 3, "intensity": 2.938775510204082, "date": "2021-10-19" }, { "rank": 4, "intensity": 2.9081632653061225, "date": "2021-01-25" }, { "rank": 5, "intensity": 2.877551020408163, "date": "2021-04-27" }, { "rank": 6, "intensity": 2.8469387755102042, "date": "2021-08-11" }, { "rank": 7, "intensity": 2.816326530612245, "date": "2021-03-26" }, { "rank": 8, "intensity": 2.7857142857142856, "date": "2021-05-16" }, { "rank": 9, "intensity": 2.7551020408163267, "date": "2021-04-14" }, { "rank": 10, "intensity": 2.7244897959183674, "date": "2021-02-23" }, { "rank": 11, "intensity": 2.693877551020408, "date": "2021-07-30" }, { "rank": 12, "intensity": 2.663265306122449, "date": "2021-09-28" }, { "rank": 13, "intensity": 2.63265306122449, "date": "2021-04-25" }, { "rank": 14, "intensity": 2.6020408163265305, "date": "2021-11-20" }, { "rank": 15, "intensity": 2.5714285714285716, "date": "2021-05-21" }, { "rank": 16, "intensity": 2.5408163265306123, "date": "2021-05-01" }, { "rank": 17, "intensity": 2.510204081632653, "date": "2021-09-20" }, { "rank": 18, "intensity": 2.479591836734694, "date": "2021-02-13" }, { "rank": 19, "intensity": 2.4489795918367347, "date": "2021-08-23" }, { "rank": 20, "intensity": 2.4183673469387754, "date": "2021-08-03" }, { "rank": 21, "intensity": 2.387755102040816, "date": "2021-12-28" }, { "rank": 22, "intensity": 2.357142857142857, "date": "2021-10-21" }, { "rank": 23, "intensity": 2.326530612244898, "date": "2021-01-19" }, { "rank": 24, "intensity": 2.295918367346939, "date": "2021-07-11" }, { "rank": 25, "intensity": 2.2653061224489797, "date": "2021-06-10" }, { "rank": 26, "intensity": 2.2346938775510203, "date": "2021-05-09" }, { "rank": 27, "intensity": 2.204081632653061, "date": "2021-07-28" }, { "rank": 28, "intensity": 2.173469387755102, "date": "2021-12-16" }, { "rank": 29, "intensity": 2.142857142857143, "date": "2021-01-29" }, { "rank": 30, "intensity": 2.112244897959184, "date": "2021-08-26" }, { "rank": 31, "intensity": 2.0816326530612246, "date": "2021-04-16" }, { "rank": 32, "intensity": 2.0510204081632653, "date": "2021-11-23" }, { "rank": 33, "intensity": 2.020408163265306, "date": "2021-01-23" }, { "rank": 34, "intensity": 1.989795918367347, "date": "2021-08-18" }, { "rank": 35, "intensity": 1.9591836734693877, "date": "2021-06-19" }, { "rank": 36, "intensity": 1.9285714285714286, "date": "2021-12-25" }, { "rank": 37, "intensity": 1.8979591836734695, "date": "2021-03-11" }, { "rank": 38, "intensity": 1.8673469387755102, "date": "2021-03-15" }, { "rank": 39, "intensity": 1.836734693877551, "date": "2021-04-21" }, { "rank": 40, "intensity": 1.806122448979592, "date": "2021-09-08" }, { "rank": 41, "intensity": 1.7755102040816326, "date": "2021-08-15" }, { "rank": 42, "intensity": 1.7448979591836735, "date": "2021-11-17" }, { "rank": 43, "intensity": 1.7142857142857144, "date": "2021-10-08" }, { "rank": 44, "intensity": 1.683673469387755, "date": "2021-01-15" }, { "rank": 45, "intensity": 1.653061224489796, "date": "2021-12-21" }, { "rank": 46, "intensity": 1.6224489795918369, "date": "2021-10-30" }, { "rank": 47, "intensity": 1.5918367346938775, "date": "2021-06-17" }, { "rank": 48, "intensity": 1.5612244897959184, "date": "2021-11-25" }, { "rank": 49, "intensity": 1.5306122448979593, "date": "2021-05-11" }, { "rank": 50, "intensity": 1.5, "date": "2021-11-13" } ], "source": "causal_rivers" } }, { "id": "L3_T1_Composite_Trend_00017", "level": 3, "level_name": "Semantic Reasoning", "category": "Composite Trend", "subtask": "Composite Trend", "question": "Among days in channel 237 during 2022 that exhibit the trend pattern 'fluctuating stable, then rise', identify the top-5 days where the fluctuating stable segment is the most fluctuating. (Output format: a ranked list of dates, e.g., ['YYYY-MM-DD', ...])", "answer": "['2022-12-27', '2022-04-10', '2022-05-09', '2022-03-07', '2022-12-08']", "ground_truth": [ "2022-12-27", "2022-04-10", "2022-05-09", "2022-03-07", "2022-12-08" ], "eval_metric": "set_f1", "channel": "237", "ts_data_path": "ts_data/L3_T1_Composite_Trend_00017.csv", "meta": { "segments": [ { "kind": "stable", "adj": "fluctuating" }, { "kind": "rise", "adj": "none" } ], "pattern_phrase": "fluctuating stable, then rise", "rank_target_idx": 0, "rank_kind": "stable", "rank_adj": "fluctuating", "rank_axis": "std", "rank_direction": "DESC", "rank_extreme": "most fluctuating", "top_segments_meta": [ { "adj": "fluctuating", "kind": "stable", "label": "fluctuating stable", "start_idx": 0, "end_idx": 53 }, { "adj": "none", "kind": "rise", "label": "none rise", "start_idx": 53, "end_idx": 98 } ], "points_per_day": 96, "pattern": "fluctuating stable, then rise", "year": 2022, "top_k": [ "2022-12-27", "2022-04-10", "2022-05-09", "2022-03-07", "2022-12-08" ], "all_injections": [ { "rank": 1, "intensity": 3.0, "date": "2022-12-27" }, { "rank": 2, "intensity": 2.9693877551020407, "date": "2022-04-10" }, { "rank": 3, "intensity": 2.938775510204082, "date": "2022-05-09" }, { "rank": 4, "intensity": 2.9081632653061225, "date": "2022-03-07" }, { "rank": 5, "intensity": 2.877551020408163, "date": "2022-12-08" }, { "rank": 6, "intensity": 2.8469387755102042, "date": "2022-07-06" }, { "rank": 7, "intensity": 2.816326530612245, "date": "2022-08-07" }, { "rank": 8, "intensity": 2.7857142857142856, "date": "2022-12-02" }, { "rank": 9, "intensity": 2.7551020408163267, "date": "2022-03-28" }, { "rank": 10, "intensity": 2.7244897959183674, "date": "2022-05-20" }, { "rank": 11, "intensity": 2.693877551020408, "date": "2022-08-21" }, { "rank": 12, "intensity": 2.663265306122449, "date": "2022-02-14" }, { "rank": 13, "intensity": 2.63265306122449, "date": "2022-01-18" }, { "rank": 14, "intensity": 2.6020408163265305, "date": "2022-08-27" }, { "rank": 15, "intensity": 2.5714285714285716, "date": "2022-03-05" }, { "rank": 16, "intensity": 2.5408163265306123, "date": "2022-02-19" }, { "rank": 17, "intensity": 2.510204081632653, "date": "2022-03-26" }, { "rank": 18, "intensity": 2.479591836734694, "date": "2022-02-07" }, { "rank": 19, "intensity": 2.4489795918367347, "date": "2022-08-13" }, { "rank": 20, "intensity": 2.4183673469387754, "date": "2022-09-09" }, { "rank": 21, "intensity": 2.387755102040816, "date": "2022-09-13" }, { "rank": 22, "intensity": 2.357142857142857, "date": "2022-01-12" }, { "rank": 23, "intensity": 2.326530612244898, "date": "2022-09-03" }, { "rank": 24, "intensity": 2.295918367346939, "date": "2022-08-11" }, { "rank": 25, "intensity": 2.2653061224489797, "date": "2022-09-26" }, { "rank": 26, "intensity": 2.2346938775510203, "date": "2022-10-05" }, { "rank": 27, "intensity": 2.204081632653061, "date": "2022-05-06" }, { "rank": 28, "intensity": 2.173469387755102, "date": "2022-01-22" }, { "rank": 29, "intensity": 2.142857142857143, "date": "2022-06-10" }, { "rank": 30, "intensity": 2.112244897959184, "date": "2022-11-21" }, { "rank": 31, "intensity": 2.0816326530612246, "date": "2022-02-04" }, { "rank": 32, "intensity": 2.0510204081632653, "date": "2022-10-29" }, { "rank": 33, "intensity": 2.020408163265306, "date": "2022-11-16" }, { "rank": 34, "intensity": 1.989795918367347, "date": "2022-01-02" }, { "rank": 35, "intensity": 1.9591836734693877, "date": "2022-04-13" }, { "rank": 36, "intensity": 1.9285714285714286, "date": "2022-10-03" }, { "rank": 37, "intensity": 1.8979591836734695, "date": "2022-08-04" }, { "rank": 38, "intensity": 1.8673469387755102, "date": "2022-01-31" }, { "rank": 39, "intensity": 1.836734693877551, "date": "2022-01-10" }, { "rank": 40, "intensity": 1.806122448979592, "date": "2022-04-06" }, { "rank": 41, "intensity": 1.7755102040816326, "date": "2022-10-24" }, { "rank": 42, "intensity": 1.7448979591836735, "date": "2022-05-13" }, { "rank": 43, "intensity": 1.7142857142857144, "date": "2022-11-23" }, { "rank": 44, "intensity": 1.683673469387755, "date": "2022-04-26" }, { "rank": 45, "intensity": 1.653061224489796, "date": "2022-01-20" }, { "rank": 46, "intensity": 1.6224489795918369, "date": "2022-01-27" }, { "rank": 47, "intensity": 1.5918367346938775, "date": "2022-07-04" }, { "rank": 48, "intensity": 1.5612244897959184, "date": "2022-02-12" }, { "rank": 49, "intensity": 1.5306122448979593, "date": "2022-01-07" }, { "rank": 50, "intensity": 1.5, "date": "2022-05-15" } ], "source": "causal_rivers" } }, { "id": "L3_T1_Composite_Trend_00018", "level": 3, "level_name": "Semantic Reasoning", "category": "Composite Trend", "subtask": "Composite Trend", "question": "Among days in channel 245 during 2022 that exhibit the trend pattern 'fall, then fluctuating stable', identify the top-5 days where the fluctuating stable segment is the most fluctuating. (Output format: a ranked list of dates, e.g., ['YYYY-MM-DD', ...])", "answer": "['2022-05-10', '2022-08-25', '2022-06-23', '2022-12-23', '2022-06-18']", "ground_truth": [ "2022-05-10", "2022-08-25", "2022-06-23", "2022-12-23", "2022-06-18" ], "eval_metric": "set_f1", "channel": "245", "ts_data_path": "ts_data/L3_T1_Composite_Trend_00018.csv", "meta": { "segments": [ { "kind": "fall", "adj": "none" }, { "kind": "stable", "adj": "fluctuating" } ], "pattern_phrase": "fall, then fluctuating stable", "rank_target_idx": 1, "rank_kind": "stable", "rank_adj": "fluctuating", "rank_axis": "std", "rank_direction": "DESC", "rank_extreme": "most fluctuating", "top_segments_meta": [ { "adj": "none", "kind": "fall", "label": "none fall", "start_idx": 0, "end_idx": 46 }, { "adj": "fluctuating", "kind": "stable", "label": "fluctuating stable", "start_idx": 46, "end_idx": 98 } ], "points_per_day": 96, "pattern": "fall, then fluctuating stable", "year": 2022, "top_k": [ "2022-05-10", "2022-08-25", "2022-06-23", "2022-12-23", "2022-06-18" ], "all_injections": [ { "rank": 1, "intensity": 3.0, "date": "2022-05-10" }, { "rank": 2, "intensity": 2.9693877551020407, "date": "2022-08-25" }, { "rank": 3, "intensity": 2.938775510204082, "date": "2022-06-23" }, { "rank": 4, "intensity": 2.9081632653061225, "date": "2022-12-23" }, { "rank": 5, "intensity": 2.877551020408163, "date": "2022-06-18" }, { "rank": 6, "intensity": 2.8469387755102042, "date": "2022-02-09" }, { "rank": 7, "intensity": 2.816326530612245, "date": "2022-06-09" }, { "rank": 8, "intensity": 2.7857142857142856, "date": "2022-11-27" }, { "rank": 9, "intensity": 2.7551020408163267, "date": "2022-02-20" }, { "rank": 10, "intensity": 2.7244897959183674, "date": "2022-07-14" }, { "rank": 11, "intensity": 2.693877551020408, "date": "2022-05-02" }, { "rank": 12, "intensity": 2.663265306122449, "date": "2022-04-05" }, { "rank": 13, "intensity": 2.63265306122449, "date": "2022-12-20" }, { "rank": 14, "intensity": 2.6020408163265305, "date": "2022-04-30" }, { "rank": 15, "intensity": 2.5714285714285716, "date": "2022-07-02" }, { "rank": 16, "intensity": 2.5408163265306123, "date": "2022-07-09" }, { "rank": 17, "intensity": 2.510204081632653, "date": "2022-02-27" }, { "rank": 18, "intensity": 2.479591836734694, "date": "2022-04-13" }, { "rank": 19, "intensity": 2.4489795918367347, "date": "2022-02-22" }, { "rank": 20, "intensity": 2.4183673469387754, "date": "2022-08-03" }, { "rank": 21, "intensity": 2.387755102040816, "date": "2022-03-10" }, { "rank": 22, "intensity": 2.357142857142857, "date": "2022-09-09" }, { "rank": 23, "intensity": 2.326530612244898, "date": "2022-10-08" }, { "rank": 24, "intensity": 2.295918367346939, "date": "2022-06-04" }, { "rank": 25, "intensity": 2.2653061224489797, "date": "2022-07-05" }, { "rank": 26, "intensity": 2.2346938775510203, "date": "2022-03-21" }, { "rank": 27, "intensity": 2.204081632653061, "date": "2022-01-16" }, { "rank": 28, "intensity": 2.173469387755102, "date": "2022-04-20" }, { "rank": 29, "intensity": 2.142857142857143, "date": "2022-11-16" }, { "rank": 30, "intensity": 2.112244897959184, "date": "2022-09-13" }, { "rank": 31, "intensity": 2.0816326530612246, "date": "2022-10-20" }, { "rank": 32, "intensity": 2.0510204081632653, "date": "2022-01-04" }, { "rank": 33, "intensity": 2.020408163265306, "date": "2022-02-03" }, { "rank": 34, "intensity": 1.989795918367347, "date": "2022-07-19" }, { "rank": 35, "intensity": 1.9591836734693877, "date": "2022-05-15" }, { "rank": 36, "intensity": 1.9285714285714286, "date": "2022-05-21" }, { "rank": 37, "intensity": 1.8979591836734695, "date": "2022-08-11" }, { "rank": 38, "intensity": 1.8673469387755102, "date": "2022-05-28" }, { "rank": 39, "intensity": 1.836734693877551, "date": "2022-08-18" }, { "rank": 40, "intensity": 1.806122448979592, "date": "2022-07-21" }, { "rank": 41, "intensity": 1.7755102040816326, "date": "2022-02-06" }, { "rank": 42, "intensity": 1.7448979591836735, "date": "2022-11-21" }, { "rank": 43, "intensity": 1.7142857142857144, "date": "2022-09-04" }, { "rank": 44, "intensity": 1.683673469387755, "date": "2022-07-17" }, { "rank": 45, "intensity": 1.653061224489796, "date": "2022-10-23" }, { "rank": 46, "intensity": 1.6224489795918369, "date": "2022-12-05" }, { "rank": 47, "intensity": 1.5918367346938775, "date": "2022-10-03" }, { "rank": 48, "intensity": 1.5612244897959184, "date": "2022-05-19" }, { "rank": 49, "intensity": 1.5306122448979593, "date": "2022-03-18" }, { "rank": 50, "intensity": 1.5, "date": "2022-09-28" } ], "source": "causal_rivers" } }, { "id": "L3_T1_Composite_Trend_00019", "level": 3, "level_name": "Semantic Reasoning", "category": "Composite Trend", "subtask": "Composite Trend", "question": "Among days in channel 312 during 2023 that exhibit the trend pattern 'slow fall, then steady stable', identify the top-5 days where the slow fall segment is the slowest. (Output format: a ranked list of dates, e.g., ['YYYY-MM-DD', ...])", "answer": "['2023-03-08', '2023-04-10', '2023-11-29', '2023-07-27', '2023-09-26']", "ground_truth": [ "2023-03-08", "2023-04-10", "2023-11-29", "2023-07-27", "2023-09-26" ], "eval_metric": "set_f1", "channel": "312", "ts_data_path": "ts_data/L3_T1_Composite_Trend_00019.csv", "meta": { "segments": [ { "kind": "fall", "adj": "slow" }, { "kind": "stable", "adj": "steady" } ], "pattern_phrase": "slow fall, then steady stable", "rank_target_idx": 0, "rank_kind": "fall", "rank_adj": "slow", "rank_axis": "slope", "rank_direction": "ASC", "rank_extreme": "slowest", "top_segments_meta": [ { "adj": "slow", "kind": "fall", "label": "slow fall", "start_idx": 0, "end_idx": 46 }, { "adj": "steady", "kind": "stable", "label": "steady stable", "start_idx": 46, "end_idx": 98 } ], "points_per_day": 96, "pattern": "slow fall, then steady stable", "year": 2023, "top_k": [ "2023-03-08", "2023-04-10", "2023-11-29", "2023-07-27", "2023-09-26" ], "all_injections": [ { "rank": 1, "intensity": 1.5, "date": "2023-03-08" }, { "rank": 2, "intensity": 1.530612244897959, "date": "2023-04-10" }, { "rank": 3, "intensity": 1.5612244897959184, "date": "2023-11-29" }, { "rank": 4, "intensity": 1.5918367346938775, "date": "2023-07-27" }, { "rank": 5, "intensity": 1.6224489795918366, "date": "2023-09-26" }, { "rank": 6, "intensity": 1.653061224489796, "date": "2023-03-02" }, { "rank": 7, "intensity": 1.683673469387755, "date": "2023-09-02" }, { "rank": 8, "intensity": 1.7142857142857142, "date": "2023-11-07" }, { "rank": 9, "intensity": 1.7448979591836735, "date": "2023-07-07" }, { "rank": 10, "intensity": 1.7755102040816326, "date": "2023-08-06" }, { "rank": 11, "intensity": 1.806122448979592, "date": "2023-01-30" }, { "rank": 12, "intensity": 1.836734693877551, "date": "2023-02-06" }, { "rank": 13, "intensity": 1.8673469387755102, "date": "2023-06-26" }, { "rank": 14, "intensity": 1.8979591836734695, "date": "2023-01-28" }, { "rank": 15, "intensity": 1.9285714285714286, "date": "2023-08-24" }, { "rank": 16, "intensity": 1.9591836734693877, "date": "2023-12-03" }, { "rank": 17, "intensity": 1.989795918367347, "date": "2023-04-20" }, { "rank": 18, "intensity": 2.020408163265306, "date": "2023-02-27" }, { "rank": 19, "intensity": 2.0510204081632653, "date": "2023-03-31" }, { "rank": 20, "intensity": 2.0816326530612246, "date": "2023-12-14" }, { "rank": 21, "intensity": 2.112244897959184, "date": "2023-09-07" }, { "rank": 22, "intensity": 2.142857142857143, "date": "2023-09-04" }, { "rank": 23, "intensity": 2.173469387755102, "date": "2023-11-03" }, { "rank": 24, "intensity": 2.204081632653061, "date": "2023-12-08" }, { "rank": 25, "intensity": 2.2346938775510203, "date": "2023-03-10" }, { "rank": 26, "intensity": 2.2653061224489797, "date": "2023-12-10" }, { "rank": 27, "intensity": 2.295918367346939, "date": "2023-01-08" }, { "rank": 28, "intensity": 2.326530612244898, "date": "2023-08-10" }, { "rank": 29, "intensity": 2.357142857142857, "date": "2023-02-08" }, { "rank": 30, "intensity": 2.387755102040816, "date": "2023-04-02" }, { "rank": 31, "intensity": 2.4183673469387754, "date": "2023-04-07" }, { "rank": 32, "intensity": 2.4489795918367347, "date": "2023-08-12" }, { "rank": 33, "intensity": 2.479591836734694, "date": "2023-12-20" }, { "rank": 34, "intensity": 2.510204081632653, "date": "2023-07-25" }, { "rank": 35, "intensity": 2.5408163265306123, "date": "2023-07-09" }, { "rank": 36, "intensity": 2.571428571428571, "date": "2023-05-26" }, { "rank": 37, "intensity": 2.6020408163265305, "date": "2023-07-31" }, { "rank": 38, "intensity": 2.63265306122449, "date": "2023-02-04" }, { "rank": 39, "intensity": 2.663265306122449, "date": "2023-06-04" }, { "rank": 40, "intensity": 2.693877551020408, "date": "2023-03-17" }, { "rank": 41, "intensity": 2.7244897959183674, "date": "2023-03-24" }, { "rank": 42, "intensity": 2.7551020408163263, "date": "2023-05-16" }, { "rank": 43, "intensity": 2.7857142857142856, "date": "2023-06-06" }, { "rank": 44, "intensity": 2.816326530612245, "date": "2023-06-21" }, { "rank": 45, "intensity": 2.8469387755102042, "date": "2023-07-01" }, { "rank": 46, "intensity": 2.877551020408163, "date": "2023-12-06" }, { "rank": 47, "intensity": 2.9081632653061225, "date": "2023-06-18" }, { "rank": 48, "intensity": 2.9387755102040813, "date": "2023-02-19" }, { "rank": 49, "intensity": 2.9693877551020407, "date": "2023-05-11" }, { "rank": 50, "intensity": 3.0, "date": "2023-03-04" } ], "source": "causal_rivers" } }, { "id": "L3_T1_Composite_Trend_00020", "level": 3, "level_name": "Semantic Reasoning", "category": "Composite Trend", "subtask": "Composite Trend", "question": "Among days in channel 430 during 2021 that exhibit the trend pattern 'rapid fall, then fall, then steady stable', identify the top-5 days where the rapid fall segment is the fastest. (Output format: a ranked list of dates, e.g., ['YYYY-MM-DD', ...])", "answer": "['2021-03-31', '2021-06-08', '2021-09-19', '2021-03-28', '2021-12-05']", "ground_truth": [ "2021-03-31", "2021-06-08", "2021-09-19", "2021-03-28", "2021-12-05" ], "eval_metric": "set_f1", "channel": "430", "ts_data_path": "ts_data/L3_T1_Composite_Trend_00020.csv", "meta": { "segments": [ { "kind": "fall", "adj": "rapid" }, { "kind": "fall", "adj": "none" }, { "kind": "stable", "adj": "steady" } ], "pattern_phrase": "rapid fall, then fall, then steady stable", "rank_target_idx": 0, "rank_kind": "fall", "rank_adj": "rapid", "rank_axis": "slope", "rank_direction": "DESC", "rank_extreme": "fastest", "top_segments_meta": [ { "adj": "rapid", "kind": "fall", "label": "rapid fall", "start_idx": 0, "end_idx": 15 }, { "adj": "none", "kind": "fall", "label": "none fall", "start_idx": 15, "end_idx": 42 }, { "adj": "steady", "kind": "stable", "label": "steady stable", "start_idx": 42, "end_idx": 98 } ], "points_per_day": 96, "pattern": "rapid fall, then fall, then steady stable", "year": 2021, "top_k": [ "2021-03-31", "2021-06-08", "2021-09-19", "2021-03-28", "2021-12-05" ], "all_injections": [ { "rank": 1, "intensity": 3.0, "date": "2021-03-31" }, { "rank": 2, "intensity": 2.9693877551020407, "date": "2021-06-08" }, { "rank": 3, "intensity": 2.938775510204082, "date": "2021-09-19" }, { "rank": 4, "intensity": 2.9081632653061225, "date": "2021-03-28" }, { "rank": 5, "intensity": 2.877551020408163, "date": "2021-12-05" }, { "rank": 6, "intensity": 2.8469387755102042, "date": "2021-07-10" }, { "rank": 7, "intensity": 2.816326530612245, "date": "2021-10-28" }, { "rank": 8, "intensity": 2.7857142857142856, "date": "2021-10-12" }, { "rank": 9, "intensity": 2.7551020408163267, "date": "2021-11-26" }, { "rank": 10, "intensity": 2.7244897959183674, "date": "2021-03-26" }, { "rank": 11, "intensity": 2.693877551020408, "date": "2021-07-14" }, { "rank": 12, "intensity": 2.663265306122449, "date": "2021-09-13" }, { "rank": 13, "intensity": 2.63265306122449, "date": "2021-02-21" }, { "rank": 14, "intensity": 2.6020408163265305, "date": "2021-06-26" }, { "rank": 15, "intensity": 2.5714285714285716, "date": "2021-11-04" }, { "rank": 16, "intensity": 2.5408163265306123, "date": "2021-05-21" }, { "rank": 17, "intensity": 2.510204081632653, "date": "2021-10-07" }, { "rank": 18, "intensity": 2.479591836734694, "date": "2021-06-01" }, { "rank": 19, "intensity": 2.4489795918367347, "date": "2021-05-27" }, { "rank": 20, "intensity": 2.4183673469387754, "date": "2021-09-04" }, { "rank": 21, "intensity": 2.387755102040816, "date": "2021-12-17" }, { "rank": 22, "intensity": 2.357142857142857, "date": "2021-08-19" }, { "rank": 23, "intensity": 2.326530612244898, "date": "2021-09-08" }, { "rank": 24, "intensity": 2.295918367346939, "date": "2021-12-28" }, { "rank": 25, "intensity": 2.2653061224489797, "date": "2021-11-22" }, { "rank": 26, "intensity": 2.2346938775510203, "date": "2021-10-22" }, { "rank": 27, "intensity": 2.204081632653061, "date": "2021-02-02" }, { "rank": 28, "intensity": 2.173469387755102, "date": "2021-07-25" }, { "rank": 29, "intensity": 2.142857142857143, "date": "2021-08-06" }, { "rank": 30, "intensity": 2.112244897959184, "date": "2021-05-09" }, { "rank": 31, "intensity": 2.0816326530612246, "date": "2021-04-18" }, { "rank": 32, "intensity": 2.0510204081632653, "date": "2021-08-03" }, { "rank": 33, "intensity": 2.020408163265306, "date": "2021-05-29" }, { "rank": 34, "intensity": 1.989795918367347, "date": "2021-03-16" }, { "rank": 35, "intensity": 1.9591836734693877, "date": "2021-04-25" }, { "rank": 36, "intensity": 1.9285714285714286, "date": "2021-04-30" }, { "rank": 37, "intensity": 1.8979591836734695, "date": "2021-08-29" }, { "rank": 38, "intensity": 1.8673469387755102, "date": "2021-11-18" }, { "rank": 39, "intensity": 1.836734693877551, "date": "2021-07-29" }, { "rank": 40, "intensity": 1.806122448979592, "date": "2021-09-29" }, { "rank": 41, "intensity": 1.7755102040816326, "date": "2021-10-09" }, { "rank": 42, "intensity": 1.7448979591836735, "date": "2021-06-15" }, { "rank": 43, "intensity": 1.7142857142857144, "date": "2021-08-16" }, { "rank": 44, "intensity": 1.683673469387755, "date": "2021-04-12" }, { "rank": 45, "intensity": 1.653061224489796, "date": "2021-02-14" }, { "rank": 46, "intensity": 1.6224489795918369, "date": "2021-04-14" }, { "rank": 47, "intensity": 1.5918367346938775, "date": "2021-04-04" }, { "rank": 48, "intensity": 1.5612244897959184, "date": "2021-07-21" }, { "rank": 49, "intensity": 1.5306122448979593, "date": "2021-02-12" }, { "rank": 50, "intensity": 1.5, "date": "2021-09-24" } ], "source": "causal_rivers" } }, { "id": "L3_T1_Composite_Trend_00021", "level": 3, "level_name": "Semantic Reasoning", "category": "Composite Trend", "subtask": "Composite Trend", "question": "Among days in channel 441 during 2020 that exhibit the trend pattern 'slow fall, then fall', identify the top-5 days where the slow fall segment is the slowest. (Output format: a ranked list of dates, e.g., ['YYYY-MM-DD', ...])", "answer": "['2020-03-29', '2020-09-04', '2020-01-18', '2020-08-11', '2020-09-17']", "ground_truth": [ "2020-03-29", "2020-09-04", "2020-01-18", "2020-08-11", "2020-09-17" ], "eval_metric": "set_f1", "channel": "441", "ts_data_path": "ts_data/L3_T1_Composite_Trend_00021.csv", "meta": { "segments": [ { "kind": "fall", "adj": "slow" }, { "kind": "fall", "adj": "none" } ], "pattern_phrase": "slow fall, then fall", "rank_target_idx": 0, "rank_kind": "fall", "rank_adj": "slow", "rank_axis": "slope", "rank_direction": "ASC", "rank_extreme": "slowest", "top_segments_meta": [ { "adj": "slow", "kind": "fall", "label": "slow fall", "start_idx": 0, "end_idx": 62 }, { "adj": "none", "kind": "fall", "label": "none fall", "start_idx": 62, "end_idx": 98 } ], "points_per_day": 96, "pattern": "slow fall, then fall", "year": 2020, "top_k": [ "2020-03-29", "2020-09-04", "2020-01-18", "2020-08-11", "2020-09-17" ], "all_injections": [ { "rank": 1, "intensity": 1.5, "date": "2020-03-29" }, { "rank": 2, "intensity": 1.530612244897959, "date": "2020-09-04" }, { "rank": 3, "intensity": 1.5612244897959184, "date": "2020-01-18" }, { "rank": 4, "intensity": 1.5918367346938775, "date": "2020-08-11" }, { "rank": 5, "intensity": 1.6224489795918366, "date": "2020-09-17" }, { "rank": 6, "intensity": 1.653061224489796, "date": "2020-04-21" }, { "rank": 7, "intensity": 1.683673469387755, "date": "2020-04-28" }, { "rank": 8, "intensity": 1.7142857142857142, "date": "2020-12-17" }, { "rank": 9, "intensity": 1.7448979591836735, "date": "2020-10-13" }, { "rank": 10, "intensity": 1.7755102040816326, "date": "2020-11-13" }, { "rank": 11, "intensity": 1.806122448979592, "date": "2020-08-27" }, { "rank": 12, "intensity": 1.836734693877551, "date": "2020-06-27" }, { "rank": 13, "intensity": 1.8673469387755102, "date": "2020-04-14" }, { "rank": 14, "intensity": 1.8979591836734695, "date": "2020-07-17" }, { "rank": 15, "intensity": 1.9285714285714286, "date": "2020-11-20" }, { "rank": 16, "intensity": 1.9591836734693877, "date": "2020-07-26" }, { "rank": 17, "intensity": 1.989795918367347, "date": "2020-02-03" }, { "rank": 18, "intensity": 2.020408163265306, "date": "2020-02-23" }, { "rank": 19, "intensity": 2.0510204081632653, "date": "2020-06-30" }, { "rank": 20, "intensity": 2.0816326530612246, "date": "2020-10-09" }, { "rank": 21, "intensity": 2.112244897959184, "date": "2020-05-14" }, { "rank": 22, "intensity": 2.142857142857143, "date": "2020-11-07" }, { "rank": 23, "intensity": 2.173469387755102, "date": "2020-12-03" }, { "rank": 24, "intensity": 2.204081632653061, "date": "2020-10-23" }, { "rank": 25, "intensity": 2.2346938775510203, "date": "2020-09-25" }, { "rank": 26, "intensity": 2.2653061224489797, "date": "2020-08-16" }, { "rank": 27, "intensity": 2.295918367346939, "date": "2020-03-12" }, { "rank": 28, "intensity": 2.326530612244898, "date": "2020-08-09" }, { "rank": 29, "intensity": 2.357142857142857, "date": "2020-12-30" }, { "rank": 30, "intensity": 2.387755102040816, "date": "2020-03-01" }, { "rank": 31, "intensity": 2.4183673469387754, "date": "2020-02-26" }, { "rank": 32, "intensity": 2.4489795918367347, "date": "2020-12-20" }, { "rank": 33, "intensity": 2.479591836734694, "date": "2020-01-15" }, { "rank": 34, "intensity": 2.510204081632653, "date": "2020-06-24" }, { "rank": 35, "intensity": 2.5408163265306123, "date": "2020-02-10" }, { "rank": 36, "intensity": 2.571428571428571, "date": "2020-12-24" }, { "rank": 37, "intensity": 2.6020408163265305, "date": "2020-09-22" }, { "rank": 38, "intensity": 2.63265306122449, "date": "2020-08-18" }, { "rank": 39, "intensity": 2.663265306122449, "date": "2020-02-20" }, { "rank": 40, "intensity": 2.693877551020408, "date": "2020-05-29" }, { "rank": 41, "intensity": 2.7244897959183674, "date": "2020-09-27" }, { "rank": 42, "intensity": 2.7551020408163263, "date": "2020-09-20" }, { "rank": 43, "intensity": 2.7857142857142856, "date": "2020-06-12" }, { "rank": 44, "intensity": 2.816326530612245, "date": "2020-03-14" }, { "rank": 45, "intensity": 2.8469387755102042, "date": "2020-09-11" }, { "rank": 46, "intensity": 2.877551020408163, "date": "2020-09-13" }, { "rank": 47, "intensity": 2.9081632653061225, "date": "2020-02-01" }, { "rank": 48, "intensity": 2.9387755102040813, "date": "2020-09-02" }, { "rank": 49, "intensity": 2.9693877551020407, "date": "2020-07-23" }, { "rank": 50, "intensity": 3.0, "date": "2020-11-01" } ], "source": "causal_rivers" } }, { "id": "L3_T1_Composite_Trend_00022", "level": 3, "level_name": "Semantic Reasoning", "category": "Composite Trend", "subtask": "Composite Trend", "question": "Among days in channel 495 during 2022 that exhibit the trend pattern 'fall, then rapid rise, then slow fall', identify the top-5 days where the slow fall segment is the slowest. (Output format: a ranked list of dates, e.g., ['YYYY-MM-DD', ...])", "answer": "['2022-11-22', '2022-05-23', '2022-08-06', '2022-01-21', '2022-02-20']", "ground_truth": [ "2022-11-22", "2022-05-23", "2022-08-06", "2022-01-21", "2022-02-20" ], "eval_metric": "set_f1", "channel": "495", "ts_data_path": "ts_data/L3_T1_Composite_Trend_00022.csv", "meta": { "segments": [ { "kind": "fall", "adj": "none" }, { "kind": "rise", "adj": "rapid" }, { "kind": "fall", "adj": "slow" } ], "pattern_phrase": "fall, then rapid rise, then slow fall", "rank_target_idx": 2, "rank_kind": "fall", "rank_adj": "slow", "rank_axis": "slope", "rank_direction": "ASC", "rank_extreme": "slowest", "top_segments_meta": [ { "adj": "none", "kind": "fall", "label": "none fall", "start_idx": 0, "end_idx": 31 }, { "adj": "rapid", "kind": "rise", "label": "rapid rise", "start_idx": 31, "end_idx": 45 }, { "adj": "slow", "kind": "fall", "label": "slow fall", "start_idx": 45, "end_idx": 98 } ], "points_per_day": 96, "pattern": "fall, then rapid rise, then slow fall", "year": 2022, "top_k": [ "2022-11-22", "2022-05-23", "2022-08-06", "2022-01-21", "2022-02-20" ], "all_injections": [ { "rank": 1, "intensity": 1.5, "date": "2022-11-22" }, { "rank": 2, "intensity": 1.530612244897959, "date": "2022-05-23" }, { "rank": 3, "intensity": 1.5612244897959184, "date": "2022-08-06" }, { "rank": 4, "intensity": 1.5918367346938775, "date": "2022-01-21" }, { "rank": 5, "intensity": 1.6224489795918366, "date": "2022-02-20" }, { "rank": 6, "intensity": 1.653061224489796, "date": "2022-12-27" }, { "rank": 7, "intensity": 1.683673469387755, "date": "2022-09-04" }, { "rank": 8, "intensity": 1.7142857142857142, "date": "2022-12-18" }, { "rank": 9, "intensity": 1.7448979591836735, "date": "2022-08-16" }, { "rank": 10, "intensity": 1.7755102040816326, "date": "2022-09-29" }, { "rank": 11, "intensity": 1.806122448979592, "date": "2022-03-25" }, { "rank": 12, "intensity": 1.836734693877551, "date": "2022-10-25" }, { "rank": 13, "intensity": 1.8673469387755102, "date": "2022-12-24" }, { "rank": 14, "intensity": 1.8979591836734695, "date": "2022-11-05" }, { "rank": 15, "intensity": 1.9285714285714286, "date": "2022-01-13" }, { "rank": 16, "intensity": 1.9591836734693877, "date": "2022-05-17" }, { "rank": 17, "intensity": 1.989795918367347, "date": "2022-09-19" }, { "rank": 18, "intensity": 2.020408163265306, "date": "2022-09-26" }, { "rank": 19, "intensity": 2.0510204081632653, "date": "2022-02-25" }, { "rank": 20, "intensity": 2.0816326530612246, "date": "2022-07-19" }, { "rank": 21, "intensity": 2.112244897959184, "date": "2022-01-24" }, { "rank": 22, "intensity": 2.142857142857143, "date": "2022-03-01" }, { "rank": 23, "intensity": 2.173469387755102, "date": "2022-05-28" }, { "rank": 24, "intensity": 2.204081632653061, "date": "2022-10-23" }, { "rank": 25, "intensity": 2.2346938775510203, "date": "2022-11-12" }, { "rank": 26, "intensity": 2.2653061224489797, "date": "2022-02-10" }, { "rank": 27, "intensity": 2.295918367346939, "date": "2022-08-10" }, { "rank": 28, "intensity": 2.326530612244898, "date": "2022-04-20" }, { "rank": 29, "intensity": 2.357142857142857, "date": "2022-06-12" }, { "rank": 30, "intensity": 2.387755102040816, "date": "2022-04-14" }, { "rank": 31, "intensity": 2.4183673469387754, "date": "2022-09-16" }, { "rank": 32, "intensity": 2.4489795918367347, "date": "2022-07-06" }, { "rank": 33, "intensity": 2.479591836734694, "date": "2022-05-10" }, { "rank": 34, "intensity": 2.510204081632653, "date": "2022-01-18" }, { "rank": 35, "intensity": 2.5408163265306123, "date": "2022-08-08" }, { "rank": 36, "intensity": 2.571428571428571, "date": "2022-06-29" }, { "rank": 37, "intensity": 2.6020408163265305, "date": "2022-05-05" }, { "rank": 38, "intensity": 2.63265306122449, "date": "2022-08-28" }, { "rank": 39, "intensity": 2.663265306122449, "date": "2022-12-08" }, { "rank": 40, "intensity": 2.693877551020408, "date": "2022-10-12" }, { "rank": 41, "intensity": 2.7244897959183674, "date": "2022-06-16" }, { "rank": 42, "intensity": 2.7551020408163263, "date": "2022-11-24" }, { "rank": 43, "intensity": 2.7857142857142856, "date": "2022-11-14" }, { "rank": 44, "intensity": 2.816326530612245, "date": "2022-02-22" }, { "rank": 45, "intensity": 2.8469387755102042, "date": "2022-10-07" }, { "rank": 46, "intensity": 2.877551020408163, "date": "2022-04-28" }, { "rank": 47, "intensity": 2.9081632653061225, "date": "2022-03-03" }, { "rank": 48, "intensity": 2.9387755102040813, "date": "2022-08-25" }, { "rank": 49, "intensity": 2.9693877551020407, "date": "2022-10-09" }, { "rank": 50, "intensity": 3.0, "date": "2022-04-24" } ], "source": "causal_rivers" } }, { "id": "L3_T1_Composite_Trend_00023", "level": 3, "level_name": "Semantic Reasoning", "category": "Composite Trend", "subtask": "Composite Trend", "question": "Among days in channel 496 during 2020 that exhibit the trend pattern 'fall, then slow rise', identify the top-5 days where the slow rise segment is the slowest. (Output format: a ranked list of dates, e.g., ['YYYY-MM-DD', ...])", "answer": "['2020-12-02', '2020-10-16', '2020-10-21', '2020-04-09', '2020-11-02']", "ground_truth": [ "2020-12-02", "2020-10-16", "2020-10-21", "2020-04-09", "2020-11-02" ], "eval_metric": "set_f1", "channel": "496", "ts_data_path": "ts_data/L3_T1_Composite_Trend_00023.csv", "meta": { "segments": [ { "kind": "fall", "adj": "none" }, { "kind": "rise", "adj": "slow" } ], "pattern_phrase": "fall, then slow rise", "rank_target_idx": 1, "rank_kind": "rise", "rank_adj": "slow", "rank_axis": "slope", "rank_direction": "ASC", "rank_extreme": "slowest", "top_segments_meta": [ { "adj": "none", "kind": "fall", "label": "none fall", "start_idx": 0, "end_idx": 34 }, { "adj": "slow", "kind": "rise", "label": "slow rise", "start_idx": 34, "end_idx": 98 } ], "points_per_day": 96, "pattern": "fall, then slow rise", "year": 2020, "top_k": [ "2020-12-02", "2020-10-16", "2020-10-21", "2020-04-09", "2020-11-02" ], "all_injections": [ { "rank": 1, "intensity": 1.5, "date": "2020-12-02" }, { "rank": 2, "intensity": 1.530612244897959, "date": "2020-10-16" }, { "rank": 3, "intensity": 1.5612244897959184, "date": "2020-10-21" }, { "rank": 4, "intensity": 1.5918367346938775, "date": "2020-04-09" }, { "rank": 5, "intensity": 1.6224489795918366, "date": "2020-11-02" }, { "rank": 6, "intensity": 1.653061224489796, "date": "2020-07-27" }, { "rank": 7, "intensity": 1.683673469387755, "date": "2020-09-24" }, { "rank": 8, "intensity": 1.7142857142857142, "date": "2020-12-19" }, { "rank": 9, "intensity": 1.7448979591836735, "date": "2020-10-13" }, { "rank": 10, "intensity": 1.7755102040816326, "date": "2020-12-27" }, { "rank": 11, "intensity": 1.806122448979592, "date": "2020-06-13" }, { "rank": 12, "intensity": 1.836734693877551, "date": "2020-12-17" }, { "rank": 13, "intensity": 1.8673469387755102, "date": "2020-02-27" }, { "rank": 14, "intensity": 1.8979591836734695, "date": "2020-08-07" }, { "rank": 15, "intensity": 1.9285714285714286, "date": "2020-01-03" }, { "rank": 16, "intensity": 1.9591836734693877, "date": "2020-03-11" }, { "rank": 17, "intensity": 1.989795918367347, "date": "2020-02-05" }, { "rank": 18, "intensity": 2.020408163265306, "date": "2020-06-29" }, { "rank": 19, "intensity": 2.0510204081632653, "date": "2020-05-31" }, { "rank": 20, "intensity": 2.0816326530612246, "date": "2020-01-21" }, { "rank": 21, "intensity": 2.112244897959184, "date": "2020-02-20" }, { "rank": 22, "intensity": 2.142857142857143, "date": "2020-07-10" }, { "rank": 23, "intensity": 2.173469387755102, "date": "2020-03-23" }, { "rank": 24, "intensity": 2.204081632653061, "date": "2020-08-19" }, { "rank": 25, "intensity": 2.2346938775510203, "date": "2020-09-22" }, { "rank": 26, "intensity": 2.2653061224489797, "date": "2020-06-10" }, { "rank": 27, "intensity": 2.295918367346939, "date": "2020-08-02" }, { "rank": 28, "intensity": 2.326530612244898, "date": "2020-05-25" }, { "rank": 29, "intensity": 2.357142857142857, "date": "2020-05-16" }, { "rank": 30, "intensity": 2.387755102040816, "date": "2020-01-18" }, { "rank": 31, "intensity": 2.4183673469387754, "date": "2020-12-06" }, { "rank": 32, "intensity": 2.4489795918367347, "date": "2020-07-29" }, { "rank": 33, "intensity": 2.479591836734694, "date": "2020-11-28" }, { "rank": 34, "intensity": 2.510204081632653, "date": "2020-03-09" }, { "rank": 35, "intensity": 2.5408163265306123, "date": "2020-12-22" }, { "rank": 36, "intensity": 2.571428571428571, "date": "2020-01-16" }, { "rank": 37, "intensity": 2.6020408163265305, "date": "2020-02-29" }, { "rank": 38, "intensity": 2.63265306122449, "date": "2020-11-18" }, { "rank": 39, "intensity": 2.663265306122449, "date": "2020-09-17" }, { "rank": 40, "intensity": 2.693877551020408, "date": "2020-07-07" }, { "rank": 41, "intensity": 2.7244897959183674, "date": "2020-05-20" }, { "rank": 42, "intensity": 2.7551020408163263, "date": "2020-05-27" }, { "rank": 43, "intensity": 2.7857142857142856, "date": "2020-04-16" }, { "rank": 44, "intensity": 2.816326530612245, "date": "2020-01-12" }, { "rank": 45, "intensity": 2.8469387755102042, "date": "2020-04-28" }, { "rank": 46, "intensity": 2.877551020408163, "date": "2020-06-27" }, { "rank": 47, "intensity": 2.9081632653061225, "date": "2020-12-15" }, { "rank": 48, "intensity": 2.9387755102040813, "date": "2020-04-07" }, { "rank": 49, "intensity": 2.9693877551020407, "date": "2020-07-31" }, { "rank": 50, "intensity": 3.0, "date": "2020-07-01" } ], "source": "causal_rivers" } }, { "id": "L3_T1_Composite_Trend_00024", "level": 3, "level_name": "Semantic Reasoning", "category": "Composite Trend", "subtask": "Composite Trend", "question": "Among days in channel 501 during 2021 that exhibit the trend pattern 'slow rise, then fluctuating stable', identify the top-5 days where the fluctuating stable segment is the most fluctuating. (Output format: a ranked list of dates, e.g., ['YYYY-MM-DD', ...])", "answer": "['2021-06-13', '2021-04-06', '2021-07-21', '2021-07-06', '2021-08-01']", "ground_truth": [ "2021-06-13", "2021-04-06", "2021-07-21", "2021-07-06", "2021-08-01" ], "eval_metric": "set_f1", "channel": "501", "ts_data_path": "ts_data/L3_T1_Composite_Trend_00024.csv", "meta": { "segments": [ { "kind": "rise", "adj": "slow" }, { "kind": "stable", "adj": "fluctuating" } ], "pattern_phrase": "slow rise, then fluctuating stable", "rank_target_idx": 1, "rank_kind": "stable", "rank_adj": "fluctuating", "rank_axis": "std", "rank_direction": "DESC", "rank_extreme": "most fluctuating", "top_segments_meta": [ { "adj": "slow", "kind": "rise", "label": "slow rise", "start_idx": 0, "end_idx": 58 }, { "adj": "fluctuating", "kind": "stable", "label": "fluctuating stable", "start_idx": 58, "end_idx": 98 } ], "points_per_day": 96, "pattern": "slow rise, then fluctuating stable", "year": 2021, "top_k": [ "2021-06-13", "2021-04-06", "2021-07-21", "2021-07-06", "2021-08-01" ], "all_injections": [ { "rank": 1, "intensity": 3.0, "date": "2021-06-13" }, { "rank": 2, "intensity": 2.9693877551020407, "date": "2021-04-06" }, { "rank": 3, "intensity": 2.938775510204082, "date": "2021-07-21" }, { "rank": 4, "intensity": 2.9081632653061225, "date": "2021-07-06" }, { "rank": 5, "intensity": 2.877551020408163, "date": "2021-08-01" }, { "rank": 6, "intensity": 2.8469387755102042, "date": "2021-11-15" }, { "rank": 7, "intensity": 2.816326530612245, "date": "2021-06-05" }, { "rank": 8, "intensity": 2.7857142857142856, "date": "2021-06-20" }, { "rank": 9, "intensity": 2.7551020408163267, "date": "2021-01-04" }, { "rank": 10, "intensity": 2.7244897959183674, "date": "2021-09-20" }, { "rank": 11, "intensity": 2.693877551020408, "date": "2021-08-17" }, { "rank": 12, "intensity": 2.663265306122449, "date": "2021-04-04" }, { "rank": 13, "intensity": 2.63265306122449, "date": "2021-08-25" }, { "rank": 14, "intensity": 2.6020408163265305, "date": "2021-11-21" }, { "rank": 15, "intensity": 2.5714285714285716, "date": "2021-10-02" }, { "rank": 16, "intensity": 2.5408163265306123, "date": "2021-05-06" }, { "rank": 17, "intensity": 2.510204081632653, "date": "2021-11-12" }, { "rank": 18, "intensity": 2.479591836734694, "date": "2021-07-27" }, { "rank": 19, "intensity": 2.4489795918367347, "date": "2021-06-28" }, { "rank": 20, "intensity": 2.4183673469387754, "date": "2021-06-07" }, { "rank": 21, "intensity": 2.387755102040816, "date": "2021-03-28" }, { "rank": 22, "intensity": 2.357142857142857, "date": "2021-10-22" }, { "rank": 23, "intensity": 2.326530612244898, "date": "2021-12-21" }, { "rank": 24, "intensity": 2.295918367346939, "date": "2021-06-22" }, { "rank": 25, "intensity": 2.2653061224489797, "date": "2021-12-10" }, { "rank": 26, "intensity": 2.2346938775510203, "date": "2021-02-28" }, { "rank": 27, "intensity": 2.204081632653061, "date": "2021-10-28" }, { "rank": 28, "intensity": 2.173469387755102, "date": "2021-09-27" }, { "rank": 29, "intensity": 2.142857142857143, "date": "2021-07-09" }, { "rank": 30, "intensity": 2.112244897959184, "date": "2021-08-10" }, { "rank": 31, "intensity": 2.0816326530612246, "date": "2021-11-17" }, { "rank": 32, "intensity": 2.0510204081632653, "date": "2021-05-18" }, { "rank": 33, "intensity": 2.020408163265306, "date": "2021-01-23" }, { "rank": 34, "intensity": 1.989795918367347, "date": "2021-02-15" }, { "rank": 35, "intensity": 1.9591836734693877, "date": "2021-04-17" }, { "rank": 36, "intensity": 1.9285714285714286, "date": "2021-08-27" }, { "rank": 37, "intensity": 1.8979591836734695, "date": "2021-08-22" }, { "rank": 38, "intensity": 1.8673469387755102, "date": "2021-12-01" }, { "rank": 39, "intensity": 1.836734693877551, "date": "2021-12-14" }, { "rank": 40, "intensity": 1.806122448979592, "date": "2021-03-20" }, { "rank": 41, "intensity": 1.7755102040816326, "date": "2021-05-11" }, { "rank": 42, "intensity": 1.7448979591836735, "date": "2021-01-29" }, { "rank": 43, "intensity": 1.7142857142857144, "date": "2021-02-17" }, { "rank": 44, "intensity": 1.683673469387755, "date": "2021-06-10" }, { "rank": 45, "intensity": 1.653061224489796, "date": "2021-09-01" }, { "rank": 46, "intensity": 1.6224489795918369, "date": "2021-02-11" }, { "rank": 47, "intensity": 1.5918367346938775, "date": "2021-03-11" }, { "rank": 48, "intensity": 1.5612244897959184, "date": "2021-09-11" }, { "rank": 49, "intensity": 1.5306122448979593, "date": "2021-03-04" }, { "rank": 50, "intensity": 1.5, "date": "2021-01-25" } ], "source": "causal_rivers" } }, { "id": "L3_T1_Composite_Trend_00025", "level": 3, "level_name": "Semantic Reasoning", "category": "Composite Trend", "subtask": "Composite Trend", "question": "Among days in channel 578 during 2021 that exhibit the trend pattern 'rapid rise, then slow rise', identify the top-5 days where the rapid rise segment is the fastest. (Output format: a ranked list of dates, e.g., ['YYYY-MM-DD', ...])", "answer": "['2021-12-18', '2021-05-12', '2021-04-24', '2021-10-13', '2021-08-08']", "ground_truth": [ "2021-12-18", "2021-05-12", "2021-04-24", "2021-10-13", "2021-08-08" ], "eval_metric": "set_f1", "channel": "578", "ts_data_path": "ts_data/L3_T1_Composite_Trend_00025.csv", "meta": { "segments": [ { "kind": "rise", "adj": "rapid" }, { "kind": "rise", "adj": "slow" } ], "pattern_phrase": "rapid rise, then slow rise", "rank_target_idx": 0, "rank_kind": "rise", "rank_adj": "rapid", "rank_axis": "slope", "rank_direction": "DESC", "rank_extreme": "fastest", "top_segments_meta": [ { "adj": "rapid", "kind": "rise", "label": "rapid rise", "start_idx": 0, "end_idx": 24 }, { "adj": "slow", "kind": "rise", "label": "slow rise", "start_idx": 24, "end_idx": 98 } ], "points_per_day": 96, "pattern": "rapid rise, then slow rise", "year": 2021, "top_k": [ "2021-12-18", "2021-05-12", "2021-04-24", "2021-10-13", "2021-08-08" ], "all_injections": [ { "rank": 1, "intensity": 3.0, "date": "2021-12-18" }, { "rank": 2, "intensity": 2.9693877551020407, "date": "2021-05-12" }, { "rank": 3, "intensity": 2.938775510204082, "date": "2021-04-24" }, { "rank": 4, "intensity": 2.9081632653061225, "date": "2021-10-13" }, { "rank": 5, "intensity": 2.877551020408163, "date": "2021-08-08" }, { "rank": 6, "intensity": 2.8469387755102042, "date": "2021-04-21" }, { "rank": 7, "intensity": 2.816326530612245, "date": "2021-03-09" }, { "rank": 8, "intensity": 2.7857142857142856, "date": "2021-01-30" }, { "rank": 9, "intensity": 2.7551020408163267, "date": "2021-11-13" }, { "rank": 10, "intensity": 2.7244897959183674, "date": "2021-05-23" }, { "rank": 11, "intensity": 2.693877551020408, "date": "2021-05-31" }, { "rank": 12, "intensity": 2.663265306122449, "date": "2021-06-08" }, { "rank": 13, "intensity": 2.63265306122449, "date": "2021-10-22" }, { "rank": 14, "intensity": 2.6020408163265305, "date": "2021-08-05" }, { "rank": 15, "intensity": 2.5714285714285716, "date": "2021-01-07" }, { "rank": 16, "intensity": 2.5408163265306123, "date": "2021-11-06" }, { "rank": 17, "intensity": 2.510204081632653, "date": "2021-04-27" }, { "rank": 18, "intensity": 2.479591836734694, "date": "2021-03-15" }, { "rank": 19, "intensity": 2.4489795918367347, "date": "2021-01-25" }, { "rank": 20, "intensity": 2.4183673469387754, "date": "2021-07-21" }, { "rank": 21, "intensity": 2.387755102040816, "date": "2021-12-21" }, { "rank": 22, "intensity": 2.357142857142857, "date": "2021-07-10" }, { "rank": 23, "intensity": 2.326530612244898, "date": "2021-10-26" }, { "rank": 24, "intensity": 2.295918367346939, "date": "2021-07-13" }, { "rank": 25, "intensity": 2.2653061224489797, "date": "2021-03-02" }, { "rank": 26, "intensity": 2.2346938775510203, "date": "2021-08-16" }, { "rank": 27, "intensity": 2.204081632653061, "date": "2021-06-23" }, { "rank": 28, "intensity": 2.173469387755102, "date": "2021-03-19" }, { "rank": 29, "intensity": 2.142857142857143, "date": "2021-11-25" }, { "rank": 30, "intensity": 2.112244897959184, "date": "2021-08-02" }, { "rank": 31, "intensity": 2.0816326530612246, "date": "2021-11-02" }, { "rank": 32, "intensity": 2.0510204081632653, "date": "2021-09-16" }, { "rank": 33, "intensity": 2.020408163265306, "date": "2021-12-16" }, { "rank": 34, "intensity": 1.989795918367347, "date": "2021-05-28" }, { "rank": 35, "intensity": 1.9591836734693877, "date": "2021-06-18" }, { "rank": 36, "intensity": 1.9285714285714286, "date": "2021-01-04" }, { "rank": 37, "intensity": 1.8979591836734695, "date": "2021-02-23" }, { "rank": 38, "intensity": 1.8673469387755102, "date": "2021-04-19" }, { "rank": 39, "intensity": 1.836734693877551, "date": "2021-05-20" }, { "rank": 40, "intensity": 1.806122448979592, "date": "2021-05-10" }, { "rank": 41, "intensity": 1.7755102040816326, "date": "2021-11-19" }, { "rank": 42, "intensity": 1.7448979591836735, "date": "2021-09-14" }, { "rank": 43, "intensity": 1.7142857142857144, "date": "2021-03-07" }, { "rank": 44, "intensity": 1.683673469387755, "date": "2021-02-05" }, { "rank": 45, "intensity": 1.653061224489796, "date": "2021-08-18" }, { "rank": 46, "intensity": 1.6224489795918369, "date": "2021-05-26" }, { "rank": 47, "intensity": 1.5918367346938775, "date": "2021-12-04" }, { "rank": 48, "intensity": 1.5612244897959184, "date": "2021-03-29" }, { "rank": 49, "intensity": 1.5306122448979593, "date": "2021-09-02" }, { "rank": 50, "intensity": 1.5, "date": "2021-10-29" } ], "source": "causal_rivers" } }, { "id": "L3_T1_Composite_Trend_00026", "level": 3, "level_name": "Semantic Reasoning", "category": "Composite Trend", "subtask": "Composite Trend", "question": "Among days in channel 580 during 2021 that exhibit the trend pattern 'steady stable, then rapid rise, then slow fall', identify the top-5 days where the slow fall segment is the slowest. (Output format: a ranked list of dates, e.g., ['YYYY-MM-DD', ...])", "answer": "['2021-04-29', '2021-11-11', '2021-08-25', '2021-07-19', '2021-10-11']", "ground_truth": [ "2021-04-29", "2021-11-11", "2021-08-25", "2021-07-19", "2021-10-11" ], "eval_metric": "set_f1", "channel": "580", "ts_data_path": "ts_data/L3_T1_Composite_Trend_00026.csv", "meta": { "segments": [ { "kind": "stable", "adj": "steady" }, { "kind": "rise", "adj": "rapid" }, { "kind": "fall", "adj": "slow" } ], "pattern_phrase": "steady stable, then rapid rise, then slow fall", "rank_target_idx": 2, "rank_kind": "fall", "rank_adj": "slow", "rank_axis": "slope", "rank_direction": "ASC", "rank_extreme": "slowest", "top_segments_meta": [ { "adj": "steady", "kind": "stable", "label": "steady stable", "start_idx": 0, "end_idx": 46 }, { "adj": "rapid", "kind": "rise", "label": "rapid rise", "start_idx": 46, "end_idx": 58 }, { "adj": "slow", "kind": "fall", "label": "slow fall", "start_idx": 58, "end_idx": 98 } ], "points_per_day": 96, "pattern": "steady stable, then rapid rise, then slow fall", "year": 2021, "top_k": [ "2021-04-29", "2021-11-11", "2021-08-25", "2021-07-19", "2021-10-11" ], "all_injections": [ { "rank": 1, "intensity": 1.5, "date": "2021-04-29" }, { "rank": 2, "intensity": 1.530612244897959, "date": "2021-11-11" }, { "rank": 3, "intensity": 1.5612244897959184, "date": "2021-08-25" }, { "rank": 4, "intensity": 1.5918367346938775, "date": "2021-07-19" }, { "rank": 5, "intensity": 1.6224489795918366, "date": "2021-10-11" }, { "rank": 6, "intensity": 1.653061224489796, "date": "2021-01-17" }, { "rank": 7, "intensity": 1.683673469387755, "date": "2021-07-04" }, { "rank": 8, "intensity": 1.7142857142857142, "date": "2021-11-06" }, { "rank": 9, "intensity": 1.7448979591836735, "date": "2021-03-08" }, { "rank": 10, "intensity": 1.7755102040816326, "date": "2021-08-19" }, { "rank": 11, "intensity": 1.806122448979592, "date": "2021-07-29" }, { "rank": 12, "intensity": 1.836734693877551, "date": "2021-09-13" }, { "rank": 13, "intensity": 1.8673469387755102, "date": "2021-12-10" }, { "rank": 14, "intensity": 1.8979591836734695, "date": "2021-03-02" }, { "rank": 15, "intensity": 1.9285714285714286, "date": "2021-03-30" }, { "rank": 16, "intensity": 1.9591836734693877, "date": "2021-12-18" }, { "rank": 17, "intensity": 1.989795918367347, "date": "2021-09-04" }, { "rank": 18, "intensity": 2.020408163265306, "date": "2021-04-09" }, { "rank": 19, "intensity": 2.0510204081632653, "date": "2021-05-13" }, { "rank": 20, "intensity": 2.0816326530612246, "date": "2021-10-15" }, { "rank": 21, "intensity": 2.112244897959184, "date": "2021-11-21" }, { "rank": 22, "intensity": 2.142857142857143, "date": "2021-02-02" }, { "rank": 23, "intensity": 2.173469387755102, "date": "2021-04-13" }, { "rank": 24, "intensity": 2.204081632653061, "date": "2021-05-11" }, { "rank": 25, "intensity": 2.2346938775510203, "date": "2021-12-02" }, { "rank": 26, "intensity": 2.2653061224489797, "date": "2021-12-25" }, { "rank": 27, "intensity": 2.295918367346939, "date": "2021-10-08" }, { "rank": 28, "intensity": 2.326530612244898, "date": "2021-06-17" }, { "rank": 29, "intensity": 2.357142857142857, "date": "2021-06-19" }, { "rank": 30, "intensity": 2.387755102040816, "date": "2021-12-14" }, { "rank": 31, "intensity": 2.4183673469387754, "date": "2021-05-18" }, { "rank": 32, "intensity": 2.4489795918367347, "date": "2021-07-11" }, { "rank": 33, "intensity": 2.479591836734694, "date": "2021-03-14" }, { "rank": 34, "intensity": 2.510204081632653, "date": "2021-04-23" }, { "rank": 35, "intensity": 2.5408163265306123, "date": "2021-02-10" }, { "rank": 36, "intensity": 2.571428571428571, "date": "2021-01-19" }, { "rank": 37, "intensity": 2.6020408163265305, "date": "2021-08-22" }, { "rank": 38, "intensity": 2.63265306122449, "date": "2021-04-26" }, { "rank": 39, "intensity": 2.663265306122449, "date": "2021-01-07" }, { "rank": 40, "intensity": 2.693877551020408, "date": "2021-03-18" }, { "rank": 41, "intensity": 2.7244897959183674, "date": "2021-05-27" }, { "rank": 42, "intensity": 2.7551020408163263, "date": "2021-04-03" }, { "rank": 43, "intensity": 2.7857142857142856, "date": "2021-01-31" }, { "rank": 44, "intensity": 2.816326530612245, "date": "2021-02-26" }, { "rank": 45, "intensity": 2.8469387755102042, "date": "2021-07-02" }, { "rank": 46, "intensity": 2.877551020408163, "date": "2021-09-25" }, { "rank": 47, "intensity": 2.9081632653061225, "date": "2021-08-27" }, { "rank": 48, "intensity": 2.9387755102040813, "date": "2021-07-25" }, { "rank": 49, "intensity": 2.9693877551020407, "date": "2021-08-06" }, { "rank": 50, "intensity": 3.0, "date": "2021-08-31" } ], "source": "causal_rivers" } }, { "id": "L3_T1_Composite_Trend_00027", "level": 3, "level_name": "Semantic Reasoning", "category": "Composite Trend", "subtask": "Composite Trend", "question": "Among days in channel 589 during 2020 that exhibit the trend pattern 'slow rise, then steady stable', identify the top-5 days where the slow rise segment is the slowest. (Output format: a ranked list of dates, e.g., ['YYYY-MM-DD', ...])", "answer": "['2020-05-02', '2020-01-04', '2020-03-05', '2020-05-20', '2020-04-14']", "ground_truth": [ "2020-05-02", "2020-01-04", "2020-03-05", "2020-05-20", "2020-04-14" ], "eval_metric": "set_f1", "channel": "589", "ts_data_path": "ts_data/L3_T1_Composite_Trend_00027.csv", "meta": { "segments": [ { "kind": "rise", "adj": "slow" }, { "kind": "stable", "adj": "steady" } ], "pattern_phrase": "slow rise, then steady stable", "rank_target_idx": 0, "rank_kind": "rise", "rank_adj": "slow", "rank_axis": "slope", "rank_direction": "ASC", "rank_extreme": "slowest", "top_segments_meta": [ { "adj": "slow", "kind": "rise", "label": "slow rise", "start_idx": 0, "end_idx": 44 }, { "adj": "steady", "kind": "stable", "label": "steady stable", "start_idx": 44, "end_idx": 98 } ], "points_per_day": 96, "pattern": "slow rise, then steady stable", "year": 2020, "top_k": [ "2020-05-02", "2020-01-04", "2020-03-05", "2020-05-20", "2020-04-14" ], "all_injections": [ { "rank": 1, "intensity": 1.5, "date": "2020-05-02" }, { "rank": 2, "intensity": 1.530612244897959, "date": "2020-01-04" }, { "rank": 3, "intensity": 1.5612244897959184, "date": "2020-03-05" }, { "rank": 4, "intensity": 1.5918367346938775, "date": "2020-05-20" }, { "rank": 5, "intensity": 1.6224489795918366, "date": "2020-04-14" }, { "rank": 6, "intensity": 1.653061224489796, "date": "2020-07-31" }, { "rank": 7, "intensity": 1.683673469387755, "date": "2020-04-23" }, { "rank": 8, "intensity": 1.7142857142857142, "date": "2020-01-29" }, { "rank": 9, "intensity": 1.7448979591836735, "date": "2020-08-07" }, { "rank": 10, "intensity": 1.7755102040816326, "date": "2020-04-18" }, { "rank": 11, "intensity": 1.806122448979592, "date": "2020-07-26" }, { "rank": 12, "intensity": 1.836734693877551, "date": "2020-05-06" }, { "rank": 13, "intensity": 1.8673469387755102, "date": "2020-08-18" }, { "rank": 14, "intensity": 1.8979591836734695, "date": "2020-03-15" }, { "rank": 15, "intensity": 1.9285714285714286, "date": "2020-07-03" }, { "rank": 16, "intensity": 1.9591836734693877, "date": "2020-12-15" }, { "rank": 17, "intensity": 1.989795918367347, "date": "2020-11-13" }, { "rank": 18, "intensity": 2.020408163265306, "date": "2020-05-27" }, { "rank": 19, "intensity": 2.0510204081632653, "date": "2020-10-25" }, { "rank": 20, "intensity": 2.0816326530612246, "date": "2020-06-19" }, { "rank": 21, "intensity": 2.112244897959184, "date": "2020-11-10" }, { "rank": 22, "intensity": 2.142857142857143, "date": "2020-02-29" }, { "rank": 23, "intensity": 2.173469387755102, "date": "2020-10-09" }, { "rank": 24, "intensity": 2.204081632653061, "date": "2020-11-29" }, { "rank": 25, "intensity": 2.2346938775510203, "date": "2020-02-17" }, { "rank": 26, "intensity": 2.2653061224489797, "date": "2020-07-01" }, { "rank": 27, "intensity": 2.295918367346939, "date": "2020-05-13" }, { "rank": 28, "intensity": 2.326530612244898, "date": "2020-01-22" }, { "rank": 29, "intensity": 2.357142857142857, "date": "2020-12-20" }, { "rank": 30, "intensity": 2.387755102040816, "date": "2020-07-05" }, { "rank": 31, "intensity": 2.4183673469387754, "date": "2020-08-09" }, { "rank": 32, "intensity": 2.4489795918367347, "date": "2020-05-08" }, { "rank": 33, "intensity": 2.479591836734694, "date": "2020-06-02" }, { "rank": 34, "intensity": 2.510204081632653, "date": "2020-11-07" }, { "rank": 35, "intensity": 2.5408163265306123, "date": "2020-11-04" }, { "rank": 36, "intensity": 2.571428571428571, "date": "2020-06-16" }, { "rank": 37, "intensity": 2.6020408163265305, "date": "2020-10-23" }, { "rank": 38, "intensity": 2.63265306122449, "date": "2020-04-05" }, { "rank": 39, "intensity": 2.663265306122449, "date": "2020-05-15" }, { "rank": 40, "intensity": 2.693877551020408, "date": "2020-02-23" }, { "rank": 41, "intensity": 2.7244897959183674, "date": "2020-03-30" }, { "rank": 42, "intensity": 2.7551020408163263, "date": "2020-10-14" }, { "rank": 43, "intensity": 2.7857142857142856, "date": "2020-03-03" }, { "rank": 44, "intensity": 2.816326530612245, "date": "2020-09-28" }, { "rank": 45, "intensity": 2.8469387755102042, "date": "2020-03-18" }, { "rank": 46, "intensity": 2.877551020408163, "date": "2020-06-25" }, { "rank": 47, "intensity": 2.9081632653061225, "date": "2020-08-12" }, { "rank": 48, "intensity": 2.9387755102040813, "date": "2020-03-24" }, { "rank": 49, "intensity": 2.9693877551020407, "date": "2020-09-16" }, { "rank": 50, "intensity": 3.0, "date": "2020-08-26" } ], "source": "causal_rivers" } }, { "id": "L3_T1_Composite_Trend_00028", "level": 3, "level_name": "Semantic Reasoning", "category": "Composite Trend", "subtask": "Composite Trend", "question": "Among days in channel 591 during 2023 that exhibit the trend pattern 'fall, then rapid rise', identify the top-5 days where the rapid rise segment is the fastest. (Output format: a ranked list of dates, e.g., ['YYYY-MM-DD', ...])", "answer": "['2023-10-11', '2023-02-07', '2023-02-19', '2023-09-07', '2023-12-27']", "ground_truth": [ "2023-10-11", "2023-02-07", "2023-02-19", "2023-09-07", "2023-12-27" ], "eval_metric": "set_f1", "channel": "591", "ts_data_path": "ts_data/L3_T1_Composite_Trend_00028.csv", "meta": { "segments": [ { "kind": "fall", "adj": "none" }, { "kind": "rise", "adj": "rapid" } ], "pattern_phrase": "fall, then rapid rise", "rank_target_idx": 1, "rank_kind": "rise", "rank_adj": "rapid", "rank_axis": "slope", "rank_direction": "DESC", "rank_extreme": "fastest", "top_segments_meta": [ { "adj": "none", "kind": "fall", "label": "none fall", "start_idx": 0, "end_idx": 64 }, { "adj": "rapid", "kind": "rise", "label": "rapid rise", "start_idx": 64, "end_idx": 98 } ], "points_per_day": 96, "pattern": "fall, then rapid rise", "year": 2023, "top_k": [ "2023-10-11", "2023-02-07", "2023-02-19", "2023-09-07", "2023-12-27" ], "all_injections": [ { "rank": 1, "intensity": 3.0, "date": "2023-10-11" }, { "rank": 2, "intensity": 2.9693877551020407, "date": "2023-02-07" }, { "rank": 3, "intensity": 2.938775510204082, "date": "2023-02-19" }, { "rank": 4, "intensity": 2.9081632653061225, "date": "2023-09-07" }, { "rank": 5, "intensity": 2.877551020408163, "date": "2023-12-27" }, { "rank": 6, "intensity": 2.8469387755102042, "date": "2023-12-13" }, { "rank": 7, "intensity": 2.816326530612245, "date": "2023-05-28" }, { "rank": 8, "intensity": 2.7857142857142856, "date": "2023-11-14" }, { "rank": 9, "intensity": 2.7551020408163267, "date": "2023-06-04" }, { "rank": 10, "intensity": 2.7244897959183674, "date": "2023-11-22" }, { "rank": 11, "intensity": 2.693877551020408, "date": "2023-02-09" }, { "rank": 12, "intensity": 2.663265306122449, "date": "2023-07-30" }, { "rank": 13, "intensity": 2.63265306122449, "date": "2023-04-19" }, { "rank": 14, "intensity": 2.6020408163265305, "date": "2023-05-18" }, { "rank": 15, "intensity": 2.5714285714285716, "date": "2023-06-10" }, { "rank": 16, "intensity": 2.5408163265306123, "date": "2023-09-28" }, { "rank": 17, "intensity": 2.510204081632653, "date": "2023-04-13" }, { "rank": 18, "intensity": 2.479591836734694, "date": "2023-01-18" }, { "rank": 19, "intensity": 2.4489795918367347, "date": "2023-12-09" }, { "rank": 20, "intensity": 2.4183673469387754, "date": "2023-12-03" }, { "rank": 21, "intensity": 2.387755102040816, "date": "2023-05-08" }, { "rank": 22, "intensity": 2.357142857142857, "date": "2023-07-23" }, { "rank": 23, "intensity": 2.326530612244898, "date": "2023-05-16" }, { "rank": 24, "intensity": 2.295918367346939, "date": "2023-01-08" }, { "rank": 25, "intensity": 2.2653061224489797, "date": "2023-10-09" }, { "rank": 26, "intensity": 2.2346938775510203, "date": "2023-07-19" }, { "rank": 27, "intensity": 2.204081632653061, "date": "2023-03-13" }, { "rank": 28, "intensity": 2.173469387755102, "date": "2023-09-16" }, { "rank": 29, "intensity": 2.142857142857143, "date": "2023-08-30" }, { "rank": 30, "intensity": 2.112244897959184, "date": "2023-10-16" }, { "rank": 31, "intensity": 2.0816326530612246, "date": "2023-09-02" }, { "rank": 32, "intensity": 2.0510204081632653, "date": "2023-12-15" }, { "rank": 33, "intensity": 2.020408163265306, "date": "2023-08-20" }, { "rank": 34, "intensity": 1.989795918367347, "date": "2023-10-05" }, { "rank": 35, "intensity": 1.9591836734693877, "date": "2023-01-24" }, { "rank": 36, "intensity": 1.9285714285714286, "date": "2023-06-15" }, { "rank": 37, "intensity": 1.8979591836734695, "date": "2023-11-26" }, { "rank": 38, "intensity": 1.8673469387755102, "date": "2023-09-18" }, { "rank": 39, "intensity": 1.836734693877551, "date": "2023-10-24" }, { "rank": 40, "intensity": 1.806122448979592, "date": "2023-04-01" }, { "rank": 41, "intensity": 1.7755102040816326, "date": "2023-05-22" }, { "rank": 42, "intensity": 1.7448979591836735, "date": "2023-08-27" }, { "rank": 43, "intensity": 1.7142857142857144, "date": "2023-08-05" }, { "rank": 44, "intensity": 1.683673469387755, "date": "2023-01-03" }, { "rank": 45, "intensity": 1.653061224489796, "date": "2023-01-26" }, { "rank": 46, "intensity": 1.6224489795918369, "date": "2023-03-18" }, { "rank": 47, "intensity": 1.5918367346938775, "date": "2023-04-22" }, { "rank": 48, "intensity": 1.5612244897959184, "date": "2023-07-16" }, { "rank": 49, "intensity": 1.5306122448979593, "date": "2023-07-03" }, { "rank": 50, "intensity": 1.5, "date": "2023-08-14" } ], "source": "causal_rivers" } }, { "id": "L3_T1_Composite_Trend_00029", "level": 3, "level_name": "Semantic Reasoning", "category": "Composite Trend", "subtask": "Composite Trend", "question": "Among days in channel 595 during 2020 that exhibit the trend pattern 'slow rise, then fluctuating stable, then rapid rise, then rapid fall', identify the top-5 days where the fluctuating stable segment is the most fluctuating. (Output format: a ranked list of dates, e.g., ['YYYY-MM-DD', ...])", "answer": "['2020-09-23', '2020-03-18', '2020-12-15', '2020-03-01', '2020-12-20']", "ground_truth": [ "2020-09-23", "2020-03-18", "2020-12-15", "2020-03-01", "2020-12-20" ], "eval_metric": "set_f1", "channel": "595", "ts_data_path": "ts_data/L3_T1_Composite_Trend_00029.csv", "meta": { "segments": [ { "kind": "rise", "adj": "slow" }, { "kind": "stable", "adj": "fluctuating" }, { "kind": "rise", "adj": "rapid" }, { "kind": "fall", "adj": "rapid" } ], "pattern_phrase": "slow rise, then fluctuating stable, then rapid rise, then rapid fall", "rank_target_idx": 1, "rank_kind": "stable", "rank_adj": "fluctuating", "rank_axis": "std", "rank_direction": "DESC", "rank_extreme": "most fluctuating", "top_segments_meta": [ { "adj": "slow", "kind": "rise", "label": "slow rise", "start_idx": 0, "end_idx": 42 }, { "adj": "fluctuating", "kind": "stable", "label": "fluctuating stable", "start_idx": 42, "end_idx": 73 }, { "adj": "rapid", "kind": "rise", "label": "rapid rise", "start_idx": 73, "end_idx": 85 }, { "adj": "rapid", "kind": "fall", "label": "rapid fall", "start_idx": 85, "end_idx": 98 } ], "points_per_day": 96, "pattern": "slow rise, then fluctuating stable, then rapid rise, then rapid fall", "year": 2020, "top_k": [ "2020-09-23", "2020-03-18", "2020-12-15", "2020-03-01", "2020-12-20" ], "all_injections": [ { "rank": 1, "intensity": 3.0, "date": "2020-09-23" }, { "rank": 2, "intensity": 2.9693877551020407, "date": "2020-03-18" }, { "rank": 3, "intensity": 2.938775510204082, "date": "2020-12-15" }, { "rank": 4, "intensity": 2.9081632653061225, "date": "2020-03-01" }, { "rank": 5, "intensity": 2.877551020408163, "date": "2020-12-20" }, { "rank": 6, "intensity": 2.8469387755102042, "date": "2020-08-31" }, { "rank": 7, "intensity": 2.816326530612245, "date": "2020-06-21" }, { "rank": 8, "intensity": 2.7857142857142856, "date": "2020-05-27" }, { "rank": 9, "intensity": 2.7551020408163267, "date": "2020-06-28" }, { "rank": 10, "intensity": 2.7244897959183674, "date": "2020-07-08" }, { "rank": 11, "intensity": 2.693877551020408, "date": "2020-05-15" }, { "rank": 12, "intensity": 2.663265306122449, "date": "2020-01-26" }, { "rank": 13, "intensity": 2.63265306122449, "date": "2020-02-12" }, { "rank": 14, "intensity": 2.6020408163265305, "date": "2020-05-07" }, { "rank": 15, "intensity": 2.5714285714285716, "date": "2020-04-28" }, { "rank": 16, "intensity": 2.5408163265306123, "date": "2020-08-02" }, { "rank": 17, "intensity": 2.510204081632653, "date": "2020-05-09" }, { "rank": 18, "intensity": 2.479591836734694, "date": "2020-08-04" }, { "rank": 19, "intensity": 2.4489795918367347, "date": "2020-05-29" }, { "rank": 20, "intensity": 2.4183673469387754, "date": "2020-07-21" }, { "rank": 21, "intensity": 2.387755102040816, "date": "2020-10-13" }, { "rank": 22, "intensity": 2.357142857142857, "date": "2020-11-05" }, { "rank": 23, "intensity": 2.326530612244898, "date": "2020-03-14" }, { "rank": 24, "intensity": 2.295918367346939, "date": "2020-02-01" }, { "rank": 25, "intensity": 2.2653061224489797, "date": "2020-06-16" }, { "rank": 26, "intensity": 2.2346938775510203, "date": "2020-06-07" }, { "rank": 27, "intensity": 2.204081632653061, "date": "2020-12-10" }, { "rank": 28, "intensity": 2.173469387755102, "date": "2020-03-06" }, { "rank": 29, "intensity": 2.142857142857143, "date": "2020-01-22" }, { "rank": 30, "intensity": 2.112244897959184, "date": "2020-03-25" }, { "rank": 31, "intensity": 2.0816326530612246, "date": "2020-09-06" }, { "rank": 32, "intensity": 2.0510204081632653, "date": "2020-01-16" }, { "rank": 33, "intensity": 2.020408163265306, "date": "2020-02-27" }, { "rank": 34, "intensity": 1.989795918367347, "date": "2020-01-19" }, { "rank": 35, "intensity": 1.9591836734693877, "date": "2020-11-01" }, { "rank": 36, "intensity": 1.9285714285714286, "date": "2020-08-10" }, { "rank": 37, "intensity": 1.8979591836734695, "date": "2020-06-24" }, { "rank": 38, "intensity": 1.8673469387755102, "date": "2020-10-10" }, { "rank": 39, "intensity": 1.836734693877551, "date": "2020-02-14" }, { "rank": 40, "intensity": 1.806122448979592, "date": "2020-10-15" }, { "rank": 41, "intensity": 1.7755102040816326, "date": "2020-04-24" }, { "rank": 42, "intensity": 1.7448979591836735, "date": "2020-12-24" }, { "rank": 43, "intensity": 1.7142857142857144, "date": "2020-11-20" }, { "rank": 44, "intensity": 1.683673469387755, "date": "2020-06-09" }, { "rank": 45, "intensity": 1.653061224489796, "date": "2020-11-24" }, { "rank": 46, "intensity": 1.6224489795918369, "date": "2020-05-22" }, { "rank": 47, "intensity": 1.5918367346938775, "date": "2020-03-20" }, { "rank": 48, "intensity": 1.5612244897959184, "date": "2020-04-05" }, { "rank": 49, "intensity": 1.5306122448979593, "date": "2020-04-03" }, { "rank": 50, "intensity": 1.5, "date": "2020-09-27" } ], "source": "causal_rivers" } }, { "id": "L3_T1_Composite_Trend_00030", "level": 3, "level_name": "Semantic Reasoning", "category": "Composite Trend", "subtask": "Composite Trend", "question": "Among days in channel 625 during 2023 that exhibit the trend pattern 'slow rise, then fluctuating stable', identify the top-5 days where the slow rise segment is the slowest. (Output format: a ranked list of dates, e.g., ['YYYY-MM-DD', ...])", "answer": "['2023-06-12', '2023-07-13', '2023-10-26', '2023-04-13', '2023-07-15']", "ground_truth": [ "2023-06-12", "2023-07-13", "2023-10-26", "2023-04-13", "2023-07-15" ], "eval_metric": "set_f1", "channel": "625", "ts_data_path": "ts_data/L3_T1_Composite_Trend_00030.csv", "meta": { "segments": [ { "kind": "rise", "adj": "slow" }, { "kind": "stable", "adj": "fluctuating" } ], "pattern_phrase": "slow rise, then fluctuating stable", "rank_target_idx": 0, "rank_kind": "rise", "rank_adj": "slow", "rank_axis": "slope", "rank_direction": "ASC", "rank_extreme": "slowest", "top_segments_meta": [ { "adj": "slow", "kind": "rise", "label": "slow rise", "start_idx": 0, "end_idx": 57 }, { "adj": "fluctuating", "kind": "stable", "label": "fluctuating stable", "start_idx": 57, "end_idx": 98 } ], "points_per_day": 96, "pattern": "slow rise, then fluctuating stable", "year": 2023, "top_k": [ "2023-06-12", "2023-07-13", "2023-10-26", "2023-04-13", "2023-07-15" ], "all_injections": [ { "rank": 1, "intensity": 1.5, "date": "2023-06-12" }, { "rank": 2, "intensity": 1.530612244897959, "date": "2023-07-13" }, { "rank": 3, "intensity": 1.5612244897959184, "date": "2023-10-26" }, { "rank": 4, "intensity": 1.5918367346938775, "date": "2023-04-13" }, { "rank": 5, "intensity": 1.6224489795918366, "date": "2023-07-15" }, { "rank": 6, "intensity": 1.653061224489796, "date": "2023-03-28" }, { "rank": 7, "intensity": 1.683673469387755, "date": "2023-10-28" }, { "rank": 8, "intensity": 1.7142857142857142, "date": "2023-03-23" }, { "rank": 9, "intensity": 1.7448979591836735, "date": "2023-11-13" }, { "rank": 10, "intensity": 1.7755102040816326, "date": "2023-09-24" }, { "rank": 11, "intensity": 1.806122448979592, "date": "2023-07-22" }, { "rank": 12, "intensity": 1.836734693877551, "date": "2023-06-02" }, { "rank": 13, "intensity": 1.8673469387755102, "date": "2023-04-11" }, { "rank": 14, "intensity": 1.8979591836734695, "date": "2023-01-13" }, { "rank": 15, "intensity": 1.9285714285714286, "date": "2023-02-14" }, { "rank": 16, "intensity": 1.9591836734693877, "date": "2023-11-28" }, { "rank": 17, "intensity": 1.989795918367347, "date": "2023-01-01" }, { "rank": 18, "intensity": 2.020408163265306, "date": "2023-04-07" }, { "rank": 19, "intensity": 2.0510204081632653, "date": "2023-08-27" }, { "rank": 20, "intensity": 2.0816326530612246, "date": "2023-09-11" }, { "rank": 21, "intensity": 2.112244897959184, "date": "2023-01-27" }, { "rank": 22, "intensity": 2.142857142857143, "date": "2023-01-15" }, { "rank": 23, "intensity": 2.173469387755102, "date": "2023-11-04" }, { "rank": 24, "intensity": 2.204081632653061, "date": "2023-05-13" }, { "rank": 25, "intensity": 2.2346938775510203, "date": "2023-06-24" }, { "rank": 26, "intensity": 2.2653061224489797, "date": "2023-01-17" }, { "rank": 27, "intensity": 2.295918367346939, "date": "2023-07-20" }, { "rank": 28, "intensity": 2.326530612244898, "date": "2023-07-02" }, { "rank": 29, "intensity": 2.357142857142857, "date": "2023-05-18" }, { "rank": 30, "intensity": 2.387755102040816, "date": "2023-09-13" }, { "rank": 31, "intensity": 2.4183673469387754, "date": "2023-12-02" }, { "rank": 32, "intensity": 2.4489795918367347, "date": "2023-02-03" }, { "rank": 33, "intensity": 2.479591836734694, "date": "2023-09-17" }, { "rank": 34, "intensity": 2.510204081632653, "date": "2023-08-13" }, { "rank": 35, "intensity": 2.5408163265306123, "date": "2023-03-19" }, { "rank": 36, "intensity": 2.571428571428571, "date": "2023-12-13" }, { "rank": 37, "intensity": 2.6020408163265305, "date": "2023-05-02" }, { "rank": 38, "intensity": 2.63265306122449, "date": "2023-05-22" }, { "rank": 39, "intensity": 2.663265306122449, "date": "2023-08-09" }, { "rank": 40, "intensity": 2.693877551020408, "date": "2023-04-17" }, { "rank": 41, "intensity": 2.7244897959183674, "date": "2023-06-04" }, { "rank": 42, "intensity": 2.7551020408163263, "date": "2023-06-10" }, { "rank": 43, "intensity": 2.7857142857142856, "date": "2023-08-04" }, { "rank": 44, "intensity": 2.816326530612245, "date": "2023-03-14" }, { "rank": 45, "intensity": 2.8469387755102042, "date": "2023-10-20" }, { "rank": 46, "intensity": 2.877551020408163, "date": "2023-08-06" }, { "rank": 47, "intensity": 2.9081632653061225, "date": "2023-04-02" }, { "rank": 48, "intensity": 2.9387755102040813, "date": "2023-08-30" }, { "rank": 49, "intensity": 2.9693877551020407, "date": "2023-11-08" }, { "rank": 50, "intensity": 3.0, "date": "2023-05-28" } ], "source": "causal_rivers" } }, { "id": "L3_T1_Composite_Trend_00031", "level": 3, "level_name": "Semantic Reasoning", "category": "Composite Trend", "subtask": "Composite Trend", "question": "Among days in channel 626 during 2021 that exhibit the trend pattern 'rapid fall, then rapid rise', identify the top-5 days where the rapid rise segment is the fastest. (Output format: a ranked list of dates, e.g., ['YYYY-MM-DD', ...])", "answer": "['2021-04-17', '2021-10-12', '2021-10-09', '2021-12-14', '2021-09-12']", "ground_truth": [ "2021-04-17", "2021-10-12", "2021-10-09", "2021-12-14", "2021-09-12" ], "eval_metric": "set_f1", "channel": "626", "ts_data_path": "ts_data/L3_T1_Composite_Trend_00031.csv", "meta": { "segments": [ { "kind": "fall", "adj": "rapid" }, { "kind": "rise", "adj": "rapid" } ], "pattern_phrase": "rapid fall, then rapid rise", "rank_target_idx": 1, "rank_kind": "rise", "rank_adj": "rapid", "rank_axis": "slope", "rank_direction": "DESC", "rank_extreme": "fastest", "top_segments_meta": [ { "adj": "rapid", "kind": "fall", "label": "rapid fall", "start_idx": 0, "end_idx": 48 }, { "adj": "rapid", "kind": "rise", "label": "rapid rise", "start_idx": 48, "end_idx": 98 } ], "points_per_day": 96, "pattern": "rapid fall, then rapid rise", "year": 2021, "top_k": [ "2021-04-17", "2021-10-12", "2021-10-09", "2021-12-14", "2021-09-12" ], "all_injections": [ { "rank": 1, "intensity": 3.0, "date": "2021-04-17" }, { "rank": 2, "intensity": 2.9693877551020407, "date": "2021-10-12" }, { "rank": 3, "intensity": 2.938775510204082, "date": "2021-10-09" }, { "rank": 4, "intensity": 2.9081632653061225, "date": "2021-12-14" }, { "rank": 5, "intensity": 2.877551020408163, "date": "2021-09-12" }, { "rank": 6, "intensity": 2.8469387755102042, "date": "2021-08-10" }, { "rank": 7, "intensity": 2.816326530612245, "date": "2021-01-24" }, { "rank": 8, "intensity": 2.7857142857142856, "date": "2021-02-17" }, { "rank": 9, "intensity": 2.7551020408163267, "date": "2021-05-23" }, { "rank": 10, "intensity": 2.7244897959183674, "date": "2021-09-17" }, { "rank": 11, "intensity": 2.693877551020408, "date": "2021-12-17" }, { "rank": 12, "intensity": 2.663265306122449, "date": "2021-09-09" }, { "rank": 13, "intensity": 2.63265306122449, "date": "2021-07-20" }, { "rank": 14, "intensity": 2.6020408163265305, "date": "2021-03-26" }, { "rank": 15, "intensity": 2.5714285714285716, "date": "2021-06-26" }, { "rank": 16, "intensity": 2.5408163265306123, "date": "2021-08-22" }, { "rank": 17, "intensity": 2.510204081632653, "date": "2021-12-08" }, { "rank": 18, "intensity": 2.479591836734694, "date": "2021-04-22" }, { "rank": 19, "intensity": 2.4489795918367347, "date": "2021-07-11" }, { "rank": 20, "intensity": 2.4183673469387754, "date": "2021-03-24" }, { "rank": 21, "intensity": 2.387755102040816, "date": "2021-10-30" }, { "rank": 22, "intensity": 2.357142857142857, "date": "2021-06-01" }, { "rank": 23, "intensity": 2.326530612244898, "date": "2021-05-07" }, { "rank": 24, "intensity": 2.295918367346939, "date": "2021-12-10" }, { "rank": 25, "intensity": 2.2653061224489797, "date": "2021-05-01" }, { "rank": 26, "intensity": 2.2346938775510203, "date": "2021-07-25" }, { "rank": 27, "intensity": 2.204081632653061, "date": "2021-09-23" }, { "rank": 28, "intensity": 2.173469387755102, "date": "2021-09-26" }, { "rank": 29, "intensity": 2.142857142857143, "date": "2021-10-06" }, { "rank": 30, "intensity": 2.112244897959184, "date": "2021-11-05" }, { "rank": 31, "intensity": 2.0816326530612246, "date": "2021-04-06" }, { "rank": 32, "intensity": 2.0510204081632653, "date": "2021-01-20" }, { "rank": 33, "intensity": 2.020408163265306, "date": "2021-11-16" }, { "rank": 34, "intensity": 1.989795918367347, "date": "2021-02-23" }, { "rank": 35, "intensity": 1.9591836734693877, "date": "2021-01-07" }, { "rank": 36, "intensity": 1.9285714285714286, "date": "2021-07-28" }, { "rank": 37, "intensity": 1.8979591836734695, "date": "2021-03-12" }, { "rank": 38, "intensity": 1.8673469387755102, "date": "2021-04-12" }, { "rank": 39, "intensity": 1.836734693877551, "date": "2021-07-02" }, { "rank": 40, "intensity": 1.806122448979592, "date": "2021-09-28" }, { "rank": 41, "intensity": 1.7755102040816326, "date": "2021-11-24" }, { "rank": 42, "intensity": 1.7448979591836735, "date": "2021-09-04" }, { "rank": 43, "intensity": 1.7142857142857144, "date": "2021-10-02" }, { "rank": 44, "intensity": 1.683673469387755, "date": "2021-11-09" }, { "rank": 45, "intensity": 1.653061224489796, "date": "2021-11-22" }, { "rank": 46, "intensity": 1.6224489795918369, "date": "2021-12-23" }, { "rank": 47, "intensity": 1.5918367346938775, "date": "2021-03-31" }, { "rank": 48, "intensity": 1.5612244897959184, "date": "2021-01-04" }, { "rank": 49, "intensity": 1.5306122448979593, "date": "2021-02-06" }, { "rank": 50, "intensity": 1.5, "date": "2021-10-22" } ], "source": "causal_rivers" } }, { "id": "L3_T1_Composite_Trend_00032", "level": 3, "level_name": "Semantic Reasoning", "category": "Composite Trend", "subtask": "Composite Trend", "question": "Among days in channel 627 during 2023 that exhibit the trend pattern 'fall, then rapid rise, then fluctuating stable', identify the top-5 days where the fluctuating stable segment is the most fluctuating. (Output format: a ranked list of dates, e.g., ['YYYY-MM-DD', ...])", "answer": "['2023-06-14', '2023-10-16', '2023-05-05', '2023-10-24', '2023-06-04']", "ground_truth": [ "2023-06-14", "2023-10-16", "2023-05-05", "2023-10-24", "2023-06-04" ], "eval_metric": "set_f1", "channel": "627", "ts_data_path": "ts_data/L3_T1_Composite_Trend_00032.csv", "meta": { "segments": [ { "kind": "fall", "adj": "none" }, { "kind": "rise", "adj": "rapid" }, { "kind": "stable", "adj": "fluctuating" } ], "pattern_phrase": "fall, then rapid rise, then fluctuating stable", "rank_target_idx": 2, "rank_kind": "stable", "rank_adj": "fluctuating", "rank_axis": "std", "rank_direction": "DESC", "rank_extreme": "most fluctuating", "top_segments_meta": [ { "adj": "none", "kind": "fall", "label": "none fall", "start_idx": 0, "end_idx": 37 }, { "adj": "rapid", "kind": "rise", "label": "rapid rise", "start_idx": 37, "end_idx": 56 }, { "adj": "fluctuating", "kind": "stable", "label": "fluctuating stable", "start_idx": 56, "end_idx": 98 } ], "points_per_day": 96, "pattern": "fall, then rapid rise, then fluctuating stable", "year": 2023, "top_k": [ "2023-06-14", "2023-10-16", "2023-05-05", "2023-10-24", "2023-06-04" ], "all_injections": [ { "rank": 1, "intensity": 3.0, "date": "2023-06-14" }, { "rank": 2, "intensity": 2.9693877551020407, "date": "2023-10-16" }, { "rank": 3, "intensity": 2.938775510204082, "date": "2023-05-05" }, { "rank": 4, "intensity": 2.9081632653061225, "date": "2023-10-24" }, { "rank": 5, "intensity": 2.877551020408163, "date": "2023-06-04" }, { "rank": 6, "intensity": 2.8469387755102042, "date": "2023-08-08" }, { "rank": 7, "intensity": 2.816326530612245, "date": "2023-06-21" }, { "rank": 8, "intensity": 2.7857142857142856, "date": "2023-05-20" }, { "rank": 9, "intensity": 2.7551020408163267, "date": "2023-03-24" }, { "rank": 10, "intensity": 2.7244897959183674, "date": "2023-02-19" }, { "rank": 11, "intensity": 2.693877551020408, "date": "2023-02-04" }, { "rank": 12, "intensity": 2.663265306122449, "date": "2023-02-27" }, { "rank": 13, "intensity": 2.63265306122449, "date": "2023-11-21" }, { "rank": 14, "intensity": 2.6020408163265305, "date": "2023-12-16" }, { "rank": 15, "intensity": 2.5714285714285716, "date": "2023-08-18" }, { "rank": 16, "intensity": 2.5408163265306123, "date": "2023-03-28" }, { "rank": 17, "intensity": 2.510204081632653, "date": "2023-04-28" }, { "rank": 18, "intensity": 2.479591836734694, "date": "2023-07-01" }, { "rank": 19, "intensity": 2.4489795918367347, "date": "2023-11-09" }, { "rank": 20, "intensity": 2.4183673469387754, "date": "2023-11-14" }, { "rank": 21, "intensity": 2.387755102040816, "date": "2023-07-26" }, { "rank": 22, "intensity": 2.357142857142857, "date": "2023-10-30" }, { "rank": 23, "intensity": 2.326530612244898, "date": "2023-11-17" }, { "rank": 24, "intensity": 2.295918367346939, "date": "2023-02-14" }, { "rank": 25, "intensity": 2.2653061224489797, "date": "2023-06-17" }, { "rank": 26, "intensity": 2.2346938775510203, "date": "2023-09-05" }, { "rank": 27, "intensity": 2.204081632653061, "date": "2023-01-25" }, { "rank": 28, "intensity": 2.173469387755102, "date": "2023-07-16" }, { "rank": 29, "intensity": 2.142857142857143, "date": "2023-02-21" }, { "rank": 30, "intensity": 2.112244897959184, "date": "2023-12-07" }, { "rank": 31, "intensity": 2.0816326530612246, "date": "2023-09-01" }, { "rank": 32, "intensity": 2.0510204081632653, "date": "2023-04-21" }, { "rank": 33, "intensity": 2.020408163265306, "date": "2023-01-08" }, { "rank": 34, "intensity": 1.989795918367347, "date": "2023-08-12" }, { "rank": 35, "intensity": 1.9591836734693877, "date": "2023-08-27" }, { "rank": 36, "intensity": 1.9285714285714286, "date": "2023-02-12" }, { "rank": 37, "intensity": 1.8979591836734695, "date": "2023-05-29" }, { "rank": 38, "intensity": 1.8673469387755102, "date": "2023-03-16" }, { "rank": 39, "intensity": 1.836734693877551, "date": "2023-01-06" }, { "rank": 40, "intensity": 1.806122448979592, "date": "2023-07-10" }, { "rank": 41, "intensity": 1.7755102040816326, "date": "2023-01-27" }, { "rank": 42, "intensity": 1.7448979591836735, "date": "2023-09-21" }, { "rank": 43, "intensity": 1.7142857142857144, "date": "2023-02-01" }, { "rank": 44, "intensity": 1.683673469387755, "date": "2023-05-13" }, { "rank": 45, "intensity": 1.653061224489796, "date": "2023-09-09" }, { "rank": 46, "intensity": 1.6224489795918369, "date": "2023-12-21" }, { "rank": 47, "intensity": 1.5918367346938775, "date": "2023-10-27" }, { "rank": 48, "intensity": 1.5612244897959184, "date": "2023-02-25" }, { "rank": 49, "intensity": 1.5306122448979593, "date": "2023-05-07" }, { "rank": 50, "intensity": 1.5, "date": "2023-12-09" } ], "source": "causal_rivers" } }, { "id": "L3_T1_Composite_Trend_00033", "level": 3, "level_name": "Semantic Reasoning", "category": "Composite Trend", "subtask": "Composite Trend", "question": "Among days in channel 647 during 2019 that exhibit the trend pattern 'rapid fall, then rapid rise', identify the top-5 days where the rapid fall segment is the fastest. (Output format: a ranked list of dates, e.g., ['YYYY-MM-DD', ...])", "answer": "['2019-11-27', '2019-07-16', '2019-06-01', '2019-06-06', '2019-07-30']", "ground_truth": [ "2019-11-27", "2019-07-16", "2019-06-01", "2019-06-06", "2019-07-30" ], "eval_metric": "set_f1", "channel": "647", "ts_data_path": "ts_data/L3_T1_Composite_Trend_00033.csv", "meta": { "segments": [ { "kind": "fall", "adj": "rapid" }, { "kind": "rise", "adj": "rapid" } ], "pattern_phrase": "rapid fall, then rapid rise", "rank_target_idx": 0, "rank_kind": "fall", "rank_adj": "rapid", "rank_axis": "slope", "rank_direction": "DESC", "rank_extreme": "fastest", "top_segments_meta": [ { "adj": "rapid", "kind": "fall", "label": "rapid fall", "start_idx": 0, "end_idx": 46 }, { "adj": "rapid", "kind": "rise", "label": "rapid rise", "start_idx": 46, "end_idx": 98 } ], "points_per_day": 96, "pattern": "rapid fall, then rapid rise", "year": 2019, "top_k": [ "2019-11-27", "2019-07-16", "2019-06-01", "2019-06-06", "2019-07-30" ], "all_injections": [ { "rank": 1, "intensity": 3.0, "date": "2019-11-27" }, { "rank": 2, "intensity": 2.9693877551020407, "date": "2019-07-16" }, { "rank": 3, "intensity": 2.938775510204082, "date": "2019-06-01" }, { "rank": 4, "intensity": 2.9081632653061225, "date": "2019-06-06" }, { "rank": 5, "intensity": 2.877551020408163, "date": "2019-07-30" }, { "rank": 6, "intensity": 2.8469387755102042, "date": "2019-03-25" }, { "rank": 7, "intensity": 2.816326530612245, "date": "2019-01-24" }, { "rank": 8, "intensity": 2.7857142857142856, "date": "2019-06-22" }, { "rank": 9, "intensity": 2.7551020408163267, "date": "2019-11-07" }, { "rank": 10, "intensity": 2.7244897959183674, "date": "2019-12-05" }, { "rank": 11, "intensity": 2.693877551020408, "date": "2019-03-11" }, { "rank": 12, "intensity": 2.663265306122449, "date": "2019-01-22" }, { "rank": 13, "intensity": 2.63265306122449, "date": "2019-01-12" }, { "rank": 14, "intensity": 2.6020408163265305, "date": "2019-10-11" }, { "rank": 15, "intensity": 2.5714285714285716, "date": "2019-04-01" }, { "rank": 16, "intensity": 2.5408163265306123, "date": "2019-10-18" }, { "rank": 17, "intensity": 2.510204081632653, "date": "2019-09-11" }, { "rank": 18, "intensity": 2.479591836734694, "date": "2019-07-26" }, { "rank": 19, "intensity": 2.4489795918367347, "date": "2019-10-16" }, { "rank": 20, "intensity": 2.4183673469387754, "date": "2019-04-26" }, { "rank": 21, "intensity": 2.387755102040816, "date": "2019-02-26" }, { "rank": 22, "intensity": 2.357142857142857, "date": "2019-11-21" }, { "rank": 23, "intensity": 2.326530612244898, "date": "2019-10-29" }, { "rank": 24, "intensity": 2.295918367346939, "date": "2019-01-15" }, { "rank": 25, "intensity": 2.2653061224489797, "date": "2019-04-16" }, { "rank": 26, "intensity": 2.2346938775510203, "date": "2019-04-05" }, { "rank": 27, "intensity": 2.204081632653061, "date": "2019-03-21" }, { "rank": 28, "intensity": 2.173469387755102, "date": "2019-01-09" }, { "rank": 29, "intensity": 2.142857142857143, "date": "2019-07-19" }, { "rank": 30, "intensity": 2.112244897959184, "date": "2019-04-19" }, { "rank": 31, "intensity": 2.0816326530612246, "date": "2019-03-14" }, { "rank": 32, "intensity": 2.0510204081632653, "date": "2019-09-02" }, { "rank": 33, "intensity": 2.020408163265306, "date": "2019-09-21" }, { "rank": 34, "intensity": 1.989795918367347, "date": "2019-07-28" }, { "rank": 35, "intensity": 1.9591836734693877, "date": "2019-09-28" }, { "rank": 36, "intensity": 1.9285714285714286, "date": "2019-10-21" }, { "rank": 37, "intensity": 1.8979591836734695, "date": "2019-10-06" }, { "rank": 38, "intensity": 1.8673469387755102, "date": "2019-03-29" }, { "rank": 39, "intensity": 1.836734693877551, "date": "2019-03-08" }, { "rank": 40, "intensity": 1.806122448979592, "date": "2019-02-04" }, { "rank": 41, "intensity": 1.7755102040816326, "date": "2019-11-25" }, { "rank": 42, "intensity": 1.7448979591836735, "date": "2019-09-30" }, { "rank": 43, "intensity": 1.7142857142857144, "date": "2019-06-29" }, { "rank": 44, "intensity": 1.683673469387755, "date": "2019-05-04" }, { "rank": 45, "intensity": 1.653061224489796, "date": "2019-01-29" }, { "rank": 46, "intensity": 1.6224489795918369, "date": "2019-06-19" }, { "rank": 47, "intensity": 1.5918367346938775, "date": "2019-06-08" }, { "rank": 48, "intensity": 1.5612244897959184, "date": "2019-08-13" }, { "rank": 49, "intensity": 1.5306122448979593, "date": "2019-07-14" }, { "rank": 50, "intensity": 1.5, "date": "2019-03-04" } ], "source": "causal_rivers" } }, { "id": "L3_T1_Composite_Trend_00034", "level": 3, "level_name": "Semantic Reasoning", "category": "Composite Trend", "subtask": "Composite Trend", "question": "Among days in channel 680 during 2023 that exhibit the trend pattern 'rise, then slow fall, then rapid rise, then steady stable', identify the top-5 days where the slow fall segment is the slowest. (Output format: a ranked list of dates, e.g., ['YYYY-MM-DD', ...])", "answer": "['2023-10-31', '2023-07-08', '2023-12-15', '2023-11-22', '2023-11-26']", "ground_truth": [ "2023-10-31", "2023-07-08", "2023-12-15", "2023-11-22", "2023-11-26" ], "eval_metric": "set_f1", "channel": "680", "ts_data_path": "ts_data/L3_T1_Composite_Trend_00034.csv", "meta": { "segments": [ { "kind": "rise", "adj": "none" }, { "kind": "fall", "adj": "slow" }, { "kind": "rise", "adj": "rapid" }, { "kind": "stable", "adj": "steady" } ], "pattern_phrase": "rise, then slow fall, then rapid rise, then steady stable", "rank_target_idx": 1, "rank_kind": "fall", "rank_adj": "slow", "rank_axis": "slope", "rank_direction": "ASC", "rank_extreme": "slowest", "top_segments_meta": [ { "adj": "none", "kind": "rise", "label": "none rise", "start_idx": 0, "end_idx": 20 }, { "adj": "slow", "kind": "fall", "label": "slow fall", "start_idx": 20, "end_idx": 51 }, { "adj": "rapid", "kind": "rise", "label": "rapid rise", "start_idx": 51, "end_idx": 60 }, { "adj": "steady", "kind": "stable", "label": "steady stable", "start_idx": 60, "end_idx": 98 } ], "points_per_day": 96, "pattern": "rise, then slow fall, then rapid rise, then steady stable", "year": 2023, "top_k": [ "2023-10-31", "2023-07-08", "2023-12-15", "2023-11-22", "2023-11-26" ], "all_injections": [ { "rank": 1, "intensity": 1.5, "date": "2023-10-31" }, { "rank": 2, "intensity": 1.530612244897959, "date": "2023-07-08" }, { "rank": 3, "intensity": 1.5612244897959184, "date": "2023-12-15" }, { "rank": 4, "intensity": 1.5918367346938775, "date": "2023-11-22" }, { "rank": 5, "intensity": 1.6224489795918366, "date": "2023-11-26" }, { "rank": 6, "intensity": 1.653061224489796, "date": "2023-08-14" }, { "rank": 7, "intensity": 1.683673469387755, "date": "2023-10-14" }, { "rank": 8, "intensity": 1.7142857142857142, "date": "2023-11-28" }, { "rank": 9, "intensity": 1.7448979591836735, "date": "2023-06-24" }, { "rank": 10, "intensity": 1.7755102040816326, "date": "2023-06-17" }, { "rank": 11, "intensity": 1.806122448979592, "date": "2023-06-22" }, { "rank": 12, "intensity": 1.836734693877551, "date": "2023-02-10" }, { "rank": 13, "intensity": 1.8673469387755102, "date": "2023-07-31" }, { "rank": 14, "intensity": 1.8979591836734695, "date": "2023-01-27" }, { "rank": 15, "intensity": 1.9285714285714286, "date": "2023-02-12" }, { "rank": 16, "intensity": 1.9591836734693877, "date": "2023-12-24" }, { "rank": 17, "intensity": 1.989795918367347, "date": "2023-01-03" }, { "rank": 18, "intensity": 2.020408163265306, "date": "2023-07-05" }, { "rank": 19, "intensity": 2.0510204081632653, "date": "2023-12-06" }, { "rank": 20, "intensity": 2.0816326530612246, "date": "2023-08-18" }, { "rank": 21, "intensity": 2.112244897959184, "date": "2023-04-27" }, { "rank": 22, "intensity": 2.142857142857143, "date": "2023-04-07" }, { "rank": 23, "intensity": 2.173469387755102, "date": "2023-09-04" }, { "rank": 24, "intensity": 2.204081632653061, "date": "2023-09-21" }, { "rank": 25, "intensity": 2.2346938775510203, "date": "2023-11-15" }, { "rank": 26, "intensity": 2.2653061224489797, "date": "2023-09-06" }, { "rank": 27, "intensity": 2.295918367346939, "date": "2023-12-27" }, { "rank": 28, "intensity": 2.326530612244898, "date": "2023-01-07" }, { "rank": 29, "intensity": 2.357142857142857, "date": "2023-05-28" }, { "rank": 30, "intensity": 2.387755102040816, "date": "2023-06-08" }, { "rank": 31, "intensity": 2.4183673469387754, "date": "2023-04-05" }, { "rank": 32, "intensity": 2.4489795918367347, "date": "2023-04-02" }, { "rank": 33, "intensity": 2.479591836734694, "date": "2023-02-06" }, { "rank": 34, "intensity": 2.510204081632653, "date": "2023-06-27" }, { "rank": 35, "intensity": 2.5408163265306123, "date": "2023-02-01" }, { "rank": 36, "intensity": 2.571428571428571, "date": "2023-08-04" }, { "rank": 37, "intensity": 2.6020408163265305, "date": "2023-04-20" }, { "rank": 38, "intensity": 2.63265306122449, "date": "2023-10-10" }, { "rank": 39, "intensity": 2.663265306122449, "date": "2023-01-19" }, { "rank": 40, "intensity": 2.693877551020408, "date": "2023-02-17" }, { "rank": 41, "intensity": 2.7244897959183674, "date": "2023-03-06" }, { "rank": 42, "intensity": 2.7551020408163263, "date": "2023-03-25" }, { "rank": 43, "intensity": 2.7857142857142856, "date": "2023-10-01" }, { "rank": 44, "intensity": 2.816326530612245, "date": "2023-03-14" }, { "rank": 45, "intensity": 2.8469387755102042, "date": "2023-08-23" }, { "rank": 46, "intensity": 2.877551020408163, "date": "2023-04-16" }, { "rank": 47, "intensity": 2.9081632653061225, "date": "2023-07-20" }, { "rank": 48, "intensity": 2.9387755102040813, "date": "2023-05-31" }, { "rank": 49, "intensity": 2.9693877551020407, "date": "2023-08-07" }, { "rank": 50, "intensity": 3.0, "date": "2023-01-01" } ], "source": "causal_rivers" } }, { "id": "L3_T1_Composite_Trend_00035", "level": 3, "level_name": "Semantic Reasoning", "category": "Composite Trend", "subtask": "Composite Trend", "question": "Among days in channel 683 during 2022 that exhibit the trend pattern 'slow rise, then rise, then rapid fall', identify the top-5 days where the slow rise segment is the slowest. (Output format: a ranked list of dates, e.g., ['YYYY-MM-DD', ...])", "answer": "['2022-12-17', '2022-12-25', '2022-02-15', '2022-07-31', '2022-02-03']", "ground_truth": [ "2022-12-17", "2022-12-25", "2022-02-15", "2022-07-31", "2022-02-03" ], "eval_metric": "set_f1", "channel": "683", "ts_data_path": "ts_data/L3_T1_Composite_Trend_00035.csv", "meta": { "segments": [ { "kind": "rise", "adj": "slow" }, { "kind": "rise", "adj": "none" }, { "kind": "fall", "adj": "rapid" } ], "pattern_phrase": "slow rise, then rise, then rapid fall", "rank_target_idx": 0, "rank_kind": "rise", "rank_adj": "slow", "rank_axis": "slope", "rank_direction": "ASC", "rank_extreme": "slowest", "top_segments_meta": [ { "adj": "slow", "kind": "rise", "label": "slow rise", "start_idx": 0, "end_idx": 56 }, { "adj": "none", "kind": "rise", "label": "none rise", "start_idx": 56, "end_idx": 83 }, { "adj": "rapid", "kind": "fall", "label": "rapid fall", "start_idx": 83, "end_idx": 98 } ], "points_per_day": 96, "pattern": "slow rise, then rise, then rapid fall", "year": 2022, "top_k": [ "2022-12-17", "2022-12-25", "2022-02-15", "2022-07-31", "2022-02-03" ], "all_injections": [ { "rank": 1, "intensity": 1.5, "date": "2022-12-17" }, { "rank": 2, "intensity": 1.530612244897959, "date": "2022-12-25" }, { "rank": 3, "intensity": 1.5612244897959184, "date": "2022-02-15" }, { "rank": 4, "intensity": 1.5918367346938775, "date": "2022-07-31" }, { "rank": 5, "intensity": 1.6224489795918366, "date": "2022-02-03" }, { "rank": 6, "intensity": 1.653061224489796, "date": "2022-08-06" }, { "rank": 7, "intensity": 1.683673469387755, "date": "2022-09-14" }, { "rank": 8, "intensity": 1.7142857142857142, "date": "2022-11-23" }, { "rank": 9, "intensity": 1.7448979591836735, "date": "2022-12-10" }, { "rank": 10, "intensity": 1.7755102040816326, "date": "2022-01-28" }, { "rank": 11, "intensity": 1.806122448979592, "date": "2022-11-08" }, { "rank": 12, "intensity": 1.836734693877551, "date": "2022-05-24" }, { "rank": 13, "intensity": 1.8673469387755102, "date": "2022-08-16" }, { "rank": 14, "intensity": 1.8979591836734695, "date": "2022-09-20" }, { "rank": 15, "intensity": 1.9285714285714286, "date": "2022-10-29" }, { "rank": 16, "intensity": 1.9591836734693877, "date": "2022-07-16" }, { "rank": 17, "intensity": 1.989795918367347, "date": "2022-04-08" }, { "rank": 18, "intensity": 2.020408163265306, "date": "2022-04-21" }, { "rank": 19, "intensity": 2.0510204081632653, "date": "2022-09-02" }, { "rank": 20, "intensity": 2.0816326530612246, "date": "2022-10-07" }, { "rank": 21, "intensity": 2.112244897959184, "date": "2022-05-20" }, { "rank": 22, "intensity": 2.142857142857143, "date": "2022-03-16" }, { "rank": 23, "intensity": 2.173469387755102, "date": "2022-10-01" }, { "rank": 24, "intensity": 2.204081632653061, "date": "2022-06-17" }, { "rank": 25, "intensity": 2.2346938775510203, "date": "2022-02-20" }, { "rank": 26, "intensity": 2.2653061224489797, "date": "2022-03-30" }, { "rank": 27, "intensity": 2.295918367346939, "date": "2022-06-25" }, { "rank": 28, "intensity": 2.326530612244898, "date": "2022-10-19" }, { "rank": 29, "intensity": 2.357142857142857, "date": "2022-10-16" }, { "rank": 30, "intensity": 2.387755102040816, "date": "2022-06-05" }, { "rank": 31, "intensity": 2.4183673469387754, "date": "2022-09-11" }, { "rank": 32, "intensity": 2.4489795918367347, "date": "2022-12-06" }, { "rank": 33, "intensity": 2.479591836734694, "date": "2022-06-03" }, { "rank": 34, "intensity": 2.510204081632653, "date": "2022-11-17" }, { "rank": 35, "intensity": 2.5408163265306123, "date": "2022-01-24" }, { "rank": 36, "intensity": 2.571428571428571, "date": "2022-01-31" }, { "rank": 37, "intensity": 2.6020408163265305, "date": "2022-05-05" }, { "rank": 38, "intensity": 2.63265306122449, "date": "2022-05-26" }, { "rank": 39, "intensity": 2.663265306122449, "date": "2022-07-19" }, { "rank": 40, "intensity": 2.693877551020408, "date": "2022-11-19" }, { "rank": 41, "intensity": 2.7244897959183674, "date": "2022-01-26" }, { "rank": 42, "intensity": 2.7551020408163263, "date": "2022-05-02" }, { "rank": 43, "intensity": 2.7857142857142856, "date": "2022-08-14" }, { "rank": 44, "intensity": 2.816326530612245, "date": "2022-01-05" }, { "rank": 45, "intensity": 2.8469387755102042, "date": "2022-07-21" }, { "rank": 46, "intensity": 2.877551020408163, "date": "2022-01-16" }, { "rank": 47, "intensity": 2.9081632653061225, "date": "2022-03-12" }, { "rank": 48, "intensity": 2.9387755102040813, "date": "2022-12-28" }, { "rank": 49, "intensity": 2.9693877551020407, "date": "2022-04-13" }, { "rank": 50, "intensity": 3.0, "date": "2022-09-06" } ], "source": "causal_rivers" } }, { "id": "L3_T1_Composite_Trend_00036", "level": 3, "level_name": "Semantic Reasoning", "category": "Composite Trend", "subtask": "Composite Trend", "question": "Among days in channel 727 during 2023 that exhibit the trend pattern 'fluctuating stable, then steady stable', identify the top-5 days where the fluctuating stable segment is the most fluctuating. (Output format: a ranked list of dates, e.g., ['YYYY-MM-DD', ...])", "answer": "['2023-04-17', '2023-02-05', '2023-09-21', '2023-02-10', '2023-10-08']", "ground_truth": [ "2023-04-17", "2023-02-05", "2023-09-21", "2023-02-10", "2023-10-08" ], "eval_metric": "set_f1", "channel": "727", "ts_data_path": "ts_data/L3_T1_Composite_Trend_00036.csv", "meta": { "segments": [ { "kind": "stable", "adj": "fluctuating" }, { "kind": "stable", "adj": "steady" } ], "pattern_phrase": "fluctuating stable, then steady stable", "rank_target_idx": 0, "rank_kind": "stable", "rank_adj": "fluctuating", "rank_axis": "std", "rank_direction": "DESC", "rank_extreme": "most fluctuating", "top_segments_meta": [ { "adj": "fluctuating", "kind": "stable", "label": "fluctuating stable", "start_idx": 0, "end_idx": 36 }, { "adj": "steady", "kind": "stable", "label": "steady stable", "start_idx": 36, "end_idx": 98 } ], "points_per_day": 96, "pattern": "fluctuating stable, then steady stable", "year": 2023, "top_k": [ "2023-04-17", "2023-02-05", "2023-09-21", "2023-02-10", "2023-10-08" ], "all_injections": [ { "rank": 1, "intensity": 3.0, "date": "2023-04-17" }, { "rank": 2, "intensity": 2.9693877551020407, "date": "2023-02-05" }, { "rank": 3, "intensity": 2.938775510204082, "date": "2023-09-21" }, { "rank": 4, "intensity": 2.9081632653061225, "date": "2023-02-10" }, { "rank": 5, "intensity": 2.877551020408163, "date": "2023-10-08" }, { "rank": 6, "intensity": 2.8469387755102042, "date": "2023-03-31" }, { "rank": 7, "intensity": 2.816326530612245, "date": "2023-11-14" }, { "rank": 8, "intensity": 2.7857142857142856, "date": "2023-06-16" }, { "rank": 9, "intensity": 2.7551020408163267, "date": "2023-05-07" }, { "rank": 10, "intensity": 2.7244897959183674, "date": "2023-07-27" }, { "rank": 11, "intensity": 2.693877551020408, "date": "2023-09-13" }, { "rank": 12, "intensity": 2.663265306122449, "date": "2023-03-09" }, { "rank": 13, "intensity": 2.63265306122449, "date": "2023-08-04" }, { "rank": 14, "intensity": 2.6020408163265305, "date": "2023-03-27" }, { "rank": 15, "intensity": 2.5714285714285716, "date": "2023-08-09" }, { "rank": 16, "intensity": 2.5408163265306123, "date": "2023-09-27" }, { "rank": 17, "intensity": 2.510204081632653, "date": "2023-09-01" }, { "rank": 18, "intensity": 2.479591836734694, "date": "2023-01-13" }, { "rank": 19, "intensity": 2.4489795918367347, "date": "2023-11-27" }, { "rank": 20, "intensity": 2.4183673469387754, "date": "2023-08-17" }, { "rank": 21, "intensity": 2.387755102040816, "date": "2023-11-09" }, { "rank": 22, "intensity": 2.357142857142857, "date": "2023-04-28" }, { "rank": 23, "intensity": 2.326530612244898, "date": "2023-03-14" }, { "rank": 24, "intensity": 2.295918367346939, "date": "2023-01-29" }, { "rank": 25, "intensity": 2.2653061224489797, "date": "2023-12-13" }, { "rank": 26, "intensity": 2.2346938775510203, "date": "2023-12-25" }, { "rank": 27, "intensity": 2.204081632653061, "date": "2023-08-26" }, { "rank": 28, "intensity": 2.173469387755102, "date": "2023-05-09" }, { "rank": 29, "intensity": 2.142857142857143, "date": "2023-05-29" }, { "rank": 30, "intensity": 2.112244897959184, "date": "2023-03-21" }, { "rank": 31, "intensity": 2.0816326530612246, "date": "2023-10-24" }, { "rank": 32, "intensity": 2.0510204081632653, "date": "2023-02-14" }, { "rank": 33, "intensity": 2.020408163265306, "date": "2023-02-19" }, { "rank": 34, "intensity": 1.989795918367347, "date": "2023-11-02" }, { "rank": 35, "intensity": 1.9591836734693877, "date": "2023-09-09" }, { "rank": 36, "intensity": 1.9285714285714286, "date": "2023-07-07" }, { "rank": 37, "intensity": 1.8979591836734695, "date": "2023-01-24" }, { "rank": 38, "intensity": 1.8673469387755102, "date": "2023-11-29" }, { "rank": 39, "intensity": 1.836734693877551, "date": "2023-10-26" }, { "rank": 40, "intensity": 1.806122448979592, "date": "2023-10-03" }, { "rank": 41, "intensity": 1.7755102040816326, "date": "2023-10-16" }, { "rank": 42, "intensity": 1.7448979591836735, "date": "2023-12-10" }, { "rank": 43, "intensity": 1.7142857142857144, "date": "2023-06-30" }, { "rank": 44, "intensity": 1.683673469387755, "date": "2023-06-08" }, { "rank": 45, "intensity": 1.653061224489796, "date": "2023-04-09" }, { "rank": 46, "intensity": 1.6224489795918369, "date": "2023-06-14" }, { "rank": 47, "intensity": 1.5918367346938775, "date": "2023-12-30" }, { "rank": 48, "intensity": 1.5612244897959184, "date": "2023-07-22" }, { "rank": 49, "intensity": 1.5306122448979593, "date": "2023-09-19" }, { "rank": 50, "intensity": 1.5, "date": "2023-05-20" } ], "source": "causal_rivers" } }, { "id": "L3_T1_Composite_Trend_00037", "level": 3, "level_name": "Semantic Reasoning", "category": "Composite Trend", "subtask": "Composite Trend", "question": "Among days in channel 728 during 2021 that exhibit the trend pattern 'fluctuating stable, then steady stable', identify the top-5 days where the fluctuating stable segment is the most fluctuating. (Output format: a ranked list of dates, e.g., ['YYYY-MM-DD', ...])", "answer": "['2021-09-03', '2021-08-25', '2021-11-19', '2021-06-27', '2021-05-19']", "ground_truth": [ "2021-09-03", "2021-08-25", "2021-11-19", "2021-06-27", "2021-05-19" ], "eval_metric": "set_f1", "channel": "728", "ts_data_path": "ts_data/L3_T1_Composite_Trend_00037.csv", "meta": { "segments": [ { "kind": "stable", "adj": "fluctuating" }, { "kind": "stable", "adj": "steady" } ], "pattern_phrase": "fluctuating stable, then steady stable", "rank_target_idx": 0, "rank_kind": "stable", "rank_adj": "fluctuating", "rank_axis": "std", "rank_direction": "DESC", "rank_extreme": "most fluctuating", "top_segments_meta": [ { "adj": "fluctuating", "kind": "stable", "label": "fluctuating stable", "start_idx": 0, "end_idx": 37 }, { "adj": "steady", "kind": "stable", "label": "steady stable", "start_idx": 37, "end_idx": 98 } ], "points_per_day": 96, "pattern": "fluctuating stable, then steady stable", "year": 2021, "top_k": [ "2021-09-03", "2021-08-25", "2021-11-19", "2021-06-27", "2021-05-19" ], "all_injections": [ { "rank": 1, "intensity": 3.0, "date": "2021-09-03" }, { "rank": 2, "intensity": 2.9693877551020407, "date": "2021-08-25" }, { "rank": 3, "intensity": 2.938775510204082, "date": "2021-11-19" }, { "rank": 4, "intensity": 2.9081632653061225, "date": "2021-06-27" }, { "rank": 5, "intensity": 2.877551020408163, "date": "2021-05-19" }, { "rank": 6, "intensity": 2.8469387755102042, "date": "2021-12-28" }, { "rank": 7, "intensity": 2.816326530612245, "date": "2021-01-02" }, { "rank": 8, "intensity": 2.7857142857142856, "date": "2021-10-19" }, { "rank": 9, "intensity": 2.7551020408163267, "date": "2021-07-26" }, { "rank": 10, "intensity": 2.7244897959183674, "date": "2021-10-27" }, { "rank": 11, "intensity": 2.693877551020408, "date": "2021-11-09" }, { "rank": 12, "intensity": 2.663265306122449, "date": "2021-05-21" }, { "rank": 13, "intensity": 2.63265306122449, "date": "2021-02-11" }, { "rank": 14, "intensity": 2.6020408163265305, "date": "2021-02-03" }, { "rank": 15, "intensity": 2.5714285714285716, "date": "2021-03-21" }, { "rank": 16, "intensity": 2.5408163265306123, "date": "2021-10-16" }, { "rank": 17, "intensity": 2.510204081632653, "date": "2021-04-21" }, { "rank": 18, "intensity": 2.479591836734694, "date": "2021-05-06" }, { "rank": 19, "intensity": 2.4489795918367347, "date": "2021-12-01" }, { "rank": 20, "intensity": 2.4183673469387754, "date": "2021-04-06" }, { "rank": 21, "intensity": 2.387755102040816, "date": "2021-09-07" }, { "rank": 22, "intensity": 2.357142857142857, "date": "2021-06-04" }, { "rank": 23, "intensity": 2.326530612244898, "date": "2021-08-15" }, { "rank": 24, "intensity": 2.295918367346939, "date": "2021-07-20" }, { "rank": 25, "intensity": 2.2653061224489797, "date": "2021-12-25" }, { "rank": 26, "intensity": 2.2346938775510203, "date": "2021-01-11" }, { "rank": 27, "intensity": 2.204081632653061, "date": "2021-01-23" }, { "rank": 28, "intensity": 2.173469387755102, "date": "2021-05-31" }, { "rank": 29, "intensity": 2.142857142857143, "date": "2021-11-27" }, { "rank": 30, "intensity": 2.112244897959184, "date": "2021-05-09" }, { "rank": 31, "intensity": 2.0816326530612246, "date": "2021-09-22" }, { "rank": 32, "intensity": 2.0510204081632653, "date": "2021-08-28" }, { "rank": 33, "intensity": 2.020408163265306, "date": "2021-08-08" }, { "rank": 34, "intensity": 1.989795918367347, "date": "2021-08-18" }, { "rank": 35, "intensity": 1.9591836734693877, "date": "2021-03-14" }, { "rank": 36, "intensity": 1.9285714285714286, "date": "2021-08-23" }, { "rank": 37, "intensity": 1.8979591836734695, "date": "2021-06-17" }, { "rank": 38, "intensity": 1.8673469387755102, "date": "2021-03-26" }, { "rank": 39, "intensity": 1.836734693877551, "date": "2021-12-18" }, { "rank": 40, "intensity": 1.806122448979592, "date": "2021-05-17" }, { "rank": 41, "intensity": 1.7755102040816326, "date": "2021-12-16" }, { "rank": 42, "intensity": 1.7448979591836735, "date": "2021-06-09" }, { "rank": 43, "intensity": 1.7142857142857144, "date": "2021-03-17" }, { "rank": 44, "intensity": 1.683673469387755, "date": "2021-01-17" }, { "rank": 45, "intensity": 1.653061224489796, "date": "2021-02-21" }, { "rank": 46, "intensity": 1.6224489795918369, "date": "2021-01-20" }, { "rank": 47, "intensity": 1.5918367346938775, "date": "2021-10-21" }, { "rank": 48, "intensity": 1.5612244897959184, "date": "2021-12-05" }, { "rank": 49, "intensity": 1.5306122448979593, "date": "2021-02-18" }, { "rank": 50, "intensity": 1.5, "date": "2021-09-17" } ], "source": "causal_rivers" } }, { "id": "L3_T1_Composite_Trend_00038", "level": 3, "level_name": "Semantic Reasoning", "category": "Composite Trend", "subtask": "Composite Trend", "question": "Among days in channel 729 during 2019 that exhibit the trend pattern 'rapid fall, then steady stable', identify the top-5 days where the rapid fall segment is the fastest. (Output format: a ranked list of dates, e.g., ['YYYY-MM-DD', ...])", "answer": "['2019-01-09', '2019-07-27', '2019-05-04', '2019-07-03', '2019-12-20']", "ground_truth": [ "2019-01-09", "2019-07-27", "2019-05-04", "2019-07-03", "2019-12-20" ], "eval_metric": "set_f1", "channel": "729", "ts_data_path": "ts_data/L3_T1_Composite_Trend_00038.csv", "meta": { "segments": [ { "kind": "fall", "adj": "rapid" }, { "kind": "stable", "adj": "steady" } ], "pattern_phrase": "rapid fall, then steady stable", "rank_target_idx": 0, "rank_kind": "fall", "rank_adj": "rapid", "rank_axis": "slope", "rank_direction": "DESC", "rank_extreme": "fastest", "top_segments_meta": [ { "adj": "rapid", "kind": "fall", "label": "rapid fall", "start_idx": 0, "end_idx": 19 }, { "adj": "steady", "kind": "stable", "label": "steady stable", "start_idx": 19, "end_idx": 98 } ], "points_per_day": 96, "pattern": "rapid fall, then steady stable", "year": 2019, "top_k": [ "2019-01-09", "2019-07-27", "2019-05-04", "2019-07-03", "2019-12-20" ], "all_injections": [ { "rank": 1, "intensity": 3.0, "date": "2019-01-09" }, { "rank": 2, "intensity": 2.9693877551020407, "date": "2019-07-27" }, { "rank": 3, "intensity": 2.938775510204082, "date": "2019-05-04" }, { "rank": 4, "intensity": 2.9081632653061225, "date": "2019-07-03" }, { "rank": 5, "intensity": 2.877551020408163, "date": "2019-12-20" }, { "rank": 6, "intensity": 2.8469387755102042, "date": "2019-12-26" }, { "rank": 7, "intensity": 2.816326530612245, "date": "2019-01-05" }, { "rank": 8, "intensity": 2.7857142857142856, "date": "2019-08-28" }, { "rank": 9, "intensity": 2.7551020408163267, "date": "2019-03-21" }, { "rank": 10, "intensity": 2.7244897959183674, "date": "2019-12-16" }, { "rank": 11, "intensity": 2.693877551020408, "date": "2019-03-11" }, { "rank": 12, "intensity": 2.663265306122449, "date": "2019-07-25" }, { "rank": 13, "intensity": 2.63265306122449, "date": "2019-02-10" }, { "rank": 14, "intensity": 2.6020408163265305, "date": "2019-06-08" }, { "rank": 15, "intensity": 2.5714285714285716, "date": "2019-08-23" }, { "rank": 16, "intensity": 2.5408163265306123, "date": "2019-06-19" }, { "rank": 17, "intensity": 2.510204081632653, "date": "2019-03-27" }, { "rank": 18, "intensity": 2.479591836734694, "date": "2019-12-08" }, { "rank": 19, "intensity": 2.4489795918367347, "date": "2019-08-15" }, { "rank": 20, "intensity": 2.4183673469387754, "date": "2019-11-21" }, { "rank": 21, "intensity": 2.387755102040816, "date": "2019-02-04" }, { "rank": 22, "intensity": 2.357142857142857, "date": "2019-05-26" }, { "rank": 23, "intensity": 2.326530612244898, "date": "2019-02-28" }, { "rank": 24, "intensity": 2.295918367346939, "date": "2019-05-22" }, { "rank": 25, "intensity": 2.2653061224489797, "date": "2019-06-30" }, { "rank": 26, "intensity": 2.2346938775510203, "date": "2019-06-17" }, { "rank": 27, "intensity": 2.204081632653061, "date": "2019-11-07" }, { "rank": 28, "intensity": 2.173469387755102, "date": "2019-09-05" }, { "rank": 29, "intensity": 2.142857142857143, "date": "2019-09-10" }, { "rank": 30, "intensity": 2.112244897959184, "date": "2019-12-23" }, { "rank": 31, "intensity": 2.0816326530612246, "date": "2019-07-19" }, { "rank": 32, "intensity": 2.0510204081632653, "date": "2019-03-03" }, { "rank": 33, "intensity": 2.020408163265306, "date": "2019-10-30" }, { "rank": 34, "intensity": 1.989795918367347, "date": "2019-12-28" }, { "rank": 35, "intensity": 1.9591836734693877, "date": "2019-05-17" }, { "rank": 36, "intensity": 1.9285714285714286, "date": "2019-05-19" }, { "rank": 37, "intensity": 1.8979591836734695, "date": "2019-11-05" }, { "rank": 38, "intensity": 1.8673469387755102, "date": "2019-03-15" }, { "rank": 39, "intensity": 1.836734693877551, "date": "2019-08-17" }, { "rank": 40, "intensity": 1.806122448979592, "date": "2019-04-26" }, { "rank": 41, "intensity": 1.7755102040816326, "date": "2019-03-25" }, { "rank": 42, "intensity": 1.7448979591836735, "date": "2019-08-07" }, { "rank": 43, "intensity": 1.7142857142857144, "date": "2019-12-12" }, { "rank": 44, "intensity": 1.683673469387755, "date": "2019-01-12" }, { "rank": 45, "intensity": 1.653061224489796, "date": "2019-07-21" }, { "rank": 46, "intensity": 1.6224489795918369, "date": "2019-06-15" }, { "rank": 47, "intensity": 1.5918367346938775, "date": "2019-03-29" }, { "rank": 48, "intensity": 1.5612244897959184, "date": "2019-06-22" }, { "rank": 49, "intensity": 1.5306122448979593, "date": "2019-01-30" }, { "rank": 50, "intensity": 1.5, "date": "2019-05-12" } ], "source": "causal_rivers" } }, { "id": "L3_T1_Composite_Trend_00039", "level": 3, "level_name": "Semantic Reasoning", "category": "Composite Trend", "subtask": "Composite Trend", "question": "Among days in channel 754 during 2021 that exhibit the trend pattern 'rise, then rapid rise', identify the top-5 days where the rapid rise segment is the fastest. (Output format: a ranked list of dates, e.g., ['YYYY-MM-DD', ...])", "answer": "['2021-12-02', '2021-11-02', '2021-09-02', '2021-02-09', '2021-03-05']", "ground_truth": [ "2021-12-02", "2021-11-02", "2021-09-02", "2021-02-09", "2021-03-05" ], "eval_metric": "set_f1", "channel": "754", "ts_data_path": "ts_data/L3_T1_Composite_Trend_00039.csv", "meta": { "segments": [ { "kind": "rise", "adj": "none" }, { "kind": "rise", "adj": "rapid" } ], "pattern_phrase": "rise, then rapid rise", "rank_target_idx": 1, "rank_kind": "rise", "rank_adj": "rapid", "rank_axis": "slope", "rank_direction": "DESC", "rank_extreme": "fastest", "top_segments_meta": [ { "adj": "none", "kind": "rise", "label": "none rise", "start_idx": 0, "end_idx": 64 }, { "adj": "rapid", "kind": "rise", "label": "rapid rise", "start_idx": 64, "end_idx": 98 } ], "points_per_day": 96, "pattern": "rise, then rapid rise", "year": 2021, "top_k": [ "2021-12-02", "2021-11-02", "2021-09-02", "2021-02-09", "2021-03-05" ], "all_injections": [ { "rank": 1, "intensity": 3.0, "date": "2021-12-02" }, { "rank": 2, "intensity": 2.9693877551020407, "date": "2021-11-02" }, { "rank": 3, "intensity": 2.938775510204082, "date": "2021-09-02" }, { "rank": 4, "intensity": 2.9081632653061225, "date": "2021-02-09" }, { "rank": 5, "intensity": 2.877551020408163, "date": "2021-03-05" }, { "rank": 6, "intensity": 2.8469387755102042, "date": "2021-05-15" }, { "rank": 7, "intensity": 2.816326530612245, "date": "2021-06-14" }, { "rank": 8, "intensity": 2.7857142857142856, "date": "2021-10-23" }, { "rank": 9, "intensity": 2.7551020408163267, "date": "2021-01-27" }, { "rank": 10, "intensity": 2.7244897959183674, "date": "2021-08-26" }, { "rank": 11, "intensity": 2.693877551020408, "date": "2021-04-14" }, { "rank": 12, "intensity": 2.663265306122449, "date": "2021-08-04" }, { "rank": 13, "intensity": 2.63265306122449, "date": "2021-10-12" }, { "rank": 14, "intensity": 2.6020408163265305, "date": "2021-12-10" }, { "rank": 15, "intensity": 2.5714285714285716, "date": "2021-05-31" }, { "rank": 16, "intensity": 2.5408163265306123, "date": "2021-09-08" }, { "rank": 17, "intensity": 2.510204081632653, "date": "2021-11-30" }, { "rank": 18, "intensity": 2.479591836734694, "date": "2021-08-08" }, { "rank": 19, "intensity": 2.4489795918367347, "date": "2021-03-31" }, { "rank": 20, "intensity": 2.4183673469387754, "date": "2021-07-29" }, { "rank": 21, "intensity": 2.387755102040816, "date": "2021-08-11" }, { "rank": 22, "intensity": 2.357142857142857, "date": "2021-09-17" }, { "rank": 23, "intensity": 2.326530612244898, "date": "2021-10-08" }, { "rank": 24, "intensity": 2.295918367346939, "date": "2021-08-31" }, { "rank": 25, "intensity": 2.2653061224489797, "date": "2021-01-16" }, { "rank": 26, "intensity": 2.2346938775510203, "date": "2021-11-11" }, { "rank": 27, "intensity": 2.204081632653061, "date": "2021-05-27" }, { "rank": 28, "intensity": 2.173469387755102, "date": "2021-06-11" }, { "rank": 29, "intensity": 2.142857142857143, "date": "2021-03-15" }, { "rank": 30, "intensity": 2.112244897959184, "date": "2021-02-04" }, { "rank": 31, "intensity": 2.0816326530612246, "date": "2021-12-23" }, { "rank": 32, "intensity": 2.0510204081632653, "date": "2021-09-15" }, { "rank": 33, "intensity": 2.020408163265306, "date": "2021-05-10" }, { "rank": 34, "intensity": 1.989795918367347, "date": "2021-02-12" }, { "rank": 35, "intensity": 1.9591836734693877, "date": "2021-01-25" }, { "rank": 36, "intensity": 1.9285714285714286, "date": "2021-02-15" }, { "rank": 37, "intensity": 1.8979591836734695, "date": "2021-10-14" }, { "rank": 38, "intensity": 1.8673469387755102, "date": "2021-01-05" }, { "rank": 39, "intensity": 1.836734693877551, "date": "2021-01-02" }, { "rank": 40, "intensity": 1.806122448979592, "date": "2021-06-21" }, { "rank": 41, "intensity": 1.7755102040816326, "date": "2021-10-01" }, { "rank": 42, "intensity": 1.7448979591836735, "date": "2021-06-19" }, { "rank": 43, "intensity": 1.7142857142857144, "date": "2021-07-13" }, { "rank": 44, "intensity": 1.683673469387755, "date": "2021-05-13" }, { "rank": 45, "intensity": 1.653061224489796, "date": "2021-09-23" }, { "rank": 46, "intensity": 1.6224489795918369, "date": "2021-12-28" }, { "rank": 47, "intensity": 1.5918367346938775, "date": "2021-12-18" }, { "rank": 48, "intensity": 1.5612244897959184, "date": "2021-03-26" }, { "rank": 49, "intensity": 1.5306122448979593, "date": "2021-01-31" }, { "rank": 50, "intensity": 1.5, "date": "2021-09-04" } ], "source": "causal_rivers" } }, { "id": "L3_T1_Composite_Trend_00040", "level": 3, "level_name": "Semantic Reasoning", "category": "Composite Trend", "subtask": "Composite Trend", "question": "Among days in channel 762 during 2020 that exhibit the trend pattern 'fluctuating stable, then slow fall, then steady stable, then rise', identify the top-5 days where the slow fall segment is the slowest. (Output format: a ranked list of dates, e.g., ['YYYY-MM-DD', ...])", "answer": "['2020-02-04', '2020-03-06', '2020-02-20', '2020-01-19', '2020-04-29']", "ground_truth": [ "2020-02-04", "2020-03-06", "2020-02-20", "2020-01-19", "2020-04-29" ], "eval_metric": "set_f1", "channel": "762", "ts_data_path": "ts_data/L3_T1_Composite_Trend_00040.csv", "meta": { "segments": [ { "kind": "stable", "adj": "fluctuating" }, { "kind": "fall", "adj": "slow" }, { "kind": "stable", "adj": "steady" }, { "kind": "rise", "adj": "none" } ], "pattern_phrase": "fluctuating stable, then slow fall, then steady stable, then rise", "rank_target_idx": 1, "rank_kind": "fall", "rank_adj": "slow", "rank_axis": "slope", "rank_direction": "ASC", "rank_extreme": "slowest", "top_segments_meta": [ { "adj": "fluctuating", "kind": "stable", "label": "fluctuating stable", "start_idx": 0, "end_idx": 21 }, { "adj": "slow", "kind": "fall", "label": "slow fall", "start_idx": 21, "end_idx": 50 }, { "adj": "steady", "kind": "stable", "label": "steady stable", "start_idx": 50, "end_idx": 81 }, { "adj": "none", "kind": "rise", "label": "none rise", "start_idx": 81, "end_idx": 98 } ], "points_per_day": 96, "pattern": "fluctuating stable, then slow fall, then steady stable, then rise", "year": 2020, "top_k": [ "2020-02-04", "2020-03-06", "2020-02-20", "2020-01-19", "2020-04-29" ], "all_injections": [ { "rank": 1, "intensity": 1.5, "date": "2020-02-04" }, { "rank": 2, "intensity": 1.530612244897959, "date": "2020-03-06" }, { "rank": 3, "intensity": 1.5612244897959184, "date": "2020-02-20" }, { "rank": 4, "intensity": 1.5918367346938775, "date": "2020-01-19" }, { "rank": 5, "intensity": 1.6224489795918366, "date": "2020-04-29" }, { "rank": 6, "intensity": 1.653061224489796, "date": "2020-05-02" }, { "rank": 7, "intensity": 1.683673469387755, "date": "2020-03-28" }, { "rank": 8, "intensity": 1.7142857142857142, "date": "2020-12-25" }, { "rank": 9, "intensity": 1.7448979591836735, "date": "2020-06-27" }, { "rank": 10, "intensity": 1.7755102040816326, "date": "2020-08-22" }, { "rank": 11, "intensity": 1.806122448979592, "date": "2020-04-22" }, { "rank": 12, "intensity": 1.836734693877551, "date": "2020-03-18" }, { "rank": 13, "intensity": 1.8673469387755102, "date": "2020-09-25" }, { "rank": 14, "intensity": 1.8979591836734695, "date": "2020-06-18" }, { "rank": 15, "intensity": 1.9285714285714286, "date": "2020-04-13" }, { "rank": 16, "intensity": 1.9591836734693877, "date": "2020-02-28" }, { "rank": 17, "intensity": 1.989795918367347, "date": "2020-09-05" }, { "rank": 18, "intensity": 2.020408163265306, "date": "2020-07-23" }, { "rank": 19, "intensity": 2.0510204081632653, "date": "2020-07-31" }, { "rank": 20, "intensity": 2.0816326530612246, "date": "2020-01-22" }, { "rank": 21, "intensity": 2.112244897959184, "date": "2020-10-30" }, { "rank": 22, "intensity": 2.142857142857143, "date": "2020-04-04" }, { "rank": 23, "intensity": 2.173469387755102, "date": "2020-12-23" }, { "rank": 24, "intensity": 2.204081632653061, "date": "2020-02-15" }, { "rank": 25, "intensity": 2.2346938775510203, "date": "2020-11-12" }, { "rank": 26, "intensity": 2.2653061224489797, "date": "2020-07-16" }, { "rank": 27, "intensity": 2.295918367346939, "date": "2020-11-26" }, { "rank": 28, "intensity": 2.326530612244898, "date": "2020-08-05" }, { "rank": 29, "intensity": 2.357142857142857, "date": "2020-09-11" }, { "rank": 30, "intensity": 2.387755102040816, "date": "2020-07-11" }, { "rank": 31, "intensity": 2.4183673469387754, "date": "2020-06-24" }, { "rank": 32, "intensity": 2.4489795918367347, "date": "2020-11-17" }, { "rank": 33, "intensity": 2.479591836734694, "date": "2020-04-08" }, { "rank": 34, "intensity": 2.510204081632653, "date": "2020-11-23" }, { "rank": 35, "intensity": 2.5408163265306123, "date": "2020-05-14" }, { "rank": 36, "intensity": 2.571428571428571, "date": "2020-02-13" }, { "rank": 37, "intensity": 2.6020408163265305, "date": "2020-09-16" }, { "rank": 38, "intensity": 2.63265306122449, "date": "2020-09-23" }, { "rank": 39, "intensity": 2.663265306122449, "date": "2020-01-02" }, { "rank": 40, "intensity": 2.693877551020408, "date": "2020-12-20" }, { "rank": 41, "intensity": 2.7244897959183674, "date": "2020-08-09" }, { "rank": 42, "intensity": 2.7551020408163263, "date": "2020-10-28" }, { "rank": 43, "intensity": 2.7857142857142856, "date": "2020-12-18" }, { "rank": 44, "intensity": 2.816326530612245, "date": "2020-09-09" }, { "rank": 45, "intensity": 2.8469387755102042, "date": "2020-01-05" }, { "rank": 46, "intensity": 2.877551020408163, "date": "2020-06-16" }, { "rank": 47, "intensity": 2.9081632653061225, "date": "2020-03-11" }, { "rank": 48, "intensity": 2.9387755102040813, "date": "2020-12-01" }, { "rank": 49, "intensity": 2.9693877551020407, "date": "2020-02-01" }, { "rank": 50, "intensity": 3.0, "date": "2020-05-08" } ], "source": "causal_rivers" } }, { "id": "L3_T1_Composite_Trend_00041", "level": 3, "level_name": "Semantic Reasoning", "category": "Composite Trend", "subtask": "Composite Trend", "question": "Among days in channel 811 during 2023 that exhibit the trend pattern 'slow fall, then steady stable, then rapid rise', identify the top-5 days where the rapid rise segment is the fastest. (Output format: a ranked list of dates, e.g., ['YYYY-MM-DD', ...])", "answer": "['2023-09-28', '2023-09-17', '2023-10-08', '2023-04-06', '2023-01-31']", "ground_truth": [ "2023-09-28", "2023-09-17", "2023-10-08", "2023-04-06", "2023-01-31" ], "eval_metric": "set_f1", "channel": "811", "ts_data_path": "ts_data/L3_T1_Composite_Trend_00041.csv", "meta": { "segments": [ { "kind": "fall", "adj": "slow" }, { "kind": "stable", "adj": "steady" }, { "kind": "rise", "adj": "rapid" } ], "pattern_phrase": "slow fall, then steady stable, then rapid rise", "rank_target_idx": 2, "rank_kind": "rise", "rank_adj": "rapid", "rank_axis": "slope", "rank_direction": "DESC", "rank_extreme": "fastest", "top_segments_meta": [ { "adj": "slow", "kind": "fall", "label": "slow fall", "start_idx": 0, "end_idx": 42 }, { "adj": "steady", "kind": "stable", "label": "steady stable", "start_idx": 42, "end_idx": 87 }, { "adj": "rapid", "kind": "rise", "label": "rapid rise", "start_idx": 87, "end_idx": 98 } ], "points_per_day": 96, "pattern": "slow fall, then steady stable, then rapid rise", "year": 2023, "top_k": [ "2023-09-28", "2023-09-17", "2023-10-08", "2023-04-06", "2023-01-31" ], "all_injections": [ { "rank": 1, "intensity": 3.0, "date": "2023-09-28" }, { "rank": 2, "intensity": 2.9693877551020407, "date": "2023-09-17" }, { "rank": 3, "intensity": 2.938775510204082, "date": "2023-10-08" }, { "rank": 4, "intensity": 2.9081632653061225, "date": "2023-04-06" }, { "rank": 5, "intensity": 2.877551020408163, "date": "2023-01-31" }, { "rank": 6, "intensity": 2.8469387755102042, "date": "2023-03-14" }, { "rank": 7, "intensity": 2.816326530612245, "date": "2023-01-18" }, { "rank": 8, "intensity": 2.7857142857142856, "date": "2023-12-14" }, { "rank": 9, "intensity": 2.7551020408163267, "date": "2023-07-03" }, { "rank": 10, "intensity": 2.7244897959183674, "date": "2023-12-20" }, { "rank": 11, "intensity": 2.693877551020408, "date": "2023-08-03" }, { "rank": 12, "intensity": 2.663265306122449, "date": "2023-06-24" }, { "rank": 13, "intensity": 2.63265306122449, "date": "2023-05-13" }, { "rank": 14, "intensity": 2.6020408163265305, "date": "2023-07-28" }, { "rank": 15, "intensity": 2.5714285714285716, "date": "2023-08-10" }, { "rank": 16, "intensity": 2.5408163265306123, "date": "2023-02-13" }, { "rank": 17, "intensity": 2.510204081632653, "date": "2023-05-07" }, { "rank": 18, "intensity": 2.479591836734694, "date": "2023-04-24" }, { "rank": 19, "intensity": 2.4489795918367347, "date": "2023-09-06" }, { "rank": 20, "intensity": 2.4183673469387754, "date": "2023-07-13" }, { "rank": 21, "intensity": 2.387755102040816, "date": "2023-03-26" }, { "rank": 22, "intensity": 2.357142857142857, "date": "2023-11-30" }, { "rank": 23, "intensity": 2.326530612244898, "date": "2023-03-08" }, { "rank": 24, "intensity": 2.295918367346939, "date": "2023-06-22" }, { "rank": 25, "intensity": 2.2653061224489797, "date": "2023-12-06" }, { "rank": 26, "intensity": 2.2346938775510203, "date": "2023-12-17" }, { "rank": 27, "intensity": 2.204081632653061, "date": "2023-10-30" }, { "rank": 28, "intensity": 2.173469387755102, "date": "2023-03-18" }, { "rank": 29, "intensity": 2.142857142857143, "date": "2023-01-01" }, { "rank": 30, "intensity": 2.112244897959184, "date": "2023-07-21" }, { "rank": 31, "intensity": 2.0816326530612246, "date": "2023-05-31" }, { "rank": 32, "intensity": 2.0510204081632653, "date": "2023-04-16" }, { "rank": 33, "intensity": 2.020408163265306, "date": "2023-01-06" }, { "rank": 34, "intensity": 1.989795918367347, "date": "2023-07-01" }, { "rank": 35, "intensity": 1.9591836734693877, "date": "2023-11-17" }, { "rank": 36, "intensity": 1.9285714285714286, "date": "2023-06-04" }, { "rank": 37, "intensity": 1.8979591836734695, "date": "2023-05-24" }, { "rank": 38, "intensity": 1.8673469387755102, "date": "2023-07-30" }, { "rank": 39, "intensity": 1.836734693877551, "date": "2023-05-21" }, { "rank": 40, "intensity": 1.806122448979592, "date": "2023-03-05" }, { "rank": 41, "intensity": 1.7755102040816326, "date": "2023-06-02" }, { "rank": 42, "intensity": 1.7448979591836735, "date": "2023-10-13" }, { "rank": 43, "intensity": 1.7142857142857144, "date": "2023-09-09" }, { "rank": 44, "intensity": 1.683673469387755, "date": "2023-10-21" }, { "rank": 45, "intensity": 1.653061224489796, "date": "2023-02-06" }, { "rank": 46, "intensity": 1.6224489795918369, "date": "2023-09-22" }, { "rank": 47, "intensity": 1.5918367346938775, "date": "2023-01-25" }, { "rank": 48, "intensity": 1.5612244897959184, "date": "2023-12-08" }, { "rank": 49, "intensity": 1.5306122448979593, "date": "2023-02-23" }, { "rank": 50, "intensity": 1.5, "date": "2023-05-27" } ], "source": "causal_rivers" } }, { "id": "L3_T1_Composite_Trend_00042", "level": 3, "level_name": "Semantic Reasoning", "category": "Composite Trend", "subtask": "Composite Trend", "question": "Among days in channel 813 during 2020 that exhibit the trend pattern 'rapid fall, then steady stable, then slow fall, then fluctuating stable', identify the top-5 days where the rapid fall segment is the fastest. (Output format: a ranked list of dates, e.g., ['YYYY-MM-DD', ...])", "answer": "['2020-11-17', '2020-01-27', '2020-11-14', '2020-07-09', '2020-07-24']", "ground_truth": [ "2020-11-17", "2020-01-27", "2020-11-14", "2020-07-09", "2020-07-24" ], "eval_metric": "set_f1", "channel": "813", "ts_data_path": "ts_data/L3_T1_Composite_Trend_00042.csv", "meta": { "segments": [ { "kind": "fall", "adj": "rapid" }, { "kind": "stable", "adj": "steady" }, { "kind": "fall", "adj": "slow" }, { "kind": "stable", "adj": "fluctuating" } ], "pattern_phrase": "rapid fall, then steady stable, then slow fall, then fluctuating stable", "rank_target_idx": 0, "rank_kind": "fall", "rank_adj": "rapid", "rank_axis": "slope", "rank_direction": "DESC", "rank_extreme": "fastest", "top_segments_meta": [ { "adj": "rapid", "kind": "fall", "label": "rapid fall", "start_idx": 0, "end_idx": 10 }, { "adj": "steady", "kind": "stable", "label": "steady stable", "start_idx": 10, "end_idx": 46 }, { "adj": "slow", "kind": "fall", "label": "slow fall", "start_idx": 46, "end_idx": 76 }, { "adj": "fluctuating", "kind": "stable", "label": "fluctuating stable", "start_idx": 76, "end_idx": 98 } ], "points_per_day": 96, "pattern": "rapid fall, then steady stable, then slow fall, then fluctuating stable", "year": 2020, "top_k": [ "2020-11-17", "2020-01-27", "2020-11-14", "2020-07-09", "2020-07-24" ], "all_injections": [ { "rank": 1, "intensity": 3.0, "date": "2020-11-17" }, { "rank": 2, "intensity": 2.9693877551020407, "date": "2020-01-27" }, { "rank": 3, "intensity": 2.938775510204082, "date": "2020-11-14" }, { "rank": 4, "intensity": 2.9081632653061225, "date": "2020-07-09" }, { "rank": 5, "intensity": 2.877551020408163, "date": "2020-07-24" }, { "rank": 6, "intensity": 2.8469387755102042, "date": "2020-04-19" }, { "rank": 7, "intensity": 2.816326530612245, "date": "2020-03-05" }, { "rank": 8, "intensity": 2.7857142857142856, "date": "2020-07-01" }, { "rank": 9, "intensity": 2.7551020408163267, "date": "2020-01-14" }, { "rank": 10, "intensity": 2.7244897959183674, "date": "2020-01-12" }, { "rank": 11, "intensity": 2.693877551020408, "date": "2020-10-25" }, { "rank": 12, "intensity": 2.663265306122449, "date": "2020-08-06" }, { "rank": 13, "intensity": 2.63265306122449, "date": "2020-02-14" }, { "rank": 14, "intensity": 2.6020408163265305, "date": "2020-11-06" }, { "rank": 15, "intensity": 2.5714285714285716, "date": "2020-04-03" }, { "rank": 16, "intensity": 2.5408163265306123, "date": "2020-06-23" }, { "rank": 17, "intensity": 2.510204081632653, "date": "2020-06-01" }, { "rank": 18, "intensity": 2.479591836734694, "date": "2020-12-24" }, { "rank": 19, "intensity": 2.4489795918367347, "date": "2020-10-10" }, { "rank": 20, "intensity": 2.4183673469387754, "date": "2020-07-11" }, { "rank": 21, "intensity": 2.387755102040816, "date": "2020-08-10" }, { "rank": 22, "intensity": 2.357142857142857, "date": "2020-03-23" }, { "rank": 23, "intensity": 2.326530612244898, "date": "2020-05-17" }, { "rank": 24, "intensity": 2.295918367346939, "date": "2020-09-27" }, { "rank": 25, "intensity": 2.2653061224489797, "date": "2020-05-14" }, { "rank": 26, "intensity": 2.2346938775510203, "date": "2020-12-20" }, { "rank": 27, "intensity": 2.204081632653061, "date": "2020-07-05" }, { "rank": 28, "intensity": 2.173469387755102, "date": "2020-10-27" }, { "rank": 29, "intensity": 2.142857142857143, "date": "2020-07-17" }, { "rank": 30, "intensity": 2.112244897959184, "date": "2020-06-29" }, { "rank": 31, "intensity": 2.0816326530612246, "date": "2020-12-16" }, { "rank": 32, "intensity": 2.0510204081632653, "date": "2020-11-27" }, { "rank": 33, "intensity": 2.020408163265306, "date": "2020-02-04" }, { "rank": 34, "intensity": 1.989795918367347, "date": "2020-12-08" }, { "rank": 35, "intensity": 1.9591836734693877, "date": "2020-04-29" }, { "rank": 36, "intensity": 1.9285714285714286, "date": "2020-03-31" }, { "rank": 37, "intensity": 1.8979591836734695, "date": "2020-01-19" }, { "rank": 38, "intensity": 1.8673469387755102, "date": "2020-05-21" }, { "rank": 39, "intensity": 1.836734693877551, "date": "2020-05-23" }, { "rank": 40, "intensity": 1.806122448979592, "date": "2020-07-26" }, { "rank": 41, "intensity": 1.7755102040816326, "date": "2020-06-11" }, { "rank": 42, "intensity": 1.7448979591836735, "date": "2020-10-22" }, { "rank": 43, "intensity": 1.7142857142857144, "date": "2020-08-13" }, { "rank": 44, "intensity": 1.683673469387755, "date": "2020-08-08" }, { "rank": 45, "intensity": 1.653061224489796, "date": "2020-11-23" }, { "rank": 46, "intensity": 1.6224489795918369, "date": "2020-01-24" }, { "rank": 47, "intensity": 1.5918367346938775, "date": "2020-06-27" }, { "rank": 48, "intensity": 1.5612244897959184, "date": "2020-05-19" }, { "rank": 49, "intensity": 1.5306122448979593, "date": "2020-11-03" }, { "rank": 50, "intensity": 1.5, "date": "2020-10-15" } ], "source": "causal_rivers" } }, { "id": "L3_T1_Composite_Trend_00043", "level": 3, "level_name": "Semantic Reasoning", "category": "Composite Trend", "subtask": "Composite Trend", "question": "Among days in channel 865 during 2020 that exhibit the trend pattern 'steady stable, then slow rise, then fall, then slow fall', identify the top-5 days where the slow fall segment is the slowest. (Output format: a ranked list of dates, e.g., ['YYYY-MM-DD', ...])", "answer": "['2020-03-26', '2020-05-15', '2020-03-03', '2020-07-16', '2020-08-05']", "ground_truth": [ "2020-03-26", "2020-05-15", "2020-03-03", "2020-07-16", "2020-08-05" ], "eval_metric": "set_f1", "channel": "865", "ts_data_path": "ts_data/L3_T1_Composite_Trend_00043.csv", "meta": { "segments": [ { "kind": "stable", "adj": "steady" }, { "kind": "rise", "adj": "slow" }, { "kind": "fall", "adj": "none" }, { "kind": "fall", "adj": "slow" } ], "pattern_phrase": "steady stable, then slow rise, then fall, then slow fall", "rank_target_idx": 3, "rank_kind": "fall", "rank_adj": "slow", "rank_axis": "slope", "rank_direction": "ASC", "rank_extreme": "slowest", "top_segments_meta": [ { "adj": "steady", "kind": "stable", "label": "steady stable", "start_idx": 0, "end_idx": 30 }, { "adj": "slow", "kind": "rise", "label": "slow rise", "start_idx": 30, "end_idx": 57 }, { "adj": "none", "kind": "fall", "label": "none fall", "start_idx": 57, "end_idx": 72 }, { "adj": "slow", "kind": "fall", "label": "slow fall", "start_idx": 72, "end_idx": 98 } ], "points_per_day": 96, "pattern": "steady stable, then slow rise, then fall, then slow fall", "year": 2020, "top_k": [ "2020-03-26", "2020-05-15", "2020-03-03", "2020-07-16", "2020-08-05" ], "all_injections": [ { "rank": 1, "intensity": 1.5, "date": "2020-03-26" }, { "rank": 2, "intensity": 1.530612244897959, "date": "2020-05-15" }, { "rank": 3, "intensity": 1.5612244897959184, "date": "2020-03-03" }, { "rank": 4, "intensity": 1.5918367346938775, "date": "2020-07-16" }, { "rank": 5, "intensity": 1.6224489795918366, "date": "2020-08-05" }, { "rank": 6, "intensity": 1.653061224489796, "date": "2020-01-19" }, { "rank": 7, "intensity": 1.683673469387755, "date": "2020-02-22" }, { "rank": 8, "intensity": 1.7142857142857142, "date": "2020-01-31" }, { "rank": 9, "intensity": 1.7448979591836735, "date": "2020-01-04" }, { "rank": 10, "intensity": 1.7755102040816326, "date": "2020-11-20" }, { "rank": 11, "intensity": 1.806122448979592, "date": "2020-10-17" }, { "rank": 12, "intensity": 1.836734693877551, "date": "2020-09-12" }, { "rank": 13, "intensity": 1.8673469387755102, "date": "2020-09-24" }, { "rank": 14, "intensity": 1.8979591836734695, "date": "2020-08-24" }, { "rank": 15, "intensity": 1.9285714285714286, "date": "2020-11-05" }, { "rank": 16, "intensity": 1.9591836734693877, "date": "2020-11-08" }, { "rank": 17, "intensity": 1.989795918367347, "date": "2020-10-15" }, { "rank": 18, "intensity": 2.020408163265306, "date": "2020-04-03" }, { "rank": 19, "intensity": 2.0510204081632653, "date": "2020-10-23" }, { "rank": 20, "intensity": 2.0816326530612246, "date": "2020-01-23" }, { "rank": 21, "intensity": 2.112244897959184, "date": "2020-04-08" }, { "rank": 22, "intensity": 2.142857142857143, "date": "2020-04-24" }, { "rank": 23, "intensity": 2.173469387755102, "date": "2020-06-18" }, { "rank": 24, "intensity": 2.204081632653061, "date": "2020-08-30" }, { "rank": 25, "intensity": 2.2346938775510203, "date": "2020-09-09" }, { "rank": 26, "intensity": 2.2653061224489797, "date": "2020-01-28" }, { "rank": 27, "intensity": 2.295918367346939, "date": "2020-11-13" }, { "rank": 28, "intensity": 2.326530612244898, "date": "2020-07-27" }, { "rank": 29, "intensity": 2.357142857142857, "date": "2020-06-20" }, { "rank": 30, "intensity": 2.387755102040816, "date": "2020-08-16" }, { "rank": 31, "intensity": 2.4183673469387754, "date": "2020-02-02" }, { "rank": 32, "intensity": 2.4489795918367347, "date": "2020-02-19" }, { "rank": 33, "intensity": 2.479591836734694, "date": "2020-08-13" }, { "rank": 34, "intensity": 2.510204081632653, "date": "2020-09-18" }, { "rank": 35, "intensity": 2.5408163265306123, "date": "2020-06-25" }, { "rank": 36, "intensity": 2.571428571428571, "date": "2020-09-29" }, { "rank": 37, "intensity": 2.6020408163265305, "date": "2020-06-03" }, { "rank": 38, "intensity": 2.63265306122449, "date": "2020-11-28" }, { "rank": 39, "intensity": 2.663265306122449, "date": "2020-06-07" }, { "rank": 40, "intensity": 2.693877551020408, "date": "2020-10-29" }, { "rank": 41, "intensity": 2.7244897959183674, "date": "2020-12-10" }, { "rank": 42, "intensity": 2.7551020408163263, "date": "2020-02-29" }, { "rank": 43, "intensity": 2.7857142857142856, "date": "2020-07-29" }, { "rank": 44, "intensity": 2.816326530612245, "date": "2020-01-01" }, { "rank": 45, "intensity": 2.8469387755102042, "date": "2020-05-25" }, { "rank": 46, "intensity": 2.877551020408163, "date": "2020-09-21" }, { "rank": 47, "intensity": 2.9081632653061225, "date": "2020-06-05" }, { "rank": 48, "intensity": 2.9387755102040813, "date": "2020-08-26" }, { "rank": 49, "intensity": 2.9693877551020407, "date": "2020-03-13" }, { "rank": 50, "intensity": 3.0, "date": "2020-04-18" } ], "source": "causal_rivers" } }, { "id": "L3_T1_Composite_Trend_00044", "level": 3, "level_name": "Semantic Reasoning", "category": "Composite Trend", "subtask": "Composite Trend", "question": "Among days in channel 891 during 2022 that exhibit the trend pattern 'rapid fall, then slow fall, then fall', identify the top-5 days where the slow fall segment is the slowest. (Output format: a ranked list of dates, e.g., ['YYYY-MM-DD', ...])", "answer": "['2022-06-15', '2022-08-22', '2022-01-30', '2022-08-08', '2022-08-24']", "ground_truth": [ "2022-06-15", "2022-08-22", "2022-01-30", "2022-08-08", "2022-08-24" ], "eval_metric": "set_f1", "channel": "891", "ts_data_path": "ts_data/L3_T1_Composite_Trend_00044.csv", "meta": { "segments": [ { "kind": "fall", "adj": "rapid" }, { "kind": "fall", "adj": "slow" }, { "kind": "fall", "adj": "none" } ], "pattern_phrase": "rapid fall, then slow fall, then fall", "rank_target_idx": 1, "rank_kind": "fall", "rank_adj": "slow", "rank_axis": "slope", "rank_direction": "ASC", "rank_extreme": "slowest", "top_segments_meta": [ { "adj": "rapid", "kind": "fall", "label": "rapid fall", "start_idx": 0, "end_idx": 15 }, { "adj": "slow", "kind": "fall", "label": "slow fall", "start_idx": 15, "end_idx": 70 }, { "adj": "none", "kind": "fall", "label": "none fall", "start_idx": 70, "end_idx": 98 } ], "points_per_day": 96, "pattern": "rapid fall, then slow fall, then fall", "year": 2022, "top_k": [ "2022-06-15", "2022-08-22", "2022-01-30", "2022-08-08", "2022-08-24" ], "all_injections": [ { "rank": 1, "intensity": 1.5, "date": "2022-06-15" }, { "rank": 2, "intensity": 1.530612244897959, "date": "2022-08-22" }, { "rank": 3, "intensity": 1.5612244897959184, "date": "2022-01-30" }, { "rank": 4, "intensity": 1.5918367346938775, "date": "2022-08-08" }, { "rank": 5, "intensity": 1.6224489795918366, "date": "2022-08-24" }, { "rank": 6, "intensity": 1.653061224489796, "date": "2022-11-20" }, { "rank": 7, "intensity": 1.683673469387755, "date": "2022-10-28" }, { "rank": 8, "intensity": 1.7142857142857142, "date": "2022-05-17" }, { "rank": 9, "intensity": 1.7448979591836735, "date": "2022-05-10" }, { "rank": 10, "intensity": 1.7755102040816326, "date": "2022-08-28" }, { "rank": 11, "intensity": 1.806122448979592, "date": "2022-10-10" }, { "rank": 12, "intensity": 1.836734693877551, "date": "2022-07-22" }, { "rank": 13, "intensity": 1.8673469387755102, "date": "2022-09-14" }, { "rank": 14, "intensity": 1.8979591836734695, "date": "2022-02-08" }, { "rank": 15, "intensity": 1.9285714285714286, "date": "2022-12-05" }, { "rank": 16, "intensity": 1.9591836734693877, "date": "2022-04-02" }, { "rank": 17, "intensity": 1.989795918367347, "date": "2022-01-12" }, { "rank": 18, "intensity": 2.020408163265306, "date": "2022-01-18" }, { "rank": 19, "intensity": 2.0510204081632653, "date": "2022-06-06" }, { "rank": 20, "intensity": 2.0816326530612246, "date": "2022-08-05" }, { "rank": 21, "intensity": 2.112244897959184, "date": "2022-02-17" }, { "rank": 22, "intensity": 2.142857142857143, "date": "2022-09-17" }, { "rank": 23, "intensity": 2.173469387755102, "date": "2022-03-29" }, { "rank": 24, "intensity": 2.204081632653061, "date": "2022-01-03" }, { "rank": 25, "intensity": 2.2346938775510203, "date": "2022-10-02" }, { "rank": 26, "intensity": 2.2653061224489797, "date": "2022-03-10" }, { "rank": 27, "intensity": 2.295918367346939, "date": "2022-03-13" }, { "rank": 28, "intensity": 2.326530612244898, "date": "2022-12-15" }, { "rank": 29, "intensity": 2.357142857142857, "date": "2022-10-08" }, { "rank": 30, "intensity": 2.387755102040816, "date": "2022-08-11" }, { "rank": 31, "intensity": 2.4183673469387754, "date": "2022-10-19" }, { "rank": 32, "intensity": 2.4489795918367347, "date": "2022-06-03" }, { "rank": 33, "intensity": 2.479591836734694, "date": "2022-10-15" }, { "rank": 34, "intensity": 2.510204081632653, "date": "2022-05-29" }, { "rank": 35, "intensity": 2.5408163265306123, "date": "2022-09-02" }, { "rank": 36, "intensity": 2.571428571428571, "date": "2022-09-09" }, { "rank": 37, "intensity": 2.6020408163265305, "date": "2022-06-10" }, { "rank": 38, "intensity": 2.63265306122449, "date": "2022-03-02" }, { "rank": 39, "intensity": 2.663265306122449, "date": "2022-09-24" }, { "rank": 40, "intensity": 2.693877551020408, "date": "2022-10-24" }, { "rank": 41, "intensity": 2.7244897959183674, "date": "2022-11-15" }, { "rank": 42, "intensity": 2.7551020408163263, "date": "2022-04-29" }, { "rank": 43, "intensity": 2.7857142857142856, "date": "2022-04-20" }, { "rank": 44, "intensity": 2.816326530612245, "date": "2022-10-17" }, { "rank": 45, "intensity": 2.8469387755102042, "date": "2022-12-30" }, { "rank": 46, "intensity": 2.877551020408163, "date": "2022-11-23" }, { "rank": 47, "intensity": 2.9081632653061225, "date": "2022-06-25" }, { "rank": 48, "intensity": 2.9387755102040813, "date": "2022-08-17" }, { "rank": 49, "intensity": 2.9693877551020407, "date": "2022-11-12" }, { "rank": 50, "intensity": 3.0, "date": "2022-01-06" } ], "source": "causal_rivers" } }, { "id": "L3_T1_Composite_Trend_00045", "level": 3, "level_name": "Semantic Reasoning", "category": "Composite Trend", "subtask": "Composite Trend", "question": "Among days in channel 894 during 2022 that exhibit the trend pattern 'fluctuating stable, then slow rise', identify the top-5 days where the fluctuating stable segment is the most fluctuating. (Output format: a ranked list of dates, e.g., ['YYYY-MM-DD', ...])", "answer": "['2022-02-27', '2022-05-25', '2022-10-23', '2022-01-29', '2022-04-16']", "ground_truth": [ "2022-02-27", "2022-05-25", "2022-10-23", "2022-01-29", "2022-04-16" ], "eval_metric": "set_f1", "channel": "894", "ts_data_path": "ts_data/L3_T1_Composite_Trend_00045.csv", "meta": { "segments": [ { "kind": "stable", "adj": "fluctuating" }, { "kind": "rise", "adj": "slow" } ], "pattern_phrase": "fluctuating stable, then slow rise", "rank_target_idx": 0, "rank_kind": "stable", "rank_adj": "fluctuating", "rank_axis": "std", "rank_direction": "DESC", "rank_extreme": "most fluctuating", "top_segments_meta": [ { "adj": "fluctuating", "kind": "stable", "label": "fluctuating stable", "start_idx": 0, "end_idx": 42 }, { "adj": "slow", "kind": "rise", "label": "slow rise", "start_idx": 42, "end_idx": 98 } ], "points_per_day": 96, "pattern": "fluctuating stable, then slow rise", "year": 2022, "top_k": [ "2022-02-27", "2022-05-25", "2022-10-23", "2022-01-29", "2022-04-16" ], "all_injections": [ { "rank": 1, "intensity": 3.0, "date": "2022-02-27" }, { "rank": 2, "intensity": 2.9693877551020407, "date": "2022-05-25" }, { "rank": 3, "intensity": 2.938775510204082, "date": "2022-10-23" }, { "rank": 4, "intensity": 2.9081632653061225, "date": "2022-01-29" }, { "rank": 5, "intensity": 2.877551020408163, "date": "2022-04-16" }, { "rank": 6, "intensity": 2.8469387755102042, "date": "2022-03-08" }, { "rank": 7, "intensity": 2.816326530612245, "date": "2022-02-23" }, { "rank": 8, "intensity": 2.7857142857142856, "date": "2022-01-22" }, { "rank": 9, "intensity": 2.7551020408163267, "date": "2022-04-27" }, { "rank": 10, "intensity": 2.7244897959183674, "date": "2022-05-31" }, { "rank": 11, "intensity": 2.693877551020408, "date": "2022-09-06" }, { "rank": 12, "intensity": 2.663265306122449, "date": "2022-02-16" }, { "rank": 13, "intensity": 2.63265306122449, "date": "2022-05-29" }, { "rank": 14, "intensity": 2.6020408163265305, "date": "2022-07-14" }, { "rank": 15, "intensity": 2.5714285714285716, "date": "2022-07-25" }, { "rank": 16, "intensity": 2.5408163265306123, "date": "2022-06-22" }, { "rank": 17, "intensity": 2.510204081632653, "date": "2022-05-14" }, { "rank": 18, "intensity": 2.479591836734694, "date": "2022-02-14" }, { "rank": 19, "intensity": 2.4489795918367347, "date": "2022-12-08" }, { "rank": 20, "intensity": 2.4183673469387754, "date": "2022-06-13" }, { "rank": 21, "intensity": 2.387755102040816, "date": "2022-01-27" }, { "rank": 22, "intensity": 2.357142857142857, "date": "2022-11-28" }, { "rank": 23, "intensity": 2.326530612244898, "date": "2022-03-29" }, { "rank": 24, "intensity": 2.295918367346939, "date": "2022-01-19" }, { "rank": 25, "intensity": 2.2653061224489797, "date": "2022-09-29" }, { "rank": 26, "intensity": 2.2346938775510203, "date": "2022-12-19" }, { "rank": 27, "intensity": 2.204081632653061, "date": "2022-08-30" }, { "rank": 28, "intensity": 2.173469387755102, "date": "2022-09-19" }, { "rank": 29, "intensity": 2.142857142857143, "date": "2022-09-01" }, { "rank": 30, "intensity": 2.112244897959184, "date": "2022-05-11" }, { "rank": 31, "intensity": 2.0816326530612246, "date": "2022-04-11" }, { "rank": 32, "intensity": 2.0510204081632653, "date": "2022-08-12" }, { "rank": 33, "intensity": 2.020408163265306, "date": "2022-10-27" }, { "rank": 34, "intensity": 1.989795918367347, "date": "2022-11-05" }, { "rank": 35, "intensity": 1.9591836734693877, "date": "2022-08-16" }, { "rank": 36, "intensity": 1.9285714285714286, "date": "2022-12-30" }, { "rank": 37, "intensity": 1.8979591836734695, "date": "2022-08-07" }, { "rank": 38, "intensity": 1.8673469387755102, "date": "2022-11-23" }, { "rank": 39, "intensity": 1.836734693877551, "date": "2022-12-04" }, { "rank": 40, "intensity": 1.806122448979592, "date": "2022-05-18" }, { "rank": 41, "intensity": 1.7755102040816326, "date": "2022-03-21" }, { "rank": 42, "intensity": 1.7448979591836735, "date": "2022-04-21" }, { "rank": 43, "intensity": 1.7142857142857144, "date": "2022-08-28" }, { "rank": 44, "intensity": 1.683673469387755, "date": "2022-08-19" }, { "rank": 45, "intensity": 1.653061224489796, "date": "2022-01-09" }, { "rank": 46, "intensity": 1.6224489795918369, "date": "2022-10-18" }, { "rank": 47, "intensity": 1.5918367346938775, "date": "2022-07-04" }, { "rank": 48, "intensity": 1.5612244897959184, "date": "2022-06-07" }, { "rank": 49, "intensity": 1.5306122448979593, "date": "2022-07-06" }, { "rank": 50, "intensity": 1.5, "date": "2022-12-17" } ], "source": "causal_rivers" } }, { "id": "L3_T1_Composite_Trend_00046", "level": 3, "level_name": "Semantic Reasoning", "category": "Composite Trend", "subtask": "Composite Trend", "question": "Among days in channel 895 during 2023 that exhibit the trend pattern 'slow rise, then rapid rise, then steady stable', identify the top-5 days where the slow rise segment is the slowest. (Output format: a ranked list of dates, e.g., ['YYYY-MM-DD', ...])", "answer": "['2023-04-13', '2023-03-30', '2023-11-08', '2023-07-14', '2023-10-21']", "ground_truth": [ "2023-04-13", "2023-03-30", "2023-11-08", "2023-07-14", "2023-10-21" ], "eval_metric": "set_f1", "channel": "895", "ts_data_path": "ts_data/L3_T1_Composite_Trend_00046.csv", "meta": { "segments": [ { "kind": "rise", "adj": "slow" }, { "kind": "rise", "adj": "rapid" }, { "kind": "stable", "adj": "steady" } ], "pattern_phrase": "slow rise, then rapid rise, then steady stable", "rank_target_idx": 0, "rank_kind": "rise", "rank_adj": "slow", "rank_axis": "slope", "rank_direction": "ASC", "rank_extreme": "slowest", "top_segments_meta": [ { "adj": "slow", "kind": "rise", "label": "slow rise", "start_idx": 0, "end_idx": 38 }, { "adj": "rapid", "kind": "rise", "label": "rapid rise", "start_idx": 38, "end_idx": 51 }, { "adj": "steady", "kind": "stable", "label": "steady stable", "start_idx": 51, "end_idx": 98 } ], "points_per_day": 96, "pattern": "slow rise, then rapid rise, then steady stable", "year": 2023, "top_k": [ "2023-04-13", "2023-03-30", "2023-11-08", "2023-07-14", "2023-10-21" ], "all_injections": [ { "rank": 1, "intensity": 1.5, "date": "2023-04-13" }, { "rank": 2, "intensity": 1.530612244897959, "date": "2023-03-30" }, { "rank": 3, "intensity": 1.5612244897959184, "date": "2023-11-08" }, { "rank": 4, "intensity": 1.5918367346938775, "date": "2023-07-14" }, { "rank": 5, "intensity": 1.6224489795918366, "date": "2023-10-21" }, { "rank": 6, "intensity": 1.653061224489796, "date": "2023-10-28" }, { "rank": 7, "intensity": 1.683673469387755, "date": "2023-01-20" }, { "rank": 8, "intensity": 1.7142857142857142, "date": "2023-04-21" }, { "rank": 9, "intensity": 1.7448979591836735, "date": "2023-04-10" }, { "rank": 10, "intensity": 1.7755102040816326, "date": "2023-03-13" }, { "rank": 11, "intensity": 1.806122448979592, "date": "2023-12-14" }, { "rank": 12, "intensity": 1.836734693877551, "date": "2023-03-27" }, { "rank": 13, "intensity": 1.8673469387755102, "date": "2023-04-26" }, { "rank": 14, "intensity": 1.8979591836734695, "date": "2023-02-05" }, { "rank": 15, "intensity": 1.9285714285714286, "date": "2023-07-20" }, { "rank": 16, "intensity": 1.9591836734693877, "date": "2023-02-13" }, { "rank": 17, "intensity": 1.989795918367347, "date": "2023-08-24" }, { "rank": 18, "intensity": 2.020408163265306, "date": "2023-10-02" }, { "rank": 19, "intensity": 2.0510204081632653, "date": "2023-09-19" }, { "rank": 20, "intensity": 2.0816326530612246, "date": "2023-01-29" }, { "rank": 21, "intensity": 2.112244897959184, "date": "2023-05-20" }, { "rank": 22, "intensity": 2.142857142857143, "date": "2023-01-09" }, { "rank": 23, "intensity": 2.173469387755102, "date": "2023-09-02" }, { "rank": 24, "intensity": 2.204081632653061, "date": "2023-03-05" }, { "rank": 25, "intensity": 2.2346938775510203, "date": "2023-06-14" }, { "rank": 26, "intensity": 2.2653061224489797, "date": "2023-05-16" }, { "rank": 27, "intensity": 2.295918367346939, "date": "2023-06-21" }, { "rank": 28, "intensity": 2.326530612244898, "date": "2023-10-12" }, { "rank": 29, "intensity": 2.357142857142857, "date": "2023-03-24" }, { "rank": 30, "intensity": 2.387755102040816, "date": "2023-11-15" }, { "rank": 31, "intensity": 2.4183673469387754, "date": "2023-09-06" }, { "rank": 32, "intensity": 2.4489795918367347, "date": "2023-09-17" }, { "rank": 33, "intensity": 2.479591836734694, "date": "2023-03-02" }, { "rank": 34, "intensity": 2.510204081632653, "date": "2023-11-12" }, { "rank": 35, "intensity": 2.5408163265306123, "date": "2023-05-24" }, { "rank": 36, "intensity": 2.571428571428571, "date": "2023-05-03" }, { "rank": 37, "intensity": 2.6020408163265305, "date": "2023-05-29" }, { "rank": 38, "intensity": 2.63265306122449, "date": "2023-07-12" }, { "rank": 39, "intensity": 2.663265306122449, "date": "2023-04-03" }, { "rank": 40, "intensity": 2.693877551020408, "date": "2023-05-09" }, { "rank": 41, "intensity": 2.7244897959183674, "date": "2023-03-18" }, { "rank": 42, "intensity": 2.7551020408163263, "date": "2023-08-19" }, { "rank": 43, "intensity": 2.7857142857142856, "date": "2023-12-28" }, { "rank": 44, "intensity": 2.816326530612245, "date": "2023-01-23" }, { "rank": 45, "intensity": 2.8469387755102042, "date": "2023-02-02" }, { "rank": 46, "intensity": 2.877551020408163, "date": "2023-08-21" }, { "rank": 47, "intensity": 2.9081632653061225, "date": "2023-06-25" }, { "rank": 48, "intensity": 2.9387755102040813, "date": "2023-10-16" }, { "rank": 49, "intensity": 2.9693877551020407, "date": "2023-09-21" }, { "rank": 50, "intensity": 3.0, "date": "2023-07-28" } ], "source": "causal_rivers" } }, { "id": "L3_T1_Composite_Trend_00047", "level": 3, "level_name": "Semantic Reasoning", "category": "Composite Trend", "subtask": "Composite Trend", "question": "Among days in channel 897 during 2021 that exhibit the trend pattern 'rise, then rapid rise, then slow rise', identify the top-5 days where the rapid rise segment is the fastest. (Output format: a ranked list of dates, e.g., ['YYYY-MM-DD', ...])", "answer": "['2021-08-15', '2021-07-11', '2021-10-02', '2021-10-18', '2021-01-24']", "ground_truth": [ "2021-08-15", "2021-07-11", "2021-10-02", "2021-10-18", "2021-01-24" ], "eval_metric": "set_f1", "channel": "897", "ts_data_path": "ts_data/L3_T1_Composite_Trend_00047.csv", "meta": { "segments": [ { "kind": "rise", "adj": "none" }, { "kind": "rise", "adj": "rapid" }, { "kind": "rise", "adj": "slow" } ], "pattern_phrase": "rise, then rapid rise, then slow rise", "rank_target_idx": 1, "rank_kind": "rise", "rank_adj": "rapid", "rank_axis": "slope", "rank_direction": "DESC", "rank_extreme": "fastest", "top_segments_meta": [ { "adj": "none", "kind": "rise", "label": "none rise", "start_idx": 0, "end_idx": 30 }, { "adj": "rapid", "kind": "rise", "label": "rapid rise", "start_idx": 30, "end_idx": 46 }, { "adj": "slow", "kind": "rise", "label": "slow rise", "start_idx": 46, "end_idx": 98 } ], "points_per_day": 96, "pattern": "rise, then rapid rise, then slow rise", "year": 2021, "top_k": [ "2021-08-15", "2021-07-11", "2021-10-02", "2021-10-18", "2021-01-24" ], "all_injections": [ { "rank": 1, "intensity": 3.0, "date": "2021-08-15" }, { "rank": 2, "intensity": 2.9693877551020407, "date": "2021-07-11" }, { "rank": 3, "intensity": 2.938775510204082, "date": "2021-10-02" }, { "rank": 4, "intensity": 2.9081632653061225, "date": "2021-10-18" }, { "rank": 5, "intensity": 2.877551020408163, "date": "2021-01-24" }, { "rank": 6, "intensity": 2.8469387755102042, "date": "2021-02-21" }, { "rank": 7, "intensity": 2.816326530612245, "date": "2021-04-23" }, { "rank": 8, "intensity": 2.7857142857142856, "date": "2021-07-09" }, { "rank": 9, "intensity": 2.7551020408163267, "date": "2021-03-09" }, { "rank": 10, "intensity": 2.7244897959183674, "date": "2021-08-09" }, { "rank": 11, "intensity": 2.693877551020408, "date": "2021-09-01" }, { "rank": 12, "intensity": 2.663265306122449, "date": "2021-03-21" }, { "rank": 13, "intensity": 2.63265306122449, "date": "2021-06-12" }, { "rank": 14, "intensity": 2.6020408163265305, "date": "2021-12-14" }, { "rank": 15, "intensity": 2.5714285714285716, "date": "2021-05-30" }, { "rank": 16, "intensity": 2.5408163265306123, "date": "2021-06-27" }, { "rank": 17, "intensity": 2.510204081632653, "date": "2021-05-09" }, { "rank": 18, "intensity": 2.479591836734694, "date": "2021-10-22" }, { "rank": 19, "intensity": 2.4489795918367347, "date": "2021-09-29" }, { "rank": 20, "intensity": 2.4183673469387754, "date": "2021-05-26" }, { "rank": 21, "intensity": 2.387755102040816, "date": "2021-12-20" }, { "rank": 22, "intensity": 2.357142857142857, "date": "2021-01-07" }, { "rank": 23, "intensity": 2.326530612244898, "date": "2021-07-01" }, { "rank": 24, "intensity": 2.295918367346939, "date": "2021-10-07" }, { "rank": 25, "intensity": 2.2653061224489797, "date": "2021-10-25" }, { "rank": 26, "intensity": 2.2346938775510203, "date": "2021-09-25" }, { "rank": 27, "intensity": 2.204081632653061, "date": "2021-03-19" }, { "rank": 28, "intensity": 2.173469387755102, "date": "2021-04-02" }, { "rank": 29, "intensity": 2.142857142857143, "date": "2021-01-02" }, { "rank": 30, "intensity": 2.112244897959184, "date": "2021-05-16" }, { "rank": 31, "intensity": 2.0816326530612246, "date": "2021-11-06" }, { "rank": 32, "intensity": 2.0510204081632653, "date": "2021-01-31" }, { "rank": 33, "intensity": 2.020408163265306, "date": "2021-10-04" }, { "rank": 34, "intensity": 1.989795918367347, "date": "2021-11-11" }, { "rank": 35, "intensity": 1.9591836734693877, "date": "2021-01-12" }, { "rank": 36, "intensity": 1.9285714285714286, "date": "2021-03-06" }, { "rank": 37, "intensity": 1.8979591836734695, "date": "2021-02-26" }, { "rank": 38, "intensity": 1.8673469387755102, "date": "2021-04-14" }, { "rank": 39, "intensity": 1.836734693877551, "date": "2021-11-28" }, { "rank": 40, "intensity": 1.806122448979592, "date": "2021-07-15" }, { "rank": 41, "intensity": 1.7755102040816326, "date": "2021-03-26" }, { "rank": 42, "intensity": 1.7448979591836735, "date": "2021-09-10" }, { "rank": 43, "intensity": 1.7142857142857144, "date": "2021-05-28" }, { "rank": 44, "intensity": 1.683673469387755, "date": "2021-01-09" }, { "rank": 45, "intensity": 1.653061224489796, "date": "2021-05-07" }, { "rank": 46, "intensity": 1.6224489795918369, "date": "2021-07-03" }, { "rank": 47, "intensity": 1.5918367346938775, "date": "2021-06-05" }, { "rank": 48, "intensity": 1.5612244897959184, "date": "2021-01-29" }, { "rank": 49, "intensity": 1.5306122448979593, "date": "2021-07-27" }, { "rank": 50, "intensity": 1.5, "date": "2021-06-10" } ], "source": "causal_rivers" } }, { "id": "L3_T1_Composite_Trend_00048", "level": 3, "level_name": "Semantic Reasoning", "category": "Composite Trend", "subtask": "Composite Trend", "question": "Among days in channel 933 during 2021 that exhibit the trend pattern 'steady stable, then rise, then rapid fall, then steady stable', identify the top-5 days where the rapid fall segment is the fastest. (Output format: a ranked list of dates, e.g., ['YYYY-MM-DD', ...])", "answer": "['2021-01-06', '2021-05-02', '2021-05-24', '2021-03-15', '2021-06-16']", "ground_truth": [ "2021-01-06", "2021-05-02", "2021-05-24", "2021-03-15", "2021-06-16" ], "eval_metric": "set_f1", "channel": "933", "ts_data_path": "ts_data/L3_T1_Composite_Trend_00048.csv", "meta": { "segments": [ { "kind": "stable", "adj": "steady" }, { "kind": "rise", "adj": "none" }, { "kind": "fall", "adj": "rapid" }, { "kind": "stable", "adj": "steady" } ], "pattern_phrase": "steady stable, then rise, then rapid fall, then steady stable", "rank_target_idx": 2, "rank_kind": "fall", "rank_adj": "rapid", "rank_axis": "slope", "rank_direction": "DESC", "rank_extreme": "fastest", "top_segments_meta": [ { "adj": "steady", "kind": "stable", "label": "steady stable", "start_idx": 0, "end_idx": 33 }, { "adj": "none", "kind": "rise", "label": "none rise", "start_idx": 33, "end_idx": 50 }, { "adj": "rapid", "kind": "fall", "label": "rapid fall", "start_idx": 50, "end_idx": 58 }, { "adj": "steady", "kind": "stable", "label": "steady stable", "start_idx": 58, "end_idx": 98 } ], "points_per_day": 96, "pattern": "steady stable, then rise, then rapid fall, then steady stable", "year": 2021, "top_k": [ "2021-01-06", "2021-05-02", "2021-05-24", "2021-03-15", "2021-06-16" ], "all_injections": [ { "rank": 1, "intensity": 3.0, "date": "2021-01-06" }, { "rank": 2, "intensity": 2.9693877551020407, "date": "2021-05-02" }, { "rank": 3, "intensity": 2.938775510204082, "date": "2021-05-24" }, { "rank": 4, "intensity": 2.9081632653061225, "date": "2021-03-15" }, { "rank": 5, "intensity": 2.877551020408163, "date": "2021-06-16" }, { "rank": 6, "intensity": 2.8469387755102042, "date": "2021-09-07" }, { "rank": 7, "intensity": 2.816326530612245, "date": "2021-08-16" }, { "rank": 8, "intensity": 2.7857142857142856, "date": "2021-12-16" }, { "rank": 9, "intensity": 2.7551020408163267, "date": "2021-04-28" }, { "rank": 10, "intensity": 2.7244897959183674, "date": "2021-06-26" }, { "rank": 11, "intensity": 2.693877551020408, "date": "2021-04-02" }, { "rank": 12, "intensity": 2.663265306122449, "date": "2021-07-02" }, { "rank": 13, "intensity": 2.63265306122449, "date": "2021-10-24" }, { "rank": 14, "intensity": 2.6020408163265305, "date": "2021-01-22" }, { "rank": 15, "intensity": 2.5714285714285716, "date": "2021-02-04" }, { "rank": 16, "intensity": 2.5408163265306123, "date": "2021-08-18" }, { "rank": 17, "intensity": 2.510204081632653, "date": "2021-12-18" }, { "rank": 18, "intensity": 2.479591836734694, "date": "2021-06-07" }, { "rank": 19, "intensity": 2.4489795918367347, "date": "2021-06-04" }, { "rank": 20, "intensity": 2.4183673469387754, "date": "2021-12-21" }, { "rank": 21, "intensity": 2.387755102040816, "date": "2021-11-28" }, { "rank": 22, "intensity": 2.357142857142857, "date": "2021-11-11" }, { "rank": 23, "intensity": 2.326530612244898, "date": "2021-10-18" }, { "rank": 24, "intensity": 2.295918367346939, "date": "2021-07-28" }, { "rank": 25, "intensity": 2.2653061224489797, "date": "2021-04-12" }, { "rank": 26, "intensity": 2.2346938775510203, "date": "2021-12-13" }, { "rank": 27, "intensity": 2.204081632653061, "date": "2021-11-20" }, { "rank": 28, "intensity": 2.173469387755102, "date": "2021-02-19" }, { "rank": 29, "intensity": 2.142857142857143, "date": "2021-01-02" }, { "rank": 30, "intensity": 2.112244897959184, "date": "2021-03-07" }, { "rank": 31, "intensity": 2.0816326530612246, "date": "2021-03-17" }, { "rank": 32, "intensity": 2.0510204081632653, "date": "2021-04-09" }, { "rank": 33, "intensity": 2.020408163265306, "date": "2021-09-27" }, { "rank": 34, "intensity": 1.989795918367347, "date": "2021-09-01" }, { "rank": 35, "intensity": 1.9591836734693877, "date": "2021-01-28" }, { "rank": 36, "intensity": 1.9285714285714286, "date": "2021-01-09" }, { "rank": 37, "intensity": 1.8979591836734695, "date": "2021-11-14" }, { "rank": 38, "intensity": 1.8673469387755102, "date": "2021-12-04" }, { "rank": 39, "intensity": 1.836734693877551, "date": "2021-03-25" }, { "rank": 40, "intensity": 1.806122448979592, "date": "2021-03-19" }, { "rank": 41, "intensity": 1.7755102040816326, "date": "2021-09-24" }, { "rank": 42, "intensity": 1.7448979591836735, "date": "2021-03-28" }, { "rank": 43, "intensity": 1.7142857142857144, "date": "2021-10-05" }, { "rank": 44, "intensity": 1.683673469387755, "date": "2021-08-01" }, { "rank": 45, "intensity": 1.653061224489796, "date": "2021-03-21" }, { "rank": 46, "intensity": 1.6224489795918369, "date": "2021-09-05" }, { "rank": 47, "intensity": 1.5918367346938775, "date": "2021-10-01" }, { "rank": 48, "intensity": 1.5612244897959184, "date": "2021-11-17" }, { "rank": 49, "intensity": 1.5306122448979593, "date": "2021-12-29" }, { "rank": 50, "intensity": 1.5, "date": "2021-07-30" } ], "source": "causal_rivers" } }, { "id": "L3_T1_Composite_Trend_00049", "level": 3, "level_name": "Semantic Reasoning", "category": "Composite Trend", "subtask": "Composite Trend", "question": "Among days in channel HUFL during 2017 that exhibit the trend pattern 'steady stable, then slow rise', identify the top-5 days where the slow rise segment is the slowest. (Output format: a ranked list of dates, e.g., ['YYYY-MM-DD', ...])", "answer": "['2017-12-16', '2017-01-19', '2017-06-26', '2017-10-01', '2017-08-15']", "ground_truth": [ "2017-12-16", "2017-01-19", "2017-06-26", "2017-10-01", "2017-08-15" ], "eval_metric": "set_f1", "channel": "HUFL", "ts_data_path": "ts_data/L3_T1_Composite_Trend_00049.csv", "meta": { "segments": [ { "kind": "stable", "adj": "steady" }, { "kind": "rise", "adj": "slow" } ], "pattern_phrase": "steady stable, then slow rise", "rank_target_idx": 1, "rank_kind": "rise", "rank_adj": "slow", "rank_axis": "slope", "rank_direction": "ASC", "rank_extreme": "slowest", "top_segments_meta": [ { "adj": "steady", "kind": "stable", "label": "steady stable", "start_idx": 0, "end_idx": 51 }, { "adj": "slow", "kind": "rise", "label": "slow rise", "start_idx": 51, "end_idx": 98 } ], "points_per_day": 96, "pattern": "steady stable, then slow rise", "year": 2017, "top_k": [ "2017-12-16", "2017-01-19", "2017-06-26", "2017-10-01", "2017-08-15" ], "all_injections": [ { "rank": 1, "intensity": 1.5, "date": "2017-12-16" }, { "rank": 2, "intensity": 1.530612244897959, "date": "2017-01-19" }, { "rank": 3, "intensity": 1.5612244897959184, "date": "2017-06-26" }, { "rank": 4, "intensity": 1.5918367346938775, "date": "2017-10-01" }, { "rank": 5, "intensity": 1.6224489795918366, "date": "2017-08-15" }, { "rank": 6, "intensity": 1.653061224489796, "date": "2017-10-10" }, { "rank": 7, "intensity": 1.683673469387755, "date": "2017-12-19" }, { "rank": 8, "intensity": 1.7142857142857142, "date": "2017-03-10" }, { "rank": 9, "intensity": 1.7448979591836735, "date": "2017-12-14" }, { "rank": 10, "intensity": 1.7755102040816326, "date": "2017-04-03" }, { "rank": 11, "intensity": 1.806122448979592, "date": "2017-03-24" }, { "rank": 12, "intensity": 1.836734693877551, "date": "2017-12-05" }, { "rank": 13, "intensity": 1.8673469387755102, "date": "2017-02-21" }, { "rank": 14, "intensity": 1.8979591836734695, "date": "2017-11-10" }, { "rank": 15, "intensity": 1.9285714285714286, "date": "2017-02-11" }, { "rank": 16, "intensity": 1.9591836734693877, "date": "2017-10-07" }, { "rank": 17, "intensity": 1.989795918367347, "date": "2017-09-22" }, { "rank": 18, "intensity": 2.020408163265306, "date": "2017-02-27" }, { "rank": 19, "intensity": 2.0510204081632653, "date": "2017-07-03" }, { "rank": 20, "intensity": 2.0816326530612246, "date": "2017-06-29" }, { "rank": 21, "intensity": 2.112244897959184, "date": "2017-03-26" }, { "rank": 22, "intensity": 2.142857142857143, "date": "2017-03-31" }, { "rank": 23, "intensity": 2.173469387755102, "date": "2017-11-23" }, { "rank": 24, "intensity": 2.204081632653061, "date": "2017-11-19" }, { "rank": 25, "intensity": 2.2346938775510203, "date": "2017-09-16" }, { "rank": 26, "intensity": 2.2653061224489797, "date": "2017-03-13" }, { "rank": 27, "intensity": 2.295918367346939, "date": "2017-07-15" }, { "rank": 28, "intensity": 2.326530612244898, "date": "2017-10-14" }, { "rank": 29, "intensity": 2.357142857142857, "date": "2017-10-04" }, { "rank": 30, "intensity": 2.387755102040816, "date": "2017-01-05" }, { "rank": 31, "intensity": 2.4183673469387754, "date": "2017-06-12" }, { "rank": 32, "intensity": 2.4489795918367347, "date": "2017-05-03" }, { "rank": 33, "intensity": 2.479591836734694, "date": "2017-10-19" }, { "rank": 34, "intensity": 2.510204081632653, "date": "2017-02-17" }, { "rank": 35, "intensity": 2.5408163265306123, "date": "2017-08-10" }, { "rank": 36, "intensity": 2.571428571428571, "date": "2017-07-26" }, { "rank": 37, "intensity": 2.6020408163265305, "date": "2017-04-23" }, { "rank": 38, "intensity": 2.63265306122449, "date": "2017-08-18" }, { "rank": 39, "intensity": 2.663265306122449, "date": "2017-05-31" }, { "rank": 40, "intensity": 2.693877551020408, "date": "2017-07-23" }, { "rank": 41, "intensity": 2.7244897959183674, "date": "2017-09-14" }, { "rank": 42, "intensity": 2.7551020408163263, "date": "2017-09-12" }, { "rank": 43, "intensity": 2.7857142857142856, "date": "2017-04-15" }, { "rank": 44, "intensity": 2.816326530612245, "date": "2017-05-22" }, { "rank": 45, "intensity": 2.8469387755102042, "date": "2017-06-20" }, { "rank": 46, "intensity": 2.877551020408163, "date": "2017-03-06" }, { "rank": 47, "intensity": 2.9081632653061225, "date": "2017-02-07" }, { "rank": 48, "intensity": 2.9387755102040813, "date": "2017-07-19" }, { "rank": 49, "intensity": 2.9693877551020407, "date": "2017-09-01" }, { "rank": 50, "intensity": 3.0, "date": "2017-07-13" } ], "source": "ettm1" } }, { "id": "L3_T1_Composite_Trend_00050", "level": 3, "level_name": "Semantic Reasoning", "category": "Composite Trend", "subtask": "Composite Trend", "question": "Among days in channel HULL during 2017 that exhibit the trend pattern 'rapid rise, then rapid fall, then steady stable, then fall', identify the top-5 days where the rapid rise segment is the fastest. (Output format: a ranked list of dates, e.g., ['YYYY-MM-DD', ...])", "answer": "['2017-10-23', '2017-02-04', '2017-12-02', '2017-04-03', '2017-05-22']", "ground_truth": [ "2017-10-23", "2017-02-04", "2017-12-02", "2017-04-03", "2017-05-22" ], "eval_metric": "set_f1", "channel": "HULL", "ts_data_path": "ts_data/L3_T1_Composite_Trend_00050.csv", "meta": { "segments": [ { "kind": "rise", "adj": "rapid" }, { "kind": "fall", "adj": "rapid" }, { "kind": "stable", "adj": "steady" }, { "kind": "fall", "adj": "none" } ], "pattern_phrase": "rapid rise, then rapid fall, then steady stable, then fall", "rank_target_idx": 0, "rank_kind": "rise", "rank_adj": "rapid", "rank_axis": "slope", "rank_direction": "DESC", "rank_extreme": "fastest", "top_segments_meta": [ { "adj": "rapid", "kind": "rise", "label": "rapid rise", "start_idx": 0, "end_idx": 13 }, { "adj": "rapid", "kind": "fall", "label": "rapid fall", "start_idx": 13, "end_idx": 25 }, { "adj": "steady", "kind": "stable", "label": "steady stable", "start_idx": 25, "end_idx": 76 }, { "adj": "none", "kind": "fall", "label": "none fall", "start_idx": 76, "end_idx": 98 } ], "points_per_day": 96, "pattern": "rapid rise, then rapid fall, then steady stable, then fall", "year": 2017, "top_k": [ "2017-10-23", "2017-02-04", "2017-12-02", "2017-04-03", "2017-05-22" ], "all_injections": [ { "rank": 1, "intensity": 3.0, "date": "2017-10-23" }, { "rank": 2, "intensity": 2.9693877551020407, "date": "2017-02-04" }, { "rank": 3, "intensity": 2.938775510204082, "date": "2017-12-02" }, { "rank": 4, "intensity": 2.9081632653061225, "date": "2017-04-03" }, { "rank": 5, "intensity": 2.877551020408163, "date": "2017-05-22" }, { "rank": 6, "intensity": 2.8469387755102042, "date": "2017-04-26" }, { "rank": 7, "intensity": 2.816326530612245, "date": "2017-10-13" }, { "rank": 8, "intensity": 2.7857142857142856, "date": "2017-11-23" }, { "rank": 9, "intensity": 2.7551020408163267, "date": "2017-04-23" }, { "rank": 10, "intensity": 2.7244897959183674, "date": "2017-05-17" }, { "rank": 11, "intensity": 2.693877551020408, "date": "2017-02-02" }, { "rank": 12, "intensity": 2.663265306122449, "date": "2017-08-07" }, { "rank": 13, "intensity": 2.63265306122449, "date": "2017-04-06" }, { "rank": 14, "intensity": 2.6020408163265305, "date": "2017-02-13" }, { "rank": 15, "intensity": 2.5714285714285716, "date": "2017-03-26" }, { "rank": 16, "intensity": 2.5408163265306123, "date": "2017-06-14" }, { "rank": 17, "intensity": 2.510204081632653, "date": "2017-12-28" }, { "rank": 18, "intensity": 2.479591836734694, "date": "2017-12-23" }, { "rank": 19, "intensity": 2.4489795918367347, "date": "2017-01-19" }, { "rank": 20, "intensity": 2.4183673469387754, "date": "2017-05-05" }, { "rank": 21, "intensity": 2.387755102040816, "date": "2017-05-11" }, { "rank": 22, "intensity": 2.357142857142857, "date": "2017-03-30" }, { "rank": 23, "intensity": 2.326530612244898, "date": "2017-09-27" }, { "rank": 24, "intensity": 2.295918367346939, "date": "2017-03-23" }, { "rank": 25, "intensity": 2.2653061224489797, "date": "2017-07-30" }, { "rank": 26, "intensity": 2.2346938775510203, "date": "2017-06-30" }, { "rank": 27, "intensity": 2.204081632653061, "date": "2017-09-09" }, { "rank": 28, "intensity": 2.173469387755102, "date": "2017-11-03" }, { "rank": 29, "intensity": 2.142857142857143, "date": "2017-02-18" }, { "rank": 30, "intensity": 2.112244897959184, "date": "2017-10-18" }, { "rank": 31, "intensity": 2.0816326530612246, "date": "2017-01-22" }, { "rank": 32, "intensity": 2.0510204081632653, "date": "2017-08-14" }, { "rank": 33, "intensity": 2.020408163265306, "date": "2017-10-04" }, { "rank": 34, "intensity": 1.989795918367347, "date": "2017-06-12" }, { "rank": 35, "intensity": 1.9591836734693877, "date": "2017-11-13" }, { "rank": 36, "intensity": 1.9285714285714286, "date": "2017-08-04" }, { "rank": 37, "intensity": 1.8979591836734695, "date": "2017-07-07" }, { "rank": 38, "intensity": 1.8673469387755102, "date": "2017-04-09" }, { "rank": 39, "intensity": 1.836734693877551, "date": "2017-09-24" }, { "rank": 40, "intensity": 1.806122448979592, "date": "2017-05-20" }, { "rank": 41, "intensity": 1.7755102040816326, "date": "2017-03-18" }, { "rank": 42, "intensity": 1.7448979591836735, "date": "2017-01-17" }, { "rank": 43, "intensity": 1.7142857142857144, "date": "2017-09-04" }, { "rank": 44, "intensity": 1.683673469387755, "date": "2017-10-16" }, { "rank": 45, "intensity": 1.653061224489796, "date": "2017-10-07" }, { "rank": 46, "intensity": 1.6224489795918369, "date": "2017-07-13" }, { "rank": 47, "intensity": 1.5918367346938775, "date": "2017-08-23" }, { "rank": 48, "intensity": 1.5612244897959184, "date": "2017-06-22" }, { "rank": 49, "intensity": 1.5306122448979593, "date": "2017-06-19" }, { "rank": 50, "intensity": 1.5, "date": "2017-07-03" } ], "source": "ettm1" } }, { "id": "L3_T1_Composite_Trend_00051", "level": 3, "level_name": "Semantic Reasoning", "category": "Composite Trend", "subtask": "Composite Trend", "question": "Among days in channel MUFL during 2017 that exhibit the trend pattern 'slow fall, then fall', identify the top-5 days where the slow fall segment is the slowest. (Output format: a ranked list of dates, e.g., ['YYYY-MM-DD', ...])", "answer": "['2017-10-18', '2017-02-12', '2017-12-28', '2017-07-08', '2017-07-17']", "ground_truth": [ "2017-10-18", "2017-02-12", "2017-12-28", "2017-07-08", "2017-07-17" ], "eval_metric": "set_f1", "channel": "MUFL", "ts_data_path": "ts_data/L3_T1_Composite_Trend_00051.csv", "meta": { "segments": [ { "kind": "fall", "adj": "slow" }, { "kind": "fall", "adj": "none" } ], "pattern_phrase": "slow fall, then fall", "rank_target_idx": 0, "rank_kind": "fall", "rank_adj": "slow", "rank_axis": "slope", "rank_direction": "ASC", "rank_extreme": "slowest", "top_segments_meta": [ { "adj": "slow", "kind": "fall", "label": "slow fall", "start_idx": 0, "end_idx": 62 }, { "adj": "none", "kind": "fall", "label": "none fall", "start_idx": 62, "end_idx": 98 } ], "points_per_day": 96, "pattern": "slow fall, then fall", "year": 2017, "top_k": [ "2017-10-18", "2017-02-12", "2017-12-28", "2017-07-08", "2017-07-17" ], "all_injections": [ { "rank": 1, "intensity": 1.5, "date": "2017-10-18" }, { "rank": 2, "intensity": 1.530612244897959, "date": "2017-02-12" }, { "rank": 3, "intensity": 1.5612244897959184, "date": "2017-12-28" }, { "rank": 4, "intensity": 1.5918367346938775, "date": "2017-07-08" }, { "rank": 5, "intensity": 1.6224489795918366, "date": "2017-07-17" }, { "rank": 6, "intensity": 1.653061224489796, "date": "2017-08-27" }, { "rank": 7, "intensity": 1.683673469387755, "date": "2017-10-11" }, { "rank": 8, "intensity": 1.7142857142857142, "date": "2017-01-25" }, { "rank": 9, "intensity": 1.7448979591836735, "date": "2017-03-02" }, { "rank": 10, "intensity": 1.7755102040816326, "date": "2017-07-25" }, { "rank": 11, "intensity": 1.806122448979592, "date": "2017-11-25" }, { "rank": 12, "intensity": 1.836734693877551, "date": "2017-06-09" }, { "rank": 13, "intensity": 1.8673469387755102, "date": "2017-07-20" }, { "rank": 14, "intensity": 1.8979591836734695, "date": "2017-11-10" }, { "rank": 15, "intensity": 1.9285714285714286, "date": "2017-01-22" }, { "rank": 16, "intensity": 1.9591836734693877, "date": "2017-06-22" }, { "rank": 17, "intensity": 1.989795918367347, "date": "2017-09-26" }, { "rank": 18, "intensity": 2.020408163265306, "date": "2017-10-01" }, { "rank": 19, "intensity": 2.0510204081632653, "date": "2017-06-30" }, { "rank": 20, "intensity": 2.0816326530612246, "date": "2017-08-20" }, { "rank": 21, "intensity": 2.112244897959184, "date": "2017-12-24" }, { "rank": 22, "intensity": 2.142857142857143, "date": "2017-10-31" }, { "rank": 23, "intensity": 2.173469387755102, "date": "2017-03-19" }, { "rank": 24, "intensity": 2.204081632653061, "date": "2017-03-29" }, { "rank": 25, "intensity": 2.2346938775510203, "date": "2017-10-07" }, { "rank": 26, "intensity": 2.2653061224489797, "date": "2017-11-29" }, { "rank": 27, "intensity": 2.295918367346939, "date": "2017-09-24" }, { "rank": 28, "intensity": 2.326530612244898, "date": "2017-12-12" }, { "rank": 29, "intensity": 2.357142857142857, "date": "2017-06-01" }, { "rank": 30, "intensity": 2.387755102040816, "date": "2017-06-27" }, { "rank": 31, "intensity": 2.4183673469387754, "date": "2017-08-01" }, { "rank": 32, "intensity": 2.4489795918367347, "date": "2017-04-04" }, { "rank": 33, "intensity": 2.479591836734694, "date": "2017-08-14" }, { "rank": 34, "intensity": 2.510204081632653, "date": "2017-11-17" }, { "rank": 35, "intensity": 2.5408163265306123, "date": "2017-09-11" }, { "rank": 36, "intensity": 2.571428571428571, "date": "2017-11-21" }, { "rank": 37, "intensity": 2.6020408163265305, "date": "2017-06-15" }, { "rank": 38, "intensity": 2.63265306122449, "date": "2017-10-03" }, { "rank": 39, "intensity": 2.663265306122449, "date": "2017-10-24" }, { "rank": 40, "intensity": 2.693877551020408, "date": "2017-08-30" }, { "rank": 41, "intensity": 2.7244897959183674, "date": "2017-05-04" }, { "rank": 42, "intensity": 2.7551020408163263, "date": "2017-04-27" }, { "rank": 43, "intensity": 2.7857142857142856, "date": "2017-04-01" }, { "rank": 44, "intensity": 2.816326530612245, "date": "2017-12-14" }, { "rank": 45, "intensity": 2.8469387755102042, "date": "2017-04-19" }, { "rank": 46, "intensity": 2.877551020408163, "date": "2017-08-06" }, { "rank": 47, "intensity": 2.9081632653061225, "date": "2017-03-15" }, { "rank": 48, "intensity": 2.9387755102040813, "date": "2017-05-17" }, { "rank": 49, "intensity": 2.9693877551020407, "date": "2017-10-28" }, { "rank": 50, "intensity": 3.0, "date": "2017-07-12" } ], "source": "ettm1" } }, { "id": "L3_T1_Composite_Trend_00052", "level": 3, "level_name": "Semantic Reasoning", "category": "Composite Trend", "subtask": "Composite Trend", "question": "Among days in channel MULL during 2017 that exhibit the trend pattern 'rapid rise, then slow rise, then slow fall', identify the top-5 days where the rapid rise segment is the fastest. (Output format: a ranked list of dates, e.g., ['YYYY-MM-DD', ...])", "answer": "['2017-12-08', '2017-05-26', '2017-09-28', '2017-08-05', '2017-12-15']", "ground_truth": [ "2017-12-08", "2017-05-26", "2017-09-28", "2017-08-05", "2017-12-15" ], "eval_metric": "set_f1", "channel": "MULL", "ts_data_path": "ts_data/L3_T1_Composite_Trend_00052.csv", "meta": { "segments": [ { "kind": "rise", "adj": "rapid" }, { "kind": "rise", "adj": "slow" }, { "kind": "fall", "adj": "slow" } ], "pattern_phrase": "rapid rise, then slow rise, then slow fall", "rank_target_idx": 0, "rank_kind": "rise", "rank_adj": "rapid", "rank_axis": "slope", "rank_direction": "DESC", "rank_extreme": "fastest", "top_segments_meta": [ { "adj": "rapid", "kind": "rise", "label": "rapid rise", "start_idx": 0, "end_idx": 12 }, { "adj": "slow", "kind": "rise", "label": "slow rise", "start_idx": 12, "end_idx": 55 }, { "adj": "slow", "kind": "fall", "label": "slow fall", "start_idx": 55, "end_idx": 98 } ], "points_per_day": 96, "pattern": "rapid rise, then slow rise, then slow fall", "year": 2017, "top_k": [ "2017-12-08", "2017-05-26", "2017-09-28", "2017-08-05", "2017-12-15" ], "all_injections": [ { "rank": 1, "intensity": 3.0, "date": "2017-12-08" }, { "rank": 2, "intensity": 2.9693877551020407, "date": "2017-05-26" }, { "rank": 3, "intensity": 2.938775510204082, "date": "2017-09-28" }, { "rank": 4, "intensity": 2.9081632653061225, "date": "2017-08-05" }, { "rank": 5, "intensity": 2.877551020408163, "date": "2017-12-15" }, { "rank": 6, "intensity": 2.8469387755102042, "date": "2017-09-20" }, { "rank": 7, "intensity": 2.816326530612245, "date": "2017-02-04" }, { "rank": 8, "intensity": 2.7857142857142856, "date": "2017-10-28" }, { "rank": 9, "intensity": 2.7551020408163267, "date": "2017-12-28" }, { "rank": 10, "intensity": 2.7244897959183674, "date": "2017-01-17" }, { "rank": 11, "intensity": 2.693877551020408, "date": "2017-04-24" }, { "rank": 12, "intensity": 2.663265306122449, "date": "2017-07-16" }, { "rank": 13, "intensity": 2.63265306122449, "date": "2017-07-19" }, { "rank": 14, "intensity": 2.6020408163265305, "date": "2017-07-27" }, { "rank": 15, "intensity": 2.5714285714285716, "date": "2017-10-31" }, { "rank": 16, "intensity": 2.5408163265306123, "date": "2017-08-23" }, { "rank": 17, "intensity": 2.510204081632653, "date": "2017-12-22" }, { "rank": 18, "intensity": 2.479591836734694, "date": "2017-05-05" }, { "rank": 19, "intensity": 2.4489795918367347, "date": "2017-04-22" }, { "rank": 20, "intensity": 2.4183673469387754, "date": "2017-06-26" }, { "rank": 21, "intensity": 2.387755102040816, "date": "2017-08-20" }, { "rank": 22, "intensity": 2.357142857142857, "date": "2017-06-30" }, { "rank": 23, "intensity": 2.326530612244898, "date": "2017-11-27" }, { "rank": 24, "intensity": 2.295918367346939, "date": "2017-05-09" }, { "rank": 25, "intensity": 2.2653061224489797, "date": "2017-03-27" }, { "rank": 26, "intensity": 2.2346938775510203, "date": "2017-06-06" }, { "rank": 27, "intensity": 2.204081632653061, "date": "2017-09-06" }, { "rank": 28, "intensity": 2.173469387755102, "date": "2017-03-17" }, { "rank": 29, "intensity": 2.142857142857143, "date": "2017-02-19" }, { "rank": 30, "intensity": 2.112244897959184, "date": "2017-07-06" }, { "rank": 31, "intensity": 2.0816326530612246, "date": "2017-03-05" }, { "rank": 32, "intensity": 2.0510204081632653, "date": "2017-11-18" }, { "rank": 33, "intensity": 2.020408163265306, "date": "2017-07-22" }, { "rank": 34, "intensity": 1.989795918367347, "date": "2017-01-27" }, { "rank": 35, "intensity": 1.9591836734693877, "date": "2017-07-10" }, { "rank": 36, "intensity": 1.9285714285714286, "date": "2017-01-03" }, { "rank": 37, "intensity": 1.8979591836734695, "date": "2017-01-19" }, { "rank": 38, "intensity": 1.8673469387755102, "date": "2017-03-12" }, { "rank": 39, "intensity": 1.836734693877551, "date": "2017-01-12" }, { "rank": 40, "intensity": 1.806122448979592, "date": "2017-04-04" }, { "rank": 41, "intensity": 1.7755102040816326, "date": "2017-09-02" }, { "rank": 42, "intensity": 1.7448979591836735, "date": "2017-10-05" }, { "rank": 43, "intensity": 1.7142857142857144, "date": "2017-05-16" }, { "rank": 44, "intensity": 1.683673469387755, "date": "2017-01-25" }, { "rank": 45, "intensity": 1.653061224489796, "date": "2017-06-14" }, { "rank": 46, "intensity": 1.6224489795918369, "date": "2017-06-21" }, { "rank": 47, "intensity": 1.5918367346938775, "date": "2017-03-30" }, { "rank": 48, "intensity": 1.5612244897959184, "date": "2017-12-20" }, { "rank": 49, "intensity": 1.5306122448979593, "date": "2017-09-12" }, { "rank": 50, "intensity": 1.5, "date": "2017-10-26" } ], "source": "ettm1" } }, { "id": "L3_T1_Composite_Trend_00053", "level": 3, "level_name": "Semantic Reasoning", "category": "Composite Trend", "subtask": "Composite Trend", "question": "Among days in channel LUFL during 2017 that exhibit the trend pattern 'fluctuating stable, then rise', identify the top-5 days where the fluctuating stable segment is the most fluctuating. (Output format: a ranked list of dates, e.g., ['YYYY-MM-DD', ...])", "answer": "['2017-02-05', '2017-06-08', '2017-04-07', '2017-09-11', '2017-05-13']", "ground_truth": [ "2017-02-05", "2017-06-08", "2017-04-07", "2017-09-11", "2017-05-13" ], "eval_metric": "set_f1", "channel": "LUFL", "ts_data_path": "ts_data/L3_T1_Composite_Trend_00053.csv", "meta": { "segments": [ { "kind": "stable", "adj": "fluctuating" }, { "kind": "rise", "adj": "none" } ], "pattern_phrase": "fluctuating stable, then rise", "rank_target_idx": 0, "rank_kind": "stable", "rank_adj": "fluctuating", "rank_axis": "std", "rank_direction": "DESC", "rank_extreme": "most fluctuating", "top_segments_meta": [ { "adj": "fluctuating", "kind": "stable", "label": "fluctuating stable", "start_idx": 0, "end_idx": 57 }, { "adj": "none", "kind": "rise", "label": "none rise", "start_idx": 57, "end_idx": 98 } ], "points_per_day": 96, "pattern": "fluctuating stable, then rise", "year": 2017, "top_k": [ "2017-02-05", "2017-06-08", "2017-04-07", "2017-09-11", "2017-05-13" ], "all_injections": [ { "rank": 1, "intensity": 3.0, "date": "2017-02-05" }, { "rank": 2, "intensity": 2.9693877551020407, "date": "2017-06-08" }, { "rank": 3, "intensity": 2.938775510204082, "date": "2017-04-07" }, { "rank": 4, "intensity": 2.9081632653061225, "date": "2017-09-11" }, { "rank": 5, "intensity": 2.877551020408163, "date": "2017-05-13" }, { "rank": 6, "intensity": 2.8469387755102042, "date": "2017-04-01" }, { "rank": 7, "intensity": 2.816326530612245, "date": "2017-02-21" }, { "rank": 8, "intensity": 2.7857142857142856, "date": "2017-04-09" }, { "rank": 9, "intensity": 2.7551020408163267, "date": "2017-12-03" }, { "rank": 10, "intensity": 2.7244897959183674, "date": "2017-05-09" }, { "rank": 11, "intensity": 2.693877551020408, "date": "2017-05-06" }, { "rank": 12, "intensity": 2.663265306122449, "date": "2017-07-10" }, { "rank": 13, "intensity": 2.63265306122449, "date": "2017-04-23" }, { "rank": 14, "intensity": 2.6020408163265305, "date": "2017-08-19" }, { "rank": 15, "intensity": 2.5714285714285716, "date": "2017-05-16" }, { "rank": 16, "intensity": 2.5408163265306123, "date": "2017-05-30" }, { "rank": 17, "intensity": 2.510204081632653, "date": "2017-06-05" }, { "rank": 18, "intensity": 2.479591836734694, "date": "2017-02-19" }, { "rank": 19, "intensity": 2.4489795918367347, "date": "2017-04-14" }, { "rank": 20, "intensity": 2.4183673469387754, "date": "2017-03-06" }, { "rank": 21, "intensity": 2.387755102040816, "date": "2017-08-01" }, { "rank": 22, "intensity": 2.357142857142857, "date": "2017-02-25" }, { "rank": 23, "intensity": 2.326530612244898, "date": "2017-04-21" }, { "rank": 24, "intensity": 2.295918367346939, "date": "2017-09-25" }, { "rank": 25, "intensity": 2.2653061224489797, "date": "2017-07-17" }, { "rank": 26, "intensity": 2.2346938775510203, "date": "2017-09-02" }, { "rank": 27, "intensity": 2.204081632653061, "date": "2017-05-28" }, { "rank": 28, "intensity": 2.173469387755102, "date": "2017-02-07" }, { "rank": 29, "intensity": 2.142857142857143, "date": "2017-06-27" }, { "rank": 30, "intensity": 2.112244897959184, "date": "2017-06-30" }, { "rank": 31, "intensity": 2.0816326530612246, "date": "2017-11-27" }, { "rank": 32, "intensity": 2.0510204081632653, "date": "2017-09-22" }, { "rank": 33, "intensity": 2.020408163265306, "date": "2017-03-08" }, { "rank": 34, "intensity": 1.989795918367347, "date": "2017-10-29" }, { "rank": 35, "intensity": 1.9591836734693877, "date": "2017-02-11" }, { "rank": 36, "intensity": 1.9285714285714286, "date": "2017-09-08" }, { "rank": 37, "intensity": 1.8979591836734695, "date": "2017-01-10" }, { "rank": 38, "intensity": 1.8673469387755102, "date": "2017-10-23" }, { "rank": 39, "intensity": 1.836734693877551, "date": "2017-12-19" }, { "rank": 40, "intensity": 1.806122448979592, "date": "2017-12-10" }, { "rank": 41, "intensity": 1.7755102040816326, "date": "2017-12-08" }, { "rank": 42, "intensity": 1.7448979591836735, "date": "2017-04-27" }, { "rank": 43, "intensity": 1.7142857142857144, "date": "2017-10-18" }, { "rank": 44, "intensity": 1.683673469387755, "date": "2017-06-03" }, { "rank": 45, "intensity": 1.653061224489796, "date": "2017-10-10" }, { "rank": 46, "intensity": 1.6224489795918369, "date": "2017-11-10" }, { "rank": 47, "intensity": 1.5918367346938775, "date": "2017-08-16" }, { "rank": 48, "intensity": 1.5612244897959184, "date": "2017-01-21" }, { "rank": 49, "intensity": 1.5306122448979593, "date": "2017-03-10" }, { "rank": 50, "intensity": 1.5, "date": "2017-03-14" } ], "source": "ettm1" } }, { "id": "L3_T1_Composite_Trend_00054", "level": 3, "level_name": "Semantic Reasoning", "category": "Composite Trend", "subtask": "Composite Trend", "question": "Among days in channel LULL during 2017 that exhibit the trend pattern 'fall, then slow fall, then rise, then steady stable', identify the top-5 days where the slow fall segment is the slowest. (Output format: a ranked list of dates, e.g., ['YYYY-MM-DD', ...])", "answer": "['2017-02-07', '2017-04-25', '2017-05-06', '2017-08-30', '2017-09-16']", "ground_truth": [ "2017-02-07", "2017-04-25", "2017-05-06", "2017-08-30", "2017-09-16" ], "eval_metric": "set_f1", "channel": "LULL", "ts_data_path": "ts_data/L3_T1_Composite_Trend_00054.csv", "meta": { "segments": [ { "kind": "fall", "adj": "none" }, { "kind": "fall", "adj": "slow" }, { "kind": "rise", "adj": "none" }, { "kind": "stable", "adj": "steady" } ], "pattern_phrase": "fall, then slow fall, then rise, then steady stable", "rank_target_idx": 1, "rank_kind": "fall", "rank_adj": "slow", "rank_axis": "slope", "rank_direction": "ASC", "rank_extreme": "slowest", "top_segments_meta": [ { "adj": "none", "kind": "fall", "label": "none fall", "start_idx": 0, "end_idx": 18 }, { "adj": "slow", "kind": "fall", "label": "slow fall", "start_idx": 18, "end_idx": 47 }, { "adj": "none", "kind": "rise", "label": "none rise", "start_idx": 47, "end_idx": 64 }, { "adj": "steady", "kind": "stable", "label": "steady stable", "start_idx": 64, "end_idx": 98 } ], "points_per_day": 96, "pattern": "fall, then slow fall, then rise, then steady stable", "year": 2017, "top_k": [ "2017-02-07", "2017-04-25", "2017-05-06", "2017-08-30", "2017-09-16" ], "all_injections": [ { "rank": 1, "intensity": 1.5, "date": "2017-02-07" }, { "rank": 2, "intensity": 1.530612244897959, "date": "2017-04-25" }, { "rank": 3, "intensity": 1.5612244897959184, "date": "2017-05-06" }, { "rank": 4, "intensity": 1.5918367346938775, "date": "2017-08-30" }, { "rank": 5, "intensity": 1.6224489795918366, "date": "2017-09-16" }, { "rank": 6, "intensity": 1.653061224489796, "date": "2017-02-22" }, { "rank": 7, "intensity": 1.683673469387755, "date": "2017-11-06" }, { "rank": 8, "intensity": 1.7142857142857142, "date": "2017-07-29" }, { "rank": 9, "intensity": 1.7448979591836735, "date": "2017-08-12" }, { "rank": 10, "intensity": 1.7755102040816326, "date": "2017-11-24" }, { "rank": 11, "intensity": 1.806122448979592, "date": "2017-06-30" }, { "rank": 12, "intensity": 1.836734693877551, "date": "2017-11-18" }, { "rank": 13, "intensity": 1.8673469387755102, "date": "2017-03-31" }, { "rank": 14, "intensity": 1.8979591836734695, "date": "2017-10-29" }, { "rank": 15, "intensity": 1.9285714285714286, "date": "2017-05-16" }, { "rank": 16, "intensity": 1.9591836734693877, "date": "2017-09-21" }, { "rank": 17, "intensity": 1.989795918367347, "date": "2017-08-24" }, { "rank": 18, "intensity": 2.020408163265306, "date": "2017-12-08" }, { "rank": 19, "intensity": 2.0510204081632653, "date": "2017-03-10" }, { "rank": 20, "intensity": 2.0816326530612246, "date": "2017-12-10" }, { "rank": 21, "intensity": 2.112244897959184, "date": "2017-07-04" }, { "rank": 22, "intensity": 2.142857142857143, "date": "2017-01-10" }, { "rank": 23, "intensity": 2.173469387755102, "date": "2017-06-22" }, { "rank": 24, "intensity": 2.204081632653061, "date": "2017-12-06" }, { "rank": 25, "intensity": 2.2346938775510203, "date": "2017-10-02" }, { "rank": 26, "intensity": 2.2653061224489797, "date": "2017-12-18" }, { "rank": 27, "intensity": 2.295918367346939, "date": "2017-11-08" }, { "rank": 28, "intensity": 2.326530612244898, "date": "2017-02-16" }, { "rank": 29, "intensity": 2.357142857142857, "date": "2017-07-24" }, { "rank": 30, "intensity": 2.387755102040816, "date": "2017-04-12" }, { "rank": 31, "intensity": 2.4183673469387754, "date": "2017-02-05" }, { "rank": 32, "intensity": 2.4489795918367347, "date": "2017-11-21" }, { "rank": 33, "intensity": 2.479591836734694, "date": "2017-06-25" }, { "rank": 34, "intensity": 2.510204081632653, "date": "2017-02-24" }, { "rank": 35, "intensity": 2.5408163265306123, "date": "2017-05-21" }, { "rank": 36, "intensity": 2.571428571428571, "date": "2017-01-14" }, { "rank": 37, "intensity": 2.6020408163265305, "date": "2017-12-04" }, { "rank": 38, "intensity": 2.63265306122449, "date": "2017-11-02" }, { "rank": 39, "intensity": 2.663265306122449, "date": "2017-02-14" }, { "rank": 40, "intensity": 2.693877551020408, "date": "2017-08-03" }, { "rank": 41, "intensity": 2.7244897959183674, "date": "2017-12-22" }, { "rank": 42, "intensity": 2.7551020408163263, "date": "2017-01-28" }, { "rank": 43, "intensity": 2.7857142857142856, "date": "2017-02-27" }, { "rank": 44, "intensity": 2.816326530612245, "date": "2017-10-07" }, { "rank": 45, "intensity": 2.8469387755102042, "date": "2017-01-26" }, { "rank": 46, "intensity": 2.877551020408163, "date": "2017-10-26" }, { "rank": 47, "intensity": 2.9081632653061225, "date": "2017-05-26" }, { "rank": 48, "intensity": 2.9387755102040813, "date": "2017-08-07" }, { "rank": 49, "intensity": 2.9693877551020407, "date": "2017-06-15" }, { "rank": 50, "intensity": 3.0, "date": "2017-09-25" } ], "source": "ettm1" } }, { "id": "L3_T1_Composite_Trend_00055", "level": 3, "level_name": "Semantic Reasoning", "category": "Composite Trend", "subtask": "Composite Trend", "question": "Among days in channel OT during 2017 that exhibit the trend pattern 'rapid fall, then steady stable', identify the top-5 days where the rapid fall segment is the fastest. (Output format: a ranked list of dates, e.g., ['YYYY-MM-DD', ...])", "answer": "['2017-03-11', '2017-05-18', '2017-09-23', '2017-02-11', '2017-07-03']", "ground_truth": [ "2017-03-11", "2017-05-18", "2017-09-23", "2017-02-11", "2017-07-03" ], "eval_metric": "set_f1", "channel": "OT", "ts_data_path": "ts_data/L3_T1_Composite_Trend_00055.csv", "meta": { "segments": [ { "kind": "fall", "adj": "rapid" }, { "kind": "stable", "adj": "steady" } ], "pattern_phrase": "rapid fall, then steady stable", "rank_target_idx": 0, "rank_kind": "fall", "rank_adj": "rapid", "rank_axis": "slope", "rank_direction": "DESC", "rank_extreme": "fastest", "top_segments_meta": [ { "adj": "rapid", "kind": "fall", "label": "rapid fall", "start_idx": 0, "end_idx": 19 }, { "adj": "steady", "kind": "stable", "label": "steady stable", "start_idx": 19, "end_idx": 98 } ], "points_per_day": 96, "pattern": "rapid fall, then steady stable", "year": 2017, "top_k": [ "2017-03-11", "2017-05-18", "2017-09-23", "2017-02-11", "2017-07-03" ], "all_injections": [ { "rank": 1, "intensity": 3.0, "date": "2017-03-11" }, { "rank": 2, "intensity": 2.9693877551020407, "date": "2017-05-18" }, { "rank": 3, "intensity": 2.938775510204082, "date": "2017-09-23" }, { "rank": 4, "intensity": 2.9081632653061225, "date": "2017-02-11" }, { "rank": 5, "intensity": 2.877551020408163, "date": "2017-07-03" }, { "rank": 6, "intensity": 2.8469387755102042, "date": "2017-12-07" }, { "rank": 7, "intensity": 2.816326530612245, "date": "2017-02-22" }, { "rank": 8, "intensity": 2.7857142857142856, "date": "2017-05-15" }, { "rank": 9, "intensity": 2.7551020408163267, "date": "2017-01-03" }, { "rank": 10, "intensity": 2.7244897959183674, "date": "2017-06-24" }, { "rank": 11, "intensity": 2.693877551020408, "date": "2017-08-26" }, { "rank": 12, "intensity": 2.663265306122449, "date": "2017-11-06" }, { "rank": 13, "intensity": 2.63265306122449, "date": "2017-07-08" }, { "rank": 14, "intensity": 2.6020408163265305, "date": "2017-12-10" }, { "rank": 15, "intensity": 2.5714285714285716, "date": "2017-12-16" }, { "rank": 16, "intensity": 2.5408163265306123, "date": "2017-08-08" }, { "rank": 17, "intensity": 2.510204081632653, "date": "2017-10-12" }, { "rank": 18, "intensity": 2.479591836734694, "date": "2017-02-05" }, { "rank": 19, "intensity": 2.4489795918367347, "date": "2017-10-28" }, { "rank": 20, "intensity": 2.4183673469387754, "date": "2017-03-09" }, { "rank": 21, "intensity": 2.387755102040816, "date": "2017-04-13" }, { "rank": 22, "intensity": 2.357142857142857, "date": "2017-04-03" }, { "rank": 23, "intensity": 2.326530612244898, "date": "2017-11-19" }, { "rank": 24, "intensity": 2.295918367346939, "date": "2017-06-28" }, { "rank": 25, "intensity": 2.2653061224489797, "date": "2017-07-19" }, { "rank": 26, "intensity": 2.2346938775510203, "date": "2017-05-04" }, { "rank": 27, "intensity": 2.204081632653061, "date": "2017-04-11" }, { "rank": 28, "intensity": 2.173469387755102, "date": "2017-07-25" }, { "rank": 29, "intensity": 2.142857142857143, "date": "2017-02-19" }, { "rank": 30, "intensity": 2.112244897959184, "date": "2017-11-12" }, { "rank": 31, "intensity": 2.0816326530612246, "date": "2017-04-28" }, { "rank": 32, "intensity": 2.0510204081632653, "date": "2017-08-17" }, { "rank": 33, "intensity": 2.020408163265306, "date": "2017-09-01" }, { "rank": 34, "intensity": 1.989795918367347, "date": "2017-08-14" }, { "rank": 35, "intensity": 1.9591836734693877, "date": "2017-12-03" }, { "rank": 36, "intensity": 1.9285714285714286, "date": "2017-09-16" }, { "rank": 37, "intensity": 1.8979591836734695, "date": "2017-04-07" }, { "rank": 38, "intensity": 1.8673469387755102, "date": "2017-10-19" }, { "rank": 39, "intensity": 1.836734693877551, "date": "2017-08-02" }, { "rank": 40, "intensity": 1.806122448979592, "date": "2017-03-16" }, { "rank": 41, "intensity": 1.7755102040816326, "date": "2017-03-29" }, { "rank": 42, "intensity": 1.7448979591836735, "date": "2017-01-06" }, { "rank": 43, "intensity": 1.7142857142857144, "date": "2017-10-22" }, { "rank": 44, "intensity": 1.683673469387755, "date": "2017-04-20" }, { "rank": 45, "intensity": 1.653061224489796, "date": "2017-03-22" }, { "rank": 46, "intensity": 1.6224489795918369, "date": "2017-05-01" }, { "rank": 47, "intensity": 1.5918367346938775, "date": "2017-12-20" }, { "rank": 48, "intensity": 1.5612244897959184, "date": "2017-09-11" }, { "rank": 49, "intensity": 1.5306122448979593, "date": "2017-10-04" }, { "rank": 50, "intensity": 1.5, "date": "2017-11-22" } ], "source": "ettm1" } }, { "id": "L3_T1_Composite_Trend_00056", "level": 3, "level_name": "Semantic Reasoning", "category": "Composite Trend", "subtask": "Composite Trend", "question": "Among days in channel 67 during 2021 that exhibit the trend pattern 'rapid rise, then fluctuating stable', identify the top-5 days where the fluctuating stable segment is the most fluctuating. (Output format: a ranked list of dates, e.g., ['YYYY-MM-DD', ...])", "answer": "['2021-06-01', '2021-12-05', '2021-01-23', '2021-02-15', '2021-09-25']", "ground_truth": [ "2021-06-01", "2021-12-05", "2021-01-23", "2021-02-15", "2021-09-25" ], "eval_metric": "set_f1", "channel": "67", "ts_data_path": "ts_data/L3_T1_Composite_Trend_00056.csv", "meta": { "segments": [ { "kind": "rise", "adj": "rapid" }, { "kind": "stable", "adj": "fluctuating" } ], "pattern_phrase": "rapid rise, then fluctuating stable", "rank_target_idx": 1, "rank_kind": "stable", "rank_adj": "fluctuating", "rank_axis": "std", "rank_direction": "DESC", "rank_extreme": "most fluctuating", "top_segments_meta": [ { "adj": "rapid", "kind": "rise", "label": "rapid rise", "start_idx": 0, "end_idx": 29 }, { "adj": "fluctuating", "kind": "stable", "label": "fluctuating stable", "start_idx": 29, "end_idx": 98 } ], "points_per_day": 96, "pattern": "rapid rise, then fluctuating stable", "year": 2021, "top_k": [ "2021-06-01", "2021-12-05", "2021-01-23", "2021-02-15", "2021-09-25" ], "all_injections": [ { "rank": 1, "intensity": 3.0, "date": "2021-06-01" }, { "rank": 2, "intensity": 2.9693877551020407, "date": "2021-12-05" }, { "rank": 3, "intensity": 2.938775510204082, "date": "2021-01-23" }, { "rank": 4, "intensity": 2.9081632653061225, "date": "2021-02-15" }, { "rank": 5, "intensity": 2.877551020408163, "date": "2021-09-25" }, { "rank": 6, "intensity": 2.8469387755102042, "date": "2021-01-31" }, { "rank": 7, "intensity": 2.816326530612245, "date": "2021-06-03" }, { "rank": 8, "intensity": 2.7857142857142856, "date": "2021-07-09" }, { "rank": 9, "intensity": 2.7551020408163267, "date": "2021-10-04" }, { "rank": 10, "intensity": 2.7244897959183674, "date": "2021-09-07" }, { "rank": 11, "intensity": 2.693877551020408, "date": "2021-03-22" }, { "rank": 12, "intensity": 2.663265306122449, "date": "2021-01-19" }, { "rank": 13, "intensity": 2.63265306122449, "date": "2021-10-25" }, { "rank": 14, "intensity": 2.6020408163265305, "date": "2021-10-13" }, { "rank": 15, "intensity": 2.5714285714285716, "date": "2021-01-10" }, { "rank": 16, "intensity": 2.5408163265306123, "date": "2021-08-24" }, { "rank": 17, "intensity": 2.510204081632653, "date": "2021-03-10" }, { "rank": 18, "intensity": 2.479591836734694, "date": "2021-03-29" }, { "rank": 19, "intensity": 2.4489795918367347, "date": "2021-05-05" }, { "rank": 20, "intensity": 2.4183673469387754, "date": "2021-01-15" }, { "rank": 21, "intensity": 2.387755102040816, "date": "2021-08-02" }, { "rank": 22, "intensity": 2.357142857142857, "date": "2021-01-06" }, { "rank": 23, "intensity": 2.326530612244898, "date": "2021-07-25" }, { "rank": 24, "intensity": 2.295918367346939, "date": "2021-09-20" }, { "rank": 25, "intensity": 2.2653061224489797, "date": "2021-07-22" }, { "rank": 26, "intensity": 2.2346938775510203, "date": "2021-02-05" }, { "rank": 27, "intensity": 2.204081632653061, "date": "2021-07-03" }, { "rank": 28, "intensity": 2.173469387755102, "date": "2021-01-17" }, { "rank": 29, "intensity": 2.142857142857143, "date": "2021-12-10" }, { "rank": 30, "intensity": 2.112244897959184, "date": "2021-01-13" }, { "rank": 31, "intensity": 2.0816326530612246, "date": "2021-09-04" }, { "rank": 32, "intensity": 2.0510204081632653, "date": "2021-05-28" }, { "rank": 33, "intensity": 2.020408163265306, "date": "2021-03-14" }, { "rank": 34, "intensity": 1.989795918367347, "date": "2021-05-18" }, { "rank": 35, "intensity": 1.9591836734693877, "date": "2021-04-04" }, { "rank": 36, "intensity": 1.9285714285714286, "date": "2021-05-12" }, { "rank": 37, "intensity": 1.8979591836734695, "date": "2021-10-21" }, { "rank": 38, "intensity": 1.8673469387755102, "date": "2021-02-20" }, { "rank": 39, "intensity": 1.836734693877551, "date": "2021-12-02" }, { "rank": 40, "intensity": 1.806122448979592, "date": "2021-04-12" }, { "rank": 41, "intensity": 1.7755102040816326, "date": "2021-02-08" }, { "rank": 42, "intensity": 1.7448979591836735, "date": "2021-08-28" }, { "rank": 43, "intensity": 1.7142857142857144, "date": "2021-10-09" }, { "rank": 44, "intensity": 1.683673469387755, "date": "2021-03-01" }, { "rank": 45, "intensity": 1.653061224489796, "date": "2021-10-28" }, { "rank": 46, "intensity": 1.6224489795918369, "date": "2021-09-29" }, { "rank": 47, "intensity": 1.5918367346938775, "date": "2021-04-20" }, { "rank": 48, "intensity": 1.5612244897959184, "date": "2021-05-03" }, { "rank": 49, "intensity": 1.5306122448979593, "date": "2021-11-03" }, { "rank": 50, "intensity": 1.5, "date": "2021-11-29" } ], "source": "causal_rivers" } }, { "id": "L3_T1_Composite_Trend_00057", "level": 3, "level_name": "Semantic Reasoning", "category": "Composite Trend", "subtask": "Composite Trend", "question": "Among days in channel 71 during 2023 that exhibit the trend pattern 'steady stable, then fluctuating stable, then slow fall', identify the top-5 days where the slow fall segment is the slowest. (Output format: a ranked list of dates, e.g., ['YYYY-MM-DD', ...])", "answer": "['2023-02-20', '2023-10-07', '2023-11-18', '2023-06-02', '2023-11-12']", "ground_truth": [ "2023-02-20", "2023-10-07", "2023-11-18", "2023-06-02", "2023-11-12" ], "eval_metric": "set_f1", "channel": "71", "ts_data_path": "ts_data/L3_T1_Composite_Trend_00057.csv", "meta": { "segments": [ { "kind": "stable", "adj": "steady" }, { "kind": "stable", "adj": "fluctuating" }, { "kind": "fall", "adj": "slow" } ], "pattern_phrase": "steady stable, then fluctuating stable, then slow fall", "rank_target_idx": 2, "rank_kind": "fall", "rank_adj": "slow", "rank_axis": "slope", "rank_direction": "ASC", "rank_extreme": "slowest", "top_segments_meta": [ { "adj": "steady", "kind": "stable", "label": "steady stable", "start_idx": 0, "end_idx": 37 }, { "adj": "fluctuating", "kind": "stable", "label": "fluctuating stable", "start_idx": 37, "end_idx": 60 }, { "adj": "slow", "kind": "fall", "label": "slow fall", "start_idx": 60, "end_idx": 98 } ], "points_per_day": 96, "pattern": "steady stable, then fluctuating stable, then slow fall", "year": 2023, "top_k": [ "2023-02-20", "2023-10-07", "2023-11-18", "2023-06-02", "2023-11-12" ], "all_injections": [ { "rank": 1, "intensity": 1.5, "date": "2023-02-20" }, { "rank": 2, "intensity": 1.530612244897959, "date": "2023-10-07" }, { "rank": 3, "intensity": 1.5612244897959184, "date": "2023-11-18" }, { "rank": 4, "intensity": 1.5918367346938775, "date": "2023-06-02" }, { "rank": 5, "intensity": 1.6224489795918366, "date": "2023-11-12" }, { "rank": 6, "intensity": 1.653061224489796, "date": "2023-05-17" }, { "rank": 7, "intensity": 1.683673469387755, "date": "2023-04-18" }, { "rank": 8, "intensity": 1.7142857142857142, "date": "2023-12-25" }, { "rank": 9, "intensity": 1.7448979591836735, "date": "2023-08-19" }, { "rank": 10, "intensity": 1.7755102040816326, "date": "2023-05-11" }, { "rank": 11, "intensity": 1.806122448979592, "date": "2023-04-21" }, { "rank": 12, "intensity": 1.836734693877551, "date": "2023-02-17" }, { "rank": 13, "intensity": 1.8673469387755102, "date": "2023-08-07" }, { "rank": 14, "intensity": 1.8979591836734695, "date": "2023-08-26" }, { "rank": 15, "intensity": 1.9285714285714286, "date": "2023-10-11" }, { "rank": 16, "intensity": 1.9591836734693877, "date": "2023-03-24" }, { "rank": 17, "intensity": 1.989795918367347, "date": "2023-06-20" }, { "rank": 18, "intensity": 2.020408163265306, "date": "2023-12-14" }, { "rank": 19, "intensity": 2.0510204081632653, "date": "2023-10-02" }, { "rank": 20, "intensity": 2.0816326530612246, "date": "2023-11-08" }, { "rank": 21, "intensity": 2.112244897959184, "date": "2023-03-11" }, { "rank": 22, "intensity": 2.142857142857143, "date": "2023-01-15" }, { "rank": 23, "intensity": 2.173469387755102, "date": "2023-05-03" }, { "rank": 24, "intensity": 2.204081632653061, "date": "2023-12-07" }, { "rank": 25, "intensity": 2.2346938775510203, "date": "2023-01-11" }, { "rank": 26, "intensity": 2.2653061224489797, "date": "2023-01-28" }, { "rank": 27, "intensity": 2.295918367346939, "date": "2023-10-30" }, { "rank": 28, "intensity": 2.326530612244898, "date": "2023-08-11" }, { "rank": 29, "intensity": 2.357142857142857, "date": "2023-02-10" }, { "rank": 30, "intensity": 2.387755102040816, "date": "2023-10-05" }, { "rank": 31, "intensity": 2.4183673469387754, "date": "2023-05-31" }, { "rank": 32, "intensity": 2.4489795918367347, "date": "2023-06-15" }, { "rank": 33, "intensity": 2.479591836734694, "date": "2023-05-19" }, { "rank": 34, "intensity": 2.510204081632653, "date": "2023-10-13" }, { "rank": 35, "intensity": 2.5408163265306123, "date": "2023-12-05" }, { "rank": 36, "intensity": 2.571428571428571, "date": "2023-07-16" }, { "rank": 37, "intensity": 2.6020408163265305, "date": "2023-07-28" }, { "rank": 38, "intensity": 2.63265306122449, "date": "2023-11-10" }, { "rank": 39, "intensity": 2.663265306122449, "date": "2023-11-15" }, { "rank": 40, "intensity": 2.693877551020408, "date": "2023-08-29" }, { "rank": 41, "intensity": 2.7244897959183674, "date": "2023-03-18" }, { "rank": 42, "intensity": 2.7551020408163263, "date": "2023-04-06" }, { "rank": 43, "intensity": 2.7857142857142856, "date": "2023-02-08" }, { "rank": 44, "intensity": 2.816326530612245, "date": "2023-03-16" }, { "rank": 45, "intensity": 2.8469387755102042, "date": "2023-09-25" }, { "rank": 46, "intensity": 2.877551020408163, "date": "2023-04-11" }, { "rank": 47, "intensity": 2.9081632653061225, "date": "2023-07-19" }, { "rank": 48, "intensity": 2.9387755102040813, "date": "2023-09-10" }, { "rank": 49, "intensity": 2.9693877551020407, "date": "2023-04-04" }, { "rank": 50, "intensity": 3.0, "date": "2023-07-09" } ], "source": "causal_rivers" } }, { "id": "L3_T1_Composite_Trend_00058", "level": 3, "level_name": "Semantic Reasoning", "category": "Composite Trend", "subtask": "Composite Trend", "question": "Among days in channel 99 during 2023 that exhibit the trend pattern 'fluctuating stable, then rise', identify the top-5 days where the fluctuating stable segment is the most fluctuating. (Output format: a ranked list of dates, e.g., ['YYYY-MM-DD', ...])", "answer": "['2023-07-09', '2023-01-31', '2023-10-31', '2023-04-27', '2023-02-17']", "ground_truth": [ "2023-07-09", "2023-01-31", "2023-10-31", "2023-04-27", "2023-02-17" ], "eval_metric": "set_f1", "channel": "99", "ts_data_path": "ts_data/L3_T1_Composite_Trend_00058.csv", "meta": { "segments": [ { "kind": "stable", "adj": "fluctuating" }, { "kind": "rise", "adj": "none" } ], "pattern_phrase": "fluctuating stable, then rise", "rank_target_idx": 0, "rank_kind": "stable", "rank_adj": "fluctuating", "rank_axis": "std", "rank_direction": "DESC", "rank_extreme": "most fluctuating", "top_segments_meta": [ { "adj": "fluctuating", "kind": "stable", "label": "fluctuating stable", "start_idx": 0, "end_idx": 52 }, { "adj": "none", "kind": "rise", "label": "none rise", "start_idx": 52, "end_idx": 98 } ], "points_per_day": 96, "pattern": "fluctuating stable, then rise", "year": 2023, "top_k": [ "2023-07-09", "2023-01-31", "2023-10-31", "2023-04-27", "2023-02-17" ], "all_injections": [ { "rank": 1, "intensity": 3.0, "date": "2023-07-09" }, { "rank": 2, "intensity": 2.9693877551020407, "date": "2023-01-31" }, { "rank": 3, "intensity": 2.938775510204082, "date": "2023-10-31" }, { "rank": 4, "intensity": 2.9081632653061225, "date": "2023-04-27" }, { "rank": 5, "intensity": 2.877551020408163, "date": "2023-02-17" }, { "rank": 6, "intensity": 2.8469387755102042, "date": "2023-12-13" }, { "rank": 7, "intensity": 2.816326530612245, "date": "2023-07-03" }, { "rank": 8, "intensity": 2.7857142857142856, "date": "2023-02-13" }, { "rank": 9, "intensity": 2.7551020408163267, "date": "2023-07-12" }, { "rank": 10, "intensity": 2.7244897959183674, "date": "2023-01-02" }, { "rank": 11, "intensity": 2.693877551020408, "date": "2023-03-07" }, { "rank": 12, "intensity": 2.663265306122449, "date": "2023-07-31" }, { "rank": 13, "intensity": 2.63265306122449, "date": "2023-06-10" }, { "rank": 14, "intensity": 2.6020408163265305, "date": "2023-08-13" }, { "rank": 15, "intensity": 2.5714285714285716, "date": "2023-01-20" }, { "rank": 16, "intensity": 2.5408163265306123, "date": "2023-08-24" }, { "rank": 17, "intensity": 2.510204081632653, "date": "2023-10-14" }, { "rank": 18, "intensity": 2.479591836734694, "date": "2023-05-13" }, { "rank": 19, "intensity": 2.4489795918367347, "date": "2023-06-21" }, { "rank": 20, "intensity": 2.4183673469387754, "date": "2023-12-19" }, { "rank": 21, "intensity": 2.387755102040816, "date": "2023-02-21" }, { "rank": 22, "intensity": 2.357142857142857, "date": "2023-05-16" }, { "rank": 23, "intensity": 2.326530612244898, "date": "2023-11-05" }, { "rank": 24, "intensity": 2.295918367346939, "date": "2023-05-07" }, { "rank": 25, "intensity": 2.2653061224489797, "date": "2023-01-10" }, { "rank": 26, "intensity": 2.2346938775510203, "date": "2023-03-05" }, { "rank": 27, "intensity": 2.204081632653061, "date": "2023-03-14" }, { "rank": 28, "intensity": 2.173469387755102, "date": "2023-02-04" }, { "rank": 29, "intensity": 2.142857142857143, "date": "2023-09-29" }, { "rank": 30, "intensity": 2.112244897959184, "date": "2023-09-17" }, { "rank": 31, "intensity": 2.0816326530612246, "date": "2023-12-22" }, { "rank": 32, "intensity": 2.0510204081632653, "date": "2023-11-20" }, { "rank": 33, "intensity": 2.020408163265306, "date": "2023-07-21" }, { "rank": 34, "intensity": 1.989795918367347, "date": "2023-10-29" }, { "rank": 35, "intensity": 1.9591836734693877, "date": "2023-03-29" }, { "rank": 36, "intensity": 1.9285714285714286, "date": "2023-09-12" }, { "rank": 37, "intensity": 1.8979591836734695, "date": "2023-10-21" }, { "rank": 38, "intensity": 1.8673469387755102, "date": "2023-04-11" }, { "rank": 39, "intensity": 1.836734693877551, "date": "2023-05-09" }, { "rank": 40, "intensity": 1.806122448979592, "date": "2023-04-14" }, { "rank": 41, "intensity": 1.7755102040816326, "date": "2023-12-30" }, { "rank": 42, "intensity": 1.7448979591836735, "date": "2023-06-05" }, { "rank": 43, "intensity": 1.7142857142857144, "date": "2023-12-25" }, { "rank": 44, "intensity": 1.683673469387755, "date": "2023-03-31" }, { "rank": 45, "intensity": 1.653061224489796, "date": "2023-12-08" }, { "rank": 46, "intensity": 1.6224489795918369, "date": "2023-02-27" }, { "rank": 47, "intensity": 1.5918367346938775, "date": "2023-06-28" }, { "rank": 48, "intensity": 1.5612244897959184, "date": "2023-01-05" }, { "rank": 49, "intensity": 1.5306122448979593, "date": "2023-06-08" }, { "rank": 50, "intensity": 1.5, "date": "2023-03-03" } ], "source": "causal_rivers" } }, { "id": "L3_T1_Composite_Trend_00059", "level": 3, "level_name": "Semantic Reasoning", "category": "Composite Trend", "subtask": "Composite Trend", "question": "Among days in channel 123 during 2019 that exhibit the trend pattern 'slow rise, then rise, then slow fall', identify the top-5 days where the slow rise segment is the slowest. (Output format: a ranked list of dates, e.g., ['YYYY-MM-DD', ...])", "answer": "['2019-02-06', '2019-03-13', '2019-11-02', '2019-06-24', '2019-12-15']", "ground_truth": [ "2019-02-06", "2019-03-13", "2019-11-02", "2019-06-24", "2019-12-15" ], "eval_metric": "set_f1", "channel": "123", "ts_data_path": "ts_data/L3_T1_Composite_Trend_00059.csv", "meta": { "segments": [ { "kind": "rise", "adj": "slow" }, { "kind": "rise", "adj": "none" }, { "kind": "fall", "adj": "slow" } ], "pattern_phrase": "slow rise, then rise, then slow fall", "rank_target_idx": 0, "rank_kind": "rise", "rank_adj": "slow", "rank_axis": "slope", "rank_direction": "ASC", "rank_extreme": "slowest", "top_segments_meta": [ { "adj": "slow", "kind": "rise", "label": "slow rise", "start_idx": 0, "end_idx": 37 }, { "adj": "none", "kind": "rise", "label": "none rise", "start_idx": 37, "end_idx": 58 }, { "adj": "slow", "kind": "fall", "label": "slow fall", "start_idx": 58, "end_idx": 98 } ], "points_per_day": 96, "pattern": "slow rise, then rise, then slow fall", "year": 2019, "top_k": [ "2019-02-06", "2019-03-13", "2019-11-02", "2019-06-24", "2019-12-15" ], "all_injections": [ { "rank": 1, "intensity": 1.5, "date": "2019-02-06" }, { "rank": 2, "intensity": 1.530612244897959, "date": "2019-03-13" }, { "rank": 3, "intensity": 1.5612244897959184, "date": "2019-11-02" }, { "rank": 4, "intensity": 1.5918367346938775, "date": "2019-06-24" }, { "rank": 5, "intensity": 1.6224489795918366, "date": "2019-12-15" }, { "rank": 6, "intensity": 1.653061224489796, "date": "2019-01-12" }, { "rank": 7, "intensity": 1.683673469387755, "date": "2019-08-23" }, { "rank": 8, "intensity": 1.7142857142857142, "date": "2019-08-11" }, { "rank": 9, "intensity": 1.7448979591836735, "date": "2019-03-10" }, { "rank": 10, "intensity": 1.7755102040816326, "date": "2019-11-05" }, { "rank": 11, "intensity": 1.806122448979592, "date": "2019-06-16" }, { "rank": 12, "intensity": 1.836734693877551, "date": "2019-06-06" }, { "rank": 13, "intensity": 1.8673469387755102, "date": "2019-06-11" }, { "rank": 14, "intensity": 1.8979591836734695, "date": "2019-05-28" }, { "rank": 15, "intensity": 1.9285714285714286, "date": "2019-01-26" }, { "rank": 16, "intensity": 1.9591836734693877, "date": "2019-09-13" }, { "rank": 17, "intensity": 1.989795918367347, "date": "2019-09-10" }, { "rank": 18, "intensity": 2.020408163265306, "date": "2019-07-30" }, { "rank": 19, "intensity": 2.0510204081632653, "date": "2019-01-02" }, { "rank": 20, "intensity": 2.0816326530612246, "date": "2019-12-28" }, { "rank": 21, "intensity": 2.112244897959184, "date": "2019-07-17" }, { "rank": 22, "intensity": 2.142857142857143, "date": "2019-07-14" }, { "rank": 23, "intensity": 2.173469387755102, "date": "2019-05-06" }, { "rank": 24, "intensity": 2.204081632653061, "date": "2019-08-15" }, { "rank": 25, "intensity": 2.2346938775510203, "date": "2019-06-26" }, { "rank": 26, "intensity": 2.2653061224489797, "date": "2019-08-09" }, { "rank": 27, "intensity": 2.295918367346939, "date": "2019-06-28" }, { "rank": 28, "intensity": 2.326530612244898, "date": "2019-05-10" }, { "rank": 29, "intensity": 2.357142857142857, "date": "2019-09-26" }, { "rank": 30, "intensity": 2.387755102040816, "date": "2019-10-08" }, { "rank": 31, "intensity": 2.4183673469387754, "date": "2019-07-21" }, { "rank": 32, "intensity": 2.4489795918367347, "date": "2019-08-04" }, { "rank": 33, "intensity": 2.479591836734694, "date": "2019-12-24" }, { "rank": 34, "intensity": 2.510204081632653, "date": "2019-03-22" }, { "rank": 35, "intensity": 2.5408163265306123, "date": "2019-12-11" }, { "rank": 36, "intensity": 2.571428571428571, "date": "2019-06-03" }, { "rank": 37, "intensity": 2.6020408163265305, "date": "2019-09-17" }, { "rank": 38, "intensity": 2.63265306122449, "date": "2019-05-22" }, { "rank": 39, "intensity": 2.663265306122449, "date": "2019-05-20" }, { "rank": 40, "intensity": 2.693877551020408, "date": "2019-03-24" }, { "rank": 41, "intensity": 2.7244897959183674, "date": "2019-08-31" }, { "rank": 42, "intensity": 2.7551020408163263, "date": "2019-11-30" }, { "rank": 43, "intensity": 2.7857142857142856, "date": "2019-08-13" }, { "rank": 44, "intensity": 2.816326530612245, "date": "2019-08-28" }, { "rank": 45, "intensity": 2.8469387755102042, "date": "2019-04-17" }, { "rank": 46, "intensity": 2.877551020408163, "date": "2019-07-08" }, { "rank": 47, "intensity": 2.9081632653061225, "date": "2019-01-30" }, { "rank": 48, "intensity": 2.9387755102040813, "date": "2019-10-02" }, { "rank": 49, "intensity": 2.9693877551020407, "date": "2019-02-04" }, { "rank": 50, "intensity": 3.0, "date": "2019-02-19" } ], "source": "causal_rivers" } }, { "id": "L3_T1_Composite_Trend_00060", "level": 3, "level_name": "Semantic Reasoning", "category": "Composite Trend", "subtask": "Composite Trend", "question": "Among days in channel 124 during 2021 that exhibit the trend pattern 'slow rise, then fall, then fluctuating stable, then fall', identify the top-5 days where the slow rise segment is the slowest. (Output format: a ranked list of dates, e.g., ['YYYY-MM-DD', ...])", "answer": "['2021-01-13', '2021-05-09', '2021-10-14', '2021-11-04', '2021-06-10']", "ground_truth": [ "2021-01-13", "2021-05-09", "2021-10-14", "2021-11-04", "2021-06-10" ], "eval_metric": "set_f1", "channel": "124", "ts_data_path": "ts_data/L3_T1_Composite_Trend_00060.csv", "meta": { "segments": [ { "kind": "rise", "adj": "slow" }, { "kind": "fall", "adj": "none" }, { "kind": "stable", "adj": "fluctuating" }, { "kind": "fall", "adj": "none" } ], "pattern_phrase": "slow rise, then fall, then fluctuating stable, then fall", "rank_target_idx": 0, "rank_kind": "rise", "rank_adj": "slow", "rank_axis": "slope", "rank_direction": "ASC", "rank_extreme": "slowest", "top_segments_meta": [ { "adj": "slow", "kind": "rise", "label": "slow rise", "start_idx": 0, "end_idx": 31 }, { "adj": "none", "kind": "fall", "label": "none fall", "start_idx": 31, "end_idx": 52 }, { "adj": "fluctuating", "kind": "stable", "label": "fluctuating stable", "start_idx": 52, "end_idx": 78 }, { "adj": "none", "kind": "fall", "label": "none fall", "start_idx": 78, "end_idx": 98 } ], "points_per_day": 96, "pattern": "slow rise, then fall, then fluctuating stable, then fall", "year": 2021, "top_k": [ "2021-01-13", "2021-05-09", "2021-10-14", "2021-11-04", "2021-06-10" ], "all_injections": [ { "rank": 1, "intensity": 1.5, "date": "2021-01-13" }, { "rank": 2, "intensity": 1.530612244897959, "date": "2021-05-09" }, { "rank": 3, "intensity": 1.5612244897959184, "date": "2021-10-14" }, { "rank": 4, "intensity": 1.5918367346938775, "date": "2021-11-04" }, { "rank": 5, "intensity": 1.6224489795918366, "date": "2021-06-10" }, { "rank": 6, "intensity": 1.653061224489796, "date": "2021-10-16" }, { "rank": 7, "intensity": 1.683673469387755, "date": "2021-08-03" }, { "rank": 8, "intensity": 1.7142857142857142, "date": "2021-12-28" }, { "rank": 9, "intensity": 1.7448979591836735, "date": "2021-09-25" }, { "rank": 10, "intensity": 1.7755102040816326, "date": "2021-11-12" }, { "rank": 11, "intensity": 1.806122448979592, "date": "2021-05-16" }, { "rank": 12, "intensity": 1.836734693877551, "date": "2021-11-28" }, { "rank": 13, "intensity": 1.8673469387755102, "date": "2021-07-29" }, { "rank": 14, "intensity": 1.8979591836734695, "date": "2021-09-19" }, { "rank": 15, "intensity": 1.9285714285714286, "date": "2021-04-28" }, { "rank": 16, "intensity": 1.9591836734693877, "date": "2021-08-09" }, { "rank": 17, "intensity": 1.989795918367347, "date": "2021-01-18" }, { "rank": 18, "intensity": 2.020408163265306, "date": "2021-08-31" }, { "rank": 19, "intensity": 2.0510204081632653, "date": "2021-08-18" }, { "rank": 20, "intensity": 2.0816326530612246, "date": "2021-04-12" }, { "rank": 21, "intensity": 2.112244897959184, "date": "2021-12-19" }, { "rank": 22, "intensity": 2.142857142857143, "date": "2021-02-12" }, { "rank": 23, "intensity": 2.173469387755102, "date": "2021-07-24" }, { "rank": 24, "intensity": 2.204081632653061, "date": "2021-06-30" }, { "rank": 25, "intensity": 2.2346938775510203, "date": "2021-08-12" }, { "rank": 26, "intensity": 2.2653061224489797, "date": "2021-10-19" }, { "rank": 27, "intensity": 2.295918367346939, "date": "2021-11-19" }, { "rank": 28, "intensity": 2.326530612244898, "date": "2021-10-12" }, { "rank": 29, "intensity": 2.357142857142857, "date": "2021-03-31" }, { "rank": 30, "intensity": 2.387755102040816, "date": "2021-08-06" }, { "rank": 31, "intensity": 2.4183673469387754, "date": "2021-02-25" }, { "rank": 32, "intensity": 2.4489795918367347, "date": "2021-06-17" }, { "rank": 33, "intensity": 2.479591836734694, "date": "2021-03-03" }, { "rank": 34, "intensity": 2.510204081632653, "date": "2021-03-21" }, { "rank": 35, "intensity": 2.5408163265306123, "date": "2021-12-06" }, { "rank": 36, "intensity": 2.571428571428571, "date": "2021-02-09" }, { "rank": 37, "intensity": 2.6020408163265305, "date": "2021-03-11" }, { "rank": 38, "intensity": 2.63265306122449, "date": "2021-01-28" }, { "rank": 39, "intensity": 2.663265306122449, "date": "2021-09-22" }, { "rank": 40, "intensity": 2.693877551020408, "date": "2021-09-13" }, { "rank": 41, "intensity": 2.7244897959183674, "date": "2021-02-27" }, { "rank": 42, "intensity": 2.7551020408163263, "date": "2021-07-05" }, { "rank": 43, "intensity": 2.7857142857142856, "date": "2021-01-05" }, { "rank": 44, "intensity": 2.816326530612245, "date": "2021-08-25" }, { "rank": 45, "intensity": 2.8469387755102042, "date": "2021-06-26" }, { "rank": 46, "intensity": 2.877551020408163, "date": "2021-04-18" }, { "rank": 47, "intensity": 2.9081632653061225, "date": "2021-04-06" }, { "rank": 48, "intensity": 2.9387755102040813, "date": "2021-05-02" }, { "rank": 49, "intensity": 2.9693877551020407, "date": "2021-07-03" }, { "rank": 50, "intensity": 3.0, "date": "2021-04-03" } ], "source": "causal_rivers" } }, { "id": "L3_T1_Composite_Trend_00061", "level": 3, "level_name": "Semantic Reasoning", "category": "Composite Trend", "subtask": "Composite Trend", "question": "Among days in channel 146 during 2021 that exhibit the trend pattern 'steady stable, then rapid rise', identify the top-5 days where the rapid rise segment is the fastest. (Output format: a ranked list of dates, e.g., ['YYYY-MM-DD', ...])", "answer": "['2021-03-28', '2021-04-23', '2021-12-10', '2021-03-03', '2021-04-18']", "ground_truth": [ "2021-03-28", "2021-04-23", "2021-12-10", "2021-03-03", "2021-04-18" ], "eval_metric": "set_f1", "channel": "146", "ts_data_path": "ts_data/L3_T1_Composite_Trend_00061.csv", "meta": { "segments": [ { "kind": "stable", "adj": "steady" }, { "kind": "rise", "adj": "rapid" } ], "pattern_phrase": "steady stable, then rapid rise", "rank_target_idx": 1, "rank_kind": "rise", "rank_adj": "rapid", "rank_axis": "slope", "rank_direction": "DESC", "rank_extreme": "fastest", "top_segments_meta": [ { "adj": "steady", "kind": "stable", "label": "steady stable", "start_idx": 0, "end_idx": 79 }, { "adj": "rapid", "kind": "rise", "label": "rapid rise", "start_idx": 79, "end_idx": 98 } ], "points_per_day": 96, "pattern": "steady stable, then rapid rise", "year": 2021, "top_k": [ "2021-03-28", "2021-04-23", "2021-12-10", "2021-03-03", "2021-04-18" ], "all_injections": [ { "rank": 1, "intensity": 3.0, "date": "2021-03-28" }, { "rank": 2, "intensity": 2.9693877551020407, "date": "2021-04-23" }, { "rank": 3, "intensity": 2.938775510204082, "date": "2021-12-10" }, { "rank": 4, "intensity": 2.9081632653061225, "date": "2021-03-03" }, { "rank": 5, "intensity": 2.877551020408163, "date": "2021-04-18" }, { "rank": 6, "intensity": 2.8469387755102042, "date": "2021-07-22" }, { "rank": 7, "intensity": 2.816326530612245, "date": "2021-09-26" }, { "rank": 8, "intensity": 2.7857142857142856, "date": "2021-11-23" }, { "rank": 9, "intensity": 2.7551020408163267, "date": "2021-05-30" }, { "rank": 10, "intensity": 2.7244897959183674, "date": "2021-03-24" }, { "rank": 11, "intensity": 2.693877551020408, "date": "2021-08-09" }, { "rank": 12, "intensity": 2.663265306122449, "date": "2021-05-18" }, { "rank": 13, "intensity": 2.63265306122449, "date": "2021-12-07" }, { "rank": 14, "intensity": 2.6020408163265305, "date": "2021-05-08" }, { "rank": 15, "intensity": 2.5714285714285716, "date": "2021-03-22" }, { "rank": 16, "intensity": 2.5408163265306123, "date": "2021-01-26" }, { "rank": 17, "intensity": 2.510204081632653, "date": "2021-11-07" }, { "rank": 18, "intensity": 2.479591836734694, "date": "2021-10-13" }, { "rank": 19, "intensity": 2.4489795918367347, "date": "2021-11-15" }, { "rank": 20, "intensity": 2.4183673469387754, "date": "2021-04-01" }, { "rank": 21, "intensity": 2.387755102040816, "date": "2021-08-28" }, { "rank": 22, "intensity": 2.357142857142857, "date": "2021-07-19" }, { "rank": 23, "intensity": 2.326530612244898, "date": "2021-06-21" }, { "rank": 24, "intensity": 2.295918367346939, "date": "2021-05-12" }, { "rank": 25, "intensity": 2.2653061224489797, "date": "2021-07-31" }, { "rank": 26, "intensity": 2.2346938775510203, "date": "2021-02-20" }, { "rank": 27, "intensity": 2.204081632653061, "date": "2021-06-28" }, { "rank": 28, "intensity": 2.173469387755102, "date": "2021-05-25" }, { "rank": 29, "intensity": 2.142857142857143, "date": "2021-06-14" }, { "rank": 30, "intensity": 2.112244897959184, "date": "2021-10-15" }, { "rank": 31, "intensity": 2.0816326530612246, "date": "2021-09-18" }, { "rank": 32, "intensity": 2.0510204081632653, "date": "2021-01-11" }, { "rank": 33, "intensity": 2.020408163265306, "date": "2021-01-06" }, { "rank": 34, "intensity": 1.989795918367347, "date": "2021-06-24" }, { "rank": 35, "intensity": 1.9591836734693877, "date": "2021-12-15" }, { "rank": 36, "intensity": 1.9285714285714286, "date": "2021-07-27" }, { "rank": 37, "intensity": 1.8979591836734695, "date": "2021-01-03" }, { "rank": 38, "intensity": 1.8673469387755102, "date": "2021-04-11" }, { "rank": 39, "intensity": 1.836734693877551, "date": "2021-08-05" }, { "rank": 40, "intensity": 1.806122448979592, "date": "2021-08-22" }, { "rank": 41, "intensity": 1.7755102040816326, "date": "2021-08-20" }, { "rank": 42, "intensity": 1.7448979591836735, "date": "2021-12-19" }, { "rank": 43, "intensity": 1.7142857142857144, "date": "2021-11-18" }, { "rank": 44, "intensity": 1.683673469387755, "date": "2021-02-26" }, { "rank": 45, "intensity": 1.653061224489796, "date": "2021-02-05" }, { "rank": 46, "intensity": 1.6224489795918369, "date": "2021-04-04" }, { "rank": 47, "intensity": 1.5918367346938775, "date": "2021-05-14" }, { "rank": 48, "intensity": 1.5612244897959184, "date": "2021-12-04" }, { "rank": 49, "intensity": 1.5306122448979593, "date": "2021-07-09" }, { "rank": 50, "intensity": 1.5, "date": "2021-09-11" } ], "source": "causal_rivers" } }, { "id": "L3_T1_Composite_Trend_00062", "level": 3, "level_name": "Semantic Reasoning", "category": "Composite Trend", "subtask": "Composite Trend", "question": "Among days in channel 147 during 2019 that exhibit the trend pattern 'rapid rise, then fluctuating stable, then rapid fall', identify the top-5 days where the rapid fall segment is the fastest. (Output format: a ranked list of dates, e.g., ['YYYY-MM-DD', ...])", "answer": "['2019-10-19', '2019-01-02', '2019-05-31', '2019-07-04', '2019-12-06']", "ground_truth": [ "2019-10-19", "2019-01-02", "2019-05-31", "2019-07-04", "2019-12-06" ], "eval_metric": "set_f1", "channel": "147", "ts_data_path": "ts_data/L3_T1_Composite_Trend_00062.csv", "meta": { "segments": [ { "kind": "rise", "adj": "rapid" }, { "kind": "stable", "adj": "fluctuating" }, { "kind": "fall", "adj": "rapid" } ], "pattern_phrase": "rapid rise, then fluctuating stable, then rapid fall", "rank_target_idx": 2, "rank_kind": "fall", "rank_adj": "rapid", "rank_axis": "slope", "rank_direction": "DESC", "rank_extreme": "fastest", "top_segments_meta": [ { "adj": "rapid", "kind": "rise", "label": "rapid rise", "start_idx": 0, "end_idx": 23 }, { "adj": "fluctuating", "kind": "stable", "label": "fluctuating stable", "start_idx": 23, "end_idx": 75 }, { "adj": "rapid", "kind": "fall", "label": "rapid fall", "start_idx": 75, "end_idx": 98 } ], "points_per_day": 96, "pattern": "rapid rise, then fluctuating stable, then rapid fall", "year": 2019, "top_k": [ "2019-10-19", "2019-01-02", "2019-05-31", "2019-07-04", "2019-12-06" ], "all_injections": [ { "rank": 1, "intensity": 3.0, "date": "2019-10-19" }, { "rank": 2, "intensity": 2.9693877551020407, "date": "2019-01-02" }, { "rank": 3, "intensity": 2.938775510204082, "date": "2019-05-31" }, { "rank": 4, "intensity": 2.9081632653061225, "date": "2019-07-04" }, { "rank": 5, "intensity": 2.877551020408163, "date": "2019-12-06" }, { "rank": 6, "intensity": 2.8469387755102042, "date": "2019-03-12" }, { "rank": 7, "intensity": 2.816326530612245, "date": "2019-05-23" }, { "rank": 8, "intensity": 2.7857142857142856, "date": "2019-12-13" }, { "rank": 9, "intensity": 2.7551020408163267, "date": "2019-12-19" }, { "rank": 10, "intensity": 2.7244897959183674, "date": "2019-10-25" }, { "rank": 11, "intensity": 2.693877551020408, "date": "2019-02-10" }, { "rank": 12, "intensity": 2.663265306122449, "date": "2019-07-28" }, { "rank": 13, "intensity": 2.63265306122449, "date": "2019-03-04" }, { "rank": 14, "intensity": 2.6020408163265305, "date": "2019-02-13" }, { "rank": 15, "intensity": 2.5714285714285716, "date": "2019-10-13" }, { "rank": 16, "intensity": 2.5408163265306123, "date": "2019-11-22" }, { "rank": 17, "intensity": 2.510204081632653, "date": "2019-11-12" }, { "rank": 18, "intensity": 2.479591836734694, "date": "2019-02-23" }, { "rank": 19, "intensity": 2.4489795918367347, "date": "2019-11-28" }, { "rank": 20, "intensity": 2.4183673469387754, "date": "2019-09-17" }, { "rank": 21, "intensity": 2.387755102040816, "date": "2019-12-04" }, { "rank": 22, "intensity": 2.357142857142857, "date": "2019-02-18" }, { "rank": 23, "intensity": 2.326530612244898, "date": "2019-04-28" }, { "rank": 24, "intensity": 2.295918367346939, "date": "2019-10-10" }, { "rank": 25, "intensity": 2.2653061224489797, "date": "2019-12-26" }, { "rank": 26, "intensity": 2.2346938775510203, "date": "2019-10-05" }, { "rank": 27, "intensity": 2.204081632653061, "date": "2019-02-15" }, { "rank": 28, "intensity": 2.173469387755102, "date": "2019-05-15" }, { "rank": 29, "intensity": 2.142857142857143, "date": "2019-07-08" }, { "rank": 30, "intensity": 2.112244897959184, "date": "2019-06-04" }, { "rank": 31, "intensity": 2.0816326530612246, "date": "2019-05-21" }, { "rank": 32, "intensity": 2.0510204081632653, "date": "2019-06-24" }, { "rank": 33, "intensity": 2.020408163265306, "date": "2019-11-01" }, { "rank": 34, "intensity": 1.989795918367347, "date": "2019-08-08" }, { "rank": 35, "intensity": 1.9591836734693877, "date": "2019-01-09" }, { "rank": 36, "intensity": 1.9285714285714286, "date": "2019-04-07" }, { "rank": 37, "intensity": 1.8979591836734695, "date": "2019-01-05" }, { "rank": 38, "intensity": 1.8673469387755102, "date": "2019-08-01" }, { "rank": 39, "intensity": 1.836734693877551, "date": "2019-08-12" }, { "rank": 40, "intensity": 1.806122448979592, "date": "2019-09-15" }, { "rank": 41, "intensity": 1.7755102040816326, "date": "2019-12-22" }, { "rank": 42, "intensity": 1.7448979591836735, "date": "2019-04-23" }, { "rank": 43, "intensity": 1.7142857142857144, "date": "2019-01-23" }, { "rank": 44, "intensity": 1.683673469387755, "date": "2019-06-26" }, { "rank": 45, "intensity": 1.653061224489796, "date": "2019-06-09" }, { "rank": 46, "intensity": 1.6224489795918369, "date": "2019-06-20" }, { "rank": 47, "intensity": 1.5918367346938775, "date": "2019-03-08" }, { "rank": 48, "intensity": 1.5612244897959184, "date": "2019-07-15" }, { "rank": 49, "intensity": 1.5306122448979593, "date": "2019-07-19" }, { "rank": 50, "intensity": 1.5, "date": "2019-04-01" } ], "source": "causal_rivers" } }, { "id": "L3_T1_Composite_Trend_00063", "level": 3, "level_name": "Semantic Reasoning", "category": "Composite Trend", "subtask": "Composite Trend", "question": "Among days in channel 151 during 2023 that exhibit the trend pattern 'steady stable, then fluctuating stable, then fall', identify the top-5 days where the fluctuating stable segment is the most fluctuating. (Output format: a ranked list of dates, e.g., ['YYYY-MM-DD', ...])", "answer": "['2023-05-31', '2023-10-10', '2023-02-11', '2023-07-31', '2023-02-07']", "ground_truth": [ "2023-05-31", "2023-10-10", "2023-02-11", "2023-07-31", "2023-02-07" ], "eval_metric": "set_f1", "channel": "151", "ts_data_path": "ts_data/L3_T1_Composite_Trend_00063.csv", "meta": { "segments": [ { "kind": "stable", "adj": "steady" }, { "kind": "stable", "adj": "fluctuating" }, { "kind": "fall", "adj": "none" } ], "pattern_phrase": "steady stable, then fluctuating stable, then fall", "rank_target_idx": 1, "rank_kind": "stable", "rank_adj": "fluctuating", "rank_axis": "std", "rank_direction": "DESC", "rank_extreme": "most fluctuating", "top_segments_meta": [ { "adj": "steady", "kind": "stable", "label": "steady stable", "start_idx": 0, "end_idx": 45 }, { "adj": "fluctuating", "kind": "stable", "label": "fluctuating stable", "start_idx": 45, "end_idx": 75 }, { "adj": "none", "kind": "fall", "label": "none fall", "start_idx": 75, "end_idx": 98 } ], "points_per_day": 96, "pattern": "steady stable, then fluctuating stable, then fall", "year": 2023, "top_k": [ "2023-05-31", "2023-10-10", "2023-02-11", "2023-07-31", "2023-02-07" ], "all_injections": [ { "rank": 1, "intensity": 3.0, "date": "2023-05-31" }, { "rank": 2, "intensity": 2.9693877551020407, "date": "2023-10-10" }, { "rank": 3, "intensity": 2.938775510204082, "date": "2023-02-11" }, { "rank": 4, "intensity": 2.9081632653061225, "date": "2023-07-31" }, { "rank": 5, "intensity": 2.877551020408163, "date": "2023-02-07" }, { "rank": 6, "intensity": 2.8469387755102042, "date": "2023-06-23" }, { "rank": 7, "intensity": 2.816326530612245, "date": "2023-11-29" }, { "rank": 8, "intensity": 2.7857142857142856, "date": "2023-04-22" }, { "rank": 9, "intensity": 2.7551020408163267, "date": "2023-02-25" }, { "rank": 10, "intensity": 2.7244897959183674, "date": "2023-10-30" }, { "rank": 11, "intensity": 2.693877551020408, "date": "2023-12-04" }, { "rank": 12, "intensity": 2.663265306122449, "date": "2023-03-23" }, { "rank": 13, "intensity": 2.63265306122449, "date": "2023-09-17" }, { "rank": 14, "intensity": 2.6020408163265305, "date": "2023-09-28" }, { "rank": 15, "intensity": 2.5714285714285716, "date": "2023-04-14" }, { "rank": 16, "intensity": 2.5408163265306123, "date": "2023-04-18" }, { "rank": 17, "intensity": 2.510204081632653, "date": "2023-06-06" }, { "rank": 18, "intensity": 2.479591836734694, "date": "2023-04-09" }, { "rank": 19, "intensity": 2.4489795918367347, "date": "2023-10-05" }, { "rank": 20, "intensity": 2.4183673469387754, "date": "2023-02-15" }, { "rank": 21, "intensity": 2.387755102040816, "date": "2023-11-03" }, { "rank": 22, "intensity": 2.357142857142857, "date": "2023-05-03" }, { "rank": 23, "intensity": 2.326530612244898, "date": "2023-03-17" }, { "rank": 24, "intensity": 2.295918367346939, "date": "2023-06-19" }, { "rank": 25, "intensity": 2.2653061224489797, "date": "2023-02-23" }, { "rank": 26, "intensity": 2.2346938775510203, "date": "2023-11-18" }, { "rank": 27, "intensity": 2.204081632653061, "date": "2023-09-25" }, { "rank": 28, "intensity": 2.173469387755102, "date": "2023-12-20" }, { "rank": 29, "intensity": 2.142857142857143, "date": "2023-01-18" }, { "rank": 30, "intensity": 2.112244897959184, "date": "2023-08-29" }, { "rank": 31, "intensity": 2.0816326530612246, "date": "2023-09-14" }, { "rank": 32, "intensity": 2.0510204081632653, "date": "2023-04-04" }, { "rank": 33, "intensity": 2.020408163265306, "date": "2023-04-07" }, { "rank": 34, "intensity": 1.989795918367347, "date": "2023-04-24" }, { "rank": 35, "intensity": 1.9591836734693877, "date": "2023-07-21" }, { "rank": 36, "intensity": 1.9285714285714286, "date": "2023-07-08" }, { "rank": 37, "intensity": 1.8979591836734695, "date": "2023-05-19" }, { "rank": 38, "intensity": 1.8673469387755102, "date": "2023-02-17" }, { "rank": 39, "intensity": 1.836734693877551, "date": "2023-03-30" }, { "rank": 40, "intensity": 1.806122448979592, "date": "2023-03-26" }, { "rank": 41, "intensity": 1.7755102040816326, "date": "2023-10-16" }, { "rank": 42, "intensity": 1.7448979591836735, "date": "2023-06-10" }, { "rank": 43, "intensity": 1.7142857142857144, "date": "2023-12-13" }, { "rank": 44, "intensity": 1.683673469387755, "date": "2023-03-02" }, { "rank": 45, "intensity": 1.653061224489796, "date": "2023-06-27" }, { "rank": 46, "intensity": 1.6224489795918369, "date": "2023-03-20" }, { "rank": 47, "intensity": 1.5918367346938775, "date": "2023-02-13" }, { "rank": 48, "intensity": 1.5612244897959184, "date": "2023-11-22" }, { "rank": 49, "intensity": 1.5306122448979593, "date": "2023-05-05" }, { "rank": 50, "intensity": 1.5, "date": "2023-08-20" } ], "source": "causal_rivers" } }, { "id": "L3_T1_Composite_Trend_00064", "level": 3, "level_name": "Semantic Reasoning", "category": "Composite Trend", "subtask": "Composite Trend", "question": "Among days in channel 154 during 2020 that exhibit the trend pattern 'fluctuating stable, then slow rise', identify the top-5 days where the fluctuating stable segment is the most fluctuating. (Output format: a ranked list of dates, e.g., ['YYYY-MM-DD', ...])", "answer": "['2020-08-23', '2020-10-14', '2020-04-13', '2020-04-05', '2020-12-27']", "ground_truth": [ "2020-08-23", "2020-10-14", "2020-04-13", "2020-04-05", "2020-12-27" ], "eval_metric": "set_f1", "channel": "154", "ts_data_path": "ts_data/L3_T1_Composite_Trend_00064.csv", "meta": { "segments": [ { "kind": "stable", "adj": "fluctuating" }, { "kind": "rise", "adj": "slow" } ], "pattern_phrase": "fluctuating stable, then slow rise", "rank_target_idx": 0, "rank_kind": "stable", "rank_adj": "fluctuating", "rank_axis": "std", "rank_direction": "DESC", "rank_extreme": "most fluctuating", "top_segments_meta": [ { "adj": "fluctuating", "kind": "stable", "label": "fluctuating stable", "start_idx": 0, "end_idx": 40 }, { "adj": "slow", "kind": "rise", "label": "slow rise", "start_idx": 40, "end_idx": 98 } ], "points_per_day": 96, "pattern": "fluctuating stable, then slow rise", "year": 2020, "top_k": [ "2020-08-23", "2020-10-14", "2020-04-13", "2020-04-05", "2020-12-27" ], "all_injections": [ { "rank": 1, "intensity": 3.0, "date": "2020-08-23" }, { "rank": 2, "intensity": 2.9693877551020407, "date": "2020-10-14" }, { "rank": 3, "intensity": 2.938775510204082, "date": "2020-04-13" }, { "rank": 4, "intensity": 2.9081632653061225, "date": "2020-04-05" }, { "rank": 5, "intensity": 2.877551020408163, "date": "2020-12-27" }, { "rank": 6, "intensity": 2.8469387755102042, "date": "2020-04-28" }, { "rank": 7, "intensity": 2.816326530612245, "date": "2020-10-02" }, { "rank": 8, "intensity": 2.7857142857142856, "date": "2020-04-30" }, { "rank": 9, "intensity": 2.7551020408163267, "date": "2020-07-19" }, { "rank": 10, "intensity": 2.7244897959183674, "date": "2020-03-23" }, { "rank": 11, "intensity": 2.693877551020408, "date": "2020-01-06" }, { "rank": 12, "intensity": 2.663265306122449, "date": "2020-02-26" }, { "rank": 13, "intensity": 2.63265306122449, "date": "2020-11-08" }, { "rank": 14, "intensity": 2.6020408163265305, "date": "2020-06-13" }, { "rank": 15, "intensity": 2.5714285714285716, "date": "2020-05-18" }, { "rank": 16, "intensity": 2.5408163265306123, "date": "2020-05-06" }, { "rank": 17, "intensity": 2.510204081632653, "date": "2020-09-23" }, { "rank": 18, "intensity": 2.479591836734694, "date": "2020-09-26" }, { "rank": 19, "intensity": 2.4489795918367347, "date": "2020-07-29" }, { "rank": 20, "intensity": 2.4183673469387754, "date": "2020-06-11" }, { "rank": 21, "intensity": 2.387755102040816, "date": "2020-12-01" }, { "rank": 22, "intensity": 2.357142857142857, "date": "2020-09-09" }, { "rank": 23, "intensity": 2.326530612244898, "date": "2020-12-04" }, { "rank": 24, "intensity": 2.295918367346939, "date": "2020-01-15" }, { "rank": 25, "intensity": 2.2653061224489797, "date": "2020-05-30" }, { "rank": 26, "intensity": 2.2346938775510203, "date": "2020-07-25" }, { "rank": 27, "intensity": 2.204081632653061, "date": "2020-10-19" }, { "rank": 28, "intensity": 2.173469387755102, "date": "2020-07-21" }, { "rank": 29, "intensity": 2.142857142857143, "date": "2020-03-16" }, { "rank": 30, "intensity": 2.112244897959184, "date": "2020-02-22" }, { "rank": 31, "intensity": 2.0816326530612246, "date": "2020-04-03" }, { "rank": 32, "intensity": 2.0510204081632653, "date": "2020-11-17" }, { "rank": 33, "intensity": 2.020408163265306, "date": "2020-02-13" }, { "rank": 34, "intensity": 1.989795918367347, "date": "2020-06-08" }, { "rank": 35, "intensity": 1.9591836734693877, "date": "2020-12-19" }, { "rank": 36, "intensity": 1.9285714285714286, "date": "2020-11-27" }, { "rank": 37, "intensity": 1.8979591836734695, "date": "2020-06-26" }, { "rank": 38, "intensity": 1.8673469387755102, "date": "2020-08-21" }, { "rank": 39, "intensity": 1.836734693877551, "date": "2020-01-10" }, { "rank": 40, "intensity": 1.806122448979592, "date": "2020-10-08" }, { "rank": 41, "intensity": 1.7755102040816326, "date": "2020-10-06" }, { "rank": 42, "intensity": 1.7448979591836735, "date": "2020-03-03" }, { "rank": 43, "intensity": 1.7142857142857144, "date": "2020-11-29" }, { "rank": 44, "intensity": 1.683673469387755, "date": "2020-10-24" }, { "rank": 45, "intensity": 1.653061224489796, "date": "2020-08-31" }, { "rank": 46, "intensity": 1.6224489795918369, "date": "2020-01-30" }, { "rank": 47, "intensity": 1.5918367346938775, "date": "2020-07-03" }, { "rank": 48, "intensity": 1.5612244897959184, "date": "2020-04-17" }, { "rank": 49, "intensity": 1.5306122448979593, "date": "2020-11-19" }, { "rank": 50, "intensity": 1.5, "date": "2020-02-09" } ], "source": "causal_rivers" } }, { "id": "L3_T1_Composite_Trend_00065", "level": 3, "level_name": "Semantic Reasoning", "category": "Composite Trend", "subtask": "Composite Trend", "question": "Among days in channel 155 during 2020 that exhibit the trend pattern 'rise, then rapid rise, then steady stable, then fluctuating stable', identify the top-5 days where the fluctuating stable segment is the most fluctuating. (Output format: a ranked list of dates, e.g., ['YYYY-MM-DD', ...])", "answer": "['2020-07-17', '2020-05-11', '2020-05-30', '2020-12-09', '2020-11-17']", "ground_truth": [ "2020-07-17", "2020-05-11", "2020-05-30", "2020-12-09", "2020-11-17" ], "eval_metric": "set_f1", "channel": "155", "ts_data_path": "ts_data/L3_T1_Composite_Trend_00065.csv", "meta": { "segments": [ { "kind": "rise", "adj": "none" }, { "kind": "rise", "adj": "rapid" }, { "kind": "stable", "adj": "steady" }, { "kind": "stable", "adj": "fluctuating" } ], "pattern_phrase": "rise, then rapid rise, then steady stable, then fluctuating stable", "rank_target_idx": 3, "rank_kind": "stable", "rank_adj": "fluctuating", "rank_axis": "std", "rank_direction": "DESC", "rank_extreme": "most fluctuating", "top_segments_meta": [ { "adj": "none", "kind": "rise", "label": "none rise", "start_idx": 0, "end_idx": 20 }, { "adj": "rapid", "kind": "rise", "label": "rapid rise", "start_idx": 20, "end_idx": 30 }, { "adj": "steady", "kind": "stable", "label": "steady stable", "start_idx": 30, "end_idx": 71 }, { "adj": "fluctuating", "kind": "stable", "label": "fluctuating stable", "start_idx": 71, "end_idx": 98 } ], "points_per_day": 96, "pattern": "rise, then rapid rise, then steady stable, then fluctuating stable", "year": 2020, "top_k": [ "2020-07-17", "2020-05-11", "2020-05-30", "2020-12-09", "2020-11-17" ], "all_injections": [ { "rank": 1, "intensity": 3.0, "date": "2020-07-17" }, { "rank": 2, "intensity": 2.9693877551020407, "date": "2020-05-11" }, { "rank": 3, "intensity": 2.938775510204082, "date": "2020-05-30" }, { "rank": 4, "intensity": 2.9081632653061225, "date": "2020-12-09" }, { "rank": 5, "intensity": 2.877551020408163, "date": "2020-11-17" }, { "rank": 6, "intensity": 2.8469387755102042, "date": "2020-10-31" }, { "rank": 7, "intensity": 2.816326530612245, "date": "2020-10-05" }, { "rank": 8, "intensity": 2.7857142857142856, "date": "2020-02-24" }, { "rank": 9, "intensity": 2.7551020408163267, "date": "2020-04-21" }, { "rank": 10, "intensity": 2.7244897959183674, "date": "2020-11-05" }, { "rank": 11, "intensity": 2.693877551020408, "date": "2020-10-28" }, { "rank": 12, "intensity": 2.663265306122449, "date": "2020-12-04" }, { "rank": 13, "intensity": 2.63265306122449, "date": "2020-10-18" }, { "rank": 14, "intensity": 2.6020408163265305, "date": "2020-09-18" }, { "rank": 15, "intensity": 2.5714285714285716, "date": "2020-05-28" }, { "rank": 16, "intensity": 2.5408163265306123, "date": "2020-05-07" }, { "rank": 17, "intensity": 2.510204081632653, "date": "2020-01-12" }, { "rank": 18, "intensity": 2.479591836734694, "date": "2020-08-30" }, { "rank": 19, "intensity": 2.4489795918367347, "date": "2020-11-02" }, { "rank": 20, "intensity": 2.4183673469387754, "date": "2020-03-19" }, { "rank": 21, "intensity": 2.387755102040816, "date": "2020-03-13" }, { "rank": 22, "intensity": 2.357142857142857, "date": "2020-02-05" }, { "rank": 23, "intensity": 2.326530612244898, "date": "2020-01-07" }, { "rank": 24, "intensity": 2.295918367346939, "date": "2020-08-14" }, { "rank": 25, "intensity": 2.2653061224489797, "date": "2020-12-22" }, { "rank": 26, "intensity": 2.2346938775510203, "date": "2020-06-02" }, { "rank": 27, "intensity": 2.204081632653061, "date": "2020-07-20" }, { "rank": 28, "intensity": 2.173469387755102, "date": "2020-03-08" }, { "rank": 29, "intensity": 2.142857142857143, "date": "2020-12-16" }, { "rank": 30, "intensity": 2.112244897959184, "date": "2020-05-20" }, { "rank": 31, "intensity": 2.0816326530612246, "date": "2020-07-28" }, { "rank": 32, "intensity": 2.0510204081632653, "date": "2020-09-28" }, { "rank": 33, "intensity": 2.020408163265306, "date": "2020-04-07" }, { "rank": 34, "intensity": 1.989795918367347, "date": "2020-11-10" }, { "rank": 35, "intensity": 1.9591836734693877, "date": "2020-07-05" }, { "rank": 36, "intensity": 1.9285714285714286, "date": "2020-10-03" }, { "rank": 37, "intensity": 1.8979591836734695, "date": "2020-10-01" }, { "rank": 38, "intensity": 1.8673469387755102, "date": "2020-09-09" }, { "rank": 39, "intensity": 1.836734693877551, "date": "2020-06-14" }, { "rank": 40, "intensity": 1.806122448979592, "date": "2020-06-27" }, { "rank": 41, "intensity": 1.7755102040816326, "date": "2020-09-01" }, { "rank": 42, "intensity": 1.7448979591836735, "date": "2020-03-05" }, { "rank": 43, "intensity": 1.7142857142857144, "date": "2020-08-09" }, { "rank": 44, "intensity": 1.683673469387755, "date": "2020-09-12" }, { "rank": 45, "intensity": 1.653061224489796, "date": "2020-12-02" }, { "rank": 46, "intensity": 1.6224489795918369, "date": "2020-11-13" }, { "rank": 47, "intensity": 1.5918367346938775, "date": "2020-02-17" }, { "rank": 48, "intensity": 1.5612244897959184, "date": "2020-07-24" }, { "rank": 49, "intensity": 1.5306122448979593, "date": "2020-04-15" }, { "rank": 50, "intensity": 1.5, "date": "2020-09-03" } ], "source": "causal_rivers" } }, { "id": "L3_T1_Composite_Trend_00066", "level": 3, "level_name": "Semantic Reasoning", "category": "Composite Trend", "subtask": "Composite Trend", "question": "Among days in channel 166 during 2020 that exhibit the trend pattern 'rapid fall, then slow rise', identify the top-5 days where the rapid fall segment is the fastest. (Output format: a ranked list of dates, e.g., ['YYYY-MM-DD', ...])", "answer": "['2020-01-07', '2020-08-26', '2020-12-08', '2020-12-04', '2020-01-12']", "ground_truth": [ "2020-01-07", "2020-08-26", "2020-12-08", "2020-12-04", "2020-01-12" ], "eval_metric": "set_f1", "channel": "166", "ts_data_path": "ts_data/L3_T1_Composite_Trend_00066.csv", "meta": { "segments": [ { "kind": "fall", "adj": "rapid" }, { "kind": "rise", "adj": "slow" } ], "pattern_phrase": "rapid fall, then slow rise", "rank_target_idx": 0, "rank_kind": "fall", "rank_adj": "rapid", "rank_axis": "slope", "rank_direction": "DESC", "rank_extreme": "fastest", "top_segments_meta": [ { "adj": "rapid", "kind": "fall", "label": "rapid fall", "start_idx": 0, "end_idx": 22 }, { "adj": "slow", "kind": "rise", "label": "slow rise", "start_idx": 22, "end_idx": 98 } ], "points_per_day": 96, "pattern": "rapid fall, then slow rise", "year": 2020, "top_k": [ "2020-01-07", "2020-08-26", "2020-12-08", "2020-12-04", "2020-01-12" ], "all_injections": [ { "rank": 1, "intensity": 3.0, "date": "2020-01-07" }, { "rank": 2, "intensity": 2.9693877551020407, "date": "2020-08-26" }, { "rank": 3, "intensity": 2.938775510204082, "date": "2020-12-08" }, { "rank": 4, "intensity": 2.9081632653061225, "date": "2020-12-04" }, { "rank": 5, "intensity": 2.877551020408163, "date": "2020-01-12" }, { "rank": 6, "intensity": 2.8469387755102042, "date": "2020-11-14" }, { "rank": 7, "intensity": 2.816326530612245, "date": "2020-04-26" }, { "rank": 8, "intensity": 2.7857142857142856, "date": "2020-09-12" }, { "rank": 9, "intensity": 2.7551020408163267, "date": "2020-08-13" }, { "rank": 10, "intensity": 2.7244897959183674, "date": "2020-02-16" }, { "rank": 11, "intensity": 2.693877551020408, "date": "2020-07-26" }, { "rank": 12, "intensity": 2.663265306122449, "date": "2020-05-27" }, { "rank": 13, "intensity": 2.63265306122449, "date": "2020-01-27" }, { "rank": 14, "intensity": 2.6020408163265305, "date": "2020-06-21" }, { "rank": 15, "intensity": 2.5714285714285716, "date": "2020-02-01" }, { "rank": 16, "intensity": 2.5408163265306123, "date": "2020-08-06" }, { "rank": 17, "intensity": 2.510204081632653, "date": "2020-12-22" }, { "rank": 18, "intensity": 2.479591836734694, "date": "2020-04-21" }, { "rank": 19, "intensity": 2.4489795918367347, "date": "2020-09-05" }, { "rank": 20, "intensity": 2.4183673469387754, "date": "2020-05-02" }, { "rank": 21, "intensity": 2.387755102040816, "date": "2020-03-18" }, { "rank": 22, "intensity": 2.357142857142857, "date": "2020-01-29" }, { "rank": 23, "intensity": 2.326530612244898, "date": "2020-05-07" }, { "rank": 24, "intensity": 2.295918367346939, "date": "2020-04-03" }, { "rank": 25, "intensity": 2.2653061224489797, "date": "2020-03-15" }, { "rank": 26, "intensity": 2.2346938775510203, "date": "2020-08-02" }, { "rank": 27, "intensity": 2.204081632653061, "date": "2020-04-18" }, { "rank": 28, "intensity": 2.173469387755102, "date": "2020-03-29" }, { "rank": 29, "intensity": 2.142857142857143, "date": "2020-11-29" }, { "rank": 30, "intensity": 2.112244897959184, "date": "2020-08-22" }, { "rank": 31, "intensity": 2.0816326530612246, "date": "2020-07-13" }, { "rank": 32, "intensity": 2.0510204081632653, "date": "2020-12-02" }, { "rank": 33, "intensity": 2.020408163265306, "date": "2020-01-14" }, { "rank": 34, "intensity": 1.989795918367347, "date": "2020-11-09" }, { "rank": 35, "intensity": 1.9591836734693877, "date": "2020-09-10" }, { "rank": 36, "intensity": 1.9285714285714286, "date": "2020-01-05" }, { "rank": 37, "intensity": 1.8979591836734695, "date": "2020-11-17" }, { "rank": 38, "intensity": 1.8673469387755102, "date": "2020-06-11" }, { "rank": 39, "intensity": 1.836734693877551, "date": "2020-09-03" }, { "rank": 40, "intensity": 1.806122448979592, "date": "2020-02-20" }, { "rank": 41, "intensity": 1.7755102040816326, "date": "2020-09-15" }, { "rank": 42, "intensity": 1.7448979591836735, "date": "2020-01-09" }, { "rank": 43, "intensity": 1.7142857142857144, "date": "2020-12-12" }, { "rank": 44, "intensity": 1.683673469387755, "date": "2020-01-18" }, { "rank": 45, "intensity": 1.653061224489796, "date": "2020-09-21" }, { "rank": 46, "intensity": 1.6224489795918369, "date": "2020-03-09" }, { "rank": 47, "intensity": 1.5918367346938775, "date": "2020-10-07" }, { "rank": 48, "intensity": 1.5612244897959184, "date": "2020-03-04" }, { "rank": 49, "intensity": 1.5306122448979593, "date": "2020-01-02" }, { "rank": 50, "intensity": 1.5, "date": "2020-05-09" } ], "source": "causal_rivers" } }, { "id": "L3_T1_Composite_Trend_00067", "level": 3, "level_name": "Semantic Reasoning", "category": "Composite Trend", "subtask": "Composite Trend", "question": "Among days in channel 169 during 2023 that exhibit the trend pattern 'rapid fall, then steady stable', identify the top-5 days where the rapid fall segment is the fastest. (Output format: a ranked list of dates, e.g., ['YYYY-MM-DD', ...])", "answer": "['2023-03-18', '2023-07-28', '2023-02-07', '2023-09-04', '2023-10-18']", "ground_truth": [ "2023-03-18", "2023-07-28", "2023-02-07", "2023-09-04", "2023-10-18" ], "eval_metric": "set_f1", "channel": "169", "ts_data_path": "ts_data/L3_T1_Composite_Trend_00067.csv", "meta": { "segments": [ { "kind": "fall", "adj": "rapid" }, { "kind": "stable", "adj": "steady" } ], "pattern_phrase": "rapid fall, then steady stable", "rank_target_idx": 0, "rank_kind": "fall", "rank_adj": "rapid", "rank_axis": "slope", "rank_direction": "DESC", "rank_extreme": "fastest", "top_segments_meta": [ { "adj": "rapid", "kind": "fall", "label": "rapid fall", "start_idx": 0, "end_idx": 20 }, { "adj": "steady", "kind": "stable", "label": "steady stable", "start_idx": 20, "end_idx": 98 } ], "points_per_day": 96, "pattern": "rapid fall, then steady stable", "year": 2023, "top_k": [ "2023-03-18", "2023-07-28", "2023-02-07", "2023-09-04", "2023-10-18" ], "all_injections": [ { "rank": 1, "intensity": 3.0, "date": "2023-03-18" }, { "rank": 2, "intensity": 2.9693877551020407, "date": "2023-07-28" }, { "rank": 3, "intensity": 2.938775510204082, "date": "2023-02-07" }, { "rank": 4, "intensity": 2.9081632653061225, "date": "2023-09-04" }, { "rank": 5, "intensity": 2.877551020408163, "date": "2023-10-18" }, { "rank": 6, "intensity": 2.8469387755102042, "date": "2023-09-07" }, { "rank": 7, "intensity": 2.816326530612245, "date": "2023-10-20" }, { "rank": 8, "intensity": 2.7857142857142856, "date": "2023-10-07" }, { "rank": 9, "intensity": 2.7551020408163267, "date": "2023-08-03" }, { "rank": 10, "intensity": 2.7244897959183674, "date": "2023-08-13" }, { "rank": 11, "intensity": 2.693877551020408, "date": "2023-10-23" }, { "rank": 12, "intensity": 2.663265306122449, "date": "2023-10-03" }, { "rank": 13, "intensity": 2.63265306122449, "date": "2023-02-02" }, { "rank": 14, "intensity": 2.6020408163265305, "date": "2023-07-31" }, { "rank": 15, "intensity": 2.5714285714285716, "date": "2023-03-31" }, { "rank": 16, "intensity": 2.5408163265306123, "date": "2023-06-14" }, { "rank": 17, "intensity": 2.510204081632653, "date": "2023-07-26" }, { "rank": 18, "intensity": 2.479591836734694, "date": "2023-01-17" }, { "rank": 19, "intensity": 2.4489795918367347, "date": "2023-08-10" }, { "rank": 20, "intensity": 2.4183673469387754, "date": "2023-06-12" }, { "rank": 21, "intensity": 2.387755102040816, "date": "2023-06-04" }, { "rank": 22, "intensity": 2.357142857142857, "date": "2023-06-20" }, { "rank": 23, "intensity": 2.326530612244898, "date": "2023-12-13" }, { "rank": 24, "intensity": 2.295918367346939, "date": "2023-12-24" }, { "rank": 25, "intensity": 2.2653061224489797, "date": "2023-12-15" }, { "rank": 26, "intensity": 2.2346938775510203, "date": "2023-08-22" }, { "rank": 27, "intensity": 2.204081632653061, "date": "2023-12-11" }, { "rank": 28, "intensity": 2.173469387755102, "date": "2023-06-27" }, { "rank": 29, "intensity": 2.142857142857143, "date": "2023-09-25" }, { "rank": 30, "intensity": 2.112244897959184, "date": "2023-01-20" }, { "rank": 31, "intensity": 2.0816326530612246, "date": "2023-08-26" }, { "rank": 32, "intensity": 2.0510204081632653, "date": "2023-03-11" }, { "rank": 33, "intensity": 2.020408163265306, "date": "2023-09-09" }, { "rank": 34, "intensity": 1.989795918367347, "date": "2023-12-18" }, { "rank": 35, "intensity": 1.9591836734693877, "date": "2023-12-26" }, { "rank": 36, "intensity": 1.9285714285714286, "date": "2023-05-25" }, { "rank": 37, "intensity": 1.8979591836734695, "date": "2023-02-14" }, { "rank": 38, "intensity": 1.8673469387755102, "date": "2023-05-18" }, { "rank": 39, "intensity": 1.836734693877551, "date": "2023-04-24" }, { "rank": 40, "intensity": 1.806122448979592, "date": "2023-06-18" }, { "rank": 41, "intensity": 1.7755102040816326, "date": "2023-04-18" }, { "rank": 42, "intensity": 1.7448979591836735, "date": "2023-06-07" }, { "rank": 43, "intensity": 1.7142857142857144, "date": "2023-01-08" }, { "rank": 44, "intensity": 1.683673469387755, "date": "2023-06-09" }, { "rank": 45, "intensity": 1.653061224489796, "date": "2023-11-27" }, { "rank": 46, "intensity": 1.6224489795918369, "date": "2023-02-19" }, { "rank": 47, "intensity": 1.5918367346938775, "date": "2023-11-24" }, { "rank": 48, "intensity": 1.5612244897959184, "date": "2023-01-27" }, { "rank": 49, "intensity": 1.5306122448979593, "date": "2023-04-09" }, { "rank": 50, "intensity": 1.5, "date": "2023-05-04" } ], "source": "causal_rivers" } }, { "id": "L3_T1_Composite_Trend_00068", "level": 3, "level_name": "Semantic Reasoning", "category": "Composite Trend", "subtask": "Composite Trend", "question": "Among days in channel 170 during 2023 that exhibit the trend pattern 'rise, then steady stable, then rapid rise, then rapid fall', identify the top-5 days where the rapid fall segment is the fastest. (Output format: a ranked list of dates, e.g., ['YYYY-MM-DD', ...])", "answer": "['2023-09-23', '2023-11-20', '2023-05-11', '2023-01-31', '2023-03-16']", "ground_truth": [ "2023-09-23", "2023-11-20", "2023-05-11", "2023-01-31", "2023-03-16" ], "eval_metric": "set_f1", "channel": "170", "ts_data_path": "ts_data/L3_T1_Composite_Trend_00068.csv", "meta": { "segments": [ { "kind": "rise", "adj": "none" }, { "kind": "stable", "adj": "steady" }, { "kind": "rise", "adj": "rapid" }, { "kind": "fall", "adj": "rapid" } ], "pattern_phrase": "rise, then steady stable, then rapid rise, then rapid fall", "rank_target_idx": 3, "rank_kind": "fall", "rank_adj": "rapid", "rank_axis": "slope", "rank_direction": "DESC", "rank_extreme": "fastest", "top_segments_meta": [ { "adj": "none", "kind": "rise", "label": "none rise", "start_idx": 0, "end_idx": 27 }, { "adj": "steady", "kind": "stable", "label": "steady stable", "start_idx": 27, "end_idx": 73 }, { "adj": "rapid", "kind": "rise", "label": "rapid rise", "start_idx": 73, "end_idx": 86 }, { "adj": "rapid", "kind": "fall", "label": "rapid fall", "start_idx": 86, "end_idx": 98 } ], "points_per_day": 96, "pattern": "rise, then steady stable, then rapid rise, then rapid fall", "year": 2023, "top_k": [ "2023-09-23", "2023-11-20", "2023-05-11", "2023-01-31", "2023-03-16" ], "all_injections": [ { "rank": 1, "intensity": 3.0, "date": "2023-09-23" }, { "rank": 2, "intensity": 2.9693877551020407, "date": "2023-11-20" }, { "rank": 3, "intensity": 2.938775510204082, "date": "2023-05-11" }, { "rank": 4, "intensity": 2.9081632653061225, "date": "2023-01-31" }, { "rank": 5, "intensity": 2.877551020408163, "date": "2023-03-16" }, { "rank": 6, "intensity": 2.8469387755102042, "date": "2023-03-11" }, { "rank": 7, "intensity": 2.816326530612245, "date": "2023-06-09" }, { "rank": 8, "intensity": 2.7857142857142856, "date": "2023-11-05" }, { "rank": 9, "intensity": 2.7551020408163267, "date": "2023-06-13" }, { "rank": 10, "intensity": 2.7244897959183674, "date": "2023-05-29" }, { "rank": 11, "intensity": 2.693877551020408, "date": "2023-10-22" }, { "rank": 12, "intensity": 2.663265306122449, "date": "2023-01-05" }, { "rank": 13, "intensity": 2.63265306122449, "date": "2023-07-10" }, { "rank": 14, "intensity": 2.6020408163265305, "date": "2023-12-26" }, { "rank": 15, "intensity": 2.5714285714285716, "date": "2023-08-07" }, { "rank": 16, "intensity": 2.5408163265306123, "date": "2023-07-14" }, { "rank": 17, "intensity": 2.510204081632653, "date": "2023-09-02" }, { "rank": 18, "intensity": 2.479591836734694, "date": "2023-09-08" }, { "rank": 19, "intensity": 2.4489795918367347, "date": "2023-08-09" }, { "rank": 20, "intensity": 2.4183673469387754, "date": "2023-02-07" }, { "rank": 21, "intensity": 2.387755102040816, "date": "2023-02-09" }, { "rank": 22, "intensity": 2.357142857142857, "date": "2023-03-07" }, { "rank": 23, "intensity": 2.326530612244898, "date": "2023-09-04" }, { "rank": 24, "intensity": 2.295918367346939, "date": "2023-05-16" }, { "rank": 25, "intensity": 2.2653061224489797, "date": "2023-12-01" }, { "rank": 26, "intensity": 2.2346938775510203, "date": "2023-03-14" }, { "rank": 27, "intensity": 2.204081632653061, "date": "2023-06-15" }, { "rank": 28, "intensity": 2.173469387755102, "date": "2023-11-12" }, { "rank": 29, "intensity": 2.142857142857143, "date": "2023-07-06" }, { "rank": 30, "intensity": 2.112244897959184, "date": "2023-09-13" }, { "rank": 31, "intensity": 2.0816326530612246, "date": "2023-12-05" }, { "rank": 32, "intensity": 2.0510204081632653, "date": "2023-08-04" }, { "rank": 33, "intensity": 2.020408163265306, "date": "2023-06-20" }, { "rank": 34, "intensity": 1.989795918367347, "date": "2023-04-10" }, { "rank": 35, "intensity": 1.9591836734693877, "date": "2023-08-11" }, { "rank": 36, "intensity": 1.9285714285714286, "date": "2023-02-04" }, { "rank": 37, "intensity": 1.8979591836734695, "date": "2023-12-13" }, { "rank": 38, "intensity": 1.8673469387755102, "date": "2023-05-19" }, { "rank": 39, "intensity": 1.836734693877551, "date": "2023-07-21" }, { "rank": 40, "intensity": 1.806122448979592, "date": "2023-07-23" }, { "rank": 41, "intensity": 1.7755102040816326, "date": "2023-05-01" }, { "rank": 42, "intensity": 1.7448979591836735, "date": "2023-10-20" }, { "rank": 43, "intensity": 1.7142857142857144, "date": "2023-08-21" }, { "rank": 44, "intensity": 1.683673469387755, "date": "2023-12-03" }, { "rank": 45, "intensity": 1.653061224489796, "date": "2023-04-04" }, { "rank": 46, "intensity": 1.6224489795918369, "date": "2023-04-24" }, { "rank": 47, "intensity": 1.5918367346938775, "date": "2023-09-15" }, { "rank": 48, "intensity": 1.5612244897959184, "date": "2023-06-27" }, { "rank": 49, "intensity": 1.5306122448979593, "date": "2023-02-28" }, { "rank": 50, "intensity": 1.5, "date": "2023-03-25" } ], "source": "causal_rivers" } }, { "id": "L3_T1_Composite_Trend_00069", "level": 3, "level_name": "Semantic Reasoning", "category": "Composite Trend", "subtask": "Composite Trend", "question": "Among days in channel 172 during 2021 that exhibit the trend pattern 'rise, then fluctuating stable', identify the top-5 days where the fluctuating stable segment is the most fluctuating. (Output format: a ranked list of dates, e.g., ['YYYY-MM-DD', ...])", "answer": "['2021-07-06', '2021-01-01', '2021-03-14', '2021-10-22', '2021-04-14']", "ground_truth": [ "2021-07-06", "2021-01-01", "2021-03-14", "2021-10-22", "2021-04-14" ], "eval_metric": "set_f1", "channel": "172", "ts_data_path": "ts_data/L3_T1_Composite_Trend_00069.csv", "meta": { "segments": [ { "kind": "rise", "adj": "none" }, { "kind": "stable", "adj": "fluctuating" } ], "pattern_phrase": "rise, then fluctuating stable", "rank_target_idx": 1, "rank_kind": "stable", "rank_adj": "fluctuating", "rank_axis": "std", "rank_direction": "DESC", "rank_extreme": "most fluctuating", "top_segments_meta": [ { "adj": "none", "kind": "rise", "label": "none rise", "start_idx": 0, "end_idx": 41 }, { "adj": "fluctuating", "kind": "stable", "label": "fluctuating stable", "start_idx": 41, "end_idx": 98 } ], "points_per_day": 96, "pattern": "rise, then fluctuating stable", "year": 2021, "top_k": [ "2021-07-06", "2021-01-01", "2021-03-14", "2021-10-22", "2021-04-14" ], "all_injections": [ { "rank": 1, "intensity": 3.0, "date": "2021-07-06" }, { "rank": 2, "intensity": 2.9693877551020407, "date": "2021-01-01" }, { "rank": 3, "intensity": 2.938775510204082, "date": "2021-03-14" }, { "rank": 4, "intensity": 2.9081632653061225, "date": "2021-10-22" }, { "rank": 5, "intensity": 2.877551020408163, "date": "2021-04-14" }, { "rank": 6, "intensity": 2.8469387755102042, "date": "2021-06-18" }, { "rank": 7, "intensity": 2.816326530612245, "date": "2021-06-07" }, { "rank": 8, "intensity": 2.7857142857142856, "date": "2021-04-26" }, { "rank": 9, "intensity": 2.7551020408163267, "date": "2021-05-19" }, { "rank": 10, "intensity": 2.7244897959183674, "date": "2021-12-29" }, { "rank": 11, "intensity": 2.693877551020408, "date": "2021-11-17" }, { "rank": 12, "intensity": 2.663265306122449, "date": "2021-07-28" }, { "rank": 13, "intensity": 2.63265306122449, "date": "2021-08-20" }, { "rank": 14, "intensity": 2.6020408163265305, "date": "2021-11-23" }, { "rank": 15, "intensity": 2.5714285714285716, "date": "2021-08-10" }, { "rank": 16, "intensity": 2.5408163265306123, "date": "2021-07-18" }, { "rank": 17, "intensity": 2.510204081632653, "date": "2021-06-29" }, { "rank": 18, "intensity": 2.479591836734694, "date": "2021-05-22" }, { "rank": 19, "intensity": 2.4489795918367347, "date": "2021-03-27" }, { "rank": 20, "intensity": 2.4183673469387754, "date": "2021-12-22" }, { "rank": 21, "intensity": 2.387755102040816, "date": "2021-09-06" }, { "rank": 22, "intensity": 2.357142857142857, "date": "2021-02-06" }, { "rank": 23, "intensity": 2.326530612244898, "date": "2021-05-27" }, { "rank": 24, "intensity": 2.295918367346939, "date": "2021-01-21" }, { "rank": 25, "intensity": 2.2653061224489797, "date": "2021-07-21" }, { "rank": 26, "intensity": 2.2346938775510203, "date": "2021-05-01" }, { "rank": 27, "intensity": 2.204081632653061, "date": "2021-12-10" }, { "rank": 28, "intensity": 2.173469387755102, "date": "2021-12-15" }, { "rank": 29, "intensity": 2.142857142857143, "date": "2021-03-12" }, { "rank": 30, "intensity": 2.112244897959184, "date": "2021-08-25" }, { "rank": 31, "intensity": 2.0816326530612246, "date": "2021-10-03" }, { "rank": 32, "intensity": 2.0510204081632653, "date": "2021-07-12" }, { "rank": 33, "intensity": 2.020408163265306, "date": "2021-08-22" }, { "rank": 34, "intensity": 1.989795918367347, "date": "2021-11-12" }, { "rank": 35, "intensity": 1.9591836734693877, "date": "2021-09-15" }, { "rank": 36, "intensity": 1.9285714285714286, "date": "2021-06-10" }, { "rank": 37, "intensity": 1.8979591836734695, "date": "2021-07-26" }, { "rank": 38, "intensity": 1.8673469387755102, "date": "2021-03-04" }, { "rank": 39, "intensity": 1.836734693877551, "date": "2021-03-30" }, { "rank": 40, "intensity": 1.806122448979592, "date": "2021-05-25" }, { "rank": 41, "intensity": 1.7755102040816326, "date": "2021-02-25" }, { "rank": 42, "intensity": 1.7448979591836735, "date": "2021-11-02" }, { "rank": 43, "intensity": 1.7142857142857144, "date": "2021-04-08" }, { "rank": 44, "intensity": 1.683673469387755, "date": "2021-06-13" }, { "rank": 45, "intensity": 1.653061224489796, "date": "2021-04-04" }, { "rank": 46, "intensity": 1.6224489795918369, "date": "2021-08-14" }, { "rank": 47, "intensity": 1.5918367346938775, "date": "2021-09-13" }, { "rank": 48, "intensity": 1.5612244897959184, "date": "2021-04-02" }, { "rank": 49, "intensity": 1.5306122448979593, "date": "2021-12-08" }, { "rank": 50, "intensity": 1.5, "date": "2021-07-14" } ], "source": "causal_rivers" } }, { "id": "L3_T1_Composite_Trend_00070", "level": 3, "level_name": "Semantic Reasoning", "category": "Composite Trend", "subtask": "Composite Trend", "question": "Among days in channel 173 during 2019 that exhibit the trend pattern 'rise, then rapid fall, then rapid rise', identify the top-5 days where the rapid rise segment is the fastest. (Output format: a ranked list of dates, e.g., ['YYYY-MM-DD', ...])", "answer": "['2019-08-01', '2019-05-16', '2019-10-26', '2019-06-12', '2019-03-15']", "ground_truth": [ "2019-08-01", "2019-05-16", "2019-10-26", "2019-06-12", "2019-03-15" ], "eval_metric": "set_f1", "channel": "173", "ts_data_path": "ts_data/L3_T1_Composite_Trend_00070.csv", "meta": { "segments": [ { "kind": "rise", "adj": "none" }, { "kind": "fall", "adj": "rapid" }, { "kind": "rise", "adj": "rapid" } ], "pattern_phrase": "rise, then rapid fall, then rapid rise", "rank_target_idx": 2, "rank_kind": "rise", "rank_adj": "rapid", "rank_axis": "slope", "rank_direction": "DESC", "rank_extreme": "fastest", "top_segments_meta": [ { "adj": "none", "kind": "rise", "label": "none rise", "start_idx": 0, "end_idx": 52 }, { "adj": "rapid", "kind": "fall", "label": "rapid fall", "start_idx": 52, "end_idx": 75 }, { "adj": "rapid", "kind": "rise", "label": "rapid rise", "start_idx": 75, "end_idx": 98 } ], "points_per_day": 96, "pattern": "rise, then rapid fall, then rapid rise", "year": 2019, "top_k": [ "2019-08-01", "2019-05-16", "2019-10-26", "2019-06-12", "2019-03-15" ], "all_injections": [ { "rank": 1, "intensity": 3.0, "date": "2019-08-01" }, { "rank": 2, "intensity": 2.9693877551020407, "date": "2019-05-16" }, { "rank": 3, "intensity": 2.938775510204082, "date": "2019-10-26" }, { "rank": 4, "intensity": 2.9081632653061225, "date": "2019-06-12" }, { "rank": 5, "intensity": 2.877551020408163, "date": "2019-03-15" }, { "rank": 6, "intensity": 2.8469387755102042, "date": "2019-04-04" }, { "rank": 7, "intensity": 2.816326530612245, "date": "2019-12-16" }, { "rank": 8, "intensity": 2.7857142857142856, "date": "2019-07-20" }, { "rank": 9, "intensity": 2.7551020408163267, "date": "2019-07-14" }, { "rank": 10, "intensity": 2.7244897959183674, "date": "2019-09-02" }, { "rank": 11, "intensity": 2.693877551020408, "date": "2019-05-06" }, { "rank": 12, "intensity": 2.663265306122449, "date": "2019-04-06" }, { "rank": 13, "intensity": 2.63265306122449, "date": "2019-04-19" }, { "rank": 14, "intensity": 2.6020408163265305, "date": "2019-08-22" }, { "rank": 15, "intensity": 2.5714285714285716, "date": "2019-05-13" }, { "rank": 16, "intensity": 2.5408163265306123, "date": "2019-09-08" }, { "rank": 17, "intensity": 2.510204081632653, "date": "2019-02-06" }, { "rank": 18, "intensity": 2.479591836734694, "date": "2019-05-30" }, { "rank": 19, "intensity": 2.4489795918367347, "date": "2019-08-18" }, { "rank": 20, "intensity": 2.4183673469387754, "date": "2019-02-08" }, { "rank": 21, "intensity": 2.387755102040816, "date": "2019-08-10" }, { "rank": 22, "intensity": 2.357142857142857, "date": "2019-08-12" }, { "rank": 23, "intensity": 2.326530612244898, "date": "2019-04-26" }, { "rank": 24, "intensity": 2.295918367346939, "date": "2019-05-03" }, { "rank": 25, "intensity": 2.2653061224489797, "date": "2019-10-09" }, { "rank": 26, "intensity": 2.2346938775510203, "date": "2019-03-11" }, { "rank": 27, "intensity": 2.204081632653061, "date": "2019-12-19" }, { "rank": 28, "intensity": 2.173469387755102, "date": "2019-02-15" }, { "rank": 29, "intensity": 2.142857142857143, "date": "2019-01-08" }, { "rank": 30, "intensity": 2.112244897959184, "date": "2019-03-28" }, { "rank": 31, "intensity": 2.0816326530612246, "date": "2019-11-26" }, { "rank": 32, "intensity": 2.0510204081632653, "date": "2019-12-03" }, { "rank": 33, "intensity": 2.020408163265306, "date": "2019-01-26" }, { "rank": 34, "intensity": 1.989795918367347, "date": "2019-07-04" }, { "rank": 35, "intensity": 1.9591836734693877, "date": "2019-06-21" }, { "rank": 36, "intensity": 1.9285714285714286, "date": "2019-08-07" }, { "rank": 37, "intensity": 1.8979591836734695, "date": "2019-03-26" }, { "rank": 38, "intensity": 1.8673469387755102, "date": "2019-01-29" }, { "rank": 39, "intensity": 1.836734693877551, "date": "2019-10-04" }, { "rank": 40, "intensity": 1.806122448979592, "date": "2019-08-30" }, { "rank": 41, "intensity": 1.7755102040816326, "date": "2019-06-25" }, { "rank": 42, "intensity": 1.7448979591836735, "date": "2019-06-02" }, { "rank": 43, "intensity": 1.7142857142857144, "date": "2019-11-12" }, { "rank": 44, "intensity": 1.683673469387755, "date": "2019-08-15" }, { "rank": 45, "intensity": 1.653061224489796, "date": "2019-06-10" }, { "rank": 46, "intensity": 1.6224489795918369, "date": "2019-06-29" }, { "rank": 47, "intensity": 1.5918367346938775, "date": "2019-02-01" }, { "rank": 48, "intensity": 1.5612244897959184, "date": "2019-02-04" }, { "rank": 49, "intensity": 1.5306122448979593, "date": "2019-03-01" }, { "rank": 50, "intensity": 1.5, "date": "2019-07-10" } ], "source": "causal_rivers" } }, { "id": "L3_T1_Composite_Trend_00071", "level": 3, "level_name": "Semantic Reasoning", "category": "Composite Trend", "subtask": "Composite Trend", "question": "Among days in channel 177 during 2021 that exhibit the trend pattern 'steady stable, then fluctuating stable', identify the top-5 days where the fluctuating stable segment is the most fluctuating. (Output format: a ranked list of dates, e.g., ['YYYY-MM-DD', ...])", "answer": "['2021-07-25', '2021-01-26', '2021-03-11', '2021-11-01', '2021-10-15']", "ground_truth": [ "2021-07-25", "2021-01-26", "2021-03-11", "2021-11-01", "2021-10-15" ], "eval_metric": "set_f1", "channel": "177", "ts_data_path": "ts_data/L3_T1_Composite_Trend_00071.csv", "meta": { "segments": [ { "kind": "stable", "adj": "steady" }, { "kind": "stable", "adj": "fluctuating" } ], "pattern_phrase": "steady stable, then fluctuating stable", "rank_target_idx": 1, "rank_kind": "stable", "rank_adj": "fluctuating", "rank_axis": "std", "rank_direction": "DESC", "rank_extreme": "most fluctuating", "top_segments_meta": [ { "adj": "steady", "kind": "stable", "label": "steady stable", "start_idx": 0, "end_idx": 61 }, { "adj": "fluctuating", "kind": "stable", "label": "fluctuating stable", "start_idx": 61, "end_idx": 98 } ], "points_per_day": 96, "pattern": "steady stable, then fluctuating stable", "year": 2021, "top_k": [ "2021-07-25", "2021-01-26", "2021-03-11", "2021-11-01", "2021-10-15" ], "all_injections": [ { "rank": 1, "intensity": 3.0, "date": "2021-07-25" }, { "rank": 2, "intensity": 2.9693877551020407, "date": "2021-01-26" }, { "rank": 3, "intensity": 2.938775510204082, "date": "2021-03-11" }, { "rank": 4, "intensity": 2.9081632653061225, "date": "2021-11-01" }, { "rank": 5, "intensity": 2.877551020408163, "date": "2021-10-15" }, { "rank": 6, "intensity": 2.8469387755102042, "date": "2021-02-13" }, { "rank": 7, "intensity": 2.816326530612245, "date": "2021-08-24" }, { "rank": 8, "intensity": 2.7857142857142856, "date": "2021-03-07" }, { "rank": 9, "intensity": 2.7551020408163267, "date": "2021-05-16" }, { "rank": 10, "intensity": 2.7244897959183674, "date": "2021-03-31" }, { "rank": 11, "intensity": 2.693877551020408, "date": "2021-03-04" }, { "rank": 12, "intensity": 2.663265306122449, "date": "2021-10-09" }, { "rank": 13, "intensity": 2.63265306122449, "date": "2021-07-28" }, { "rank": 14, "intensity": 2.6020408163265305, "date": "2021-04-06" }, { "rank": 15, "intensity": 2.5714285714285716, "date": "2021-09-15" }, { "rank": 16, "intensity": 2.5408163265306123, "date": "2021-02-26" }, { "rank": 17, "intensity": 2.510204081632653, "date": "2021-05-23" }, { "rank": 18, "intensity": 2.479591836734694, "date": "2021-07-03" }, { "rank": 19, "intensity": 2.4489795918367347, "date": "2021-07-21" }, { "rank": 20, "intensity": 2.4183673469387754, "date": "2021-12-17" }, { "rank": 21, "intensity": 2.387755102040816, "date": "2021-02-02" }, { "rank": 22, "intensity": 2.357142857142857, "date": "2021-06-10" }, { "rank": 23, "intensity": 2.326530612244898, "date": "2021-03-28" }, { "rank": 24, "intensity": 2.295918367346939, "date": "2021-06-19" }, { "rank": 25, "intensity": 2.2653061224489797, "date": "2021-10-17" }, { "rank": 26, "intensity": 2.2346938775510203, "date": "2021-08-22" }, { "rank": 27, "intensity": 2.204081632653061, "date": "2021-09-25" }, { "rank": 28, "intensity": 2.173469387755102, "date": "2021-09-29" }, { "rank": 29, "intensity": 2.142857142857143, "date": "2021-11-16" }, { "rank": 30, "intensity": 2.112244897959184, "date": "2021-01-09" }, { "rank": 31, "intensity": 2.0816326530612246, "date": "2021-11-30" }, { "rank": 32, "intensity": 2.0510204081632653, "date": "2021-12-30" }, { "rank": 33, "intensity": 2.020408163265306, "date": "2021-09-04" }, { "rank": 34, "intensity": 1.989795918367347, "date": "2021-01-11" }, { "rank": 35, "intensity": 1.9591836734693877, "date": "2021-12-19" }, { "rank": 36, "intensity": 1.9285714285714286, "date": "2021-08-02" }, { "rank": 37, "intensity": 1.8979591836734695, "date": "2021-10-24" }, { "rank": 38, "intensity": 1.8673469387755102, "date": "2021-09-08" }, { "rank": 39, "intensity": 1.836734693877551, "date": "2021-06-02" }, { "rank": 40, "intensity": 1.806122448979592, "date": "2021-08-19" }, { "rank": 41, "intensity": 1.7755102040816326, "date": "2021-02-22" }, { "rank": 42, "intensity": 1.7448979591836735, "date": "2021-01-06" }, { "rank": 43, "intensity": 1.7142857142857144, "date": "2021-06-28" }, { "rank": 44, "intensity": 1.683673469387755, "date": "2021-10-30" }, { "rank": 45, "intensity": 1.653061224489796, "date": "2021-05-14" }, { "rank": 46, "intensity": 1.6224489795918369, "date": "2021-07-14" }, { "rank": 47, "intensity": 1.5918367346938775, "date": "2021-10-03" }, { "rank": 48, "intensity": 1.5612244897959184, "date": "2021-01-19" }, { "rank": 49, "intensity": 1.5306122448979593, "date": "2021-04-19" }, { "rank": 50, "intensity": 1.5, "date": "2021-02-07" } ], "source": "causal_rivers" } }, { "id": "L3_T1_Composite_Trend_00072", "level": 3, "level_name": "Semantic Reasoning", "category": "Composite Trend", "subtask": "Composite Trend", "question": "Among days in channel 237 during 2020 that exhibit the trend pattern 'fluctuating stable, then fall', identify the top-5 days where the fluctuating stable segment is the most fluctuating. (Output format: a ranked list of dates, e.g., ['YYYY-MM-DD', ...])", "answer": "['2020-06-12', '2020-12-18', '2020-08-31', '2020-10-10', '2020-12-12']", "ground_truth": [ "2020-06-12", "2020-12-18", "2020-08-31", "2020-10-10", "2020-12-12" ], "eval_metric": "set_f1", "channel": "237", "ts_data_path": "ts_data/L3_T1_Composite_Trend_00072.csv", "meta": { "segments": [ { "kind": "stable", "adj": "fluctuating" }, { "kind": "fall", "adj": "none" } ], "pattern_phrase": "fluctuating stable, then fall", "rank_target_idx": 0, "rank_kind": "stable", "rank_adj": "fluctuating", "rank_axis": "std", "rank_direction": "DESC", "rank_extreme": "most fluctuating", "top_segments_meta": [ { "adj": "fluctuating", "kind": "stable", "label": "fluctuating stable", "start_idx": 0, "end_idx": 58 }, { "adj": "none", "kind": "fall", "label": "none fall", "start_idx": 58, "end_idx": 98 } ], "points_per_day": 96, "pattern": "fluctuating stable, then fall", "year": 2020, "top_k": [ "2020-06-12", "2020-12-18", "2020-08-31", "2020-10-10", "2020-12-12" ], "all_injections": [ { "rank": 1, "intensity": 3.0, "date": "2020-06-12" }, { "rank": 2, "intensity": 2.9693877551020407, "date": "2020-12-18" }, { "rank": 3, "intensity": 2.938775510204082, "date": "2020-08-31" }, { "rank": 4, "intensity": 2.9081632653061225, "date": "2020-10-10" }, { "rank": 5, "intensity": 2.877551020408163, "date": "2020-12-12" }, { "rank": 6, "intensity": 2.8469387755102042, "date": "2020-07-24" }, { "rank": 7, "intensity": 2.816326530612245, "date": "2020-05-08" }, { "rank": 8, "intensity": 2.7857142857142856, "date": "2020-02-02" }, { "rank": 9, "intensity": 2.7551020408163267, "date": "2020-11-30" }, { "rank": 10, "intensity": 2.7244897959183674, "date": "2020-04-14" }, { "rank": 11, "intensity": 2.693877551020408, "date": "2020-08-16" }, { "rank": 12, "intensity": 2.663265306122449, "date": "2020-07-03" }, { "rank": 13, "intensity": 2.63265306122449, "date": "2020-01-07" }, { "rank": 14, "intensity": 2.6020408163265305, "date": "2020-07-30" }, { "rank": 15, "intensity": 2.5714285714285716, "date": "2020-09-18" }, { "rank": 16, "intensity": 2.5408163265306123, "date": "2020-06-24" }, { "rank": 17, "intensity": 2.510204081632653, "date": "2020-04-29" }, { "rank": 18, "intensity": 2.479591836734694, "date": "2020-12-16" }, { "rank": 19, "intensity": 2.4489795918367347, "date": "2020-09-09" }, { "rank": 20, "intensity": 2.4183673469387754, "date": "2020-12-10" }, { "rank": 21, "intensity": 2.387755102040816, "date": "2020-11-03" }, { "rank": 22, "intensity": 2.357142857142857, "date": "2020-03-02" }, { "rank": 23, "intensity": 2.326530612244898, "date": "2020-08-14" }, { "rank": 24, "intensity": 2.295918367346939, "date": "2020-11-11" }, { "rank": 25, "intensity": 2.2653061224489797, "date": "2020-01-27" }, { "rank": 26, "intensity": 2.2346938775510203, "date": "2020-02-22" }, { "rank": 27, "intensity": 2.204081632653061, "date": "2020-02-19" }, { "rank": 28, "intensity": 2.173469387755102, "date": "2020-06-30" }, { "rank": 29, "intensity": 2.142857142857143, "date": "2020-08-25" }, { "rank": 30, "intensity": 2.112244897959184, "date": "2020-01-25" }, { "rank": 31, "intensity": 2.0816326530612246, "date": "2020-05-13" }, { "rank": 32, "intensity": 2.0510204081632653, "date": "2020-07-12" }, { "rank": 33, "intensity": 2.020408163265306, "date": "2020-08-04" }, { "rank": 34, "intensity": 1.989795918367347, "date": "2020-02-12" }, { "rank": 35, "intensity": 1.9591836734693877, "date": "2020-10-13" }, { "rank": 36, "intensity": 1.9285714285714286, "date": "2020-09-06" }, { "rank": 37, "intensity": 1.8979591836734695, "date": "2020-10-03" }, { "rank": 38, "intensity": 1.8673469387755102, "date": "2020-03-05" }, { "rank": 39, "intensity": 1.836734693877551, "date": "2020-05-26" }, { "rank": 40, "intensity": 1.806122448979592, "date": "2020-01-01" }, { "rank": 41, "intensity": 1.7755102040816326, "date": "2020-09-24" }, { "rank": 42, "intensity": 1.7448979591836735, "date": "2020-02-28" }, { "rank": 43, "intensity": 1.7142857142857144, "date": "2020-04-05" }, { "rank": 44, "intensity": 1.683673469387755, "date": "2020-07-17" }, { "rank": 45, "intensity": 1.653061224489796, "date": "2020-09-28" }, { "rank": 46, "intensity": 1.6224489795918369, "date": "2020-09-14" }, { "rank": 47, "intensity": 1.5918367346938775, "date": "2020-09-20" }, { "rank": 48, "intensity": 1.5612244897959184, "date": "2020-10-08" }, { "rank": 49, "intensity": 1.5306122448979593, "date": "2020-08-20" }, { "rank": 50, "intensity": 1.5, "date": "2020-07-22" } ], "source": "causal_rivers" } }, { "id": "L3_T1_Composite_Trend_00073", "level": 3, "level_name": "Semantic Reasoning", "category": "Composite Trend", "subtask": "Composite Trend", "question": "Among days in channel 245 during 2019 that exhibit the trend pattern 'fall, then rise, then slow rise, then steady stable', identify the top-5 days where the slow rise segment is the slowest. (Output format: a ranked list of dates, e.g., ['YYYY-MM-DD', ...])", "answer": "['2019-06-29', '2019-03-26', '2019-04-15', '2019-02-26', '2019-01-13']", "ground_truth": [ "2019-06-29", "2019-03-26", "2019-04-15", "2019-02-26", "2019-01-13" ], "eval_metric": "set_f1", "channel": "245", "ts_data_path": "ts_data/L3_T1_Composite_Trend_00073.csv", "meta": { "segments": [ { "kind": "fall", "adj": "none" }, { "kind": "rise", "adj": "none" }, { "kind": "rise", "adj": "slow" }, { "kind": "stable", "adj": "steady" } ], "pattern_phrase": "fall, then rise, then slow rise, then steady stable", "rank_target_idx": 2, "rank_kind": "rise", "rank_adj": "slow", "rank_axis": "slope", "rank_direction": "ASC", "rank_extreme": "slowest", "top_segments_meta": [ { "adj": "none", "kind": "fall", "label": "none fall", "start_idx": 0, "end_idx": 17 }, { "adj": "none", "kind": "rise", "label": "none rise", "start_idx": 17, "end_idx": 34 }, { "adj": "slow", "kind": "rise", "label": "slow rise", "start_idx": 34, "end_idx": 64 }, { "adj": "steady", "kind": "stable", "label": "steady stable", "start_idx": 64, "end_idx": 98 } ], "points_per_day": 96, "pattern": "fall, then rise, then slow rise, then steady stable", "year": 2019, "top_k": [ "2019-06-29", "2019-03-26", "2019-04-15", "2019-02-26", "2019-01-13" ], "all_injections": [ { "rank": 1, "intensity": 1.5, "date": "2019-06-29" }, { "rank": 2, "intensity": 1.530612244897959, "date": "2019-03-26" }, { "rank": 3, "intensity": 1.5612244897959184, "date": "2019-04-15" }, { "rank": 4, "intensity": 1.5918367346938775, "date": "2019-02-26" }, { "rank": 5, "intensity": 1.6224489795918366, "date": "2019-01-13" }, { "rank": 6, "intensity": 1.653061224489796, "date": "2019-10-08" }, { "rank": 7, "intensity": 1.683673469387755, "date": "2019-12-25" }, { "rank": 8, "intensity": 1.7142857142857142, "date": "2019-01-06" }, { "rank": 9, "intensity": 1.7448979591836735, "date": "2019-12-21" }, { "rank": 10, "intensity": 1.7755102040816326, "date": "2019-05-18" }, { "rank": 11, "intensity": 1.806122448979592, "date": "2019-10-19" }, { "rank": 12, "intensity": 1.836734693877551, "date": "2019-09-17" }, { "rank": 13, "intensity": 1.8673469387755102, "date": "2019-04-07" }, { "rank": 14, "intensity": 1.8979591836734695, "date": "2019-07-13" }, { "rank": 15, "intensity": 1.9285714285714286, "date": "2019-02-10" }, { "rank": 16, "intensity": 1.9591836734693877, "date": "2019-05-07" }, { "rank": 17, "intensity": 1.989795918367347, "date": "2019-03-14" }, { "rank": 18, "intensity": 2.020408163265306, "date": "2019-09-14" }, { "rank": 19, "intensity": 2.0510204081632653, "date": "2019-10-21" }, { "rank": 20, "intensity": 2.0816326530612246, "date": "2019-08-24" }, { "rank": 21, "intensity": 2.112244897959184, "date": "2019-12-17" }, { "rank": 22, "intensity": 2.142857142857143, "date": "2019-08-28" }, { "rank": 23, "intensity": 2.173469387755102, "date": "2019-02-16" }, { "rank": 24, "intensity": 2.204081632653061, "date": "2019-06-10" }, { "rank": 25, "intensity": 2.2346938775510203, "date": "2019-02-22" }, { "rank": 26, "intensity": 2.2653061224489797, "date": "2019-12-28" }, { "rank": 27, "intensity": 2.295918367346939, "date": "2019-03-04" }, { "rank": 28, "intensity": 2.326530612244898, "date": "2019-04-25" }, { "rank": 29, "intensity": 2.357142857142857, "date": "2019-11-28" }, { "rank": 30, "intensity": 2.387755102040816, "date": "2019-05-10" }, { "rank": 31, "intensity": 2.4183673469387754, "date": "2019-06-20" }, { "rank": 32, "intensity": 2.4489795918367347, "date": "2019-03-01" }, { "rank": 33, "intensity": 2.479591836734694, "date": "2019-01-22" }, { "rank": 34, "intensity": 2.510204081632653, "date": "2019-10-14" }, { "rank": 35, "intensity": 2.5408163265306123, "date": "2019-08-10" }, { "rank": 36, "intensity": 2.571428571428571, "date": "2019-12-05" }, { "rank": 37, "intensity": 2.6020408163265305, "date": "2019-09-09" }, { "rank": 38, "intensity": 2.63265306122449, "date": "2019-02-19" }, { "rank": 39, "intensity": 2.663265306122449, "date": "2019-03-30" }, { "rank": 40, "intensity": 2.693877551020408, "date": "2019-06-07" }, { "rank": 41, "intensity": 2.7244897959183674, "date": "2019-05-23" }, { "rank": 42, "intensity": 2.7551020408163263, "date": "2019-04-05" }, { "rank": 43, "intensity": 2.7857142857142856, "date": "2019-12-02" }, { "rank": 44, "intensity": 2.816326530612245, "date": "2019-07-10" }, { "rank": 45, "intensity": 2.8469387755102042, "date": "2019-06-02" }, { "rank": 46, "intensity": 2.877551020408163, "date": "2019-10-17" }, { "rank": 47, "intensity": 2.9081632653061225, "date": "2019-06-15" }, { "rank": 48, "intensity": 2.9387755102040813, "date": "2019-07-02" }, { "rank": 49, "intensity": 2.9693877551020407, "date": "2019-03-24" }, { "rank": 50, "intensity": 3.0, "date": "2019-09-23" } ], "source": "causal_rivers" } }, { "id": "L3_T1_Composite_Trend_00074", "level": 3, "level_name": "Semantic Reasoning", "category": "Composite Trend", "subtask": "Composite Trend", "question": "Among days in channel 312 during 2022 that exhibit the trend pattern 'fall, then fluctuating stable, then rise', identify the top-5 days where the fluctuating stable segment is the most fluctuating. (Output format: a ranked list of dates, e.g., ['YYYY-MM-DD', ...])", "answer": "['2022-02-23', '2022-02-09', '2022-09-19', '2022-06-20', '2022-03-02']", "ground_truth": [ "2022-02-23", "2022-02-09", "2022-09-19", "2022-06-20", "2022-03-02" ], "eval_metric": "set_f1", "channel": "312", "ts_data_path": "ts_data/L3_T1_Composite_Trend_00074.csv", "meta": { "segments": [ { "kind": "fall", "adj": "none" }, { "kind": "stable", "adj": "fluctuating" }, { "kind": "rise", "adj": "none" } ], "pattern_phrase": "fall, then fluctuating stable, then rise", "rank_target_idx": 1, "rank_kind": "stable", "rank_adj": "fluctuating", "rank_axis": "std", "rank_direction": "DESC", "rank_extreme": "most fluctuating", "top_segments_meta": [ { "adj": "none", "kind": "fall", "label": "none fall", "start_idx": 0, "end_idx": 29 }, { "adj": "fluctuating", "kind": "stable", "label": "fluctuating stable", "start_idx": 29, "end_idx": 67 }, { "adj": "none", "kind": "rise", "label": "none rise", "start_idx": 67, "end_idx": 98 } ], "points_per_day": 96, "pattern": "fall, then fluctuating stable, then rise", "year": 2022, "top_k": [ "2022-02-23", "2022-02-09", "2022-09-19", "2022-06-20", "2022-03-02" ], "all_injections": [ { "rank": 1, "intensity": 3.0, "date": "2022-02-23" }, { "rank": 2, "intensity": 2.9693877551020407, "date": "2022-02-09" }, { "rank": 3, "intensity": 2.938775510204082, "date": "2022-09-19" }, { "rank": 4, "intensity": 2.9081632653061225, "date": "2022-06-20" }, { "rank": 5, "intensity": 2.877551020408163, "date": "2022-03-02" }, { "rank": 6, "intensity": 2.8469387755102042, "date": "2022-05-18" }, { "rank": 7, "intensity": 2.816326530612245, "date": "2022-05-11" }, { "rank": 8, "intensity": 2.7857142857142856, "date": "2022-12-29" }, { "rank": 9, "intensity": 2.7551020408163267, "date": "2022-12-08" }, { "rank": 10, "intensity": 2.7244897959183674, "date": "2022-07-23" }, { "rank": 11, "intensity": 2.693877551020408, "date": "2022-05-31" }, { "rank": 12, "intensity": 2.663265306122449, "date": "2022-12-01" }, { "rank": 13, "intensity": 2.63265306122449, "date": "2022-07-27" }, { "rank": 14, "intensity": 2.6020408163265305, "date": "2022-09-06" }, { "rank": 15, "intensity": 2.5714285714285716, "date": "2022-10-30" }, { "rank": 16, "intensity": 2.5408163265306123, "date": "2022-01-30" }, { "rank": 17, "intensity": 2.510204081632653, "date": "2022-07-01" }, { "rank": 18, "intensity": 2.479591836734694, "date": "2022-07-30" }, { "rank": 19, "intensity": 2.4489795918367347, "date": "2022-03-05" }, { "rank": 20, "intensity": 2.4183673469387754, "date": "2022-12-15" }, { "rank": 21, "intensity": 2.387755102040816, "date": "2022-04-10" }, { "rank": 22, "intensity": 2.357142857142857, "date": "2022-03-30" }, { "rank": 23, "intensity": 2.326530612244898, "date": "2022-03-27" }, { "rank": 24, "intensity": 2.295918367346939, "date": "2022-08-25" }, { "rank": 25, "intensity": 2.2653061224489797, "date": "2022-04-23" }, { "rank": 26, "intensity": 2.2346938775510203, "date": "2022-10-22" }, { "rank": 27, "intensity": 2.204081632653061, "date": "2022-11-08" }, { "rank": 28, "intensity": 2.173469387755102, "date": "2022-09-12" }, { "rank": 29, "intensity": 2.142857142857143, "date": "2022-04-28" }, { "rank": 30, "intensity": 2.112244897959184, "date": "2022-06-24" }, { "rank": 31, "intensity": 2.0816326530612246, "date": "2022-06-03" }, { "rank": 32, "intensity": 2.0510204081632653, "date": "2022-04-13" }, { "rank": 33, "intensity": 2.020408163265306, "date": "2022-04-25" }, { "rank": 34, "intensity": 1.989795918367347, "date": "2022-10-24" }, { "rank": 35, "intensity": 1.9591836734693877, "date": "2022-09-25" }, { "rank": 36, "intensity": 1.9285714285714286, "date": "2022-09-22" }, { "rank": 37, "intensity": 1.8979591836734695, "date": "2022-12-22" }, { "rank": 38, "intensity": 1.8673469387755102, "date": "2022-07-20" }, { "rank": 39, "intensity": 1.836734693877551, "date": "2022-12-12" }, { "rank": 40, "intensity": 1.806122448979592, "date": "2022-06-14" }, { "rank": 41, "intensity": 1.7755102040816326, "date": "2022-05-23" }, { "rank": 42, "intensity": 1.7448979591836735, "date": "2022-11-28" }, { "rank": 43, "intensity": 1.7142857142857144, "date": "2022-09-27" }, { "rank": 44, "intensity": 1.683673469387755, "date": "2022-08-08" }, { "rank": 45, "intensity": 1.653061224489796, "date": "2022-11-04" }, { "rank": 46, "intensity": 1.6224489795918369, "date": "2022-10-14" }, { "rank": 47, "intensity": 1.5918367346938775, "date": "2022-04-06" }, { "rank": 48, "intensity": 1.5612244897959184, "date": "2022-01-16" }, { "rank": 49, "intensity": 1.5306122448979593, "date": "2022-07-18" }, { "rank": 50, "intensity": 1.5, "date": "2022-08-15" } ], "source": "causal_rivers" } }, { "id": "L3_T1_Composite_Trend_00075", "level": 3, "level_name": "Semantic Reasoning", "category": "Composite Trend", "subtask": "Composite Trend", "question": "Among days in channel 430 during 2022 that exhibit the trend pattern 'rapid rise, then slow fall, then rise, then slow rise', identify the top-5 days where the slow fall segment is the slowest. (Output format: a ranked list of dates, e.g., ['YYYY-MM-DD', ...])", "answer": "['2022-08-12', '2022-02-27', '2022-07-13', '2022-12-21', '2022-12-14']", "ground_truth": [ "2022-08-12", "2022-02-27", "2022-07-13", "2022-12-21", "2022-12-14" ], "eval_metric": "set_f1", "channel": "430", "ts_data_path": "ts_data/L3_T1_Composite_Trend_00075.csv", "meta": { "segments": [ { "kind": "rise", "adj": "rapid" }, { "kind": "fall", "adj": "slow" }, { "kind": "rise", "adj": "none" }, { "kind": "rise", "adj": "slow" } ], "pattern_phrase": "rapid rise, then slow fall, then rise, then slow rise", "rank_target_idx": 1, "rank_kind": "fall", "rank_adj": "slow", "rank_axis": "slope", "rank_direction": "ASC", "rank_extreme": "slowest", "top_segments_meta": [ { "adj": "rapid", "kind": "rise", "label": "rapid rise", "start_idx": 0, "end_idx": 10 }, { "adj": "slow", "kind": "fall", "label": "slow fall", "start_idx": 10, "end_idx": 46 }, { "adj": "none", "kind": "rise", "label": "none rise", "start_idx": 46, "end_idx": 64 }, { "adj": "slow", "kind": "rise", "label": "slow rise", "start_idx": 64, "end_idx": 98 } ], "points_per_day": 96, "pattern": "rapid rise, then slow fall, then rise, then slow rise", "year": 2022, "top_k": [ "2022-08-12", "2022-02-27", "2022-07-13", "2022-12-21", "2022-12-14" ], "all_injections": [ { "rank": 1, "intensity": 1.5, "date": "2022-08-12" }, { "rank": 2, "intensity": 1.530612244897959, "date": "2022-02-27" }, { "rank": 3, "intensity": 1.5612244897959184, "date": "2022-07-13" }, { "rank": 4, "intensity": 1.5918367346938775, "date": "2022-12-21" }, { "rank": 5, "intensity": 1.6224489795918366, "date": "2022-12-14" }, { "rank": 6, "intensity": 1.653061224489796, "date": "2022-02-05" }, { "rank": 7, "intensity": 1.683673469387755, "date": "2022-04-20" }, { "rank": 8, "intensity": 1.7142857142857142, "date": "2022-11-21" }, { "rank": 9, "intensity": 1.7448979591836735, "date": "2022-04-30" }, { "rank": 10, "intensity": 1.7755102040816326, "date": "2022-05-26" }, { "rank": 11, "intensity": 1.806122448979592, "date": "2022-09-09" }, { "rank": 12, "intensity": 1.836734693877551, "date": "2022-04-18" }, { "rank": 13, "intensity": 1.8673469387755102, "date": "2022-08-16" }, { "rank": 14, "intensity": 1.8979591836734695, "date": "2022-10-02" }, { "rank": 15, "intensity": 1.9285714285714286, "date": "2022-01-26" }, { "rank": 16, "intensity": 1.9591836734693877, "date": "2022-06-11" }, { "rank": 17, "intensity": 1.989795918367347, "date": "2022-01-29" }, { "rank": 18, "intensity": 2.020408163265306, "date": "2022-10-18" }, { "rank": 19, "intensity": 2.0510204081632653, "date": "2022-06-18" }, { "rank": 20, "intensity": 2.0816326530612246, "date": "2022-06-27" }, { "rank": 21, "intensity": 2.112244897959184, "date": "2022-03-05" }, { "rank": 22, "intensity": 2.142857142857143, "date": "2022-12-25" }, { "rank": 23, "intensity": 2.173469387755102, "date": "2022-08-05" }, { "rank": 24, "intensity": 2.204081632653061, "date": "2022-01-05" }, { "rank": 25, "intensity": 2.2346938775510203, "date": "2022-05-09" }, { "rank": 26, "intensity": 2.2653061224489797, "date": "2022-09-27" }, { "rank": 27, "intensity": 2.295918367346939, "date": "2022-10-16" }, { "rank": 28, "intensity": 2.326530612244898, "date": "2022-09-05" }, { "rank": 29, "intensity": 2.357142857142857, "date": "2022-11-06" }, { "rank": 30, "intensity": 2.387755102040816, "date": "2022-09-13" }, { "rank": 31, "intensity": 2.4183673469387754, "date": "2022-04-04" }, { "rank": 32, "intensity": 2.4489795918367347, "date": "2022-08-26" }, { "rank": 33, "intensity": 2.479591836734694, "date": "2022-08-31" }, { "rank": 34, "intensity": 2.510204081632653, "date": "2022-05-07" }, { "rank": 35, "intensity": 2.5408163265306123, "date": "2022-07-28" }, { "rank": 36, "intensity": 2.571428571428571, "date": "2022-10-25" }, { "rank": 37, "intensity": 2.6020408163265305, "date": "2022-11-29" }, { "rank": 38, "intensity": 2.63265306122449, "date": "2022-09-17" }, { "rank": 39, "intensity": 2.663265306122449, "date": "2022-02-09" }, { "rank": 40, "intensity": 2.693877551020408, "date": "2022-02-14" }, { "rank": 41, "intensity": 2.7244897959183674, "date": "2022-06-29" }, { "rank": 42, "intensity": 2.7551020408163263, "date": "2022-10-04" }, { "rank": 43, "intensity": 2.7857142857142856, "date": "2022-03-16" }, { "rank": 44, "intensity": 2.816326530612245, "date": "2022-04-15" }, { "rank": 45, "intensity": 2.8469387755102042, "date": "2022-08-02" }, { "rank": 46, "intensity": 2.877551020408163, "date": "2022-02-25" }, { "rank": 47, "intensity": 2.9081632653061225, "date": "2022-05-12" }, { "rank": 48, "intensity": 2.9387755102040813, "date": "2022-12-18" }, { "rank": 49, "intensity": 2.9693877551020407, "date": "2022-01-17" }, { "rank": 50, "intensity": 3.0, "date": "2022-03-10" } ], "source": "causal_rivers" } }, { "id": "L3_T1_Composite_Trend_00076", "level": 3, "level_name": "Semantic Reasoning", "category": "Composite Trend", "subtask": "Composite Trend", "question": "Among days in channel 441 during 2020 that exhibit the trend pattern 'rapid rise, then steady stable, then slow rise', identify the top-5 days where the rapid rise segment is the fastest. (Output format: a ranked list of dates, e.g., ['YYYY-MM-DD', ...])", "answer": "['2020-08-16', '2020-07-06', '2020-12-21', '2020-03-30', '2020-02-04']", "ground_truth": [ "2020-08-16", "2020-07-06", "2020-12-21", "2020-03-30", "2020-02-04" ], "eval_metric": "set_f1", "channel": "441", "ts_data_path": "ts_data/L3_T1_Composite_Trend_00076.csv", "meta": { "segments": [ { "kind": "rise", "adj": "rapid" }, { "kind": "stable", "adj": "steady" }, { "kind": "rise", "adj": "slow" } ], "pattern_phrase": "rapid rise, then steady stable, then slow rise", "rank_target_idx": 0, "rank_kind": "rise", "rank_adj": "rapid", "rank_axis": "slope", "rank_direction": "DESC", "rank_extreme": "fastest", "top_segments_meta": [ { "adj": "rapid", "kind": "rise", "label": "rapid rise", "start_idx": 0, "end_idx": 11 }, { "adj": "steady", "kind": "stable", "label": "steady stable", "start_idx": 11, "end_idx": 57 }, { "adj": "slow", "kind": "rise", "label": "slow rise", "start_idx": 57, "end_idx": 98 } ], "points_per_day": 96, "pattern": "rapid rise, then steady stable, then slow rise", "year": 2020, "top_k": [ "2020-08-16", "2020-07-06", "2020-12-21", "2020-03-30", "2020-02-04" ], "all_injections": [ { "rank": 1, "intensity": 3.0, "date": "2020-08-16" }, { "rank": 2, "intensity": 2.9693877551020407, "date": "2020-07-06" }, { "rank": 3, "intensity": 2.938775510204082, "date": "2020-12-21" }, { "rank": 4, "intensity": 2.9081632653061225, "date": "2020-03-30" }, { "rank": 5, "intensity": 2.877551020408163, "date": "2020-02-04" }, { "rank": 6, "intensity": 2.8469387755102042, "date": "2020-03-22" }, { "rank": 7, "intensity": 2.816326530612245, "date": "2020-04-02" }, { "rank": 8, "intensity": 2.7857142857142856, "date": "2020-11-04" }, { "rank": 9, "intensity": 2.7551020408163267, "date": "2020-10-24" }, { "rank": 10, "intensity": 2.7244897959183674, "date": "2020-06-09" }, { "rank": 11, "intensity": 2.693877551020408, "date": "2020-05-26" }, { "rank": 12, "intensity": 2.663265306122449, "date": "2020-10-07" }, { "rank": 13, "intensity": 2.63265306122449, "date": "2020-01-29" }, { "rank": 14, "intensity": 2.6020408163265305, "date": "2020-05-30" }, { "rank": 15, "intensity": 2.5714285714285716, "date": "2020-12-19" }, { "rank": 16, "intensity": 2.5408163265306123, "date": "2020-08-09" }, { "rank": 17, "intensity": 2.510204081632653, "date": "2020-03-28" }, { "rank": 18, "intensity": 2.479591836734694, "date": "2020-09-05" }, { "rank": 19, "intensity": 2.4489795918367347, "date": "2020-05-23" }, { "rank": 20, "intensity": 2.4183673469387754, "date": "2020-11-26" }, { "rank": 21, "intensity": 2.387755102040816, "date": "2020-06-24" }, { "rank": 22, "intensity": 2.357142857142857, "date": "2020-04-16" }, { "rank": 23, "intensity": 2.326530612244898, "date": "2020-05-13" }, { "rank": 24, "intensity": 2.295918367346939, "date": "2020-10-02" }, { "rank": 25, "intensity": 2.2653061224489797, "date": "2020-06-04" }, { "rank": 26, "intensity": 2.2346938775510203, "date": "2020-10-13" }, { "rank": 27, "intensity": 2.204081632653061, "date": "2020-04-30" }, { "rank": 28, "intensity": 2.173469387755102, "date": "2020-05-19" }, { "rank": 29, "intensity": 2.142857142857143, "date": "2020-09-24" }, { "rank": 30, "intensity": 2.112244897959184, "date": "2020-04-24" }, { "rank": 31, "intensity": 2.0816326530612246, "date": "2020-10-26" }, { "rank": 32, "intensity": 2.0510204081632653, "date": "2020-08-12" }, { "rank": 33, "intensity": 2.020408163265306, "date": "2020-02-09" }, { "rank": 34, "intensity": 1.989795918367347, "date": "2020-09-17" }, { "rank": 35, "intensity": 1.9591836734693877, "date": "2020-05-15" }, { "rank": 36, "intensity": 1.9285714285714286, "date": "2020-11-15" }, { "rank": 37, "intensity": 1.8979591836734695, "date": "2020-04-18" }, { "rank": 38, "intensity": 1.8673469387755102, "date": "2020-01-04" }, { "rank": 39, "intensity": 1.836734693877551, "date": "2020-04-12" }, { "rank": 40, "intensity": 1.806122448979592, "date": "2020-10-19" }, { "rank": 41, "intensity": 1.7755102040816326, "date": "2020-10-17" }, { "rank": 42, "intensity": 1.7448979591836735, "date": "2020-11-18" }, { "rank": 43, "intensity": 1.7142857142857144, "date": "2020-08-19" }, { "rank": 44, "intensity": 1.683673469387755, "date": "2020-08-04" }, { "rank": 45, "intensity": 1.653061224489796, "date": "2020-02-27" }, { "rank": 46, "intensity": 1.6224489795918369, "date": "2020-06-02" }, { "rank": 47, "intensity": 1.5918367346938775, "date": "2020-09-11" }, { "rank": 48, "intensity": 1.5612244897959184, "date": "2020-12-26" }, { "rank": 49, "intensity": 1.5306122448979593, "date": "2020-04-22" }, { "rank": 50, "intensity": 1.5, "date": "2020-04-10" } ], "source": "causal_rivers" } }, { "id": "L3_T1_Composite_Trend_00077", "level": 3, "level_name": "Semantic Reasoning", "category": "Composite Trend", "subtask": "Composite Trend", "question": "Among days in channel 495 during 2023 that exhibit the trend pattern 'steady stable, then slow fall, then steady stable, then rise', identify the top-5 days where the slow fall segment is the slowest. (Output format: a ranked list of dates, e.g., ['YYYY-MM-DD', ...])", "answer": "['2023-09-05', '2023-05-31', '2023-11-06', '2023-11-24', '2023-11-19']", "ground_truth": [ "2023-09-05", "2023-05-31", "2023-11-06", "2023-11-24", "2023-11-19" ], "eval_metric": "set_f1", "channel": "495", "ts_data_path": "ts_data/L3_T1_Composite_Trend_00077.csv", "meta": { "segments": [ { "kind": "stable", "adj": "steady" }, { "kind": "fall", "adj": "slow" }, { "kind": "stable", "adj": "steady" }, { "kind": "rise", "adj": "none" } ], "pattern_phrase": "steady stable, then slow fall, then steady stable, then rise", "rank_target_idx": 1, "rank_kind": "fall", "rank_adj": "slow", "rank_axis": "slope", "rank_direction": "ASC", "rank_extreme": "slowest", "top_segments_meta": [ { "adj": "steady", "kind": "stable", "label": "steady stable", "start_idx": 0, "end_idx": 28 }, { "adj": "slow", "kind": "fall", "label": "slow fall", "start_idx": 28, "end_idx": 53 }, { "adj": "steady", "kind": "stable", "label": "steady stable", "start_idx": 53, "end_idx": 84 }, { "adj": "none", "kind": "rise", "label": "none rise", "start_idx": 84, "end_idx": 98 } ], "points_per_day": 96, "pattern": "steady stable, then slow fall, then steady stable, then rise", "year": 2023, "top_k": [ "2023-09-05", "2023-05-31", "2023-11-06", "2023-11-24", "2023-11-19" ], "all_injections": [ { "rank": 1, "intensity": 1.5, "date": "2023-09-05" }, { "rank": 2, "intensity": 1.530612244897959, "date": "2023-05-31" }, { "rank": 3, "intensity": 1.5612244897959184, "date": "2023-11-06" }, { "rank": 4, "intensity": 1.5918367346938775, "date": "2023-11-24" }, { "rank": 5, "intensity": 1.6224489795918366, "date": "2023-11-19" }, { "rank": 6, "intensity": 1.653061224489796, "date": "2023-05-13" }, { "rank": 7, "intensity": 1.683673469387755, "date": "2023-09-29" }, { "rank": 8, "intensity": 1.7142857142857142, "date": "2023-08-13" }, { "rank": 9, "intensity": 1.7448979591836735, "date": "2023-10-14" }, { "rank": 10, "intensity": 1.7755102040816326, "date": "2023-06-03" }, { "rank": 11, "intensity": 1.806122448979592, "date": "2023-02-24" }, { "rank": 12, "intensity": 1.836734693877551, "date": "2023-07-05" }, { "rank": 13, "intensity": 1.8673469387755102, "date": "2023-12-29" }, { "rank": 14, "intensity": 1.8979591836734695, "date": "2023-07-14" }, { "rank": 15, "intensity": 1.9285714285714286, "date": "2023-02-08" }, { "rank": 16, "intensity": 1.9591836734693877, "date": "2023-04-26" }, { "rank": 17, "intensity": 1.989795918367347, "date": "2023-12-26" }, { "rank": 18, "intensity": 2.020408163265306, "date": "2023-09-23" }, { "rank": 19, "intensity": 2.0510204081632653, "date": "2023-01-18" }, { "rank": 20, "intensity": 2.0816326530612246, "date": "2023-08-07" }, { "rank": 21, "intensity": 2.112244897959184, "date": "2023-03-16" }, { "rank": 22, "intensity": 2.142857142857143, "date": "2023-12-13" }, { "rank": 23, "intensity": 2.173469387755102, "date": "2023-07-23" }, { "rank": 24, "intensity": 2.204081632653061, "date": "2023-02-10" }, { "rank": 25, "intensity": 2.2346938775510203, "date": "2023-06-08" }, { "rank": 26, "intensity": 2.2653061224489797, "date": "2023-05-20" }, { "rank": 27, "intensity": 2.295918367346939, "date": "2023-07-01" }, { "rank": 28, "intensity": 2.326530612244898, "date": "2023-06-24" }, { "rank": 29, "intensity": 2.357142857142857, "date": "2023-09-25" }, { "rank": 30, "intensity": 2.387755102040816, "date": "2023-03-08" }, { "rank": 31, "intensity": 2.4183673469387754, "date": "2023-03-04" }, { "rank": 32, "intensity": 2.4489795918367347, "date": "2023-11-22" }, { "rank": 33, "intensity": 2.479591836734694, "date": "2023-01-22" }, { "rank": 34, "intensity": 2.510204081632653, "date": "2023-01-15" }, { "rank": 35, "intensity": 2.5408163265306123, "date": "2023-11-08" }, { "rank": 36, "intensity": 2.571428571428571, "date": "2023-10-01" }, { "rank": 37, "intensity": 2.6020408163265305, "date": "2023-01-30" }, { "rank": 38, "intensity": 2.63265306122449, "date": "2023-07-11" }, { "rank": 39, "intensity": 2.663265306122449, "date": "2023-05-11" }, { "rank": 40, "intensity": 2.693877551020408, "date": "2023-05-16" }, { "rank": 41, "intensity": 2.7244897959183674, "date": "2023-08-04" }, { "rank": 42, "intensity": 2.7551020408163263, "date": "2023-02-27" }, { "rank": 43, "intensity": 2.7857142857142856, "date": "2023-08-22" }, { "rank": 44, "intensity": 2.816326530612245, "date": "2023-09-20" }, { "rank": 45, "intensity": 2.8469387755102042, "date": "2023-10-16" }, { "rank": 46, "intensity": 2.877551020408163, "date": "2023-06-11" }, { "rank": 47, "intensity": 2.9081632653061225, "date": "2023-10-30" }, { "rank": 48, "intensity": 2.9387755102040813, "date": "2023-10-20" }, { "rank": 49, "intensity": 2.9693877551020407, "date": "2023-08-27" }, { "rank": 50, "intensity": 3.0, "date": "2023-01-24" } ], "source": "causal_rivers" } }, { "id": "L3_T1_Composite_Trend_00078", "level": 3, "level_name": "Semantic Reasoning", "category": "Composite Trend", "subtask": "Composite Trend", "question": "Among days in channel 496 during 2020 that exhibit the trend pattern 'steady stable, then rapid rise', identify the top-5 days where the rapid rise segment is the fastest. (Output format: a ranked list of dates, e.g., ['YYYY-MM-DD', ...])", "answer": "['2020-08-17', '2020-10-23', '2020-09-11', '2020-11-30', '2020-05-25']", "ground_truth": [ "2020-08-17", "2020-10-23", "2020-09-11", "2020-11-30", "2020-05-25" ], "eval_metric": "set_f1", "channel": "496", "ts_data_path": "ts_data/L3_T1_Composite_Trend_00078.csv", "meta": { "segments": [ { "kind": "stable", "adj": "steady" }, { "kind": "rise", "adj": "rapid" } ], "pattern_phrase": "steady stable, then rapid rise", "rank_target_idx": 1, "rank_kind": "rise", "rank_adj": "rapid", "rank_axis": "slope", "rank_direction": "DESC", "rank_extreme": "fastest", "top_segments_meta": [ { "adj": "steady", "kind": "stable", "label": "steady stable", "start_idx": 0, "end_idx": 78 }, { "adj": "rapid", "kind": "rise", "label": "rapid rise", "start_idx": 78, "end_idx": 98 } ], "points_per_day": 96, "pattern": "steady stable, then rapid rise", "year": 2020, "top_k": [ "2020-08-17", "2020-10-23", "2020-09-11", "2020-11-30", "2020-05-25" ], "all_injections": [ { "rank": 1, "intensity": 3.0, "date": "2020-08-17" }, { "rank": 2, "intensity": 2.9693877551020407, "date": "2020-10-23" }, { "rank": 3, "intensity": 2.938775510204082, "date": "2020-09-11" }, { "rank": 4, "intensity": 2.9081632653061225, "date": "2020-11-30" }, { "rank": 5, "intensity": 2.877551020408163, "date": "2020-05-25" }, { "rank": 6, "intensity": 2.8469387755102042, "date": "2020-11-24" }, { "rank": 7, "intensity": 2.816326530612245, "date": "2020-07-14" }, { "rank": 8, "intensity": 2.7857142857142856, "date": "2020-07-02" }, { "rank": 9, "intensity": 2.7551020408163267, "date": "2020-09-17" }, { "rank": 10, "intensity": 2.7244897959183674, "date": "2020-04-22" }, { "rank": 11, "intensity": 2.693877551020408, "date": "2020-05-12" }, { "rank": 12, "intensity": 2.663265306122449, "date": "2020-05-31" }, { "rank": 13, "intensity": 2.63265306122449, "date": "2020-11-10" }, { "rank": 14, "intensity": 2.6020408163265305, "date": "2020-03-05" }, { "rank": 15, "intensity": 2.5714285714285716, "date": "2020-12-19" }, { "rank": 16, "intensity": 2.5408163265306123, "date": "2020-08-03" }, { "rank": 17, "intensity": 2.510204081632653, "date": "2020-12-08" }, { "rank": 18, "intensity": 2.479591836734694, "date": "2020-03-08" }, { "rank": 19, "intensity": 2.4489795918367347, "date": "2020-11-18" }, { "rank": 20, "intensity": 2.4183673469387754, "date": "2020-02-16" }, { "rank": 21, "intensity": 2.387755102040816, "date": "2020-09-27" }, { "rank": 22, "intensity": 2.357142857142857, "date": "2020-02-23" }, { "rank": 23, "intensity": 2.326530612244898, "date": "2020-04-16" }, { "rank": 24, "intensity": 2.295918367346939, "date": "2020-08-11" }, { "rank": 25, "intensity": 2.2653061224489797, "date": "2020-10-28" }, { "rank": 26, "intensity": 2.2346938775510203, "date": "2020-07-27" }, { "rank": 27, "intensity": 2.204081632653061, "date": "2020-07-16" }, { "rank": 28, "intensity": 2.173469387755102, "date": "2020-05-22" }, { "rank": 29, "intensity": 2.142857142857143, "date": "2020-09-23" }, { "rank": 30, "intensity": 2.112244897959184, "date": "2020-01-03" }, { "rank": 31, "intensity": 2.0816326530612246, "date": "2020-02-05" }, { "rank": 32, "intensity": 2.0510204081632653, "date": "2020-06-20" }, { "rank": 33, "intensity": 2.020408163265306, "date": "2020-01-11" }, { "rank": 34, "intensity": 1.989795918367347, "date": "2020-06-05" }, { "rank": 35, "intensity": 1.9591836734693877, "date": "2020-01-19" }, { "rank": 36, "intensity": 1.9285714285714286, "date": "2020-11-12" }, { "rank": 37, "intensity": 1.8979591836734695, "date": "2020-07-29" }, { "rank": 38, "intensity": 1.8673469387755102, "date": "2020-08-23" }, { "rank": 39, "intensity": 1.836734693877551, "date": "2020-02-29" }, { "rank": 40, "intensity": 1.806122448979592, "date": "2020-06-08" }, { "rank": 41, "intensity": 1.7755102040816326, "date": "2020-12-26" }, { "rank": 42, "intensity": 1.7448979591836735, "date": "2020-06-28" }, { "rank": 43, "intensity": 1.7142857142857144, "date": "2020-07-23" }, { "rank": 44, "intensity": 1.683673469387755, "date": "2020-04-24" }, { "rank": 45, "intensity": 1.653061224489796, "date": "2020-04-11" }, { "rank": 46, "intensity": 1.6224489795918369, "date": "2020-06-23" }, { "rank": 47, "intensity": 1.5918367346938775, "date": "2020-05-14" }, { "rank": 48, "intensity": 1.5612244897959184, "date": "2020-09-30" }, { "rank": 49, "intensity": 1.5306122448979593, "date": "2020-12-14" }, { "rank": 50, "intensity": 1.5, "date": "2020-10-20" } ], "source": "causal_rivers" } }, { "id": "L3_T1_Composite_Trend_00079", "level": 3, "level_name": "Semantic Reasoning", "category": "Composite Trend", "subtask": "Composite Trend", "question": "Among days in channel 501 during 2022 that exhibit the trend pattern 'fall, then rapid fall, then steady stable', identify the top-5 days where the rapid fall segment is the fastest. (Output format: a ranked list of dates, e.g., ['YYYY-MM-DD', ...])", "answer": "['2022-04-27', '2022-08-13', '2022-10-28', '2022-02-05', '2022-09-24']", "ground_truth": [ "2022-04-27", "2022-08-13", "2022-10-28", "2022-02-05", "2022-09-24" ], "eval_metric": "set_f1", "channel": "501", "ts_data_path": "ts_data/L3_T1_Composite_Trend_00079.csv", "meta": { "segments": [ { "kind": "fall", "adj": "none" }, { "kind": "fall", "adj": "rapid" }, { "kind": "stable", "adj": "steady" } ], "pattern_phrase": "fall, then rapid fall, then steady stable", "rank_target_idx": 1, "rank_kind": "fall", "rank_adj": "rapid", "rank_axis": "slope", "rank_direction": "DESC", "rank_extreme": "fastest", "top_segments_meta": [ { "adj": "none", "kind": "fall", "label": "none fall", "start_idx": 0, "end_idx": 25 }, { "adj": "rapid", "kind": "fall", "label": "rapid fall", "start_idx": 25, "end_idx": 40 }, { "adj": "steady", "kind": "stable", "label": "steady stable", "start_idx": 40, "end_idx": 98 } ], "points_per_day": 96, "pattern": "fall, then rapid fall, then steady stable", "year": 2022, "top_k": [ "2022-04-27", "2022-08-13", "2022-10-28", "2022-02-05", "2022-09-24" ], "all_injections": [ { "rank": 1, "intensity": 3.0, "date": "2022-04-27" }, { "rank": 2, "intensity": 2.9693877551020407, "date": "2022-08-13" }, { "rank": 3, "intensity": 2.938775510204082, "date": "2022-10-28" }, { "rank": 4, "intensity": 2.9081632653061225, "date": "2022-02-05" }, { "rank": 5, "intensity": 2.877551020408163, "date": "2022-09-24" }, { "rank": 6, "intensity": 2.8469387755102042, "date": "2022-08-09" }, { "rank": 7, "intensity": 2.816326530612245, "date": "2022-07-26" }, { "rank": 8, "intensity": 2.7857142857142856, "date": "2022-02-09" }, { "rank": 9, "intensity": 2.7551020408163267, "date": "2022-12-01" }, { "rank": 10, "intensity": 2.7244897959183674, "date": "2022-08-20" }, { "rank": 11, "intensity": 2.693877551020408, "date": "2022-12-03" }, { "rank": 12, "intensity": 2.663265306122449, "date": "2022-11-04" }, { "rank": 13, "intensity": 2.63265306122449, "date": "2022-07-01" }, { "rank": 14, "intensity": 2.6020408163265305, "date": "2022-05-12" }, { "rank": 15, "intensity": 2.5714285714285716, "date": "2022-01-29" }, { "rank": 16, "intensity": 2.5408163265306123, "date": "2022-07-18" }, { "rank": 17, "intensity": 2.510204081632653, "date": "2022-11-29" }, { "rank": 18, "intensity": 2.479591836734694, "date": "2022-01-14" }, { "rank": 19, "intensity": 2.4489795918367347, "date": "2022-11-09" }, { "rank": 20, "intensity": 2.4183673469387754, "date": "2022-07-14" }, { "rank": 21, "intensity": 2.387755102040816, "date": "2022-05-23" }, { "rank": 22, "intensity": 2.357142857142857, "date": "2022-02-17" }, { "rank": 23, "intensity": 2.326530612244898, "date": "2022-04-12" }, { "rank": 24, "intensity": 2.295918367346939, "date": "2022-01-20" }, { "rank": 25, "intensity": 2.2653061224489797, "date": "2022-02-13" }, { "rank": 26, "intensity": 2.2346938775510203, "date": "2022-09-12" }, { "rank": 27, "intensity": 2.204081632653061, "date": "2022-06-14" }, { "rank": 28, "intensity": 2.173469387755102, "date": "2022-08-06" }, { "rank": 29, "intensity": 2.142857142857143, "date": "2022-05-21" }, { "rank": 30, "intensity": 2.112244897959184, "date": "2022-01-18" }, { "rank": 31, "intensity": 2.0816326530612246, "date": "2022-11-22" }, { "rank": 32, "intensity": 2.0510204081632653, "date": "2022-02-03" }, { "rank": 33, "intensity": 2.020408163265306, "date": "2022-10-06" }, { "rank": 34, "intensity": 1.989795918367347, "date": "2022-01-27" }, { "rank": 35, "intensity": 1.9591836734693877, "date": "2022-05-10" }, { "rank": 36, "intensity": 1.9285714285714286, "date": "2022-04-04" }, { "rank": 37, "intensity": 1.8979591836734695, "date": "2022-03-12" }, { "rank": 38, "intensity": 1.8673469387755102, "date": "2022-08-02" }, { "rank": 39, "intensity": 1.836734693877551, "date": "2022-06-24" }, { "rank": 40, "intensity": 1.806122448979592, "date": "2022-03-21" }, { "rank": 41, "intensity": 1.7755102040816326, "date": "2022-06-03" }, { "rank": 42, "intensity": 1.7448979591836735, "date": "2022-10-08" }, { "rank": 43, "intensity": 1.7142857142857144, "date": "2022-08-31" }, { "rank": 44, "intensity": 1.683673469387755, "date": "2022-03-18" }, { "rank": 45, "intensity": 1.653061224489796, "date": "2022-12-20" }, { "rank": 46, "intensity": 1.6224489795918369, "date": "2022-02-07" }, { "rank": 47, "intensity": 1.5918367346938775, "date": "2022-10-03" }, { "rank": 48, "intensity": 1.5612244897959184, "date": "2022-06-29" }, { "rank": 49, "intensity": 1.5306122448979593, "date": "2022-09-06" }, { "rank": 50, "intensity": 1.5, "date": "2022-02-25" } ], "source": "causal_rivers" } }, { "id": "L3_T1_Composite_Trend_00080", "level": 3, "level_name": "Semantic Reasoning", "category": "Composite Trend", "subtask": "Composite Trend", "question": "Among days in channel 578 during 2021 that exhibit the trend pattern 'slow fall, then fluctuating stable', identify the top-5 days where the fluctuating stable segment is the most fluctuating. (Output format: a ranked list of dates, e.g., ['YYYY-MM-DD', ...])", "answer": "['2021-12-26', '2021-10-20', '2021-09-06', '2021-08-04', '2021-04-03']", "ground_truth": [ "2021-12-26", "2021-10-20", "2021-09-06", "2021-08-04", "2021-04-03" ], "eval_metric": "set_f1", "channel": "578", "ts_data_path": "ts_data/L3_T1_Composite_Trend_00080.csv", "meta": { "segments": [ { "kind": "fall", "adj": "slow" }, { "kind": "stable", "adj": "fluctuating" } ], "pattern_phrase": "slow fall, then fluctuating stable", "rank_target_idx": 1, "rank_kind": "stable", "rank_adj": "fluctuating", "rank_axis": "std", "rank_direction": "DESC", "rank_extreme": "most fluctuating", "top_segments_meta": [ { "adj": "slow", "kind": "fall", "label": "slow fall", "start_idx": 0, "end_idx": 58 }, { "adj": "fluctuating", "kind": "stable", "label": "fluctuating stable", "start_idx": 58, "end_idx": 98 } ], "points_per_day": 96, "pattern": "slow fall, then fluctuating stable", "year": 2021, "top_k": [ "2021-12-26", "2021-10-20", "2021-09-06", "2021-08-04", "2021-04-03" ], "all_injections": [ { "rank": 1, "intensity": 3.0, "date": "2021-12-26" }, { "rank": 2, "intensity": 2.9693877551020407, "date": "2021-10-20" }, { "rank": 3, "intensity": 2.938775510204082, "date": "2021-09-06" }, { "rank": 4, "intensity": 2.9081632653061225, "date": "2021-08-04" }, { "rank": 5, "intensity": 2.877551020408163, "date": "2021-04-03" }, { "rank": 6, "intensity": 2.8469387755102042, "date": "2021-11-26" }, { "rank": 7, "intensity": 2.816326530612245, "date": "2021-02-16" }, { "rank": 8, "intensity": 2.7857142857142856, "date": "2021-08-26" }, { "rank": 9, "intensity": 2.7551020408163267, "date": "2021-04-18" }, { "rank": 10, "intensity": 2.7244897959183674, "date": "2021-02-08" }, { "rank": 11, "intensity": 2.693877551020408, "date": "2021-06-08" }, { "rank": 12, "intensity": 2.663265306122449, "date": "2021-05-12" }, { "rank": 13, "intensity": 2.63265306122449, "date": "2021-07-17" }, { "rank": 14, "intensity": 2.6020408163265305, "date": "2021-04-22" }, { "rank": 15, "intensity": 2.5714285714285716, "date": "2021-01-23" }, { "rank": 16, "intensity": 2.5408163265306123, "date": "2021-03-14" }, { "rank": 17, "intensity": 2.510204081632653, "date": "2021-06-27" }, { "rank": 18, "intensity": 2.479591836734694, "date": "2021-12-11" }, { "rank": 19, "intensity": 2.4489795918367347, "date": "2021-10-11" }, { "rank": 20, "intensity": 2.4183673469387754, "date": "2021-12-04" }, { "rank": 21, "intensity": 2.387755102040816, "date": "2021-05-14" }, { "rank": 22, "intensity": 2.357142857142857, "date": "2021-05-06" }, { "rank": 23, "intensity": 2.326530612244898, "date": "2021-06-06" }, { "rank": 24, "intensity": 2.295918367346939, "date": "2021-06-13" }, { "rank": 25, "intensity": 2.2653061224489797, "date": "2021-04-16" }, { "rank": 26, "intensity": 2.2346938775510203, "date": "2021-07-12" }, { "rank": 27, "intensity": 2.204081632653061, "date": "2021-08-21" }, { "rank": 28, "intensity": 2.173469387755102, "date": "2021-08-28" }, { "rank": 29, "intensity": 2.142857142857143, "date": "2021-08-31" }, { "rank": 30, "intensity": 2.112244897959184, "date": "2021-07-20" }, { "rank": 31, "intensity": 2.0816326530612246, "date": "2021-08-10" }, { "rank": 32, "intensity": 2.0510204081632653, "date": "2021-11-07" }, { "rank": 33, "intensity": 2.020408163265306, "date": "2021-07-01" }, { "rank": 34, "intensity": 1.989795918367347, "date": "2021-11-18" }, { "rank": 35, "intensity": 1.9591836734693877, "date": "2021-01-17" }, { "rank": 36, "intensity": 1.9285714285714286, "date": "2021-02-04" }, { "rank": 37, "intensity": 1.8979591836734695, "date": "2021-09-20" }, { "rank": 38, "intensity": 1.8673469387755102, "date": "2021-09-27" }, { "rank": 39, "intensity": 1.836734693877551, "date": "2021-09-09" }, { "rank": 40, "intensity": 1.806122448979592, "date": "2021-12-28" }, { "rank": 41, "intensity": 1.7755102040816326, "date": "2021-02-12" }, { "rank": 42, "intensity": 1.7448979591836735, "date": "2021-01-21" }, { "rank": 43, "intensity": 1.7142857142857144, "date": "2021-02-18" }, { "rank": 44, "intensity": 1.683673469387755, "date": "2021-06-17" }, { "rank": 45, "intensity": 1.653061224489796, "date": "2021-07-15" }, { "rank": 46, "intensity": 1.6224489795918369, "date": "2021-05-29" }, { "rank": 47, "intensity": 1.5918367346938775, "date": "2021-04-07" }, { "rank": 48, "intensity": 1.5612244897959184, "date": "2021-05-19" }, { "rank": 49, "intensity": 1.5306122448979593, "date": "2021-11-12" }, { "rank": 50, "intensity": 1.5, "date": "2021-01-25" } ], "source": "causal_rivers" } }, { "id": "L3_T1_Composite_Trend_00081", "level": 3, "level_name": "Semantic Reasoning", "category": "Composite Trend", "subtask": "Composite Trend", "question": "Among days in channel 580 during 2021 that exhibit the trend pattern 'steady stable, then slow fall', identify the top-5 days where the slow fall segment is the slowest. (Output format: a ranked list of dates, e.g., ['YYYY-MM-DD', ...])", "answer": "['2021-09-25', '2021-11-27', '2021-07-17', '2021-04-30', '2021-10-20']", "ground_truth": [ "2021-09-25", "2021-11-27", "2021-07-17", "2021-04-30", "2021-10-20" ], "eval_metric": "set_f1", "channel": "580", "ts_data_path": "ts_data/L3_T1_Composite_Trend_00081.csv", "meta": { "segments": [ { "kind": "stable", "adj": "steady" }, { "kind": "fall", "adj": "slow" } ], "pattern_phrase": "steady stable, then slow fall", "rank_target_idx": 1, "rank_kind": "fall", "rank_adj": "slow", "rank_axis": "slope", "rank_direction": "ASC", "rank_extreme": "slowest", "top_segments_meta": [ { "adj": "steady", "kind": "stable", "label": "steady stable", "start_idx": 0, "end_idx": 51 }, { "adj": "slow", "kind": "fall", "label": "slow fall", "start_idx": 51, "end_idx": 98 } ], "points_per_day": 96, "pattern": "steady stable, then slow fall", "year": 2021, "top_k": [ "2021-09-25", "2021-11-27", "2021-07-17", "2021-04-30", "2021-10-20" ], "all_injections": [ { "rank": 1, "intensity": 1.5, "date": "2021-09-25" }, { "rank": 2, "intensity": 1.530612244897959, "date": "2021-11-27" }, { "rank": 3, "intensity": 1.5612244897959184, "date": "2021-07-17" }, { "rank": 4, "intensity": 1.5918367346938775, "date": "2021-04-30" }, { "rank": 5, "intensity": 1.6224489795918366, "date": "2021-10-20" }, { "rank": 6, "intensity": 1.653061224489796, "date": "2021-01-23" }, { "rank": 7, "intensity": 1.683673469387755, "date": "2021-09-30" }, { "rank": 8, "intensity": 1.7142857142857142, "date": "2021-01-07" }, { "rank": 9, "intensity": 1.7448979591836735, "date": "2021-10-10" }, { "rank": 10, "intensity": 1.7755102040816326, "date": "2021-04-27" }, { "rank": 11, "intensity": 1.806122448979592, "date": "2021-06-20" }, { "rank": 12, "intensity": 1.836734693877551, "date": "2021-02-25" }, { "rank": 13, "intensity": 1.8673469387755102, "date": "2021-05-11" }, { "rank": 14, "intensity": 1.8979591836734695, "date": "2021-07-14" }, { "rank": 15, "intensity": 1.9285714285714286, "date": "2021-06-17" }, { "rank": 16, "intensity": 1.9591836734693877, "date": "2021-04-21" }, { "rank": 17, "intensity": 1.989795918367347, "date": "2021-11-12" }, { "rank": 18, "intensity": 2.020408163265306, "date": "2021-05-30" }, { "rank": 19, "intensity": 2.0510204081632653, "date": "2021-12-23" }, { "rank": 20, "intensity": 2.0816326530612246, "date": "2021-10-28" }, { "rank": 21, "intensity": 2.112244897959184, "date": "2021-09-10" }, { "rank": 22, "intensity": 2.142857142857143, "date": "2021-12-04" }, { "rank": 23, "intensity": 2.173469387755102, "date": "2021-07-24" }, { "rank": 24, "intensity": 2.204081632653061, "date": "2021-02-05" }, { "rank": 25, "intensity": 2.2346938775510203, "date": "2021-09-17" }, { "rank": 26, "intensity": 2.2653061224489797, "date": "2021-02-19" }, { "rank": 27, "intensity": 2.295918367346939, "date": "2021-04-19" }, { "rank": 28, "intensity": 2.326530612244898, "date": "2021-11-01" }, { "rank": 29, "intensity": 2.357142857142857, "date": "2021-10-12" }, { "rank": 30, "intensity": 2.387755102040816, "date": "2021-02-21" }, { "rank": 31, "intensity": 2.4183673469387754, "date": "2021-11-16" }, { "rank": 32, "intensity": 2.4489795918367347, "date": "2021-09-28" }, { "rank": 33, "intensity": 2.479591836734694, "date": "2021-05-09" }, { "rank": 34, "intensity": 2.510204081632653, "date": "2021-12-20" }, { "rank": 35, "intensity": 2.5408163265306123, "date": "2021-02-09" }, { "rank": 36, "intensity": 2.571428571428571, "date": "2021-04-14" }, { "rank": 37, "intensity": 2.6020408163265305, "date": "2021-08-05" }, { "rank": 38, "intensity": 2.63265306122449, "date": "2021-11-05" }, { "rank": 39, "intensity": 2.663265306122449, "date": "2021-08-26" }, { "rank": 40, "intensity": 2.693877551020408, "date": "2021-07-27" }, { "rank": 41, "intensity": 2.7244897959183674, "date": "2021-03-07" }, { "rank": 42, "intensity": 2.7551020408163263, "date": "2021-09-23" }, { "rank": 43, "intensity": 2.7857142857142856, "date": "2021-05-20" }, { "rank": 44, "intensity": 2.816326530612245, "date": "2021-05-04" }, { "rank": 45, "intensity": 2.8469387755102042, "date": "2021-01-01" }, { "rank": 46, "intensity": 2.877551020408163, "date": "2021-07-12" }, { "rank": 47, "intensity": 2.9081632653061225, "date": "2021-09-02" }, { "rank": 48, "intensity": 2.9387755102040813, "date": "2021-01-09" }, { "rank": 49, "intensity": 2.9693877551020407, "date": "2021-08-22" }, { "rank": 50, "intensity": 3.0, "date": "2021-04-09" } ], "source": "causal_rivers" } }, { "id": "L3_T1_Composite_Trend_00082", "level": 3, "level_name": "Semantic Reasoning", "category": "Composite Trend", "subtask": "Composite Trend", "question": "Among days in channel 589 during 2023 that exhibit the trend pattern 'slow rise, then fluctuating stable', identify the top-5 days where the slow rise segment is the slowest. (Output format: a ranked list of dates, e.g., ['YYYY-MM-DD', ...])", "answer": "['2023-05-25', '2023-02-10', '2023-01-13', '2023-08-12', '2023-12-23']", "ground_truth": [ "2023-05-25", "2023-02-10", "2023-01-13", "2023-08-12", "2023-12-23" ], "eval_metric": "set_f1", "channel": "589", "ts_data_path": "ts_data/L3_T1_Composite_Trend_00082.csv", "meta": { "segments": [ { "kind": "rise", "adj": "slow" }, { "kind": "stable", "adj": "fluctuating" } ], "pattern_phrase": "slow rise, then fluctuating stable", "rank_target_idx": 0, "rank_kind": "rise", "rank_adj": "slow", "rank_axis": "slope", "rank_direction": "ASC", "rank_extreme": "slowest", "top_segments_meta": [ { "adj": "slow", "kind": "rise", "label": "slow rise", "start_idx": 0, "end_idx": 55 }, { "adj": "fluctuating", "kind": "stable", "label": "fluctuating stable", "start_idx": 55, "end_idx": 98 } ], "points_per_day": 96, "pattern": "slow rise, then fluctuating stable", "year": 2023, "top_k": [ "2023-05-25", "2023-02-10", "2023-01-13", "2023-08-12", "2023-12-23" ], "all_injections": [ { "rank": 1, "intensity": 1.5, "date": "2023-05-25" }, { "rank": 2, "intensity": 1.530612244897959, "date": "2023-02-10" }, { "rank": 3, "intensity": 1.5612244897959184, "date": "2023-01-13" }, { "rank": 4, "intensity": 1.5918367346938775, "date": "2023-08-12" }, { "rank": 5, "intensity": 1.6224489795918366, "date": "2023-12-23" }, { "rank": 6, "intensity": 1.653061224489796, "date": "2023-10-07" }, { "rank": 7, "intensity": 1.683673469387755, "date": "2023-06-20" }, { "rank": 8, "intensity": 1.7142857142857142, "date": "2023-11-07" }, { "rank": 9, "intensity": 1.7448979591836735, "date": "2023-04-26" }, { "rank": 10, "intensity": 1.7755102040816326, "date": "2023-06-13" }, { "rank": 11, "intensity": 1.806122448979592, "date": "2023-12-27" }, { "rank": 12, "intensity": 1.836734693877551, "date": "2023-09-27" }, { "rank": 13, "intensity": 1.8673469387755102, "date": "2023-12-15" }, { "rank": 14, "intensity": 1.8979591836734695, "date": "2023-02-25" }, { "rank": 15, "intensity": 1.9285714285714286, "date": "2023-10-11" }, { "rank": 16, "intensity": 1.9591836734693877, "date": "2023-12-06" }, { "rank": 17, "intensity": 1.989795918367347, "date": "2023-02-13" }, { "rank": 18, "intensity": 2.020408163265306, "date": "2023-08-15" }, { "rank": 19, "intensity": 2.0510204081632653, "date": "2023-04-28" }, { "rank": 20, "intensity": 2.0816326530612246, "date": "2023-01-22" }, { "rank": 21, "intensity": 2.112244897959184, "date": "2023-12-10" }, { "rank": 22, "intensity": 2.142857142857143, "date": "2023-05-29" }, { "rank": 23, "intensity": 2.173469387755102, "date": "2023-12-02" }, { "rank": 24, "intensity": 2.204081632653061, "date": "2023-11-14" }, { "rank": 25, "intensity": 2.2346938775510203, "date": "2023-07-27" }, { "rank": 26, "intensity": 2.2653061224489797, "date": "2023-06-07" }, { "rank": 27, "intensity": 2.295918367346939, "date": "2023-09-30" }, { "rank": 28, "intensity": 2.326530612244898, "date": "2023-06-25" }, { "rank": 29, "intensity": 2.357142857142857, "date": "2023-06-28" }, { "rank": 30, "intensity": 2.387755102040816, "date": "2023-04-20" }, { "rank": 31, "intensity": 2.4183673469387754, "date": "2023-04-12" }, { "rank": 32, "intensity": 2.4489795918367347, "date": "2023-11-10" }, { "rank": 33, "intensity": 2.479591836734694, "date": "2023-01-17" }, { "rank": 34, "intensity": 2.510204081632653, "date": "2023-06-16" }, { "rank": 35, "intensity": 2.5408163265306123, "date": "2023-01-26" }, { "rank": 36, "intensity": 2.571428571428571, "date": "2023-11-18" }, { "rank": 37, "intensity": 2.6020408163265305, "date": "2023-09-19" }, { "rank": 38, "intensity": 2.63265306122449, "date": "2023-07-01" }, { "rank": 39, "intensity": 2.663265306122449, "date": "2023-08-07" }, { "rank": 40, "intensity": 2.693877551020408, "date": "2023-01-05" }, { "rank": 41, "intensity": 2.7244897959183674, "date": "2023-10-13" }, { "rank": 42, "intensity": 2.7551020408163263, "date": "2023-06-02" }, { "rank": 43, "intensity": 2.7857142857142856, "date": "2023-02-17" }, { "rank": 44, "intensity": 2.816326530612245, "date": "2023-08-27" }, { "rank": 45, "intensity": 2.8469387755102042, "date": "2023-09-13" }, { "rank": 46, "intensity": 2.877551020408163, "date": "2023-10-23" }, { "rank": 47, "intensity": 2.9081632653061225, "date": "2023-09-24" }, { "rank": 48, "intensity": 2.9387755102040813, "date": "2023-05-10" }, { "rank": 49, "intensity": 2.9693877551020407, "date": "2023-03-22" }, { "rank": 50, "intensity": 3.0, "date": "2023-11-29" } ], "source": "causal_rivers" } }, { "id": "L3_T1_Composite_Trend_00083", "level": 3, "level_name": "Semantic Reasoning", "category": "Composite Trend", "subtask": "Composite Trend", "question": "Among days in channel 591 during 2019 that exhibit the trend pattern 'fluctuating stable, then fall', identify the top-5 days where the fluctuating stable segment is the most fluctuating. (Output format: a ranked list of dates, e.g., ['YYYY-MM-DD', ...])", "answer": "['2019-07-22', '2019-07-30', '2019-08-10', '2019-09-04', '2019-06-12']", "ground_truth": [ "2019-07-22", "2019-07-30", "2019-08-10", "2019-09-04", "2019-06-12" ], "eval_metric": "set_f1", "channel": "591", "ts_data_path": "ts_data/L3_T1_Composite_Trend_00083.csv", "meta": { "segments": [ { "kind": "stable", "adj": "fluctuating" }, { "kind": "fall", "adj": "none" } ], "pattern_phrase": "fluctuating stable, then fall", "rank_target_idx": 0, "rank_kind": "stable", "rank_adj": "fluctuating", "rank_axis": "std", "rank_direction": "DESC", "rank_extreme": "most fluctuating", "top_segments_meta": [ { "adj": "fluctuating", "kind": "stable", "label": "fluctuating stable", "start_idx": 0, "end_idx": 56 }, { "adj": "none", "kind": "fall", "label": "none fall", "start_idx": 56, "end_idx": 98 } ], "points_per_day": 96, "pattern": "fluctuating stable, then fall", "year": 2019, "top_k": [ "2019-07-22", "2019-07-30", "2019-08-10", "2019-09-04", "2019-06-12" ], "all_injections": [ { "rank": 1, "intensity": 3.0, "date": "2019-07-22" }, { "rank": 2, "intensity": 2.9693877551020407, "date": "2019-07-30" }, { "rank": 3, "intensity": 2.938775510204082, "date": "2019-08-10" }, { "rank": 4, "intensity": 2.9081632653061225, "date": "2019-09-04" }, { "rank": 5, "intensity": 2.877551020408163, "date": "2019-06-12" }, { "rank": 6, "intensity": 2.8469387755102042, "date": "2019-02-17" }, { "rank": 7, "intensity": 2.816326530612245, "date": "2019-10-12" }, { "rank": 8, "intensity": 2.7857142857142856, "date": "2019-11-10" }, { "rank": 9, "intensity": 2.7551020408163267, "date": "2019-08-29" }, { "rank": 10, "intensity": 2.7244897959183674, "date": "2019-06-26" }, { "rank": 11, "intensity": 2.693877551020408, "date": "2019-12-04" }, { "rank": 12, "intensity": 2.663265306122449, "date": "2019-12-26" }, { "rank": 13, "intensity": 2.63265306122449, "date": "2019-05-12" }, { "rank": 14, "intensity": 2.6020408163265305, "date": "2019-05-18" }, { "rank": 15, "intensity": 2.5714285714285716, "date": "2019-11-04" }, { "rank": 16, "intensity": 2.5408163265306123, "date": "2019-07-04" }, { "rank": 17, "intensity": 2.510204081632653, "date": "2019-07-13" }, { "rank": 18, "intensity": 2.479591836734694, "date": "2019-03-05" }, { "rank": 19, "intensity": 2.4489795918367347, "date": "2019-04-29" }, { "rank": 20, "intensity": 2.4183673469387754, "date": "2019-01-01" }, { "rank": 21, "intensity": 2.387755102040816, "date": "2019-02-14" }, { "rank": 22, "intensity": 2.357142857142857, "date": "2019-05-27" }, { "rank": 23, "intensity": 2.326530612244898, "date": "2019-12-16" }, { "rank": 24, "intensity": 2.295918367346939, "date": "2019-03-24" }, { "rank": 25, "intensity": 2.2653061224489797, "date": "2019-02-23" }, { "rank": 26, "intensity": 2.2346938775510203, "date": "2019-01-25" }, { "rank": 27, "intensity": 2.204081632653061, "date": "2019-08-20" }, { "rank": 28, "intensity": 2.173469387755102, "date": "2019-03-09" }, { "rank": 29, "intensity": 2.142857142857143, "date": "2019-05-21" }, { "rank": 30, "intensity": 2.112244897959184, "date": "2019-09-12" }, { "rank": 31, "intensity": 2.0816326530612246, "date": "2019-09-01" }, { "rank": 32, "intensity": 2.0510204081632653, "date": "2019-10-19" }, { "rank": 33, "intensity": 2.020408163265306, "date": "2019-10-07" }, { "rank": 34, "intensity": 1.989795918367347, "date": "2019-12-24" }, { "rank": 35, "intensity": 1.9591836734693877, "date": "2019-06-20" }, { "rank": 36, "intensity": 1.9285714285714286, "date": "2019-09-22" }, { "rank": 37, "intensity": 1.8979591836734695, "date": "2019-12-02" }, { "rank": 38, "intensity": 1.8673469387755102, "date": "2019-01-30" }, { "rank": 39, "intensity": 1.836734693877551, "date": "2019-09-26" }, { "rank": 40, "intensity": 1.806122448979592, "date": "2019-04-14" }, { "rank": 41, "intensity": 1.7755102040816326, "date": "2019-05-04" }, { "rank": 42, "intensity": 1.7448979591836735, "date": "2019-12-11" }, { "rank": 43, "intensity": 1.7142857142857144, "date": "2019-05-07" }, { "rank": 44, "intensity": 1.683673469387755, "date": "2019-11-20" }, { "rank": 45, "intensity": 1.653061224489796, "date": "2019-03-16" }, { "rank": 46, "intensity": 1.6224489795918369, "date": "2019-10-29" }, { "rank": 47, "intensity": 1.5918367346938775, "date": "2019-03-18" }, { "rank": 48, "intensity": 1.5612244897959184, "date": "2019-03-20" }, { "rank": 49, "intensity": 1.5306122448979593, "date": "2019-07-15" }, { "rank": 50, "intensity": 1.5, "date": "2019-01-09" } ], "source": "causal_rivers" } }, { "id": "L3_T1_Composite_Trend_00084", "level": 3, "level_name": "Semantic Reasoning", "category": "Composite Trend", "subtask": "Composite Trend", "question": "Among days in channel 595 during 2021 that exhibit the trend pattern 'slow rise, then rise, then rapid rise', identify the top-5 days where the slow rise segment is the slowest. (Output format: a ranked list of dates, e.g., ['YYYY-MM-DD', ...])", "answer": "['2021-01-20', '2021-03-31', '2021-03-16', '2021-05-04', '2021-04-26']", "ground_truth": [ "2021-01-20", "2021-03-31", "2021-03-16", "2021-05-04", "2021-04-26" ], "eval_metric": "set_f1", "channel": "595", "ts_data_path": "ts_data/L3_T1_Composite_Trend_00084.csv", "meta": { "segments": [ { "kind": "rise", "adj": "slow" }, { "kind": "rise", "adj": "none" }, { "kind": "rise", "adj": "rapid" } ], "pattern_phrase": "slow rise, then rise, then rapid rise", "rank_target_idx": 0, "rank_kind": "rise", "rank_adj": "slow", "rank_axis": "slope", "rank_direction": "ASC", "rank_extreme": "slowest", "top_segments_meta": [ { "adj": "slow", "kind": "rise", "label": "slow rise", "start_idx": 0, "end_idx": 50 }, { "adj": "none", "kind": "rise", "label": "none rise", "start_idx": 50, "end_idx": 81 }, { "adj": "rapid", "kind": "rise", "label": "rapid rise", "start_idx": 81, "end_idx": 98 } ], "points_per_day": 96, "pattern": "slow rise, then rise, then rapid rise", "year": 2021, "top_k": [ "2021-01-20", "2021-03-31", "2021-03-16", "2021-05-04", "2021-04-26" ], "all_injections": [ { "rank": 1, "intensity": 1.5, "date": "2021-01-20" }, { "rank": 2, "intensity": 1.530612244897959, "date": "2021-03-31" }, { "rank": 3, "intensity": 1.5612244897959184, "date": "2021-03-16" }, { "rank": 4, "intensity": 1.5918367346938775, "date": "2021-05-04" }, { "rank": 5, "intensity": 1.6224489795918366, "date": "2021-04-26" }, { "rank": 6, "intensity": 1.653061224489796, "date": "2021-08-25" }, { "rank": 7, "intensity": 1.683673469387755, "date": "2021-11-24" }, { "rank": 8, "intensity": 1.7142857142857142, "date": "2021-09-13" }, { "rank": 9, "intensity": 1.7448979591836735, "date": "2021-07-13" }, { "rank": 10, "intensity": 1.7755102040816326, "date": "2021-02-26" }, { "rank": 11, "intensity": 1.806122448979592, "date": "2021-12-27" }, { "rank": 12, "intensity": 1.836734693877551, "date": "2021-07-28" }, { "rank": 13, "intensity": 1.8673469387755102, "date": "2021-11-09" }, { "rank": 14, "intensity": 1.8979591836734695, "date": "2021-10-07" }, { "rank": 15, "intensity": 1.9285714285714286, "date": "2021-12-22" }, { "rank": 16, "intensity": 1.9591836734693877, "date": "2021-06-11" }, { "rank": 17, "intensity": 1.989795918367347, "date": "2021-09-10" }, { "rank": 18, "intensity": 2.020408163265306, "date": "2021-11-26" }, { "rank": 19, "intensity": 2.0510204081632653, "date": "2021-05-17" }, { "rank": 20, "intensity": 2.0816326530612246, "date": "2021-07-31" }, { "rank": 21, "intensity": 2.112244897959184, "date": "2021-08-06" }, { "rank": 22, "intensity": 2.142857142857143, "date": "2021-06-27" }, { "rank": 23, "intensity": 2.173469387755102, "date": "2021-01-06" }, { "rank": 24, "intensity": 2.204081632653061, "date": "2021-12-07" }, { "rank": 25, "intensity": 2.2346938775510203, "date": "2021-01-18" }, { "rank": 26, "intensity": 2.2653061224489797, "date": "2021-08-30" }, { "rank": 27, "intensity": 2.295918367346939, "date": "2021-02-11" }, { "rank": 28, "intensity": 2.326530612244898, "date": "2021-08-21" }, { "rank": 29, "intensity": 2.357142857142857, "date": "2021-04-02" }, { "rank": 30, "intensity": 2.387755102040816, "date": "2021-07-04" }, { "rank": 31, "intensity": 2.4183673469387754, "date": "2021-06-05" }, { "rank": 32, "intensity": 2.4489795918367347, "date": "2021-04-20" }, { "rank": 33, "intensity": 2.479591836734694, "date": "2021-03-12" }, { "rank": 34, "intensity": 2.510204081632653, "date": "2021-10-25" }, { "rank": 35, "intensity": 2.5408163265306123, "date": "2021-09-19" }, { "rank": 36, "intensity": 2.571428571428571, "date": "2021-03-20" }, { "rank": 37, "intensity": 2.6020408163265305, "date": "2021-07-25" }, { "rank": 38, "intensity": 2.63265306122449, "date": "2021-10-02" }, { "rank": 39, "intensity": 2.663265306122449, "date": "2021-02-22" }, { "rank": 40, "intensity": 2.693877551020408, "date": "2021-10-21" }, { "rank": 41, "intensity": 2.7244897959183674, "date": "2021-02-24" }, { "rank": 42, "intensity": 2.7551020408163263, "date": "2021-04-18" }, { "rank": 43, "intensity": 2.7857142857142856, "date": "2021-03-10" }, { "rank": 44, "intensity": 2.816326530612245, "date": "2021-02-03" }, { "rank": 45, "intensity": 2.8469387755102042, "date": "2021-10-18" }, { "rank": 46, "intensity": 2.877551020408163, "date": "2021-05-15" }, { "rank": 47, "intensity": 2.9081632653061225, "date": "2021-11-04" }, { "rank": 48, "intensity": 2.9387755102040813, "date": "2021-03-18" }, { "rank": 49, "intensity": 2.9693877551020407, "date": "2021-08-15" }, { "rank": 50, "intensity": 3.0, "date": "2021-01-04" } ], "source": "causal_rivers" } }, { "id": "L3_T1_Composite_Trend_00085", "level": 3, "level_name": "Semantic Reasoning", "category": "Composite Trend", "subtask": "Composite Trend", "question": "Among days in channel 625 during 2021 that exhibit the trend pattern 'fluctuating stable, then slow rise, then steady stable, then rapid fall', identify the top-5 days where the rapid fall segment is the fastest. (Output format: a ranked list of dates, e.g., ['YYYY-MM-DD', ...])", "answer": "['2021-02-13', '2021-07-15', '2021-08-13', '2021-06-17', '2021-08-31']", "ground_truth": [ "2021-02-13", "2021-07-15", "2021-08-13", "2021-06-17", "2021-08-31" ], "eval_metric": "set_f1", "channel": "625", "ts_data_path": "ts_data/L3_T1_Composite_Trend_00085.csv", "meta": { "segments": [ { "kind": "stable", "adj": "fluctuating" }, { "kind": "rise", "adj": "slow" }, { "kind": "stable", "adj": "steady" }, { "kind": "fall", "adj": "rapid" } ], "pattern_phrase": "fluctuating stable, then slow rise, then steady stable, then rapid fall", "rank_target_idx": 3, "rank_kind": "fall", "rank_adj": "rapid", "rank_axis": "slope", "rank_direction": "DESC", "rank_extreme": "fastest", "top_segments_meta": [ { "adj": "fluctuating", "kind": "stable", "label": "fluctuating stable", "start_idx": 0, "end_idx": 22 }, { "adj": "slow", "kind": "rise", "label": "slow rise", "start_idx": 22, "end_idx": 55 }, { "adj": "steady", "kind": "stable", "label": "steady stable", "start_idx": 55, "end_idx": 89 }, { "adj": "rapid", "kind": "fall", "label": "rapid fall", "start_idx": 89, "end_idx": 98 } ], "points_per_day": 96, "pattern": "fluctuating stable, then slow rise, then steady stable, then rapid fall", "year": 2021, "top_k": [ "2021-02-13", "2021-07-15", "2021-08-13", "2021-06-17", "2021-08-31" ], "all_injections": [ { "rank": 1, "intensity": 3.0, "date": "2021-02-13" }, { "rank": 2, "intensity": 2.9693877551020407, "date": "2021-07-15" }, { "rank": 3, "intensity": 2.938775510204082, "date": "2021-08-13" }, { "rank": 4, "intensity": 2.9081632653061225, "date": "2021-06-17" }, { "rank": 5, "intensity": 2.877551020408163, "date": "2021-08-31" }, { "rank": 6, "intensity": 2.8469387755102042, "date": "2021-02-25" }, { "rank": 7, "intensity": 2.816326530612245, "date": "2021-10-18" }, { "rank": 8, "intensity": 2.7857142857142856, "date": "2021-10-04" }, { "rank": 9, "intensity": 2.7551020408163267, "date": "2021-09-23" }, { "rank": 10, "intensity": 2.7244897959183674, "date": "2021-02-23" }, { "rank": 11, "intensity": 2.693877551020408, "date": "2021-04-22" }, { "rank": 12, "intensity": 2.663265306122449, "date": "2021-06-12" }, { "rank": 13, "intensity": 2.63265306122449, "date": "2021-09-19" }, { "rank": 14, "intensity": 2.6020408163265305, "date": "2021-11-22" }, { "rank": 15, "intensity": 2.5714285714285716, "date": "2021-03-26" }, { "rank": 16, "intensity": 2.5408163265306123, "date": "2021-07-04" }, { "rank": 17, "intensity": 2.510204081632653, "date": "2021-09-17" }, { "rank": 18, "intensity": 2.479591836734694, "date": "2021-10-31" }, { "rank": 19, "intensity": 2.4489795918367347, "date": "2021-11-19" }, { "rank": 20, "intensity": 2.4183673469387754, "date": "2021-09-13" }, { "rank": 21, "intensity": 2.387755102040816, "date": "2021-04-17" }, { "rank": 22, "intensity": 2.357142857142857, "date": "2021-08-11" }, { "rank": 23, "intensity": 2.326530612244898, "date": "2021-08-24" }, { "rank": 24, "intensity": 2.295918367346939, "date": "2021-09-28" }, { "rank": 25, "intensity": 2.2653061224489797, "date": "2021-06-29" }, { "rank": 26, "intensity": 2.2346938775510203, "date": "2021-04-29" }, { "rank": 27, "intensity": 2.204081632653061, "date": "2021-12-18" }, { "rank": 28, "intensity": 2.173469387755102, "date": "2021-10-28" }, { "rank": 29, "intensity": 2.142857142857143, "date": "2021-08-06" }, { "rank": 30, "intensity": 2.112244897959184, "date": "2021-06-22" }, { "rank": 31, "intensity": 2.0816326530612246, "date": "2021-10-14" }, { "rank": 32, "intensity": 2.0510204081632653, "date": "2021-08-20" }, { "rank": 33, "intensity": 2.020408163265306, "date": "2021-08-09" }, { "rank": 34, "intensity": 1.989795918367347, "date": "2021-05-26" }, { "rank": 35, "intensity": 1.9591836734693877, "date": "2021-12-11" }, { "rank": 36, "intensity": 1.9285714285714286, "date": "2021-01-25" }, { "rank": 37, "intensity": 1.8979591836734695, "date": "2021-01-03" }, { "rank": 38, "intensity": 1.8673469387755102, "date": "2021-04-11" }, { "rank": 39, "intensity": 1.836734693877551, "date": "2021-08-18" }, { "rank": 40, "intensity": 1.806122448979592, "date": "2021-10-23" }, { "rank": 41, "intensity": 1.7755102040816326, "date": "2021-04-03" }, { "rank": 42, "intensity": 1.7448979591836735, "date": "2021-12-26" }, { "rank": 43, "intensity": 1.7142857142857144, "date": "2021-05-01" }, { "rank": 44, "intensity": 1.683673469387755, "date": "2021-03-18" }, { "rank": 45, "intensity": 1.653061224489796, "date": "2021-04-09" }, { "rank": 46, "intensity": 1.6224489795918369, "date": "2021-11-12" }, { "rank": 47, "intensity": 1.5918367346938775, "date": "2021-08-04" }, { "rank": 48, "intensity": 1.5612244897959184, "date": "2021-06-27" }, { "rank": 49, "intensity": 1.5306122448979593, "date": "2021-05-21" }, { "rank": 50, "intensity": 1.5, "date": "2021-07-30" } ], "source": "causal_rivers" } }, { "id": "L3_T1_Composite_Trend_00086", "level": 3, "level_name": "Semantic Reasoning", "category": "Composite Trend", "subtask": "Composite Trend", "question": "Among days in channel 626 during 2019 that exhibit the trend pattern 'rapid fall, then rapid rise', identify the top-5 days where the rapid fall segment is the fastest. (Output format: a ranked list of dates, e.g., ['YYYY-MM-DD', ...])", "answer": "['2019-09-04', '2019-01-21', '2019-12-08', '2019-12-04', '2019-08-14']", "ground_truth": [ "2019-09-04", "2019-01-21", "2019-12-08", "2019-12-04", "2019-08-14" ], "eval_metric": "set_f1", "channel": "626", "ts_data_path": "ts_data/L3_T1_Composite_Trend_00086.csv", "meta": { "segments": [ { "kind": "fall", "adj": "rapid" }, { "kind": "rise", "adj": "rapid" } ], "pattern_phrase": "rapid fall, then rapid rise", "rank_target_idx": 0, "rank_kind": "fall", "rank_adj": "rapid", "rank_axis": "slope", "rank_direction": "DESC", "rank_extreme": "fastest", "top_segments_meta": [ { "adj": "rapid", "kind": "fall", "label": "rapid fall", "start_idx": 0, "end_idx": 48 }, { "adj": "rapid", "kind": "rise", "label": "rapid rise", "start_idx": 48, "end_idx": 98 } ], "points_per_day": 96, "pattern": "rapid fall, then rapid rise", "year": 2019, "top_k": [ "2019-09-04", "2019-01-21", "2019-12-08", "2019-12-04", "2019-08-14" ], "all_injections": [ { "rank": 1, "intensity": 3.0, "date": "2019-09-04" }, { "rank": 2, "intensity": 2.9693877551020407, "date": "2019-01-21" }, { "rank": 3, "intensity": 2.938775510204082, "date": "2019-12-08" }, { "rank": 4, "intensity": 2.9081632653061225, "date": "2019-12-04" }, { "rank": 5, "intensity": 2.877551020408163, "date": "2019-08-14" }, { "rank": 6, "intensity": 2.8469387755102042, "date": "2019-03-02" }, { "rank": 7, "intensity": 2.816326530612245, "date": "2019-06-13" }, { "rank": 8, "intensity": 2.7857142857142856, "date": "2019-02-28" }, { "rank": 9, "intensity": 2.7551020408163267, "date": "2019-07-21" }, { "rank": 10, "intensity": 2.7244897959183674, "date": "2019-05-12" }, { "rank": 11, "intensity": 2.693877551020408, "date": "2019-12-12" }, { "rank": 12, "intensity": 2.663265306122449, "date": "2019-09-24" }, { "rank": 13, "intensity": 2.63265306122449, "date": "2019-06-17" }, { "rank": 14, "intensity": 2.6020408163265305, "date": "2019-01-12" }, { "rank": 15, "intensity": 2.5714285714285716, "date": "2019-11-14" }, { "rank": 16, "intensity": 2.5408163265306123, "date": "2019-07-17" }, { "rank": 17, "intensity": 2.510204081632653, "date": "2019-09-14" }, { "rank": 18, "intensity": 2.479591836734694, "date": "2019-12-25" }, { "rank": 19, "intensity": 2.4489795918367347, "date": "2019-06-15" }, { "rank": 20, "intensity": 2.4183673469387754, "date": "2019-12-10" }, { "rank": 21, "intensity": 2.387755102040816, "date": "2019-08-06" }, { "rank": 22, "intensity": 2.357142857142857, "date": "2019-01-10" }, { "rank": 23, "intensity": 2.326530612244898, "date": "2019-04-11" }, { "rank": 24, "intensity": 2.295918367346939, "date": "2019-09-30" }, { "rank": 25, "intensity": 2.2653061224489797, "date": "2019-04-19" }, { "rank": 26, "intensity": 2.2346938775510203, "date": "2019-05-25" }, { "rank": 27, "intensity": 2.204081632653061, "date": "2019-06-29" }, { "rank": 28, "intensity": 2.173469387755102, "date": "2019-11-24" }, { "rank": 29, "intensity": 2.142857142857143, "date": "2019-02-03" }, { "rank": 30, "intensity": 2.112244897959184, "date": "2019-01-25" }, { "rank": 31, "intensity": 2.0816326530612246, "date": "2019-05-04" }, { "rank": 32, "intensity": 2.0510204081632653, "date": "2019-04-26" }, { "rank": 33, "intensity": 2.020408163265306, "date": "2019-06-06" }, { "rank": 34, "intensity": 1.989795918367347, "date": "2019-05-20" }, { "rank": 35, "intensity": 1.9591836734693877, "date": "2019-02-18" }, { "rank": 36, "intensity": 1.9285714285714286, "date": "2019-10-12" }, { "rank": 37, "intensity": 1.8979591836734695, "date": "2019-09-06" }, { "rank": 38, "intensity": 1.8673469387755102, "date": "2019-09-28" }, { "rank": 39, "intensity": 1.836734693877551, "date": "2019-06-21" }, { "rank": 40, "intensity": 1.806122448979592, "date": "2019-08-17" }, { "rank": 41, "intensity": 1.7755102040816326, "date": "2019-05-15" }, { "rank": 42, "intensity": 1.7448979591836735, "date": "2019-07-31" }, { "rank": 43, "intensity": 1.7142857142857144, "date": "2019-03-23" }, { "rank": 44, "intensity": 1.683673469387755, "date": "2019-09-10" }, { "rank": 45, "intensity": 1.653061224489796, "date": "2019-07-04" }, { "rank": 46, "intensity": 1.6224489795918369, "date": "2019-06-04" }, { "rank": 47, "intensity": 1.5918367346938775, "date": "2019-10-17" }, { "rank": 48, "intensity": 1.5612244897959184, "date": "2019-12-17" }, { "rank": 49, "intensity": 1.5306122448979593, "date": "2019-02-05" }, { "rank": 50, "intensity": 1.5, "date": "2019-12-28" } ], "source": "causal_rivers" } }, { "id": "L3_T1_Composite_Trend_00087", "level": 3, "level_name": "Semantic Reasoning", "category": "Composite Trend", "subtask": "Composite Trend", "question": "Among days in channel 627 during 2023 that exhibit the trend pattern 'rapid rise, then steady stable', identify the top-5 days where the rapid rise segment is the fastest. (Output format: a ranked list of dates, e.g., ['YYYY-MM-DD', ...])", "answer": "['2023-06-08', '2023-12-04', '2023-09-15', '2023-07-08', '2023-12-18']", "ground_truth": [ "2023-06-08", "2023-12-04", "2023-09-15", "2023-07-08", "2023-12-18" ], "eval_metric": "set_f1", "channel": "627", "ts_data_path": "ts_data/L3_T1_Composite_Trend_00087.csv", "meta": { "segments": [ { "kind": "rise", "adj": "rapid" }, { "kind": "stable", "adj": "steady" } ], "pattern_phrase": "rapid rise, then steady stable", "rank_target_idx": 0, "rank_kind": "rise", "rank_adj": "rapid", "rank_axis": "slope", "rank_direction": "DESC", "rank_extreme": "fastest", "top_segments_meta": [ { "adj": "rapid", "kind": "rise", "label": "rapid rise", "start_idx": 0, "end_idx": 18 }, { "adj": "steady", "kind": "stable", "label": "steady stable", "start_idx": 18, "end_idx": 98 } ], "points_per_day": 96, "pattern": "rapid rise, then steady stable", "year": 2023, "top_k": [ "2023-06-08", "2023-12-04", "2023-09-15", "2023-07-08", "2023-12-18" ], "all_injections": [ { "rank": 1, "intensity": 3.0, "date": "2023-06-08" }, { "rank": 2, "intensity": 2.9693877551020407, "date": "2023-12-04" }, { "rank": 3, "intensity": 2.938775510204082, "date": "2023-09-15" }, { "rank": 4, "intensity": 2.9081632653061225, "date": "2023-07-08" }, { "rank": 5, "intensity": 2.877551020408163, "date": "2023-12-18" }, { "rank": 6, "intensity": 2.8469387755102042, "date": "2023-03-25" }, { "rank": 7, "intensity": 2.816326530612245, "date": "2023-12-09" }, { "rank": 8, "intensity": 2.7857142857142856, "date": "2023-04-13" }, { "rank": 9, "intensity": 2.7551020408163267, "date": "2023-05-05" }, { "rank": 10, "intensity": 2.7244897959183674, "date": "2023-11-04" }, { "rank": 11, "intensity": 2.693877551020408, "date": "2023-03-09" }, { "rank": 12, "intensity": 2.663265306122449, "date": "2023-09-21" }, { "rank": 13, "intensity": 2.63265306122449, "date": "2023-09-06" }, { "rank": 14, "intensity": 2.6020408163265305, "date": "2023-03-30" }, { "rank": 15, "intensity": 2.5714285714285716, "date": "2023-08-11" }, { "rank": 16, "intensity": 2.5408163265306123, "date": "2023-01-20" }, { "rank": 17, "intensity": 2.510204081632653, "date": "2023-11-22" }, { "rank": 18, "intensity": 2.479591836734694, "date": "2023-10-13" }, { "rank": 19, "intensity": 2.4489795918367347, "date": "2023-10-15" }, { "rank": 20, "intensity": 2.4183673469387754, "date": "2023-06-02" }, { "rank": 21, "intensity": 2.387755102040816, "date": "2023-09-12" }, { "rank": 22, "intensity": 2.357142857142857, "date": "2023-02-19" }, { "rank": 23, "intensity": 2.326530612244898, "date": "2023-03-03" }, { "rank": 24, "intensity": 2.295918367346939, "date": "2023-06-11" }, { "rank": 25, "intensity": 2.2653061224489797, "date": "2023-02-08" }, { "rank": 26, "intensity": 2.2346938775510203, "date": "2023-10-27" }, { "rank": 27, "intensity": 2.204081632653061, "date": "2023-01-24" }, { "rank": 28, "intensity": 2.173469387755102, "date": "2023-09-19" }, { "rank": 29, "intensity": 2.142857142857143, "date": "2023-11-09" }, { "rank": 30, "intensity": 2.112244897959184, "date": "2023-07-30" }, { "rank": 31, "intensity": 2.0816326530612246, "date": "2023-04-21" }, { "rank": 32, "intensity": 2.0510204081632653, "date": "2023-01-26" }, { "rank": 33, "intensity": 2.020408163265306, "date": "2023-06-20" }, { "rank": 34, "intensity": 1.989795918367347, "date": "2023-06-16" }, { "rank": 35, "intensity": 1.9591836734693877, "date": "2023-10-18" }, { "rank": 36, "intensity": 1.9285714285714286, "date": "2023-04-01" }, { "rank": 37, "intensity": 1.8979591836734695, "date": "2023-08-14" }, { "rank": 38, "intensity": 1.8673469387755102, "date": "2023-11-25" }, { "rank": 39, "intensity": 1.836734693877551, "date": "2023-08-02" }, { "rank": 40, "intensity": 1.806122448979592, "date": "2023-06-28" }, { "rank": 41, "intensity": 1.7755102040816326, "date": "2023-04-16" }, { "rank": 42, "intensity": 1.7448979591836735, "date": "2023-01-01" }, { "rank": 43, "intensity": 1.7142857142857144, "date": "2023-12-16" }, { "rank": 44, "intensity": 1.683673469387755, "date": "2023-08-04" }, { "rank": 45, "intensity": 1.653061224489796, "date": "2023-08-22" }, { "rank": 46, "intensity": 1.6224489795918369, "date": "2023-04-23" }, { "rank": 47, "intensity": 1.5918367346938775, "date": "2023-12-07" }, { "rank": 48, "intensity": 1.5612244897959184, "date": "2023-05-11" }, { "rank": 49, "intensity": 1.5306122448979593, "date": "2023-08-26" }, { "rank": 50, "intensity": 1.5, "date": "2023-09-27" } ], "source": "causal_rivers" } }, { "id": "L3_T1_Composite_Trend_00088", "level": 3, "level_name": "Semantic Reasoning", "category": "Composite Trend", "subtask": "Composite Trend", "question": "Among days in channel 647 during 2022 that exhibit the trend pattern 'slow fall, then fluctuating stable', identify the top-5 days where the slow fall segment is the slowest. (Output format: a ranked list of dates, e.g., ['YYYY-MM-DD', ...])", "answer": "['2022-09-15', '2022-11-19', '2022-01-05', '2022-04-26', '2022-12-09']", "ground_truth": [ "2022-09-15", "2022-11-19", "2022-01-05", "2022-04-26", "2022-12-09" ], "eval_metric": "set_f1", "channel": "647", "ts_data_path": "ts_data/L3_T1_Composite_Trend_00088.csv", "meta": { "segments": [ { "kind": "fall", "adj": "slow" }, { "kind": "stable", "adj": "fluctuating" } ], "pattern_phrase": "slow fall, then fluctuating stable", "rank_target_idx": 0, "rank_kind": "fall", "rank_adj": "slow", "rank_axis": "slope", "rank_direction": "ASC", "rank_extreme": "slowest", "top_segments_meta": [ { "adj": "slow", "kind": "fall", "label": "slow fall", "start_idx": 0, "end_idx": 58 }, { "adj": "fluctuating", "kind": "stable", "label": "fluctuating stable", "start_idx": 58, "end_idx": 98 } ], "points_per_day": 96, "pattern": "slow fall, then fluctuating stable", "year": 2022, "top_k": [ "2022-09-15", "2022-11-19", "2022-01-05", "2022-04-26", "2022-12-09" ], "all_injections": [ { "rank": 1, "intensity": 1.5, "date": "2022-09-15" }, { "rank": 2, "intensity": 1.530612244897959, "date": "2022-11-19" }, { "rank": 3, "intensity": 1.5612244897959184, "date": "2022-01-05" }, { "rank": 4, "intensity": 1.5918367346938775, "date": "2022-04-26" }, { "rank": 5, "intensity": 1.6224489795918366, "date": "2022-12-09" }, { "rank": 6, "intensity": 1.653061224489796, "date": "2022-03-04" }, { "rank": 7, "intensity": 1.683673469387755, "date": "2022-03-27" }, { "rank": 8, "intensity": 1.7142857142857142, "date": "2022-10-21" }, { "rank": 9, "intensity": 1.7448979591836735, "date": "2022-05-01" }, { "rank": 10, "intensity": 1.7755102040816326, "date": "2022-12-02" }, { "rank": 11, "intensity": 1.806122448979592, "date": "2022-10-01" }, { "rank": 12, "intensity": 1.836734693877551, "date": "2022-04-02" }, { "rank": 13, "intensity": 1.8673469387755102, "date": "2022-10-13" }, { "rank": 14, "intensity": 1.8979591836734695, "date": "2022-11-05" }, { "rank": 15, "intensity": 1.9285714285714286, "date": "2022-02-09" }, { "rank": 16, "intensity": 1.9591836734693877, "date": "2022-07-19" }, { "rank": 17, "intensity": 1.989795918367347, "date": "2022-03-19" }, { "rank": 18, "intensity": 2.020408163265306, "date": "2022-03-15" }, { "rank": 19, "intensity": 2.0510204081632653, "date": "2022-05-07" }, { "rank": 20, "intensity": 2.0816326530612246, "date": "2022-12-11" }, { "rank": 21, "intensity": 2.112244897959184, "date": "2022-05-31" }, { "rank": 22, "intensity": 2.142857142857143, "date": "2022-02-20" }, { "rank": 23, "intensity": 2.173469387755102, "date": "2022-01-23" }, { "rank": 24, "intensity": 2.204081632653061, "date": "2022-07-24" }, { "rank": 25, "intensity": 2.2346938775510203, "date": "2022-04-14" }, { "rank": 26, "intensity": 2.2653061224489797, "date": "2022-03-21" }, { "rank": 27, "intensity": 2.295918367346939, "date": "2022-06-09" }, { "rank": 28, "intensity": 2.326530612244898, "date": "2022-11-17" }, { "rank": 29, "intensity": 2.357142857142857, "date": "2022-05-26" }, { "rank": 30, "intensity": 2.387755102040816, "date": "2022-04-22" }, { "rank": 31, "intensity": 2.4183673469387754, "date": "2022-10-10" }, { "rank": 32, "intensity": 2.4489795918367347, "date": "2022-08-11" }, { "rank": 33, "intensity": 2.479591836734694, "date": "2022-08-07" }, { "rank": 34, "intensity": 2.510204081632653, "date": "2022-05-15" }, { "rank": 35, "intensity": 2.5408163265306123, "date": "2022-09-02" }, { "rank": 36, "intensity": 2.571428571428571, "date": "2022-04-12" }, { "rank": 37, "intensity": 2.6020408163265305, "date": "2022-05-11" }, { "rank": 38, "intensity": 2.63265306122449, "date": "2022-07-31" }, { "rank": 39, "intensity": 2.663265306122449, "date": "2022-03-07" }, { "rank": 40, "intensity": 2.693877551020408, "date": "2022-09-06" }, { "rank": 41, "intensity": 2.7244897959183674, "date": "2022-12-26" }, { "rank": 42, "intensity": 2.7551020408163263, "date": "2022-10-18" }, { "rank": 43, "intensity": 2.7857142857142856, "date": "2022-12-06" }, { "rank": 44, "intensity": 2.816326530612245, "date": "2022-04-29" }, { "rank": 45, "intensity": 2.8469387755102042, "date": "2022-09-22" }, { "rank": 46, "intensity": 2.877551020408163, "date": "2022-12-14" }, { "rank": 47, "intensity": 2.9081632653061225, "date": "2022-02-12" }, { "rank": 48, "intensity": 2.9387755102040813, "date": "2022-09-20" }, { "rank": 49, "intensity": 2.9693877551020407, "date": "2022-08-20" }, { "rank": 50, "intensity": 3.0, "date": "2022-07-26" } ], "source": "causal_rivers" } }, { "id": "L3_T1_Composite_Trend_00089", "level": 3, "level_name": "Semantic Reasoning", "category": "Composite Trend", "subtask": "Composite Trend", "question": "Among days in channel 680 during 2019 that exhibit the trend pattern 'slow fall, then steady stable', identify the top-5 days where the slow fall segment is the slowest. (Output format: a ranked list of dates, e.g., ['YYYY-MM-DD', ...])", "answer": "['2019-09-14', '2019-09-24', '2019-08-21', '2019-09-05', '2019-07-10']", "ground_truth": [ "2019-09-14", "2019-09-24", "2019-08-21", "2019-09-05", "2019-07-10" ], "eval_metric": "set_f1", "channel": "680", "ts_data_path": "ts_data/L3_T1_Composite_Trend_00089.csv", "meta": { "segments": [ { "kind": "fall", "adj": "slow" }, { "kind": "stable", "adj": "steady" } ], "pattern_phrase": "slow fall, then steady stable", "rank_target_idx": 0, "rank_kind": "fall", "rank_adj": "slow", "rank_axis": "slope", "rank_direction": "ASC", "rank_extreme": "slowest", "top_segments_meta": [ { "adj": "slow", "kind": "fall", "label": "slow fall", "start_idx": 0, "end_idx": 45 }, { "adj": "steady", "kind": "stable", "label": "steady stable", "start_idx": 45, "end_idx": 98 } ], "points_per_day": 96, "pattern": "slow fall, then steady stable", "year": 2019, "top_k": [ "2019-09-14", "2019-09-24", "2019-08-21", "2019-09-05", "2019-07-10" ], "all_injections": [ { "rank": 1, "intensity": 1.5, "date": "2019-09-14" }, { "rank": 2, "intensity": 1.530612244897959, "date": "2019-09-24" }, { "rank": 3, "intensity": 1.5612244897959184, "date": "2019-08-21" }, { "rank": 4, "intensity": 1.5918367346938775, "date": "2019-09-05" }, { "rank": 5, "intensity": 1.6224489795918366, "date": "2019-07-10" }, { "rank": 6, "intensity": 1.653061224489796, "date": "2019-03-07" }, { "rank": 7, "intensity": 1.683673469387755, "date": "2019-12-04" }, { "rank": 8, "intensity": 1.7142857142857142, "date": "2019-10-12" }, { "rank": 9, "intensity": 1.7448979591836735, "date": "2019-06-03" }, { "rank": 10, "intensity": 1.7755102040816326, "date": "2019-09-10" }, { "rank": 11, "intensity": 1.806122448979592, "date": "2019-03-26" }, { "rank": 12, "intensity": 1.836734693877551, "date": "2019-03-21" }, { "rank": 13, "intensity": 1.8673469387755102, "date": "2019-02-27" }, { "rank": 14, "intensity": 1.8979591836734695, "date": "2019-04-29" }, { "rank": 15, "intensity": 1.9285714285714286, "date": "2019-09-20" }, { "rank": 16, "intensity": 1.9591836734693877, "date": "2019-05-03" }, { "rank": 17, "intensity": 1.989795918367347, "date": "2019-06-26" }, { "rank": 18, "intensity": 2.020408163265306, "date": "2019-11-04" }, { "rank": 19, "intensity": 2.0510204081632653, "date": "2019-06-30" }, { "rank": 20, "intensity": 2.0816326530612246, "date": "2019-06-23" }, { "rank": 21, "intensity": 2.112244897959184, "date": "2019-10-08" }, { "rank": 22, "intensity": 2.142857142857143, "date": "2019-02-06" }, { "rank": 23, "intensity": 2.173469387755102, "date": "2019-05-07" }, { "rank": 24, "intensity": 2.204081632653061, "date": "2019-03-11" }, { "rank": 25, "intensity": 2.2346938775510203, "date": "2019-03-18" }, { "rank": 26, "intensity": 2.2653061224489797, "date": "2019-07-21" }, { "rank": 27, "intensity": 2.295918367346939, "date": "2019-06-20" }, { "rank": 28, "intensity": 2.326530612244898, "date": "2019-04-01" }, { "rank": 29, "intensity": 2.357142857142857, "date": "2019-06-09" }, { "rank": 30, "intensity": 2.387755102040816, "date": "2019-01-28" }, { "rank": 31, "intensity": 2.4183673469387754, "date": "2019-07-14" }, { "rank": 32, "intensity": 2.4489795918367347, "date": "2019-05-28" }, { "rank": 33, "intensity": 2.479591836734694, "date": "2019-02-14" }, { "rank": 34, "intensity": 2.510204081632653, "date": "2019-08-02" }, { "rank": 35, "intensity": 2.5408163265306123, "date": "2019-04-16" }, { "rank": 36, "intensity": 2.571428571428571, "date": "2019-03-23" }, { "rank": 37, "intensity": 2.6020408163265305, "date": "2019-11-30" }, { "rank": 38, "intensity": 2.63265306122449, "date": "2019-11-02" }, { "rank": 39, "intensity": 2.663265306122449, "date": "2019-08-10" }, { "rank": 40, "intensity": 2.693877551020408, "date": "2019-10-01" }, { "rank": 41, "intensity": 2.7244897959183674, "date": "2019-03-01" }, { "rank": 42, "intensity": 2.7551020408163263, "date": "2019-02-18" }, { "rank": 43, "intensity": 2.7857142857142856, "date": "2019-10-30" }, { "rank": 44, "intensity": 2.816326530612245, "date": "2019-04-12" }, { "rank": 45, "intensity": 2.8469387755102042, "date": "2019-02-12" }, { "rank": 46, "intensity": 2.877551020408163, "date": "2019-04-07" }, { "rank": 47, "intensity": 2.9081632653061225, "date": "2019-07-24" }, { "rank": 48, "intensity": 2.9387755102040813, "date": "2019-01-14" }, { "rank": 49, "intensity": 2.9693877551020407, "date": "2019-11-22" }, { "rank": 50, "intensity": 3.0, "date": "2019-02-20" } ], "source": "causal_rivers" } }, { "id": "L3_T1_Composite_Trend_00090", "level": 3, "level_name": "Semantic Reasoning", "category": "Composite Trend", "subtask": "Composite Trend", "question": "Among days in channel 683 during 2021 that exhibit the trend pattern 'slow rise, then rise', identify the top-5 days where the slow rise segment is the slowest. (Output format: a ranked list of dates, e.g., ['YYYY-MM-DD', ...])", "answer": "['2021-02-21', '2021-08-29', '2021-12-22', '2021-09-23', '2021-11-25']", "ground_truth": [ "2021-02-21", "2021-08-29", "2021-12-22", "2021-09-23", "2021-11-25" ], "eval_metric": "set_f1", "channel": "683", "ts_data_path": "ts_data/L3_T1_Composite_Trend_00090.csv", "meta": { "segments": [ { "kind": "rise", "adj": "slow" }, { "kind": "rise", "adj": "none" } ], "pattern_phrase": "slow rise, then rise", "rank_target_idx": 0, "rank_kind": "rise", "rank_adj": "slow", "rank_axis": "slope", "rank_direction": "ASC", "rank_extreme": "slowest", "top_segments_meta": [ { "adj": "slow", "kind": "rise", "label": "slow rise", "start_idx": 0, "end_idx": 65 }, { "adj": "none", "kind": "rise", "label": "none rise", "start_idx": 65, "end_idx": 98 } ], "points_per_day": 96, "pattern": "slow rise, then rise", "year": 2021, "top_k": [ "2021-02-21", "2021-08-29", "2021-12-22", "2021-09-23", "2021-11-25" ], "all_injections": [ { "rank": 1, "intensity": 1.5, "date": "2021-02-21" }, { "rank": 2, "intensity": 1.530612244897959, "date": "2021-08-29" }, { "rank": 3, "intensity": 1.5612244897959184, "date": "2021-12-22" }, { "rank": 4, "intensity": 1.5918367346938775, "date": "2021-09-23" }, { "rank": 5, "intensity": 1.6224489795918366, "date": "2021-11-25" }, { "rank": 6, "intensity": 1.653061224489796, "date": "2021-01-17" }, { "rank": 7, "intensity": 1.683673469387755, "date": "2021-07-29" }, { "rank": 8, "intensity": 1.7142857142857142, "date": "2021-05-03" }, { "rank": 9, "intensity": 1.7448979591836735, "date": "2021-03-24" }, { "rank": 10, "intensity": 1.7755102040816326, "date": "2021-11-08" }, { "rank": 11, "intensity": 1.806122448979592, "date": "2021-05-24" }, { "rank": 12, "intensity": 1.836734693877551, "date": "2021-04-05" }, { "rank": 13, "intensity": 1.8673469387755102, "date": "2021-08-11" }, { "rank": 14, "intensity": 1.8979591836734695, "date": "2021-07-02" }, { "rank": 15, "intensity": 1.9285714285714286, "date": "2021-11-01" }, { "rank": 16, "intensity": 1.9591836734693877, "date": "2021-10-02" }, { "rank": 17, "intensity": 1.989795918367347, "date": "2021-06-10" }, { "rank": 18, "intensity": 2.020408163265306, "date": "2021-01-10" }, { "rank": 19, "intensity": 2.0510204081632653, "date": "2021-01-29" }, { "rank": 20, "intensity": 2.0816326530612246, "date": "2021-10-08" }, { "rank": 21, "intensity": 2.112244897959184, "date": "2021-04-07" }, { "rank": 22, "intensity": 2.142857142857143, "date": "2021-03-10" }, { "rank": 23, "intensity": 2.173469387755102, "date": "2021-06-16" }, { "rank": 24, "intensity": 2.204081632653061, "date": "2021-02-15" }, { "rank": 25, "intensity": 2.2346938775510203, "date": "2021-09-21" }, { "rank": 26, "intensity": 2.2653061224489797, "date": "2021-01-15" }, { "rank": 27, "intensity": 2.295918367346939, "date": "2021-02-18" }, { "rank": 28, "intensity": 2.326530612244898, "date": "2021-03-28" }, { "rank": 29, "intensity": 2.357142857142857, "date": "2021-11-06" }, { "rank": 30, "intensity": 2.387755102040816, "date": "2021-01-22" }, { "rank": 31, "intensity": 2.4183673469387754, "date": "2021-02-05" }, { "rank": 32, "intensity": 2.4489795918367347, "date": "2021-06-02" }, { "rank": 33, "intensity": 2.479591836734694, "date": "2021-08-04" }, { "rank": 34, "intensity": 2.510204081632653, "date": "2021-10-25" }, { "rank": 35, "intensity": 2.5408163265306123, "date": "2021-07-14" }, { "rank": 36, "intensity": 2.571428571428571, "date": "2021-04-10" }, { "rank": 37, "intensity": 2.6020408163265305, "date": "2021-05-30" }, { "rank": 38, "intensity": 2.63265306122449, "date": "2021-11-12" }, { "rank": 39, "intensity": 2.663265306122449, "date": "2021-03-18" }, { "rank": 40, "intensity": 2.693877551020408, "date": "2021-08-19" }, { "rank": 41, "intensity": 2.7244897959183674, "date": "2021-01-24" }, { "rank": 42, "intensity": 2.7551020408163263, "date": "2021-06-07" }, { "rank": 43, "intensity": 2.7857142857142856, "date": "2021-07-20" }, { "rank": 44, "intensity": 2.816326530612245, "date": "2021-11-17" }, { "rank": 45, "intensity": 2.8469387755102042, "date": "2021-12-18" }, { "rank": 46, "intensity": 2.877551020408163, "date": "2021-09-26" }, { "rank": 47, "intensity": 2.9081632653061225, "date": "2021-10-29" }, { "rank": 48, "intensity": 2.9387755102040813, "date": "2021-02-08" }, { "rank": 49, "intensity": 2.9693877551020407, "date": "2021-03-31" }, { "rank": 50, "intensity": 3.0, "date": "2021-08-31" } ], "source": "causal_rivers" } }, { "id": "L3_T1_Composite_Trend_00091", "level": 3, "level_name": "Semantic Reasoning", "category": "Composite Trend", "subtask": "Composite Trend", "question": "Among days in channel 727 during 2021 that exhibit the trend pattern 'rapid fall, then steady stable', identify the top-5 days where the rapid fall segment is the fastest. (Output format: a ranked list of dates, e.g., ['YYYY-MM-DD', ...])", "answer": "['2021-09-09', '2021-12-27', '2021-05-06', '2021-04-18', '2021-01-19']", "ground_truth": [ "2021-09-09", "2021-12-27", "2021-05-06", "2021-04-18", "2021-01-19" ], "eval_metric": "set_f1", "channel": "727", "ts_data_path": "ts_data/L3_T1_Composite_Trend_00091.csv", "meta": { "segments": [ { "kind": "fall", "adj": "rapid" }, { "kind": "stable", "adj": "steady" } ], "pattern_phrase": "rapid fall, then steady stable", "rank_target_idx": 0, "rank_kind": "fall", "rank_adj": "rapid", "rank_axis": "slope", "rank_direction": "DESC", "rank_extreme": "fastest", "top_segments_meta": [ { "adj": "rapid", "kind": "fall", "label": "rapid fall", "start_idx": 0, "end_idx": 20 }, { "adj": "steady", "kind": "stable", "label": "steady stable", "start_idx": 20, "end_idx": 98 } ], "points_per_day": 96, "pattern": "rapid fall, then steady stable", "year": 2021, "top_k": [ "2021-09-09", "2021-12-27", "2021-05-06", "2021-04-18", "2021-01-19" ], "all_injections": [ { "rank": 1, "intensity": 3.0, "date": "2021-09-09" }, { "rank": 2, "intensity": 2.9693877551020407, "date": "2021-12-27" }, { "rank": 3, "intensity": 2.938775510204082, "date": "2021-05-06" }, { "rank": 4, "intensity": 2.9081632653061225, "date": "2021-04-18" }, { "rank": 5, "intensity": 2.877551020408163, "date": "2021-01-19" }, { "rank": 6, "intensity": 2.8469387755102042, "date": "2021-02-26" }, { "rank": 7, "intensity": 2.816326530612245, "date": "2021-05-31" }, { "rank": 8, "intensity": 2.7857142857142856, "date": "2021-12-29" }, { "rank": 9, "intensity": 2.7551020408163267, "date": "2021-07-06" }, { "rank": 10, "intensity": 2.7244897959183674, "date": "2021-04-30" }, { "rank": 11, "intensity": 2.693877551020408, "date": "2021-11-19" }, { "rank": 12, "intensity": 2.663265306122449, "date": "2021-03-25" }, { "rank": 13, "intensity": 2.63265306122449, "date": "2021-11-08" }, { "rank": 14, "intensity": 2.6020408163265305, "date": "2021-03-22" }, { "rank": 15, "intensity": 2.5714285714285716, "date": "2021-04-23" }, { "rank": 16, "intensity": 2.5408163265306123, "date": "2021-08-16" }, { "rank": 17, "intensity": 2.510204081632653, "date": "2021-09-29" }, { "rank": 18, "intensity": 2.479591836734694, "date": "2021-01-14" }, { "rank": 19, "intensity": 2.4489795918367347, "date": "2021-08-05" }, { "rank": 20, "intensity": 2.4183673469387754, "date": "2021-08-11" }, { "rank": 21, "intensity": 2.387755102040816, "date": "2021-07-23" }, { "rank": 22, "intensity": 2.357142857142857, "date": "2021-01-31" }, { "rank": 23, "intensity": 2.326530612244898, "date": "2021-07-11" }, { "rank": 24, "intensity": 2.295918367346939, "date": "2021-06-30" }, { "rank": 25, "intensity": 2.2653061224489797, "date": "2021-02-02" }, { "rank": 26, "intensity": 2.2346938775510203, "date": "2021-06-06" }, { "rank": 27, "intensity": 2.204081632653061, "date": "2021-05-13" }, { "rank": 28, "intensity": 2.173469387755102, "date": "2021-11-21" }, { "rank": 29, "intensity": 2.142857142857143, "date": "2021-10-07" }, { "rank": 30, "intensity": 2.112244897959184, "date": "2021-12-20" }, { "rank": 31, "intensity": 2.0816326530612246, "date": "2021-06-18" }, { "rank": 32, "intensity": 2.0510204081632653, "date": "2021-10-03" }, { "rank": 33, "intensity": 2.020408163265306, "date": "2021-03-17" }, { "rank": 34, "intensity": 1.989795918367347, "date": "2021-10-13" }, { "rank": 35, "intensity": 1.9591836734693877, "date": "2021-11-12" }, { "rank": 36, "intensity": 1.9285714285714286, "date": "2021-01-16" }, { "rank": 37, "intensity": 1.8979591836734695, "date": "2021-10-10" }, { "rank": 38, "intensity": 1.8673469387755102, "date": "2021-05-21" }, { "rank": 39, "intensity": 1.836734693877551, "date": "2021-02-06" }, { "rank": 40, "intensity": 1.806122448979592, "date": "2021-09-06" }, { "rank": 41, "intensity": 1.7755102040816326, "date": "2021-10-31" }, { "rank": 42, "intensity": 1.7448979591836735, "date": "2021-04-09" }, { "rank": 43, "intensity": 1.7142857142857144, "date": "2021-12-01" }, { "rank": 44, "intensity": 1.683673469387755, "date": "2021-01-21" }, { "rank": 45, "intensity": 1.653061224489796, "date": "2021-02-04" }, { "rank": 46, "intensity": 1.6224489795918369, "date": "2021-12-10" }, { "rank": 47, "intensity": 1.5918367346938775, "date": "2021-04-21" }, { "rank": 48, "intensity": 1.5612244897959184, "date": "2021-01-06" }, { "rank": 49, "intensity": 1.5306122448979593, "date": "2021-07-19" }, { "rank": 50, "intensity": 1.5, "date": "2021-04-14" } ], "source": "causal_rivers" } }, { "id": "L3_T1_Composite_Trend_00092", "level": 3, "level_name": "Semantic Reasoning", "category": "Composite Trend", "subtask": "Composite Trend", "question": "Among days in channel 728 during 2020 that exhibit the trend pattern 'fall, then fluctuating stable, then rapid fall, then slow fall', identify the top-5 days where the rapid fall segment is the fastest. (Output format: a ranked list of dates, e.g., ['YYYY-MM-DD', ...])", "answer": "['2020-06-09', '2020-11-02', '2020-05-21', '2020-02-04', '2020-10-03']", "ground_truth": [ "2020-06-09", "2020-11-02", "2020-05-21", "2020-02-04", "2020-10-03" ], "eval_metric": "set_f1", "channel": "728", "ts_data_path": "ts_data/L3_T1_Composite_Trend_00092.csv", "meta": { "segments": [ { "kind": "fall", "adj": "none" }, { "kind": "stable", "adj": "fluctuating" }, { "kind": "fall", "adj": "rapid" }, { "kind": "fall", "adj": "slow" } ], "pattern_phrase": "fall, then fluctuating stable, then rapid fall, then slow fall", "rank_target_idx": 2, "rank_kind": "fall", "rank_adj": "rapid", "rank_axis": "slope", "rank_direction": "DESC", "rank_extreme": "fastest", "top_segments_meta": [ { "adj": "none", "kind": "fall", "label": "none fall", "start_idx": 0, "end_idx": 20 }, { "adj": "fluctuating", "kind": "stable", "label": "fluctuating stable", "start_idx": 20, "end_idx": 46 }, { "adj": "rapid", "kind": "fall", "label": "rapid fall", "start_idx": 46, "end_idx": 57 }, { "adj": "slow", "kind": "fall", "label": "slow fall", "start_idx": 57, "end_idx": 98 } ], "points_per_day": 96, "pattern": "fall, then fluctuating stable, then rapid fall, then slow fall", "year": 2020, "top_k": [ "2020-06-09", "2020-11-02", "2020-05-21", "2020-02-04", "2020-10-03" ], "all_injections": [ { "rank": 1, "intensity": 3.0, "date": "2020-06-09" }, { "rank": 2, "intensity": 2.9693877551020407, "date": "2020-11-02" }, { "rank": 3, "intensity": 2.938775510204082, "date": "2020-05-21" }, { "rank": 4, "intensity": 2.9081632653061225, "date": "2020-02-04" }, { "rank": 5, "intensity": 2.877551020408163, "date": "2020-10-03" }, { "rank": 6, "intensity": 2.8469387755102042, "date": "2020-06-04" }, { "rank": 7, "intensity": 2.816326530612245, "date": "2020-02-12" }, { "rank": 8, "intensity": 2.7857142857142856, "date": "2020-06-15" }, { "rank": 9, "intensity": 2.7551020408163267, "date": "2020-01-21" }, { "rank": 10, "intensity": 2.7244897959183674, "date": "2020-05-23" }, { "rank": 11, "intensity": 2.693877551020408, "date": "2020-12-12" }, { "rank": 12, "intensity": 2.663265306122449, "date": "2020-08-31" }, { "rank": 13, "intensity": 2.63265306122449, "date": "2020-12-04" }, { "rank": 14, "intensity": 2.6020408163265305, "date": "2020-09-11" }, { "rank": 15, "intensity": 2.5714285714285716, "date": "2020-04-11" }, { "rank": 16, "intensity": 2.5408163265306123, "date": "2020-03-22" }, { "rank": 17, "intensity": 2.510204081632653, "date": "2020-06-22" }, { "rank": 18, "intensity": 2.479591836734694, "date": "2020-02-28" }, { "rank": 19, "intensity": 2.4489795918367347, "date": "2020-08-01" }, { "rank": 20, "intensity": 2.4183673469387754, "date": "2020-01-02" }, { "rank": 21, "intensity": 2.387755102040816, "date": "2020-07-13" }, { "rank": 22, "intensity": 2.357142857142857, "date": "2020-11-21" }, { "rank": 23, "intensity": 2.326530612244898, "date": "2020-03-24" }, { "rank": 24, "intensity": 2.295918367346939, "date": "2020-03-26" }, { "rank": 25, "intensity": 2.2653061224489797, "date": "2020-09-30" }, { "rank": 26, "intensity": 2.2346938775510203, "date": "2020-10-10" }, { "rank": 27, "intensity": 2.204081632653061, "date": "2020-10-28" }, { "rank": 28, "intensity": 2.173469387755102, "date": "2020-11-14" }, { "rank": 29, "intensity": 2.142857142857143, "date": "2020-08-11" }, { "rank": 30, "intensity": 2.112244897959184, "date": "2020-08-22" }, { "rank": 31, "intensity": 2.0816326530612246, "date": "2020-10-20" }, { "rank": 32, "intensity": 2.0510204081632653, "date": "2020-07-16" }, { "rank": 33, "intensity": 2.020408163265306, "date": "2020-05-08" }, { "rank": 34, "intensity": 1.989795918367347, "date": "2020-06-29" }, { "rank": 35, "intensity": 1.9591836734693877, "date": "2020-07-18" }, { "rank": 36, "intensity": 1.9285714285714286, "date": "2020-02-24" }, { "rank": 37, "intensity": 1.8979591836734695, "date": "2020-09-17" }, { "rank": 38, "intensity": 1.8673469387755102, "date": "2020-11-16" }, { "rank": 39, "intensity": 1.836734693877551, "date": "2020-01-12" }, { "rank": 40, "intensity": 1.806122448979592, "date": "2020-05-10" }, { "rank": 41, "intensity": 1.7755102040816326, "date": "2020-10-26" }, { "rank": 42, "intensity": 1.7448979591836735, "date": "2020-03-29" }, { "rank": 43, "intensity": 1.7142857142857144, "date": "2020-10-05" }, { "rank": 44, "intensity": 1.683673469387755, "date": "2020-09-24" }, { "rank": 45, "intensity": 1.653061224489796, "date": "2020-06-07" }, { "rank": 46, "intensity": 1.6224489795918369, "date": "2020-08-09" }, { "rank": 47, "intensity": 1.5918367346938775, "date": "2020-04-06" }, { "rank": 48, "intensity": 1.5612244897959184, "date": "2020-11-09" }, { "rank": 49, "intensity": 1.5306122448979593, "date": "2020-06-19" }, { "rank": 50, "intensity": 1.5, "date": "2020-05-29" } ], "source": "causal_rivers" } }, { "id": "L3_T1_Composite_Trend_00093", "level": 3, "level_name": "Semantic Reasoning", "category": "Composite Trend", "subtask": "Composite Trend", "question": "Among days in channel 729 during 2023 that exhibit the trend pattern 'slow rise, then fall, then steady stable, then fluctuating stable', identify the top-5 days where the slow rise segment is the slowest. (Output format: a ranked list of dates, e.g., ['YYYY-MM-DD', ...])", "answer": "['2023-07-16', '2023-04-30', '2023-01-10', '2023-10-09', '2023-08-13']", "ground_truth": [ "2023-07-16", "2023-04-30", "2023-01-10", "2023-10-09", "2023-08-13" ], "eval_metric": "set_f1", "channel": "729", "ts_data_path": "ts_data/L3_T1_Composite_Trend_00093.csv", "meta": { "segments": [ { "kind": "rise", "adj": "slow" }, { "kind": "fall", "adj": "none" }, { "kind": "stable", "adj": "steady" }, { "kind": "stable", "adj": "fluctuating" } ], "pattern_phrase": "slow rise, then fall, then steady stable, then fluctuating stable", "rank_target_idx": 0, "rank_kind": "rise", "rank_adj": "slow", "rank_axis": "slope", "rank_direction": "ASC", "rank_extreme": "slowest", "top_segments_meta": [ { "adj": "slow", "kind": "rise", "label": "slow rise", "start_idx": 0, "end_idx": 28 }, { "adj": "none", "kind": "fall", "label": "none fall", "start_idx": 28, "end_idx": 44 }, { "adj": "steady", "kind": "stable", "label": "steady stable", "start_idx": 44, "end_idx": 77 }, { "adj": "fluctuating", "kind": "stable", "label": "fluctuating stable", "start_idx": 77, "end_idx": 98 } ], "points_per_day": 96, "pattern": "slow rise, then fall, then steady stable, then fluctuating stable", "year": 2023, "top_k": [ "2023-07-16", "2023-04-30", "2023-01-10", "2023-10-09", "2023-08-13" ], "all_injections": [ { "rank": 1, "intensity": 1.5, "date": "2023-07-16" }, { "rank": 2, "intensity": 1.530612244897959, "date": "2023-04-30" }, { "rank": 3, "intensity": 1.5612244897959184, "date": "2023-01-10" }, { "rank": 4, "intensity": 1.5918367346938775, "date": "2023-10-09" }, { "rank": 5, "intensity": 1.6224489795918366, "date": "2023-08-13" }, { "rank": 6, "intensity": 1.653061224489796, "date": "2023-11-29" }, { "rank": 7, "intensity": 1.683673469387755, "date": "2023-07-28" }, { "rank": 8, "intensity": 1.7142857142857142, "date": "2023-06-15" }, { "rank": 9, "intensity": 1.7448979591836735, "date": "2023-12-07" }, { "rank": 10, "intensity": 1.7755102040816326, "date": "2023-10-04" }, { "rank": 11, "intensity": 1.806122448979592, "date": "2023-09-18" }, { "rank": 12, "intensity": 1.836734693877551, "date": "2023-09-26" }, { "rank": 13, "intensity": 1.8673469387755102, "date": "2023-11-12" }, { "rank": 14, "intensity": 1.8979591836734695, "date": "2023-05-15" }, { "rank": 15, "intensity": 1.9285714285714286, "date": "2023-06-20" }, { "rank": 16, "intensity": 1.9591836734693877, "date": "2023-08-08" }, { "rank": 17, "intensity": 1.989795918367347, "date": "2023-11-26" }, { "rank": 18, "intensity": 2.020408163265306, "date": "2023-08-29" }, { "rank": 19, "intensity": 2.0510204081632653, "date": "2023-06-06" }, { "rank": 20, "intensity": 2.0816326530612246, "date": "2023-12-13" }, { "rank": 21, "intensity": 2.112244897959184, "date": "2023-05-08" }, { "rank": 22, "intensity": 2.142857142857143, "date": "2023-11-15" }, { "rank": 23, "intensity": 2.173469387755102, "date": "2023-12-26" }, { "rank": 24, "intensity": 2.204081632653061, "date": "2023-02-13" }, { "rank": 25, "intensity": 2.2346938775510203, "date": "2023-03-16" }, { "rank": 26, "intensity": 2.2653061224489797, "date": "2023-01-08" }, { "rank": 27, "intensity": 2.295918367346939, "date": "2023-03-21" }, { "rank": 28, "intensity": 2.326530612244898, "date": "2023-03-03" }, { "rank": 29, "intensity": 2.357142857142857, "date": "2023-09-08" }, { "rank": 30, "intensity": 2.387755102040816, "date": "2023-06-11" }, { "rank": 31, "intensity": 2.4183673469387754, "date": "2023-05-20" }, { "rank": 32, "intensity": 2.4489795918367347, "date": "2023-07-07" }, { "rank": 33, "intensity": 2.479591836734694, "date": "2023-04-17" }, { "rank": 34, "intensity": 2.510204081632653, "date": "2023-01-21" }, { "rank": 35, "intensity": 2.5408163265306123, "date": "2023-05-06" }, { "rank": 36, "intensity": 2.571428571428571, "date": "2023-08-05" }, { "rank": 37, "intensity": 2.6020408163265305, "date": "2023-09-10" }, { "rank": 38, "intensity": 2.63265306122449, "date": "2023-06-13" }, { "rank": 39, "intensity": 2.663265306122449, "date": "2023-01-06" }, { "rank": 40, "intensity": 2.693877551020408, "date": "2023-04-08" }, { "rank": 41, "intensity": 2.7244897959183674, "date": "2023-03-27" }, { "rank": 42, "intensity": 2.7551020408163263, "date": "2023-06-26" }, { "rank": 43, "intensity": 2.7857142857142856, "date": "2023-08-18" }, { "rank": 44, "intensity": 2.816326530612245, "date": "2023-12-05" }, { "rank": 45, "intensity": 2.8469387755102042, "date": "2023-11-17" }, { "rank": 46, "intensity": 2.877551020408163, "date": "2023-09-24" }, { "rank": 47, "intensity": 2.9081632653061225, "date": "2023-04-15" }, { "rank": 48, "intensity": 2.9387755102040813, "date": "2023-09-29" }, { "rank": 49, "intensity": 2.9693877551020407, "date": "2023-07-11" }, { "rank": 50, "intensity": 3.0, "date": "2023-12-19" } ], "source": "causal_rivers" } }, { "id": "L3_T1_Composite_Trend_00094", "level": 3, "level_name": "Semantic Reasoning", "category": "Composite Trend", "subtask": "Composite Trend", "question": "Among days in channel 754 during 2020 that exhibit the trend pattern 'rise, then slow rise', identify the top-5 days where the slow rise segment is the slowest. (Output format: a ranked list of dates, e.g., ['YYYY-MM-DD', ...])", "answer": "['2020-02-19', '2020-12-28', '2020-09-25', '2020-09-19', '2020-07-03']", "ground_truth": [ "2020-02-19", "2020-12-28", "2020-09-25", "2020-09-19", "2020-07-03" ], "eval_metric": "set_f1", "channel": "754", "ts_data_path": "ts_data/L3_T1_Composite_Trend_00094.csv", "meta": { "segments": [ { "kind": "rise", "adj": "none" }, { "kind": "rise", "adj": "slow" } ], "pattern_phrase": "rise, then slow rise", "rank_target_idx": 1, "rank_kind": "rise", "rank_adj": "slow", "rank_axis": "slope", "rank_direction": "ASC", "rank_extreme": "slowest", "top_segments_meta": [ { "adj": "none", "kind": "rise", "label": "none rise", "start_idx": 0, "end_idx": 34 }, { "adj": "slow", "kind": "rise", "label": "slow rise", "start_idx": 34, "end_idx": 98 } ], "points_per_day": 96, "pattern": "rise, then slow rise", "year": 2020, "top_k": [ "2020-02-19", "2020-12-28", "2020-09-25", "2020-09-19", "2020-07-03" ], "all_injections": [ { "rank": 1, "intensity": 1.5, "date": "2020-02-19" }, { "rank": 2, "intensity": 1.530612244897959, "date": "2020-12-28" }, { "rank": 3, "intensity": 1.5612244897959184, "date": "2020-09-25" }, { "rank": 4, "intensity": 1.5918367346938775, "date": "2020-09-19" }, { "rank": 5, "intensity": 1.6224489795918366, "date": "2020-07-03" }, { "rank": 6, "intensity": 1.653061224489796, "date": "2020-03-22" }, { "rank": 7, "intensity": 1.683673469387755, "date": "2020-06-23" }, { "rank": 8, "intensity": 1.7142857142857142, "date": "2020-07-25" }, { "rank": 9, "intensity": 1.7448979591836735, "date": "2020-04-23" }, { "rank": 10, "intensity": 1.7755102040816326, "date": "2020-04-25" }, { "rank": 11, "intensity": 1.806122448979592, "date": "2020-04-03" }, { "rank": 12, "intensity": 1.836734693877551, "date": "2020-03-29" }, { "rank": 13, "intensity": 1.8673469387755102, "date": "2020-08-14" }, { "rank": 14, "intensity": 1.8979591836734695, "date": "2020-09-05" }, { "rank": 15, "intensity": 1.9285714285714286, "date": "2020-11-13" }, { "rank": 16, "intensity": 1.9591836734693877, "date": "2020-04-13" }, { "rank": 17, "intensity": 1.989795918367347, "date": "2020-04-06" }, { "rank": 18, "intensity": 2.020408163265306, "date": "2020-01-29" }, { "rank": 19, "intensity": 2.0510204081632653, "date": "2020-02-14" }, { "rank": 20, "intensity": 2.0816326530612246, "date": "2020-03-05" }, { "rank": 21, "intensity": 2.112244897959184, "date": "2020-05-25" }, { "rank": 22, "intensity": 2.142857142857143, "date": "2020-05-08" }, { "rank": 23, "intensity": 2.173469387755102, "date": "2020-11-06" }, { "rank": 24, "intensity": 2.204081632653061, "date": "2020-07-17" }, { "rank": 25, "intensity": 2.2346938775510203, "date": "2020-01-15" }, { "rank": 26, "intensity": 2.2653061224489797, "date": "2020-06-17" }, { "rank": 27, "intensity": 2.295918367346939, "date": "2020-10-31" }, { "rank": 28, "intensity": 2.326530612244898, "date": "2020-06-21" }, { "rank": 29, "intensity": 2.357142857142857, "date": "2020-05-19" }, { "rank": 30, "intensity": 2.387755102040816, "date": "2020-03-01" }, { "rank": 31, "intensity": 2.4183673469387754, "date": "2020-11-20" }, { "rank": 32, "intensity": 2.4489795918367347, "date": "2020-09-29" }, { "rank": 33, "intensity": 2.479591836734694, "date": "2020-06-27" }, { "rank": 34, "intensity": 2.510204081632653, "date": "2020-09-16" }, { "rank": 35, "intensity": 2.5408163265306123, "date": "2020-08-27" }, { "rank": 36, "intensity": 2.571428571428571, "date": "2020-11-16" }, { "rank": 37, "intensity": 2.6020408163265305, "date": "2020-09-03" }, { "rank": 38, "intensity": 2.63265306122449, "date": "2020-01-02" }, { "rank": 39, "intensity": 2.663265306122449, "date": "2020-05-10" }, { "rank": 40, "intensity": 2.693877551020408, "date": "2020-09-12" }, { "rank": 41, "intensity": 2.7244897959183674, "date": "2020-10-03" }, { "rank": 42, "intensity": 2.7551020408163263, "date": "2020-08-17" }, { "rank": 43, "intensity": 2.7857142857142856, "date": "2020-08-23" }, { "rank": 44, "intensity": 2.816326530612245, "date": "2020-12-05" }, { "rank": 45, "intensity": 2.8469387755102042, "date": "2020-05-30" }, { "rank": 46, "intensity": 2.877551020408163, "date": "2020-10-19" }, { "rank": 47, "intensity": 2.9081632653061225, "date": "2020-06-29" }, { "rank": 48, "intensity": 2.9387755102040813, "date": "2020-10-28" }, { "rank": 49, "intensity": 2.9693877551020407, "date": "2020-12-30" }, { "rank": 50, "intensity": 3.0, "date": "2020-12-20" } ], "source": "causal_rivers" } }, { "id": "L3_T1_Composite_Trend_00095", "level": 3, "level_name": "Semantic Reasoning", "category": "Composite Trend", "subtask": "Composite Trend", "question": "Among days in channel 762 during 2023 that exhibit the trend pattern 'fluctuating stable, then slow fall', identify the top-5 days where the slow fall segment is the slowest. (Output format: a ranked list of dates, e.g., ['YYYY-MM-DD', ...])", "answer": "['2023-10-21', '2023-07-24', '2023-05-04', '2023-11-24', '2023-04-26']", "ground_truth": [ "2023-10-21", "2023-07-24", "2023-05-04", "2023-11-24", "2023-04-26" ], "eval_metric": "set_f1", "channel": "762", "ts_data_path": "ts_data/L3_T1_Composite_Trend_00095.csv", "meta": { "segments": [ { "kind": "stable", "adj": "fluctuating" }, { "kind": "fall", "adj": "slow" } ], "pattern_phrase": "fluctuating stable, then slow fall", "rank_target_idx": 1, "rank_kind": "fall", "rank_adj": "slow", "rank_axis": "slope", "rank_direction": "ASC", "rank_extreme": "slowest", "top_segments_meta": [ { "adj": "fluctuating", "kind": "stable", "label": "fluctuating stable", "start_idx": 0, "end_idx": 44 }, { "adj": "slow", "kind": "fall", "label": "slow fall", "start_idx": 44, "end_idx": 98 } ], "points_per_day": 96, "pattern": "fluctuating stable, then slow fall", "year": 2023, "top_k": [ "2023-10-21", "2023-07-24", "2023-05-04", "2023-11-24", "2023-04-26" ], "all_injections": [ { "rank": 1, "intensity": 1.5, "date": "2023-10-21" }, { "rank": 2, "intensity": 1.530612244897959, "date": "2023-07-24" }, { "rank": 3, "intensity": 1.5612244897959184, "date": "2023-05-04" }, { "rank": 4, "intensity": 1.5918367346938775, "date": "2023-11-24" }, { "rank": 5, "intensity": 1.6224489795918366, "date": "2023-04-26" }, { "rank": 6, "intensity": 1.653061224489796, "date": "2023-03-28" }, { "rank": 7, "intensity": 1.683673469387755, "date": "2023-12-02" }, { "rank": 8, "intensity": 1.7142857142857142, "date": "2023-11-11" }, { "rank": 9, "intensity": 1.7448979591836735, "date": "2023-10-12" }, { "rank": 10, "intensity": 1.7755102040816326, "date": "2023-05-20" }, { "rank": 11, "intensity": 1.806122448979592, "date": "2023-05-13" }, { "rank": 12, "intensity": 1.836734693877551, "date": "2023-11-04" }, { "rank": 13, "intensity": 1.8673469387755102, "date": "2023-09-03" }, { "rank": 14, "intensity": 1.8979591836734695, "date": "2023-04-13" }, { "rank": 15, "intensity": 1.9285714285714286, "date": "2023-05-29" }, { "rank": 16, "intensity": 1.9591836734693877, "date": "2023-02-23" }, { "rank": 17, "intensity": 1.989795918367347, "date": "2023-08-23" }, { "rank": 18, "intensity": 2.020408163265306, "date": "2023-01-19" }, { "rank": 19, "intensity": 2.0510204081632653, "date": "2023-01-09" }, { "rank": 20, "intensity": 2.0816326530612246, "date": "2023-11-21" }, { "rank": 21, "intensity": 2.112244897959184, "date": "2023-06-03" }, { "rank": 22, "intensity": 2.142857142857143, "date": "2023-04-09" }, { "rank": 23, "intensity": 2.173469387755102, "date": "2023-05-11" }, { "rank": 24, "intensity": 2.204081632653061, "date": "2023-03-24" }, { "rank": 25, "intensity": 2.2346938775510203, "date": "2023-03-19" }, { "rank": 26, "intensity": 2.2653061224489797, "date": "2023-06-08" }, { "rank": 27, "intensity": 2.295918367346939, "date": "2023-06-01" }, { "rank": 28, "intensity": 2.326530612244898, "date": "2023-12-21" }, { "rank": 29, "intensity": 2.357142857142857, "date": "2023-02-07" }, { "rank": 30, "intensity": 2.387755102040816, "date": "2023-06-06" }, { "rank": 31, "intensity": 2.4183673469387754, "date": "2023-07-28" }, { "rank": 32, "intensity": 2.4489795918367347, "date": "2023-10-26" }, { "rank": 33, "intensity": 2.479591836734694, "date": "2023-11-26" }, { "rank": 34, "intensity": 2.510204081632653, "date": "2023-11-08" }, { "rank": 35, "intensity": 2.5408163265306123, "date": "2023-08-31" }, { "rank": 36, "intensity": 2.571428571428571, "date": "2023-04-11" }, { "rank": 37, "intensity": 2.6020408163265305, "date": "2023-06-29" }, { "rank": 38, "intensity": 2.63265306122449, "date": "2023-09-10" }, { "rank": 39, "intensity": 2.663265306122449, "date": "2023-04-23" }, { "rank": 40, "intensity": 2.693877551020408, "date": "2023-10-04" }, { "rank": 41, "intensity": 2.7244897959183674, "date": "2023-01-21" }, { "rank": 42, "intensity": 2.7551020408163263, "date": "2023-10-24" }, { "rank": 43, "intensity": 2.7857142857142856, "date": "2023-06-21" }, { "rank": 44, "intensity": 2.816326530612245, "date": "2023-05-02" }, { "rank": 45, "intensity": 2.8469387755102042, "date": "2023-05-06" }, { "rank": 46, "intensity": 2.877551020408163, "date": "2023-06-11" }, { "rank": 47, "intensity": 2.9081632653061225, "date": "2023-09-17" }, { "rank": 48, "intensity": 2.9387755102040813, "date": "2023-03-17" }, { "rank": 49, "intensity": 2.9693877551020407, "date": "2023-02-01" }, { "rank": 50, "intensity": 3.0, "date": "2023-10-16" } ], "source": "causal_rivers" } }, { "id": "L3_T1_Composite_Trend_00096", "level": 3, "level_name": "Semantic Reasoning", "category": "Composite Trend", "subtask": "Composite Trend", "question": "Among days in channel 811 during 2019 that exhibit the trend pattern 'slow fall, then steady stable', identify the top-5 days where the slow fall segment is the slowest. (Output format: a ranked list of dates, e.g., ['YYYY-MM-DD', ...])", "answer": "['2019-04-14', '2019-12-09', '2019-11-01', '2019-06-16', '2019-08-10']", "ground_truth": [ "2019-04-14", "2019-12-09", "2019-11-01", "2019-06-16", "2019-08-10" ], "eval_metric": "set_f1", "channel": "811", "ts_data_path": "ts_data/L3_T1_Composite_Trend_00096.csv", "meta": { "segments": [ { "kind": "fall", "adj": "slow" }, { "kind": "stable", "adj": "steady" } ], "pattern_phrase": "slow fall, then steady stable", "rank_target_idx": 0, "rank_kind": "fall", "rank_adj": "slow", "rank_axis": "slope", "rank_direction": "ASC", "rank_extreme": "slowest", "top_segments_meta": [ { "adj": "slow", "kind": "fall", "label": "slow fall", "start_idx": 0, "end_idx": 46 }, { "adj": "steady", "kind": "stable", "label": "steady stable", "start_idx": 46, "end_idx": 98 } ], "points_per_day": 96, "pattern": "slow fall, then steady stable", "year": 2019, "top_k": [ "2019-04-14", "2019-12-09", "2019-11-01", "2019-06-16", "2019-08-10" ], "all_injections": [ { "rank": 1, "intensity": 1.5, "date": "2019-04-14" }, { "rank": 2, "intensity": 1.530612244897959, "date": "2019-12-09" }, { "rank": 3, "intensity": 1.5612244897959184, "date": "2019-11-01" }, { "rank": 4, "intensity": 1.5918367346938775, "date": "2019-06-16" }, { "rank": 5, "intensity": 1.6224489795918366, "date": "2019-08-10" }, { "rank": 6, "intensity": 1.653061224489796, "date": "2019-06-13" }, { "rank": 7, "intensity": 1.683673469387755, "date": "2019-02-15" }, { "rank": 8, "intensity": 1.7142857142857142, "date": "2019-01-14" }, { "rank": 9, "intensity": 1.7448979591836735, "date": "2019-08-31" }, { "rank": 10, "intensity": 1.7755102040816326, "date": "2019-05-21" }, { "rank": 11, "intensity": 1.806122448979592, "date": "2019-12-19" }, { "rank": 12, "intensity": 1.836734693877551, "date": "2019-06-29" }, { "rank": 13, "intensity": 1.8673469387755102, "date": "2019-05-09" }, { "rank": 14, "intensity": 1.8979591836734695, "date": "2019-08-28" }, { "rank": 15, "intensity": 1.9285714285714286, "date": "2019-01-23" }, { "rank": 16, "intensity": 1.9591836734693877, "date": "2019-10-01" }, { "rank": 17, "intensity": 1.989795918367347, "date": "2019-04-28" }, { "rank": 18, "intensity": 2.020408163265306, "date": "2019-02-24" }, { "rank": 19, "intensity": 2.0510204081632653, "date": "2019-11-15" }, { "rank": 20, "intensity": 2.0816326530612246, "date": "2019-02-11" }, { "rank": 21, "intensity": 2.112244897959184, "date": "2019-12-11" }, { "rank": 22, "intensity": 2.142857142857143, "date": "2019-10-17" }, { "rank": 23, "intensity": 2.173469387755102, "date": "2019-09-15" }, { "rank": 24, "intensity": 2.204081632653061, "date": "2019-05-28" }, { "rank": 25, "intensity": 2.2346938775510203, "date": "2019-11-21" }, { "rank": 26, "intensity": 2.2653061224489797, "date": "2019-08-22" }, { "rank": 27, "intensity": 2.295918367346939, "date": "2019-03-27" }, { "rank": 28, "intensity": 2.326530612244898, "date": "2019-08-06" }, { "rank": 29, "intensity": 2.357142857142857, "date": "2019-08-17" }, { "rank": 30, "intensity": 2.387755102040816, "date": "2019-04-10" }, { "rank": 31, "intensity": 2.4183673469387754, "date": "2019-02-22" }, { "rank": 32, "intensity": 2.4489795918367347, "date": "2019-11-23" }, { "rank": 33, "intensity": 2.479591836734694, "date": "2019-03-05" }, { "rank": 34, "intensity": 2.510204081632653, "date": "2019-05-26" }, { "rank": 35, "intensity": 2.5408163265306123, "date": "2019-12-05" }, { "rank": 36, "intensity": 2.571428571428571, "date": "2019-10-09" }, { "rank": 37, "intensity": 2.6020408163265305, "date": "2019-01-25" }, { "rank": 38, "intensity": 2.63265306122449, "date": "2019-04-30" }, { "rank": 39, "intensity": 2.663265306122449, "date": "2019-11-30" }, { "rank": 40, "intensity": 2.693877551020408, "date": "2019-07-02" }, { "rank": 41, "intensity": 2.7244897959183674, "date": "2019-05-31" }, { "rank": 42, "intensity": 2.7551020408163263, "date": "2019-11-09" }, { "rank": 43, "intensity": 2.7857142857142856, "date": "2019-02-18" }, { "rank": 44, "intensity": 2.816326530612245, "date": "2019-07-26" }, { "rank": 45, "intensity": 2.8469387755102042, "date": "2019-09-24" }, { "rank": 46, "intensity": 2.877551020408163, "date": "2019-03-02" }, { "rank": 47, "intensity": 2.9081632653061225, "date": "2019-08-03" }, { "rank": 48, "intensity": 2.9387755102040813, "date": "2019-12-28" }, { "rank": 49, "intensity": 2.9693877551020407, "date": "2019-07-15" }, { "rank": 50, "intensity": 3.0, "date": "2019-09-06" } ], "source": "causal_rivers" } }, { "id": "L3_T1_Composite_Trend_00097", "level": 3, "level_name": "Semantic Reasoning", "category": "Composite Trend", "subtask": "Composite Trend", "question": "Among days in channel 813 during 2022 that exhibit the trend pattern 'fluctuating stable, then rise', identify the top-5 days where the fluctuating stable segment is the most fluctuating. (Output format: a ranked list of dates, e.g., ['YYYY-MM-DD', ...])", "answer": "['2022-07-30', '2022-01-22', '2022-06-03', '2022-06-17', '2022-09-28']", "ground_truth": [ "2022-07-30", "2022-01-22", "2022-06-03", "2022-06-17", "2022-09-28" ], "eval_metric": "set_f1", "channel": "813", "ts_data_path": "ts_data/L3_T1_Composite_Trend_00097.csv", "meta": { "segments": [ { "kind": "stable", "adj": "fluctuating" }, { "kind": "rise", "adj": "none" } ], "pattern_phrase": "fluctuating stable, then rise", "rank_target_idx": 0, "rank_kind": "stable", "rank_adj": "fluctuating", "rank_axis": "std", "rank_direction": "DESC", "rank_extreme": "most fluctuating", "top_segments_meta": [ { "adj": "fluctuating", "kind": "stable", "label": "fluctuating stable", "start_idx": 0, "end_idx": 52 }, { "adj": "none", "kind": "rise", "label": "none rise", "start_idx": 52, "end_idx": 98 } ], "points_per_day": 96, "pattern": "fluctuating stable, then rise", "year": 2022, "top_k": [ "2022-07-30", "2022-01-22", "2022-06-03", "2022-06-17", "2022-09-28" ], "all_injections": [ { "rank": 1, "intensity": 3.0, "date": "2022-07-30" }, { "rank": 2, "intensity": 2.9693877551020407, "date": "2022-01-22" }, { "rank": 3, "intensity": 2.938775510204082, "date": "2022-06-03" }, { "rank": 4, "intensity": 2.9081632653061225, "date": "2022-06-17" }, { "rank": 5, "intensity": 2.877551020408163, "date": "2022-09-28" }, { "rank": 6, "intensity": 2.8469387755102042, "date": "2022-08-08" }, { "rank": 7, "intensity": 2.816326530612245, "date": "2022-05-02" }, { "rank": 8, "intensity": 2.7857142857142856, "date": "2022-02-08" }, { "rank": 9, "intensity": 2.7551020408163267, "date": "2022-02-24" }, { "rank": 10, "intensity": 2.7244897959183674, "date": "2022-12-26" }, { "rank": 11, "intensity": 2.693877551020408, "date": "2022-10-05" }, { "rank": 12, "intensity": 2.663265306122449, "date": "2022-11-28" }, { "rank": 13, "intensity": 2.63265306122449, "date": "2022-11-22" }, { "rank": 14, "intensity": 2.6020408163265305, "date": "2022-06-22" }, { "rank": 15, "intensity": 2.5714285714285716, "date": "2022-10-13" }, { "rank": 16, "intensity": 2.5408163265306123, "date": "2022-09-17" }, { "rank": 17, "intensity": 2.510204081632653, "date": "2022-09-05" }, { "rank": 18, "intensity": 2.479591836734694, "date": "2022-03-03" }, { "rank": 19, "intensity": 2.4489795918367347, "date": "2022-08-02" }, { "rank": 20, "intensity": 2.4183673469387754, "date": "2022-03-17" }, { "rank": 21, "intensity": 2.387755102040816, "date": "2022-08-06" }, { "rank": 22, "intensity": 2.357142857142857, "date": "2022-12-15" }, { "rank": 23, "intensity": 2.326530612244898, "date": "2022-09-03" }, { "rank": 24, "intensity": 2.295918367346939, "date": "2022-08-18" }, { "rank": 25, "intensity": 2.2653061224489797, "date": "2022-08-15" }, { "rank": 26, "intensity": 2.2346938775510203, "date": "2022-10-30" }, { "rank": 27, "intensity": 2.204081632653061, "date": "2022-02-11" }, { "rank": 28, "intensity": 2.173469387755102, "date": "2022-05-15" }, { "rank": 29, "intensity": 2.142857142857143, "date": "2022-12-18" }, { "rank": 30, "intensity": 2.112244897959184, "date": "2022-05-11" }, { "rank": 31, "intensity": 2.0816326530612246, "date": "2022-06-07" }, { "rank": 32, "intensity": 2.0510204081632653, "date": "2022-06-13" }, { "rank": 33, "intensity": 2.020408163265306, "date": "2022-01-06" }, { "rank": 34, "intensity": 1.989795918367347, "date": "2022-04-26" }, { "rank": 35, "intensity": 1.9591836734693877, "date": "2022-11-12" }, { "rank": 36, "intensity": 1.9285714285714286, "date": "2022-09-01" }, { "rank": 37, "intensity": 1.8979591836734695, "date": "2022-01-12" }, { "rank": 38, "intensity": 1.8673469387755102, "date": "2022-02-17" }, { "rank": 39, "intensity": 1.836734693877551, "date": "2022-10-01" }, { "rank": 40, "intensity": 1.806122448979592, "date": "2022-08-13" }, { "rank": 41, "intensity": 1.7755102040816326, "date": "2022-07-08" }, { "rank": 42, "intensity": 1.7448979591836735, "date": "2022-03-24" }, { "rank": 43, "intensity": 1.7142857142857144, "date": "2022-11-03" }, { "rank": 44, "intensity": 1.683673469387755, "date": "2022-12-22" }, { "rank": 45, "intensity": 1.653061224489796, "date": "2022-02-15" }, { "rank": 46, "intensity": 1.6224489795918369, "date": "2022-07-13" }, { "rank": 47, "intensity": 1.5918367346938775, "date": "2022-06-20" }, { "rank": 48, "intensity": 1.5612244897959184, "date": "2022-03-13" }, { "rank": 49, "intensity": 1.5306122448979593, "date": "2022-04-24" }, { "rank": 50, "intensity": 1.5, "date": "2022-01-19" } ], "source": "causal_rivers" } }, { "id": "L3_T1_Composite_Trend_00098", "level": 3, "level_name": "Semantic Reasoning", "category": "Composite Trend", "subtask": "Composite Trend", "question": "Among days in channel 865 during 2021 that exhibit the trend pattern 'fall, then steady stable, then fluctuating stable', identify the top-5 days where the fluctuating stable segment is the most fluctuating. (Output format: a ranked list of dates, e.g., ['YYYY-MM-DD', ...])", "answer": "['2021-05-28', '2021-12-18', '2021-12-26', '2021-10-12', '2021-08-14']", "ground_truth": [ "2021-05-28", "2021-12-18", "2021-12-26", "2021-10-12", "2021-08-14" ], "eval_metric": "set_f1", "channel": "865", "ts_data_path": "ts_data/L3_T1_Composite_Trend_00098.csv", "meta": { "segments": [ { "kind": "fall", "adj": "none" }, { "kind": "stable", "adj": "steady" }, { "kind": "stable", "adj": "fluctuating" } ], "pattern_phrase": "fall, then steady stable, then fluctuating stable", "rank_target_idx": 2, "rank_kind": "stable", "rank_adj": "fluctuating", "rank_axis": "std", "rank_direction": "DESC", "rank_extreme": "most fluctuating", "top_segments_meta": [ { "adj": "none", "kind": "fall", "label": "none fall", "start_idx": 0, "end_idx": 23 }, { "adj": "steady", "kind": "stable", "label": "steady stable", "start_idx": 23, "end_idx": 66 }, { "adj": "fluctuating", "kind": "stable", "label": "fluctuating stable", "start_idx": 66, "end_idx": 98 } ], "points_per_day": 96, "pattern": "fall, then steady stable, then fluctuating stable", "year": 2021, "top_k": [ "2021-05-28", "2021-12-18", "2021-12-26", "2021-10-12", "2021-08-14" ], "all_injections": [ { "rank": 1, "intensity": 3.0, "date": "2021-05-28" }, { "rank": 2, "intensity": 2.9693877551020407, "date": "2021-12-18" }, { "rank": 3, "intensity": 2.938775510204082, "date": "2021-12-26" }, { "rank": 4, "intensity": 2.9081632653061225, "date": "2021-10-12" }, { "rank": 5, "intensity": 2.877551020408163, "date": "2021-08-14" }, { "rank": 6, "intensity": 2.8469387755102042, "date": "2021-01-02" }, { "rank": 7, "intensity": 2.816326530612245, "date": "2021-12-05" }, { "rank": 8, "intensity": 2.7857142857142856, "date": "2021-05-18" }, { "rank": 9, "intensity": 2.7551020408163267, "date": "2021-07-06" }, { "rank": 10, "intensity": 2.7244897959183674, "date": "2021-12-22" }, { "rank": 11, "intensity": 2.693877551020408, "date": "2021-12-20" }, { "rank": 12, "intensity": 2.663265306122449, "date": "2021-08-11" }, { "rank": 13, "intensity": 2.63265306122449, "date": "2021-07-18" }, { "rank": 14, "intensity": 2.6020408163265305, "date": "2021-04-19" }, { "rank": 15, "intensity": 2.5714285714285716, "date": "2021-09-24" }, { "rank": 16, "intensity": 2.5408163265306123, "date": "2021-04-01" }, { "rank": 17, "intensity": 2.510204081632653, "date": "2021-04-30" }, { "rank": 18, "intensity": 2.479591836734694, "date": "2021-03-13" }, { "rank": 19, "intensity": 2.4489795918367347, "date": "2021-03-21" }, { "rank": 20, "intensity": 2.4183673469387754, "date": "2021-10-25" }, { "rank": 21, "intensity": 2.387755102040816, "date": "2021-06-19" }, { "rank": 22, "intensity": 2.357142857142857, "date": "2021-07-03" }, { "rank": 23, "intensity": 2.326530612244898, "date": "2021-06-17" }, { "rank": 24, "intensity": 2.295918367346939, "date": "2021-09-10" }, { "rank": 25, "intensity": 2.2653061224489797, "date": "2021-09-30" }, { "rank": 26, "intensity": 2.2346938775510203, "date": "2021-07-24" }, { "rank": 27, "intensity": 2.204081632653061, "date": "2021-02-01" }, { "rank": 28, "intensity": 2.173469387755102, "date": "2021-07-16" }, { "rank": 29, "intensity": 2.142857142857143, "date": "2021-12-16" }, { "rank": 30, "intensity": 2.112244897959184, "date": "2021-05-20" }, { "rank": 31, "intensity": 2.0816326530612246, "date": "2021-06-02" }, { "rank": 32, "intensity": 2.0510204081632653, "date": "2021-04-11" }, { "rank": 33, "intensity": 2.020408163265306, "date": "2021-01-28" }, { "rank": 34, "intensity": 1.989795918367347, "date": "2021-01-26" }, { "rank": 35, "intensity": 1.9591836734693877, "date": "2021-08-04" }, { "rank": 36, "intensity": 1.9285714285714286, "date": "2021-04-23" }, { "rank": 37, "intensity": 1.8979591836734695, "date": "2021-11-05" }, { "rank": 38, "intensity": 1.8673469387755102, "date": "2021-01-07" }, { "rank": 39, "intensity": 1.836734693877551, "date": "2021-06-25" }, { "rank": 40, "intensity": 1.806122448979592, "date": "2021-07-21" }, { "rank": 41, "intensity": 1.7755102040816326, "date": "2021-10-14" }, { "rank": 42, "intensity": 1.7448979591836735, "date": "2021-01-18" }, { "rank": 43, "intensity": 1.7142857142857144, "date": "2021-04-14" }, { "rank": 44, "intensity": 1.683673469387755, "date": "2021-12-07" }, { "rank": 45, "intensity": 1.653061224489796, "date": "2021-08-21" }, { "rank": 46, "intensity": 1.6224489795918369, "date": "2021-02-20" }, { "rank": 47, "intensity": 1.5918367346938775, "date": "2021-01-10" }, { "rank": 48, "intensity": 1.5612244897959184, "date": "2021-01-23" }, { "rank": 49, "intensity": 1.5306122448979593, "date": "2021-03-27" }, { "rank": 50, "intensity": 1.5, "date": "2021-12-12" } ], "source": "causal_rivers" } }, { "id": "L3_T1_Composite_Trend_00099", "level": 3, "level_name": "Semantic Reasoning", "category": "Composite Trend", "subtask": "Composite Trend", "question": "Among days in channel 891 during 2020 that exhibit the trend pattern 'fluctuating stable, then steady stable, then rise', identify the top-5 days where the fluctuating stable segment is the most fluctuating. (Output format: a ranked list of dates, e.g., ['YYYY-MM-DD', ...])", "answer": "['2020-10-07', '2020-12-08', '2020-03-27', '2020-09-05', '2020-08-01']", "ground_truth": [ "2020-10-07", "2020-12-08", "2020-03-27", "2020-09-05", "2020-08-01" ], "eval_metric": "set_f1", "channel": "891", "ts_data_path": "ts_data/L3_T1_Composite_Trend_00099.csv", "meta": { "segments": [ { "kind": "stable", "adj": "fluctuating" }, { "kind": "stable", "adj": "steady" }, { "kind": "rise", "adj": "none" } ], "pattern_phrase": "fluctuating stable, then steady stable, then rise", "rank_target_idx": 0, "rank_kind": "stable", "rank_adj": "fluctuating", "rank_axis": "std", "rank_direction": "DESC", "rank_extreme": "most fluctuating", "top_segments_meta": [ { "adj": "fluctuating", "kind": "stable", "label": "fluctuating stable", "start_idx": 0, "end_idx": 29 }, { "adj": "steady", "kind": "stable", "label": "steady stable", "start_idx": 29, "end_idx": 74 }, { "adj": "none", "kind": "rise", "label": "none rise", "start_idx": 74, "end_idx": 98 } ], "points_per_day": 96, "pattern": "fluctuating stable, then steady stable, then rise", "year": 2020, "top_k": [ "2020-10-07", "2020-12-08", "2020-03-27", "2020-09-05", "2020-08-01" ], "all_injections": [ { "rank": 1, "intensity": 3.0, "date": "2020-10-07" }, { "rank": 2, "intensity": 2.9693877551020407, "date": "2020-12-08" }, { "rank": 3, "intensity": 2.938775510204082, "date": "2020-03-27" }, { "rank": 4, "intensity": 2.9081632653061225, "date": "2020-09-05" }, { "rank": 5, "intensity": 2.877551020408163, "date": "2020-08-01" }, { "rank": 6, "intensity": 2.8469387755102042, "date": "2020-11-07" }, { "rank": 7, "intensity": 2.816326530612245, "date": "2020-11-21" }, { "rank": 8, "intensity": 2.7857142857142856, "date": "2020-10-18" }, { "rank": 9, "intensity": 2.7551020408163267, "date": "2020-01-05" }, { "rank": 10, "intensity": 2.7244897959183674, "date": "2020-05-24" }, { "rank": 11, "intensity": 2.693877551020408, "date": "2020-08-07" }, { "rank": 12, "intensity": 2.663265306122449, "date": "2020-03-14" }, { "rank": 13, "intensity": 2.63265306122449, "date": "2020-09-01" }, { "rank": 14, "intensity": 2.6020408163265305, "date": "2020-03-12" }, { "rank": 15, "intensity": 2.5714285714285716, "date": "2020-06-09" }, { "rank": 16, "intensity": 2.5408163265306123, "date": "2020-09-12" }, { "rank": 17, "intensity": 2.510204081632653, "date": "2020-05-14" }, { "rank": 18, "intensity": 2.479591836734694, "date": "2020-02-14" }, { "rank": 19, "intensity": 2.4489795918367347, "date": "2020-01-12" }, { "rank": 20, "intensity": 2.4183673469387754, "date": "2020-07-09" }, { "rank": 21, "intensity": 2.387755102040816, "date": "2020-02-09" }, { "rank": 22, "intensity": 2.357142857142857, "date": "2020-09-18" }, { "rank": 23, "intensity": 2.326530612244898, "date": "2020-04-04" }, { "rank": 24, "intensity": 2.295918367346939, "date": "2020-08-11" }, { "rank": 25, "intensity": 2.2653061224489797, "date": "2020-01-27" }, { "rank": 26, "intensity": 2.2346938775510203, "date": "2020-12-21" }, { "rank": 27, "intensity": 2.204081632653061, "date": "2020-11-10" }, { "rank": 28, "intensity": 2.173469387755102, "date": "2020-06-12" }, { "rank": 29, "intensity": 2.142857142857143, "date": "2020-08-22" }, { "rank": 30, "intensity": 2.112244897959184, "date": "2020-06-20" }, { "rank": 31, "intensity": 2.0816326530612246, "date": "2020-05-02" }, { "rank": 32, "intensity": 2.0510204081632653, "date": "2020-07-06" }, { "rank": 33, "intensity": 2.020408163265306, "date": "2020-12-26" }, { "rank": 34, "intensity": 1.989795918367347, "date": "2020-11-23" }, { "rank": 35, "intensity": 1.9591836734693877, "date": "2020-03-03" }, { "rank": 36, "intensity": 1.9285714285714286, "date": "2020-10-28" }, { "rank": 37, "intensity": 1.8979591836734695, "date": "2020-12-15" }, { "rank": 38, "intensity": 1.8673469387755102, "date": "2020-02-20" }, { "rank": 39, "intensity": 1.836734693877551, "date": "2020-05-05" }, { "rank": 40, "intensity": 1.806122448979592, "date": "2020-11-29" }, { "rank": 41, "intensity": 1.7755102040816326, "date": "2020-01-30" }, { "rank": 42, "intensity": 1.7448979591836735, "date": "2020-12-05" }, { "rank": 43, "intensity": 1.7142857142857144, "date": "2020-02-07" }, { "rank": 44, "intensity": 1.683673469387755, "date": "2020-08-29" }, { "rank": 45, "intensity": 1.653061224489796, "date": "2020-11-26" }, { "rank": 46, "intensity": 1.6224489795918369, "date": "2020-07-11" }, { "rank": 47, "intensity": 1.5918367346938775, "date": "2020-10-20" }, { "rank": 48, "intensity": 1.5612244897959184, "date": "2020-07-02" }, { "rank": 49, "intensity": 1.5306122448979593, "date": "2020-02-12" }, { "rank": 50, "intensity": 1.5, "date": "2020-12-11" } ], "source": "causal_rivers" } }, { "id": "L3_T1_Composite_Trend_00100", "level": 3, "level_name": "Semantic Reasoning", "category": "Composite Trend", "subtask": "Composite Trend", "question": "Among days in channel 894 during 2020 that exhibit the trend pattern 'fall, then rapid rise, then fluctuating stable, then fall', identify the top-5 days where the rapid rise segment is the fastest. (Output format: a ranked list of dates, e.g., ['YYYY-MM-DD', ...])", "answer": "['2020-05-12', '2020-09-15', '2020-09-23', '2020-11-04', '2020-05-22']", "ground_truth": [ "2020-05-12", "2020-09-15", "2020-09-23", "2020-11-04", "2020-05-22" ], "eval_metric": "set_f1", "channel": "894", "ts_data_path": "ts_data/L3_T1_Composite_Trend_00100.csv", "meta": { "segments": [ { "kind": "fall", "adj": "none" }, { "kind": "rise", "adj": "rapid" }, { "kind": "stable", "adj": "fluctuating" }, { "kind": "fall", "adj": "none" } ], "pattern_phrase": "fall, then rapid rise, then fluctuating stable, then fall", "rank_target_idx": 1, "rank_kind": "rise", "rank_adj": "rapid", "rank_axis": "slope", "rank_direction": "DESC", "rank_extreme": "fastest", "top_segments_meta": [ { "adj": "none", "kind": "fall", "label": "none fall", "start_idx": 0, "end_idx": 24 }, { "adj": "rapid", "kind": "rise", "label": "rapid rise", "start_idx": 24, "end_idx": 38 }, { "adj": "fluctuating", "kind": "stable", "label": "fluctuating stable", "start_idx": 38, "end_idx": 72 }, { "adj": "none", "kind": "fall", "label": "none fall", "start_idx": 72, "end_idx": 98 } ], "points_per_day": 96, "pattern": "fall, then rapid rise, then fluctuating stable, then fall", "year": 2020, "top_k": [ "2020-05-12", "2020-09-15", "2020-09-23", "2020-11-04", "2020-05-22" ], "all_injections": [ { "rank": 1, "intensity": 3.0, "date": "2020-05-12" }, { "rank": 2, "intensity": 2.9693877551020407, "date": "2020-09-15" }, { "rank": 3, "intensity": 2.938775510204082, "date": "2020-09-23" }, { "rank": 4, "intensity": 2.9081632653061225, "date": "2020-11-04" }, { "rank": 5, "intensity": 2.877551020408163, "date": "2020-05-22" }, { "rank": 6, "intensity": 2.8469387755102042, "date": "2020-04-21" }, { "rank": 7, "intensity": 2.816326530612245, "date": "2020-11-15" }, { "rank": 8, "intensity": 2.7857142857142856, "date": "2020-02-03" }, { "rank": 9, "intensity": 2.7551020408163267, "date": "2020-06-12" }, { "rank": 10, "intensity": 2.7244897959183674, "date": "2020-01-22" }, { "rank": 11, "intensity": 2.693877551020408, "date": "2020-04-14" }, { "rank": 12, "intensity": 2.663265306122449, "date": "2020-10-03" }, { "rank": 13, "intensity": 2.63265306122449, "date": "2020-12-22" }, { "rank": 14, "intensity": 2.6020408163265305, "date": "2020-02-25" }, { "rank": 15, "intensity": 2.5714285714285716, "date": "2020-01-02" }, { "rank": 16, "intensity": 2.5408163265306123, "date": "2020-06-26" }, { "rank": 17, "intensity": 2.510204081632653, "date": "2020-04-29" }, { "rank": 18, "intensity": 2.479591836734694, "date": "2020-03-18" }, { "rank": 19, "intensity": 2.4489795918367347, "date": "2020-06-19" }, { "rank": 20, "intensity": 2.4183673469387754, "date": "2020-07-26" }, { "rank": 21, "intensity": 2.387755102040816, "date": "2020-07-22" }, { "rank": 22, "intensity": 2.357142857142857, "date": "2020-05-24" }, { "rank": 23, "intensity": 2.326530612244898, "date": "2020-09-02" }, { "rank": 24, "intensity": 2.295918367346939, "date": "2020-07-28" }, { "rank": 25, "intensity": 2.2653061224489797, "date": "2020-08-03" }, { "rank": 26, "intensity": 2.2346938775510203, "date": "2020-02-19" }, { "rank": 27, "intensity": 2.204081632653061, "date": "2020-09-27" }, { "rank": 28, "intensity": 2.173469387755102, "date": "2020-11-11" }, { "rank": 29, "intensity": 2.142857142857143, "date": "2020-06-02" }, { "rank": 30, "intensity": 2.112244897959184, "date": "2020-08-15" }, { "rank": 31, "intensity": 2.0816326530612246, "date": "2020-10-24" }, { "rank": 32, "intensity": 2.0510204081632653, "date": "2020-05-15" }, { "rank": 33, "intensity": 2.020408163265306, "date": "2020-04-23" }, { "rank": 34, "intensity": 1.989795918367347, "date": "2020-07-08" }, { "rank": 35, "intensity": 1.9591836734693877, "date": "2020-11-20" }, { "rank": 36, "intensity": 1.9285714285714286, "date": "2020-03-26" }, { "rank": 37, "intensity": 1.8979591836734695, "date": "2020-01-30" }, { "rank": 38, "intensity": 1.8673469387755102, "date": "2020-03-15" }, { "rank": 39, "intensity": 1.836734693877551, "date": "2020-05-20" }, { "rank": 40, "intensity": 1.806122448979592, "date": "2020-08-06" }, { "rank": 41, "intensity": 1.7755102040816326, "date": "2020-05-09" }, { "rank": 42, "intensity": 1.7448979591836735, "date": "2020-12-19" }, { "rank": 43, "intensity": 1.7142857142857144, "date": "2020-08-12" }, { "rank": 44, "intensity": 1.683673469387755, "date": "2020-08-09" }, { "rank": 45, "intensity": 1.653061224489796, "date": "2020-04-08" }, { "rank": 46, "intensity": 1.6224489795918369, "date": "2020-07-11" }, { "rank": 47, "intensity": 1.5918367346938775, "date": "2020-06-24" }, { "rank": 48, "intensity": 1.5612244897959184, "date": "2020-02-27" }, { "rank": 49, "intensity": 1.5306122448979593, "date": "2020-04-18" }, { "rank": 50, "intensity": 1.5, "date": "2020-11-22" } ], "source": "causal_rivers" } }, { "id": "L3_T1_Composite_Trend_00101", "level": 3, "level_name": "Semantic Reasoning", "category": "Composite Trend", "subtask": "Composite Trend", "question": "Among days in channel 895 during 2022 that exhibit the trend pattern 'rapid rise, then slow rise, then steady stable', identify the top-5 days where the rapid rise segment is the fastest. (Output format: a ranked list of dates, e.g., ['YYYY-MM-DD', ...])", "answer": "['2022-08-10', '2022-05-04', '2022-11-14', '2022-02-05', '2022-04-14']", "ground_truth": [ "2022-08-10", "2022-05-04", "2022-11-14", "2022-02-05", "2022-04-14" ], "eval_metric": "set_f1", "channel": "895", "ts_data_path": "ts_data/L3_T1_Composite_Trend_00101.csv", "meta": { "segments": [ { "kind": "rise", "adj": "rapid" }, { "kind": "rise", "adj": "slow" }, { "kind": "stable", "adj": "steady" } ], "pattern_phrase": "rapid rise, then slow rise, then steady stable", "rank_target_idx": 0, "rank_kind": "rise", "rank_adj": "rapid", "rank_axis": "slope", "rank_direction": "DESC", "rank_extreme": "fastest", "top_segments_meta": [ { "adj": "rapid", "kind": "rise", "label": "rapid rise", "start_idx": 0, "end_idx": 10 }, { "adj": "slow", "kind": "rise", "label": "slow rise", "start_idx": 10, "end_idx": 50 }, { "adj": "steady", "kind": "stable", "label": "steady stable", "start_idx": 50, "end_idx": 98 } ], "points_per_day": 96, "pattern": "rapid rise, then slow rise, then steady stable", "year": 2022, "top_k": [ "2022-08-10", "2022-05-04", "2022-11-14", "2022-02-05", "2022-04-14" ], "all_injections": [ { "rank": 1, "intensity": 3.0, "date": "2022-08-10" }, { "rank": 2, "intensity": 2.9693877551020407, "date": "2022-05-04" }, { "rank": 3, "intensity": 2.938775510204082, "date": "2022-11-14" }, { "rank": 4, "intensity": 2.9081632653061225, "date": "2022-02-05" }, { "rank": 5, "intensity": 2.877551020408163, "date": "2022-04-14" }, { "rank": 6, "intensity": 2.8469387755102042, "date": "2022-02-02" }, { "rank": 7, "intensity": 2.816326530612245, "date": "2022-09-09" }, { "rank": 8, "intensity": 2.7857142857142856, "date": "2022-06-10" }, { "rank": 9, "intensity": 2.7551020408163267, "date": "2022-11-06" }, { "rank": 10, "intensity": 2.7244897959183674, "date": "2022-10-12" }, { "rank": 11, "intensity": 2.693877551020408, "date": "2022-08-18" }, { "rank": 12, "intensity": 2.663265306122449, "date": "2022-05-14" }, { "rank": 13, "intensity": 2.63265306122449, "date": "2022-05-23" }, { "rank": 14, "intensity": 2.6020408163265305, "date": "2022-04-04" }, { "rank": 15, "intensity": 2.5714285714285716, "date": "2022-11-23" }, { "rank": 16, "intensity": 2.5408163265306123, "date": "2022-03-27" }, { "rank": 17, "intensity": 2.510204081632653, "date": "2022-12-30" }, { "rank": 18, "intensity": 2.479591836734694, "date": "2022-10-27" }, { "rank": 19, "intensity": 2.4489795918367347, "date": "2022-08-20" }, { "rank": 20, "intensity": 2.4183673469387754, "date": "2022-02-27" }, { "rank": 21, "intensity": 2.387755102040816, "date": "2022-04-22" }, { "rank": 22, "intensity": 2.357142857142857, "date": "2022-07-09" }, { "rank": 23, "intensity": 2.326530612244898, "date": "2022-11-08" }, { "rank": 24, "intensity": 2.295918367346939, "date": "2022-08-03" }, { "rank": 25, "intensity": 2.2653061224489797, "date": "2022-03-13" }, { "rank": 26, "intensity": 2.2346938775510203, "date": "2022-12-25" }, { "rank": 27, "intensity": 2.204081632653061, "date": "2022-08-29" }, { "rank": 28, "intensity": 2.173469387755102, "date": "2022-06-08" }, { "rank": 29, "intensity": 2.142857142857143, "date": "2022-04-16" }, { "rank": 30, "intensity": 2.112244897959184, "date": "2022-05-28" }, { "rank": 31, "intensity": 2.0816326530612246, "date": "2022-07-29" }, { "rank": 32, "intensity": 2.0510204081632653, "date": "2022-01-22" }, { "rank": 33, "intensity": 2.020408163265306, "date": "2022-08-08" }, { "rank": 34, "intensity": 1.989795918367347, "date": "2022-02-07" }, { "rank": 35, "intensity": 1.9591836734693877, "date": "2022-05-19" }, { "rank": 36, "intensity": 1.9285714285714286, "date": "2022-07-27" }, { "rank": 37, "intensity": 1.8979591836734695, "date": "2022-11-04" }, { "rank": 38, "intensity": 1.8673469387755102, "date": "2022-09-16" }, { "rank": 39, "intensity": 1.836734693877551, "date": "2022-07-04" }, { "rank": 40, "intensity": 1.806122448979592, "date": "2022-07-15" }, { "rank": 41, "intensity": 1.7755102040816326, "date": "2022-05-25" }, { "rank": 42, "intensity": 1.7448979591836735, "date": "2022-11-10" }, { "rank": 43, "intensity": 1.7142857142857144, "date": "2022-05-01" }, { "rank": 44, "intensity": 1.683673469387755, "date": "2022-06-24" }, { "rank": 45, "intensity": 1.653061224489796, "date": "2022-12-23" }, { "rank": 46, "intensity": 1.6224489795918369, "date": "2022-01-15" }, { "rank": 47, "intensity": 1.5918367346938775, "date": "2022-01-25" }, { "rank": 48, "intensity": 1.5612244897959184, "date": "2022-04-20" }, { "rank": 49, "intensity": 1.5306122448979593, "date": "2022-05-31" }, { "rank": 50, "intensity": 1.5, "date": "2022-05-08" } ], "source": "causal_rivers" } }, { "id": "L3_T1_Composite_Trend_00102", "level": 3, "level_name": "Semantic Reasoning", "category": "Composite Trend", "subtask": "Composite Trend", "question": "Among days in channel 897 during 2019 that exhibit the trend pattern 'steady stable, then rapid rise, then rise', identify the top-5 days where the rapid rise segment is the fastest. (Output format: a ranked list of dates, e.g., ['YYYY-MM-DD', ...])", "answer": "['2019-12-19', '2019-06-13', '2019-06-26', '2019-10-24', '2019-07-12']", "ground_truth": [ "2019-12-19", "2019-06-13", "2019-06-26", "2019-10-24", "2019-07-12" ], "eval_metric": "set_f1", "channel": "897", "ts_data_path": "ts_data/L3_T1_Composite_Trend_00102.csv", "meta": { "segments": [ { "kind": "stable", "adj": "steady" }, { "kind": "rise", "adj": "rapid" }, { "kind": "rise", "adj": "none" } ], "pattern_phrase": "steady stable, then rapid rise, then rise", "rank_target_idx": 1, "rank_kind": "rise", "rank_adj": "rapid", "rank_axis": "slope", "rank_direction": "DESC", "rank_extreme": "fastest", "top_segments_meta": [ { "adj": "steady", "kind": "stable", "label": "steady stable", "start_idx": 0, "end_idx": 57 }, { "adj": "rapid", "kind": "rise", "label": "rapid rise", "start_idx": 57, "end_idx": 71 }, { "adj": "none", "kind": "rise", "label": "none rise", "start_idx": 71, "end_idx": 98 } ], "points_per_day": 96, "pattern": "steady stable, then rapid rise, then rise", "year": 2019, "top_k": [ "2019-12-19", "2019-06-13", "2019-06-26", "2019-10-24", "2019-07-12" ], "all_injections": [ { "rank": 1, "intensity": 3.0, "date": "2019-12-19" }, { "rank": 2, "intensity": 2.9693877551020407, "date": "2019-06-13" }, { "rank": 3, "intensity": 2.938775510204082, "date": "2019-06-26" }, { "rank": 4, "intensity": 2.9081632653061225, "date": "2019-10-24" }, { "rank": 5, "intensity": 2.877551020408163, "date": "2019-07-12" }, { "rank": 6, "intensity": 2.8469387755102042, "date": "2019-12-13" }, { "rank": 7, "intensity": 2.816326530612245, "date": "2019-11-20" }, { "rank": 8, "intensity": 2.7857142857142856, "date": "2019-06-22" }, { "rank": 9, "intensity": 2.7551020408163267, "date": "2019-02-17" }, { "rank": 10, "intensity": 2.7244897959183674, "date": "2019-03-02" }, { "rank": 11, "intensity": 2.693877551020408, "date": "2019-12-24" }, { "rank": 12, "intensity": 2.663265306122449, "date": "2019-06-29" }, { "rank": 13, "intensity": 2.63265306122449, "date": "2019-01-14" }, { "rank": 14, "intensity": 2.6020408163265305, "date": "2019-05-30" }, { "rank": 15, "intensity": 2.5714285714285716, "date": "2019-06-20" }, { "rank": 16, "intensity": 2.5408163265306123, "date": "2019-05-22" }, { "rank": 17, "intensity": 2.510204081632653, "date": "2019-10-04" }, { "rank": 18, "intensity": 2.479591836734694, "date": "2019-11-11" }, { "rank": 19, "intensity": 2.4489795918367347, "date": "2019-11-30" }, { "rank": 20, "intensity": 2.4183673469387754, "date": "2019-06-03" }, { "rank": 21, "intensity": 2.387755102040816, "date": "2019-07-08" }, { "rank": 22, "intensity": 2.357142857142857, "date": "2019-02-06" }, { "rank": 23, "intensity": 2.326530612244898, "date": "2019-10-18" }, { "rank": 24, "intensity": 2.295918367346939, "date": "2019-12-21" }, { "rank": 25, "intensity": 2.2653061224489797, "date": "2019-04-17" }, { "rank": 26, "intensity": 2.2346938775510203, "date": "2019-04-26" }, { "rank": 27, "intensity": 2.204081632653061, "date": "2019-04-12" }, { "rank": 28, "intensity": 2.173469387755102, "date": "2019-01-25" }, { "rank": 29, "intensity": 2.142857142857143, "date": "2019-05-25" }, { "rank": 30, "intensity": 2.112244897959184, "date": "2019-01-23" }, { "rank": 31, "intensity": 2.0816326530612246, "date": "2019-07-05" }, { "rank": 32, "intensity": 2.0510204081632653, "date": "2019-08-05" }, { "rank": 33, "intensity": 2.020408163265306, "date": "2019-08-30" }, { "rank": 34, "intensity": 1.989795918367347, "date": "2019-08-15" }, { "rank": 35, "intensity": 1.9591836734693877, "date": "2019-03-20" }, { "rank": 36, "intensity": 1.9285714285714286, "date": "2019-02-14" }, { "rank": 37, "intensity": 1.8979591836734695, "date": "2019-01-12" }, { "rank": 38, "intensity": 1.8673469387755102, "date": "2019-09-13" }, { "rank": 39, "intensity": 1.836734693877551, "date": "2019-06-18" }, { "rank": 40, "intensity": 1.806122448979592, "date": "2019-04-06" }, { "rank": 41, "intensity": 1.7755102040816326, "date": "2019-01-10" }, { "rank": 42, "intensity": 1.7448979591836735, "date": "2019-07-20" }, { "rank": 43, "intensity": 1.7142857142857144, "date": "2019-11-03" }, { "rank": 44, "intensity": 1.683673469387755, "date": "2019-05-19" }, { "rank": 45, "intensity": 1.653061224489796, "date": "2019-03-27" }, { "rank": 46, "intensity": 1.6224489795918369, "date": "2019-05-15" }, { "rank": 47, "intensity": 1.5918367346938775, "date": "2019-12-26" }, { "rank": 48, "intensity": 1.5612244897959184, "date": "2019-07-03" }, { "rank": 49, "intensity": 1.5306122448979593, "date": "2019-01-30" }, { "rank": 50, "intensity": 1.5, "date": "2019-08-28" } ], "source": "causal_rivers" } }, { "id": "L3_T1_Composite_Trend_00103", "level": 3, "level_name": "Semantic Reasoning", "category": "Composite Trend", "subtask": "Composite Trend", "question": "Among days in channel 933 during 2019 that exhibit the trend pattern 'rapid fall, then steady stable', identify the top-5 days where the rapid fall segment is the fastest. (Output format: a ranked list of dates, e.g., ['YYYY-MM-DD', ...])", "answer": "['2019-12-06', '2019-03-15', '2019-08-07', '2019-06-08', '2019-12-15']", "ground_truth": [ "2019-12-06", "2019-03-15", "2019-08-07", "2019-06-08", "2019-12-15" ], "eval_metric": "set_f1", "channel": "933", "ts_data_path": "ts_data/L3_T1_Composite_Trend_00103.csv", "meta": { "segments": [ { "kind": "fall", "adj": "rapid" }, { "kind": "stable", "adj": "steady" } ], "pattern_phrase": "rapid fall, then steady stable", "rank_target_idx": 0, "rank_kind": "fall", "rank_adj": "rapid", "rank_axis": "slope", "rank_direction": "DESC", "rank_extreme": "fastest", "top_segments_meta": [ { "adj": "rapid", "kind": "fall", "label": "rapid fall", "start_idx": 0, "end_idx": 20 }, { "adj": "steady", "kind": "stable", "label": "steady stable", "start_idx": 20, "end_idx": 98 } ], "points_per_day": 96, "pattern": "rapid fall, then steady stable", "year": 2019, "top_k": [ "2019-12-06", "2019-03-15", "2019-08-07", "2019-06-08", "2019-12-15" ], "all_injections": [ { "rank": 1, "intensity": 3.0, "date": "2019-12-06" }, { "rank": 2, "intensity": 2.9693877551020407, "date": "2019-03-15" }, { "rank": 3, "intensity": 2.938775510204082, "date": "2019-08-07" }, { "rank": 4, "intensity": 2.9081632653061225, "date": "2019-06-08" }, { "rank": 5, "intensity": 2.877551020408163, "date": "2019-12-15" }, { "rank": 6, "intensity": 2.8469387755102042, "date": "2019-12-20" }, { "rank": 7, "intensity": 2.816326530612245, "date": "2019-10-27" }, { "rank": 8, "intensity": 2.7857142857142856, "date": "2019-04-05" }, { "rank": 9, "intensity": 2.7551020408163267, "date": "2019-06-13" }, { "rank": 10, "intensity": 2.7244897959183674, "date": "2019-08-21" }, { "rank": 11, "intensity": 2.693877551020408, "date": "2019-05-17" }, { "rank": 12, "intensity": 2.663265306122449, "date": "2019-10-06" }, { "rank": 13, "intensity": 2.63265306122449, "date": "2019-02-16" }, { "rank": 14, "intensity": 2.6020408163265305, "date": "2019-09-23" }, { "rank": 15, "intensity": 2.5714285714285716, "date": "2019-06-17" }, { "rank": 16, "intensity": 2.5408163265306123, "date": "2019-08-18" }, { "rank": 17, "intensity": 2.510204081632653, "date": "2019-03-06" }, { "rank": 18, "intensity": 2.479591836734694, "date": "2019-07-01" }, { "rank": 19, "intensity": 2.4489795918367347, "date": "2019-10-04" }, { "rank": 20, "intensity": 2.4183673469387754, "date": "2019-07-13" }, { "rank": 21, "intensity": 2.387755102040816, "date": "2019-08-16" }, { "rank": 22, "intensity": 2.357142857142857, "date": "2019-12-29" }, { "rank": 23, "intensity": 2.326530612244898, "date": "2019-05-31" }, { "rank": 24, "intensity": 2.295918367346939, "date": "2019-12-03" }, { "rank": 25, "intensity": 2.2653061224489797, "date": "2019-11-30" }, { "rank": 26, "intensity": 2.2346938775510203, "date": "2019-11-03" }, { "rank": 27, "intensity": 2.204081632653061, "date": "2019-12-09" }, { "rank": 28, "intensity": 2.173469387755102, "date": "2019-10-29" }, { "rank": 29, "intensity": 2.142857142857143, "date": "2019-09-15" }, { "rank": 30, "intensity": 2.112244897959184, "date": "2019-07-11" }, { "rank": 31, "intensity": 2.0816326530612246, "date": "2019-09-01" }, { "rank": 32, "intensity": 2.0510204081632653, "date": "2019-07-28" }, { "rank": 33, "intensity": 2.020408163265306, "date": "2019-09-27" }, { "rank": 34, "intensity": 1.989795918367347, "date": "2019-10-16" }, { "rank": 35, "intensity": 1.9591836734693877, "date": "2019-05-02" }, { "rank": 36, "intensity": 1.9285714285714286, "date": "2019-12-18" }, { "rank": 37, "intensity": 1.8979591836734695, "date": "2019-11-25" }, { "rank": 38, "intensity": 1.8673469387755102, "date": "2019-06-29" }, { "rank": 39, "intensity": 1.836734693877551, "date": "2019-01-26" }, { "rank": 40, "intensity": 1.806122448979592, "date": "2019-11-15" }, { "rank": 41, "intensity": 1.7755102040816326, "date": "2019-01-29" }, { "rank": 42, "intensity": 1.7448979591836735, "date": "2019-10-08" }, { "rank": 43, "intensity": 1.7142857142857144, "date": "2019-08-24" }, { "rank": 44, "intensity": 1.683673469387755, "date": "2019-09-08" }, { "rank": 45, "intensity": 1.653061224489796, "date": "2019-09-12" }, { "rank": 46, "intensity": 1.6224489795918369, "date": "2019-02-12" }, { "rank": 47, "intensity": 1.5918367346938775, "date": "2019-05-15" }, { "rank": 48, "intensity": 1.5612244897959184, "date": "2019-01-15" }, { "rank": 49, "intensity": 1.5306122448979593, "date": "2019-02-23" }, { "rank": 50, "intensity": 1.5, "date": "2019-05-29" } ], "source": "causal_rivers" } }, { "id": "L3_T1_Composite_Trend_00104", "level": 3, "level_name": "Semantic Reasoning", "category": "Composite Trend", "subtask": "Composite Trend", "question": "Among days in channel HUFL during 2017 that exhibit the trend pattern 'fluctuating stable, then rapid fall, then steady stable', identify the top-5 days where the fluctuating stable segment is the most fluctuating. (Output format: a ranked list of dates, e.g., ['YYYY-MM-DD', ...])", "answer": "['2017-06-12', '2017-12-26', '2017-07-02', '2017-08-26', '2017-04-07']", "ground_truth": [ "2017-06-12", "2017-12-26", "2017-07-02", "2017-08-26", "2017-04-07" ], "eval_metric": "set_f1", "channel": "HUFL", "ts_data_path": "ts_data/L3_T1_Composite_Trend_00104.csv", "meta": { "segments": [ { "kind": "stable", "adj": "fluctuating" }, { "kind": "fall", "adj": "rapid" }, { "kind": "stable", "adj": "steady" } ], "pattern_phrase": "fluctuating stable, then rapid fall, then steady stable", "rank_target_idx": 0, "rank_kind": "stable", "rank_adj": "fluctuating", "rank_axis": "std", "rank_direction": "DESC", "rank_extreme": "most fluctuating", "top_segments_meta": [ { "adj": "fluctuating", "kind": "stable", "label": "fluctuating stable", "start_idx": 0, "end_idx": 35 }, { "adj": "rapid", "kind": "fall", "label": "rapid fall", "start_idx": 35, "end_idx": 49 }, { "adj": "steady", "kind": "stable", "label": "steady stable", "start_idx": 49, "end_idx": 98 } ], "points_per_day": 96, "pattern": "fluctuating stable, then rapid fall, then steady stable", "year": 2017, "top_k": [ "2017-06-12", "2017-12-26", "2017-07-02", "2017-08-26", "2017-04-07" ], "all_injections": [ { "rank": 1, "intensity": 3.0, "date": "2017-06-12" }, { "rank": 2, "intensity": 2.9693877551020407, "date": "2017-12-26" }, { "rank": 3, "intensity": 2.938775510204082, "date": "2017-07-02" }, { "rank": 4, "intensity": 2.9081632653061225, "date": "2017-08-26" }, { "rank": 5, "intensity": 2.877551020408163, "date": "2017-04-07" }, { "rank": 6, "intensity": 2.8469387755102042, "date": "2017-09-30" }, { "rank": 7, "intensity": 2.816326530612245, "date": "2017-02-13" }, { "rank": 8, "intensity": 2.7857142857142856, "date": "2017-02-07" }, { "rank": 9, "intensity": 2.7551020408163267, "date": "2017-07-29" }, { "rank": 10, "intensity": 2.7244897959183674, "date": "2017-07-23" }, { "rank": 11, "intensity": 2.693877551020408, "date": "2017-06-25" }, { "rank": 12, "intensity": 2.663265306122449, "date": "2017-12-16" }, { "rank": 13, "intensity": 2.63265306122449, "date": "2017-04-10" }, { "rank": 14, "intensity": 2.6020408163265305, "date": "2017-11-25" }, { "rank": 15, "intensity": 2.5714285714285716, "date": "2017-10-25" }, { "rank": 16, "intensity": 2.5408163265306123, "date": "2017-12-19" }, { "rank": 17, "intensity": 2.510204081632653, "date": "2017-08-02" }, { "rank": 18, "intensity": 2.479591836734694, "date": "2017-07-06" }, { "rank": 19, "intensity": 2.4489795918367347, "date": "2017-07-20" }, { "rank": 20, "intensity": 2.4183673469387754, "date": "2017-02-10" }, { "rank": 21, "intensity": 2.387755102040816, "date": "2017-12-03" }, { "rank": 22, "intensity": 2.357142857142857, "date": "2017-05-29" }, { "rank": 23, "intensity": 2.326530612244898, "date": "2017-01-24" }, { "rank": 24, "intensity": 2.295918367346939, "date": "2017-10-16" }, { "rank": 25, "intensity": 2.2653061224489797, "date": "2017-12-13" }, { "rank": 26, "intensity": 2.2346938775510203, "date": "2017-04-05" }, { "rank": 27, "intensity": 2.204081632653061, "date": "2017-01-31" }, { "rank": 28, "intensity": 2.173469387755102, "date": "2017-10-04" }, { "rank": 29, "intensity": 2.142857142857143, "date": "2017-08-14" }, { "rank": 30, "intensity": 2.112244897959184, "date": "2017-06-06" }, { "rank": 31, "intensity": 2.0816326530612246, "date": "2017-03-17" }, { "rank": 32, "intensity": 2.0510204081632653, "date": "2017-11-19" }, { "rank": 33, "intensity": 2.020408163265306, "date": "2017-01-29" }, { "rank": 34, "intensity": 1.989795918367347, "date": "2017-01-16" }, { "rank": 35, "intensity": 1.9591836734693877, "date": "2017-02-16" }, { "rank": 36, "intensity": 1.9285714285714286, "date": "2017-05-17" }, { "rank": 37, "intensity": 1.8979591836734695, "date": "2017-07-13" }, { "rank": 38, "intensity": 1.8673469387755102, "date": "2017-03-27" }, { "rank": 39, "intensity": 1.836734693877551, "date": "2017-08-09" }, { "rank": 40, "intensity": 1.806122448979592, "date": "2017-03-14" }, { "rank": 41, "intensity": 1.7755102040816326, "date": "2017-06-30" }, { "rank": 42, "intensity": 1.7448979591836735, "date": "2017-07-04" }, { "rank": 43, "intensity": 1.7142857142857144, "date": "2017-01-03" }, { "rank": 44, "intensity": 1.683673469387755, "date": "2017-11-05" }, { "rank": 45, "intensity": 1.653061224489796, "date": "2017-08-21" }, { "rank": 46, "intensity": 1.6224489795918369, "date": "2017-02-22" }, { "rank": 47, "intensity": 1.5918367346938775, "date": "2017-10-11" }, { "rank": 48, "intensity": 1.5612244897959184, "date": "2017-11-21" }, { "rank": 49, "intensity": 1.5306122448979593, "date": "2017-04-25" }, { "rank": 50, "intensity": 1.5, "date": "2017-09-23" } ], "source": "ettm1" } }, { "id": "L3_T1_Composite_Trend_00105", "level": 3, "level_name": "Semantic Reasoning", "category": "Composite Trend", "subtask": "Composite Trend", "question": "Among days in channel HULL during 2017 that exhibit the trend pattern 'fluctuating stable, then rise', identify the top-5 days where the fluctuating stable segment is the most fluctuating. (Output format: a ranked list of dates, e.g., ['YYYY-MM-DD', ...])", "answer": "['2017-06-12', '2017-01-19', '2017-05-27', '2017-09-25', '2017-06-22']", "ground_truth": [ "2017-06-12", "2017-01-19", "2017-05-27", "2017-09-25", "2017-06-22" ], "eval_metric": "set_f1", "channel": "HULL", "ts_data_path": "ts_data/L3_T1_Composite_Trend_00105.csv", "meta": { "segments": [ { "kind": "stable", "adj": "fluctuating" }, { "kind": "rise", "adj": "none" } ], "pattern_phrase": "fluctuating stable, then rise", "rank_target_idx": 0, "rank_kind": "stable", "rank_adj": "fluctuating", "rank_axis": "std", "rank_direction": "DESC", "rank_extreme": "most fluctuating", "top_segments_meta": [ { "adj": "fluctuating", "kind": "stable", "label": "fluctuating stable", "start_idx": 0, "end_idx": 53 }, { "adj": "none", "kind": "rise", "label": "none rise", "start_idx": 53, "end_idx": 98 } ], "points_per_day": 96, "pattern": "fluctuating stable, then rise", "year": 2017, "top_k": [ "2017-06-12", "2017-01-19", "2017-05-27", "2017-09-25", "2017-06-22" ], "all_injections": [ { "rank": 1, "intensity": 3.0, "date": "2017-06-12" }, { "rank": 2, "intensity": 2.9693877551020407, "date": "2017-01-19" }, { "rank": 3, "intensity": 2.938775510204082, "date": "2017-05-27" }, { "rank": 4, "intensity": 2.9081632653061225, "date": "2017-09-25" }, { "rank": 5, "intensity": 2.877551020408163, "date": "2017-06-22" }, { "rank": 6, "intensity": 2.8469387755102042, "date": "2017-10-12" }, { "rank": 7, "intensity": 2.816326530612245, "date": "2017-02-06" }, { "rank": 8, "intensity": 2.7857142857142856, "date": "2017-06-05" }, { "rank": 9, "intensity": 2.7551020408163267, "date": "2017-11-26" }, { "rank": 10, "intensity": 2.7244897959183674, "date": "2017-07-10" }, { "rank": 11, "intensity": 2.693877551020408, "date": "2017-02-11" }, { "rank": 12, "intensity": 2.663265306122449, "date": "2017-09-22" }, { "rank": 13, "intensity": 2.63265306122449, "date": "2017-08-30" }, { "rank": 14, "intensity": 2.6020408163265305, "date": "2017-04-23" }, { "rank": 15, "intensity": 2.5714285714285716, "date": "2017-07-17" }, { "rank": 16, "intensity": 2.5408163265306123, "date": "2017-04-06" }, { "rank": 17, "intensity": 2.510204081632653, "date": "2017-05-08" }, { "rank": 18, "intensity": 2.479591836734694, "date": "2017-11-10" }, { "rank": 19, "intensity": 2.4489795918367347, "date": "2017-07-02" }, { "rank": 20, "intensity": 2.4183673469387754, "date": "2017-04-12" }, { "rank": 21, "intensity": 2.387755102040816, "date": "2017-09-01" }, { "rank": 22, "intensity": 2.357142857142857, "date": "2017-12-22" }, { "rank": 23, "intensity": 2.326530612244898, "date": "2017-06-27" }, { "rank": 24, "intensity": 2.295918367346939, "date": "2017-08-21" }, { "rank": 25, "intensity": 2.2653061224489797, "date": "2017-01-14" }, { "rank": 26, "intensity": 2.2346938775510203, "date": "2017-09-09" }, { "rank": 27, "intensity": 2.204081632653061, "date": "2017-06-03" }, { "rank": 28, "intensity": 2.173469387755102, "date": "2017-09-13" }, { "rank": 29, "intensity": 2.142857142857143, "date": "2017-12-18" }, { "rank": 30, "intensity": 2.112244897959184, "date": "2017-05-21" }, { "rank": 31, "intensity": 2.0816326530612246, "date": "2017-01-28" }, { "rank": 32, "intensity": 2.0510204081632653, "date": "2017-09-11" }, { "rank": 33, "intensity": 2.020408163265306, "date": "2017-04-29" }, { "rank": 34, "intensity": 1.989795918367347, "date": "2017-01-16" }, { "rank": 35, "intensity": 1.9591836734693877, "date": "2017-04-19" }, { "rank": 36, "intensity": 1.9285714285714286, "date": "2017-09-17" }, { "rank": 37, "intensity": 1.8979591836734695, "date": "2017-02-17" }, { "rank": 38, "intensity": 1.8673469387755102, "date": "2017-12-09" }, { "rank": 39, "intensity": 1.836734693877551, "date": "2017-02-01" }, { "rank": 40, "intensity": 1.806122448979592, "date": "2017-08-28" }, { "rank": 41, "intensity": 1.7755102040816326, "date": "2017-01-25" }, { "rank": 42, "intensity": 1.7448979591836735, "date": "2017-10-21" }, { "rank": 43, "intensity": 1.7142857142857144, "date": "2017-12-05" }, { "rank": 44, "intensity": 1.683673469387755, "date": "2017-03-01" }, { "rank": 45, "intensity": 1.653061224489796, "date": "2017-08-26" }, { "rank": 46, "intensity": 1.6224489795918369, "date": "2017-06-25" }, { "rank": 47, "intensity": 1.5918367346938775, "date": "2017-11-12" }, { "rank": 48, "intensity": 1.5612244897959184, "date": "2017-12-02" }, { "rank": 49, "intensity": 1.5306122448979593, "date": "2017-07-31" }, { "rank": 50, "intensity": 1.5, "date": "2017-11-20" } ], "source": "ettm1" } }, { "id": "L3_T1_Composite_Trend_00106", "level": 3, "level_name": "Semantic Reasoning", "category": "Composite Trend", "subtask": "Composite Trend", "question": "Among days in channel MUFL during 2017 that exhibit the trend pattern 'slow rise, then fluctuating stable', identify the top-5 days where the slow rise segment is the slowest. (Output format: a ranked list of dates, e.g., ['YYYY-MM-DD', ...])", "answer": "['2017-01-16', '2017-02-17', '2017-12-24', '2017-05-05', '2017-04-29']", "ground_truth": [ "2017-01-16", "2017-02-17", "2017-12-24", "2017-05-05", "2017-04-29" ], "eval_metric": "set_f1", "channel": "MUFL", "ts_data_path": "ts_data/L3_T1_Composite_Trend_00106.csv", "meta": { "segments": [ { "kind": "rise", "adj": "slow" }, { "kind": "stable", "adj": "fluctuating" } ], "pattern_phrase": "slow rise, then fluctuating stable", "rank_target_idx": 0, "rank_kind": "rise", "rank_adj": "slow", "rank_axis": "slope", "rank_direction": "ASC", "rank_extreme": "slowest", "top_segments_meta": [ { "adj": "slow", "kind": "rise", "label": "slow rise", "start_idx": 0, "end_idx": 54 }, { "adj": "fluctuating", "kind": "stable", "label": "fluctuating stable", "start_idx": 54, "end_idx": 98 } ], "points_per_day": 96, "pattern": "slow rise, then fluctuating stable", "year": 2017, "top_k": [ "2017-01-16", "2017-02-17", "2017-12-24", "2017-05-05", "2017-04-29" ], "all_injections": [ { "rank": 1, "intensity": 1.5, "date": "2017-01-16" }, { "rank": 2, "intensity": 1.530612244897959, "date": "2017-02-17" }, { "rank": 3, "intensity": 1.5612244897959184, "date": "2017-12-24" }, { "rank": 4, "intensity": 1.5918367346938775, "date": "2017-05-05" }, { "rank": 5, "intensity": 1.6224489795918366, "date": "2017-04-29" }, { "rank": 6, "intensity": 1.653061224489796, "date": "2017-09-14" }, { "rank": 7, "intensity": 1.683673469387755, "date": "2017-02-26" }, { "rank": 8, "intensity": 1.7142857142857142, "date": "2017-07-01" }, { "rank": 9, "intensity": 1.7448979591836735, "date": "2017-07-06" }, { "rank": 10, "intensity": 1.7755102040816326, "date": "2017-08-31" }, { "rank": 11, "intensity": 1.806122448979592, "date": "2017-02-06" }, { "rank": 12, "intensity": 1.836734693877551, "date": "2017-03-16" }, { "rank": 13, "intensity": 1.8673469387755102, "date": "2017-01-20" }, { "rank": 14, "intensity": 1.8979591836734695, "date": "2017-09-19" }, { "rank": 15, "intensity": 1.9285714285714286, "date": "2017-01-11" }, { "rank": 16, "intensity": 1.9591836734693877, "date": "2017-10-15" }, { "rank": 17, "intensity": 1.989795918367347, "date": "2017-12-26" }, { "rank": 18, "intensity": 2.020408163265306, "date": "2017-04-06" }, { "rank": 19, "intensity": 2.0510204081632653, "date": "2017-11-14" }, { "rank": 20, "intensity": 2.0816326530612246, "date": "2017-04-11" }, { "rank": 21, "intensity": 2.112244897959184, "date": "2017-09-28" }, { "rank": 22, "intensity": 2.142857142857143, "date": "2017-11-07" }, { "rank": 23, "intensity": 2.173469387755102, "date": "2017-11-30" }, { "rank": 24, "intensity": 2.204081632653061, "date": "2017-07-29" }, { "rank": 25, "intensity": 2.2346938775510203, "date": "2017-02-21" }, { "rank": 26, "intensity": 2.2653061224489797, "date": "2017-08-12" }, { "rank": 27, "intensity": 2.295918367346939, "date": "2017-04-22" }, { "rank": 28, "intensity": 2.326530612244898, "date": "2017-10-17" }, { "rank": 29, "intensity": 2.357142857142857, "date": "2017-11-26" }, { "rank": 30, "intensity": 2.387755102040816, "date": "2017-06-22" }, { "rank": 31, "intensity": 2.4183673469387754, "date": "2017-05-23" }, { "rank": 32, "intensity": 2.4489795918367347, "date": "2017-12-18" }, { "rank": 33, "intensity": 2.479591836734694, "date": "2017-11-22" }, { "rank": 34, "intensity": 2.510204081632653, "date": "2017-11-01" }, { "rank": 35, "intensity": 2.5408163265306123, "date": "2017-06-28" }, { "rank": 36, "intensity": 2.571428571428571, "date": "2017-10-12" }, { "rank": 37, "intensity": 2.6020408163265305, "date": "2017-04-24" }, { "rank": 38, "intensity": 2.63265306122449, "date": "2017-07-03" }, { "rank": 39, "intensity": 2.663265306122449, "date": "2017-08-29" }, { "rank": 40, "intensity": 2.693877551020408, "date": "2017-02-02" }, { "rank": 41, "intensity": 2.7244897959183674, "date": "2017-08-02" }, { "rank": 42, "intensity": 2.7551020408163263, "date": "2017-12-14" }, { "rank": 43, "intensity": 2.7857142857142856, "date": "2017-01-13" }, { "rank": 44, "intensity": 2.816326530612245, "date": "2017-06-15" }, { "rank": 45, "intensity": 2.8469387755102042, "date": "2017-07-22" }, { "rank": 46, "intensity": 2.877551020408163, "date": "2017-04-19" }, { "rank": 47, "intensity": 2.9081632653061225, "date": "2017-10-28" }, { "rank": 48, "intensity": 2.9387755102040813, "date": "2017-05-11" }, { "rank": 49, "intensity": 2.9693877551020407, "date": "2017-09-12" }, { "rank": 50, "intensity": 3.0, "date": "2017-07-24" } ], "source": "ettm1" } }, { "id": "L3_T1_Composite_Trend_00107", "level": 3, "level_name": "Semantic Reasoning", "category": "Composite Trend", "subtask": "Composite Trend", "question": "Among days in channel MULL during 2017 that exhibit the trend pattern 'rise, then rapid fall', identify the top-5 days where the rapid fall segment is the fastest. (Output format: a ranked list of dates, e.g., ['YYYY-MM-DD', ...])", "answer": "['2017-03-12', '2017-07-24', '2017-05-30', '2017-04-15', '2017-11-19']", "ground_truth": [ "2017-03-12", "2017-07-24", "2017-05-30", "2017-04-15", "2017-11-19" ], "eval_metric": "set_f1", "channel": "MULL", "ts_data_path": "ts_data/L3_T1_Composite_Trend_00107.csv", "meta": { "segments": [ { "kind": "rise", "adj": "none" }, { "kind": "fall", "adj": "rapid" } ], "pattern_phrase": "rise, then rapid fall", "rank_target_idx": 1, "rank_kind": "fall", "rank_adj": "rapid", "rank_axis": "slope", "rank_direction": "DESC", "rank_extreme": "fastest", "top_segments_meta": [ { "adj": "none", "kind": "rise", "label": "none rise", "start_idx": 0, "end_idx": 67 }, { "adj": "rapid", "kind": "fall", "label": "rapid fall", "start_idx": 67, "end_idx": 98 } ], "points_per_day": 96, "pattern": "rise, then rapid fall", "year": 2017, "top_k": [ "2017-03-12", "2017-07-24", "2017-05-30", "2017-04-15", "2017-11-19" ], "all_injections": [ { "rank": 1, "intensity": 3.0, "date": "2017-03-12" }, { "rank": 2, "intensity": 2.9693877551020407, "date": "2017-07-24" }, { "rank": 3, "intensity": 2.938775510204082, "date": "2017-05-30" }, { "rank": 4, "intensity": 2.9081632653061225, "date": "2017-04-15" }, { "rank": 5, "intensity": 2.877551020408163, "date": "2017-11-19" }, { "rank": 6, "intensity": 2.8469387755102042, "date": "2017-04-23" }, { "rank": 7, "intensity": 2.816326530612245, "date": "2017-02-18" }, { "rank": 8, "intensity": 2.7857142857142856, "date": "2017-08-20" }, { "rank": 9, "intensity": 2.7551020408163267, "date": "2017-07-31" }, { "rank": 10, "intensity": 2.7244897959183674, "date": "2017-09-30" }, { "rank": 11, "intensity": 2.693877551020408, "date": "2017-04-11" }, { "rank": 12, "intensity": 2.663265306122449, "date": "2017-12-01" }, { "rank": 13, "intensity": 2.63265306122449, "date": "2017-02-14" }, { "rank": 14, "intensity": 2.6020408163265305, "date": "2017-01-21" }, { "rank": 15, "intensity": 2.5714285714285716, "date": "2017-11-24" }, { "rank": 16, "intensity": 2.5408163265306123, "date": "2017-04-25" }, { "rank": 17, "intensity": 2.510204081632653, "date": "2017-10-05" }, { "rank": 18, "intensity": 2.479591836734694, "date": "2017-08-25" }, { "rank": 19, "intensity": 2.4489795918367347, "date": "2017-06-01" }, { "rank": 20, "intensity": 2.4183673469387754, "date": "2017-04-19" }, { "rank": 21, "intensity": 2.387755102040816, "date": "2017-11-15" }, { "rank": 22, "intensity": 2.357142857142857, "date": "2017-01-06" }, { "rank": 23, "intensity": 2.326530612244898, "date": "2017-12-30" }, { "rank": 24, "intensity": 2.295918367346939, "date": "2017-03-15" }, { "rank": 25, "intensity": 2.2653061224489797, "date": "2017-01-09" }, { "rank": 26, "intensity": 2.2346938775510203, "date": "2017-10-16" }, { "rank": 27, "intensity": 2.204081632653061, "date": "2017-05-06" }, { "rank": 28, "intensity": 2.173469387755102, "date": "2017-07-01" }, { "rank": 29, "intensity": 2.142857142857143, "date": "2017-09-13" }, { "rank": 30, "intensity": 2.112244897959184, "date": "2017-05-25" }, { "rank": 31, "intensity": 2.0816326530612246, "date": "2017-09-08" }, { "rank": 32, "intensity": 2.0510204081632653, "date": "2017-12-05" }, { "rank": 33, "intensity": 2.020408163265306, "date": "2017-12-08" }, { "rank": 34, "intensity": 1.989795918367347, "date": "2017-12-26" }, { "rank": 35, "intensity": 1.9591836734693877, "date": "2017-12-18" }, { "rank": 36, "intensity": 1.9285714285714286, "date": "2017-10-12" }, { "rank": 37, "intensity": 1.8979591836734695, "date": "2017-10-26" }, { "rank": 38, "intensity": 1.8673469387755102, "date": "2017-06-21" }, { "rank": 39, "intensity": 1.836734693877551, "date": "2017-12-20" }, { "rank": 40, "intensity": 1.806122448979592, "date": "2017-09-23" }, { "rank": 41, "intensity": 1.7755102040816326, "date": "2017-03-05" }, { "rank": 42, "intensity": 1.7448979591836735, "date": "2017-01-30" }, { "rank": 43, "intensity": 1.7142857142857144, "date": "2017-05-12" }, { "rank": 44, "intensity": 1.683673469387755, "date": "2017-03-31" }, { "rank": 45, "intensity": 1.653061224489796, "date": "2017-10-07" }, { "rank": 46, "intensity": 1.6224489795918369, "date": "2017-10-24" }, { "rank": 47, "intensity": 1.5918367346938775, "date": "2017-03-19" }, { "rank": 48, "intensity": 1.5612244897959184, "date": "2017-08-29" }, { "rank": 49, "intensity": 1.5306122448979593, "date": "2017-12-12" }, { "rank": 50, "intensity": 1.5, "date": "2017-02-04" } ], "source": "ettm1" } }, { "id": "L3_T1_Composite_Trend_00108", "level": 3, "level_name": "Semantic Reasoning", "category": "Composite Trend", "subtask": "Composite Trend", "question": "Among days in channel LUFL during 2017 that exhibit the trend pattern 'rise, then fluctuating stable', identify the top-5 days where the fluctuating stable segment is the most fluctuating. (Output format: a ranked list of dates, e.g., ['YYYY-MM-DD', ...])", "answer": "['2017-03-15', '2017-07-05', '2017-10-27', '2017-07-17', '2017-02-21']", "ground_truth": [ "2017-03-15", "2017-07-05", "2017-10-27", "2017-07-17", "2017-02-21" ], "eval_metric": "set_f1", "channel": "LUFL", "ts_data_path": "ts_data/L3_T1_Composite_Trend_00108.csv", "meta": { "segments": [ { "kind": "rise", "adj": "none" }, { "kind": "stable", "adj": "fluctuating" } ], "pattern_phrase": "rise, then fluctuating stable", "rank_target_idx": 1, "rank_kind": "stable", "rank_adj": "fluctuating", "rank_axis": "std", "rank_direction": "DESC", "rank_extreme": "most fluctuating", "top_segments_meta": [ { "adj": "none", "kind": "rise", "label": "none rise", "start_idx": 0, "end_idx": 43 }, { "adj": "fluctuating", "kind": "stable", "label": "fluctuating stable", "start_idx": 43, "end_idx": 98 } ], "points_per_day": 96, "pattern": "rise, then fluctuating stable", "year": 2017, "top_k": [ "2017-03-15", "2017-07-05", "2017-10-27", "2017-07-17", "2017-02-21" ], "all_injections": [ { "rank": 1, "intensity": 3.0, "date": "2017-03-15" }, { "rank": 2, "intensity": 2.9693877551020407, "date": "2017-07-05" }, { "rank": 3, "intensity": 2.938775510204082, "date": "2017-10-27" }, { "rank": 4, "intensity": 2.9081632653061225, "date": "2017-07-17" }, { "rank": 5, "intensity": 2.877551020408163, "date": "2017-02-21" }, { "rank": 6, "intensity": 2.8469387755102042, "date": "2017-09-22" }, { "rank": 7, "intensity": 2.816326530612245, "date": "2017-04-08" }, { "rank": 8, "intensity": 2.7857142857142856, "date": "2017-01-12" }, { "rank": 9, "intensity": 2.7551020408163267, "date": "2017-03-01" }, { "rank": 10, "intensity": 2.7244897959183674, "date": "2017-09-27" }, { "rank": 11, "intensity": 2.693877551020408, "date": "2017-11-22" }, { "rank": 12, "intensity": 2.663265306122449, "date": "2017-12-01" }, { "rank": 13, "intensity": 2.63265306122449, "date": "2017-03-03" }, { "rank": 14, "intensity": 2.6020408163265305, "date": "2017-05-02" }, { "rank": 15, "intensity": 2.5714285714285716, "date": "2017-08-24" }, { "rank": 16, "intensity": 2.5408163265306123, "date": "2017-09-01" }, { "rank": 17, "intensity": 2.510204081632653, "date": "2017-03-12" }, { "rank": 18, "intensity": 2.479591836734694, "date": "2017-07-11" }, { "rank": 19, "intensity": 2.4489795918367347, "date": "2017-12-15" }, { "rank": 20, "intensity": 2.4183673469387754, "date": "2017-12-05" }, { "rank": 21, "intensity": 2.387755102040816, "date": "2017-04-21" }, { "rank": 22, "intensity": 2.357142857142857, "date": "2017-10-09" }, { "rank": 23, "intensity": 2.326530612244898, "date": "2017-03-25" }, { "rank": 24, "intensity": 2.295918367346939, "date": "2017-10-30" }, { "rank": 25, "intensity": 2.2653061224489797, "date": "2017-08-22" }, { "rank": 26, "intensity": 2.2346938775510203, "date": "2017-05-16" }, { "rank": 27, "intensity": 2.204081632653061, "date": "2017-07-31" }, { "rank": 28, "intensity": 2.173469387755102, "date": "2017-12-20" }, { "rank": 29, "intensity": 2.142857142857143, "date": "2017-02-08" }, { "rank": 30, "intensity": 2.112244897959184, "date": "2017-06-11" }, { "rank": 31, "intensity": 2.0816326530612246, "date": "2017-01-31" }, { "rank": 32, "intensity": 2.0510204081632653, "date": "2017-10-19" }, { "rank": 33, "intensity": 2.020408163265306, "date": "2017-09-30" }, { "rank": 34, "intensity": 1.989795918367347, "date": "2017-06-17" }, { "rank": 35, "intensity": 1.9591836734693877, "date": "2017-11-17" }, { "rank": 36, "intensity": 1.9285714285714286, "date": "2017-02-13" }, { "rank": 37, "intensity": 1.8979591836734695, "date": "2017-07-28" }, { "rank": 38, "intensity": 1.8673469387755102, "date": "2017-05-20" }, { "rank": 39, "intensity": 1.836734693877551, "date": "2017-04-04" }, { "rank": 40, "intensity": 1.806122448979592, "date": "2017-10-24" }, { "rank": 41, "intensity": 1.7755102040816326, "date": "2017-08-09" }, { "rank": 42, "intensity": 1.7448979591836735, "date": "2017-06-09" }, { "rank": 43, "intensity": 1.7142857142857144, "date": "2017-09-25" }, { "rank": 44, "intensity": 1.683673469387755, "date": "2017-06-19" }, { "rank": 45, "intensity": 1.653061224489796, "date": "2017-12-30" }, { "rank": 46, "intensity": 1.6224489795918369, "date": "2017-04-24" }, { "rank": 47, "intensity": 1.5918367346938775, "date": "2017-01-02" }, { "rank": 48, "intensity": 1.5612244897959184, "date": "2017-05-14" }, { "rank": 49, "intensity": 1.5306122448979593, "date": "2017-11-20" }, { "rank": 50, "intensity": 1.5, "date": "2017-08-28" } ], "source": "ettm1" } }, { "id": "L3_T1_Composite_Trend_00109", "level": 3, "level_name": "Semantic Reasoning", "category": "Composite Trend", "subtask": "Composite Trend", "question": "Among days in channel LULL during 2017 that exhibit the trend pattern 'fall, then slow fall', identify the top-5 days where the slow fall segment is the slowest. (Output format: a ranked list of dates, e.g., ['YYYY-MM-DD', ...])", "answer": "['2017-05-29', '2017-12-21', '2017-11-14', '2017-05-07', '2017-11-17']", "ground_truth": [ "2017-05-29", "2017-12-21", "2017-11-14", "2017-05-07", "2017-11-17" ], "eval_metric": "set_f1", "channel": "LULL", "ts_data_path": "ts_data/L3_T1_Composite_Trend_00109.csv", "meta": { "segments": [ { "kind": "fall", "adj": "none" }, { "kind": "fall", "adj": "slow" } ], "pattern_phrase": "fall, then slow fall", "rank_target_idx": 1, "rank_kind": "fall", "rank_adj": "slow", "rank_axis": "slope", "rank_direction": "ASC", "rank_extreme": "slowest", "top_segments_meta": [ { "adj": "none", "kind": "fall", "label": "none fall", "start_idx": 0, "end_idx": 39 }, { "adj": "slow", "kind": "fall", "label": "slow fall", "start_idx": 39, "end_idx": 98 } ], "points_per_day": 96, "pattern": "fall, then slow fall", "year": 2017, "top_k": [ "2017-05-29", "2017-12-21", "2017-11-14", "2017-05-07", "2017-11-17" ], "all_injections": [ { "rank": 1, "intensity": 1.5, "date": "2017-05-29" }, { "rank": 2, "intensity": 1.530612244897959, "date": "2017-12-21" }, { "rank": 3, "intensity": 1.5612244897959184, "date": "2017-11-14" }, { "rank": 4, "intensity": 1.5918367346938775, "date": "2017-05-07" }, { "rank": 5, "intensity": 1.6224489795918366, "date": "2017-11-17" }, { "rank": 6, "intensity": 1.653061224489796, "date": "2017-03-14" }, { "rank": 7, "intensity": 1.683673469387755, "date": "2017-04-27" }, { "rank": 8, "intensity": 1.7142857142857142, "date": "2017-10-08" }, { "rank": 9, "intensity": 1.7448979591836735, "date": "2017-12-15" }, { "rank": 10, "intensity": 1.7755102040816326, "date": "2017-01-15" }, { "rank": 11, "intensity": 1.806122448979592, "date": "2017-03-02" }, { "rank": 12, "intensity": 1.836734693877551, "date": "2017-02-04" }, { "rank": 13, "intensity": 1.8673469387755102, "date": "2017-06-18" }, { "rank": 14, "intensity": 1.8979591836734695, "date": "2017-06-26" }, { "rank": 15, "intensity": 1.9285714285714286, "date": "2017-09-07" }, { "rank": 16, "intensity": 1.9591836734693877, "date": "2017-07-16" }, { "rank": 17, "intensity": 1.989795918367347, "date": "2017-06-24" }, { "rank": 18, "intensity": 2.020408163265306, "date": "2017-08-27" }, { "rank": 19, "intensity": 2.0510204081632653, "date": "2017-03-05" }, { "rank": 20, "intensity": 2.0816326530612246, "date": "2017-10-22" }, { "rank": 21, "intensity": 2.112244897959184, "date": "2017-10-15" }, { "rank": 22, "intensity": 2.142857142857143, "date": "2017-04-09" }, { "rank": 23, "intensity": 2.173469387755102, "date": "2017-06-22" }, { "rank": 24, "intensity": 2.204081632653061, "date": "2017-05-21" }, { "rank": 25, "intensity": 2.2346938775510203, "date": "2017-02-10" }, { "rank": 26, "intensity": 2.2653061224489797, "date": "2017-03-19" }, { "rank": 27, "intensity": 2.295918367346939, "date": "2017-01-07" }, { "rank": 28, "intensity": 2.326530612244898, "date": "2017-05-01" }, { "rank": 29, "intensity": 2.357142857142857, "date": "2017-06-11" }, { "rank": 30, "intensity": 2.387755102040816, "date": "2017-09-29" }, { "rank": 31, "intensity": 2.4183673469387754, "date": "2017-01-31" }, { "rank": 32, "intensity": 2.4489795918367347, "date": "2017-02-26" }, { "rank": 33, "intensity": 2.479591836734694, "date": "2017-08-09" }, { "rank": 34, "intensity": 2.510204081632653, "date": "2017-07-21" }, { "rank": 35, "intensity": 2.5408163265306123, "date": "2017-07-11" }, { "rank": 36, "intensity": 2.571428571428571, "date": "2017-12-03" }, { "rank": 37, "intensity": 2.6020408163265305, "date": "2017-12-11" }, { "rank": 38, "intensity": 2.63265306122449, "date": "2017-06-02" }, { "rank": 39, "intensity": 2.663265306122449, "date": "2017-04-13" }, { "rank": 40, "intensity": 2.693877551020408, "date": "2017-05-09" }, { "rank": 41, "intensity": 2.7244897959183674, "date": "2017-10-26" }, { "rank": 42, "intensity": 2.7551020408163263, "date": "2017-12-01" }, { "rank": 43, "intensity": 2.7857142857142856, "date": "2017-10-19" }, { "rank": 44, "intensity": 2.816326530612245, "date": "2017-06-08" }, { "rank": 45, "intensity": 2.8469387755102042, "date": "2017-09-12" }, { "rank": 46, "intensity": 2.877551020408163, "date": "2017-12-26" }, { "rank": 47, "intensity": 2.9081632653061225, "date": "2017-10-31" }, { "rank": 48, "intensity": 2.9387755102040813, "date": "2017-03-31" }, { "rank": 49, "intensity": 2.9693877551020407, "date": "2017-08-23" }, { "rank": 50, "intensity": 3.0, "date": "2017-09-21" } ], "source": "ettm1" } }, { "id": "L3_T1_Composite_Trend_00110", "level": 3, "level_name": "Semantic Reasoning", "category": "Composite Trend", "subtask": "Composite Trend", "question": "Among days in channel OT during 2017 that exhibit the trend pattern 'fluctuating stable, then rapid fall, then rise, then fall', identify the top-5 days where the fluctuating stable segment is the most fluctuating. (Output format: a ranked list of dates, e.g., ['YYYY-MM-DD', ...])", "answer": "['2017-09-11', '2017-06-10', '2017-09-26', '2017-04-13', '2017-02-02']", "ground_truth": [ "2017-09-11", "2017-06-10", "2017-09-26", "2017-04-13", "2017-02-02" ], "eval_metric": "set_f1", "channel": "OT", "ts_data_path": "ts_data/L3_T1_Composite_Trend_00110.csv", "meta": { "segments": [ { "kind": "stable", "adj": "fluctuating" }, { "kind": "fall", "adj": "rapid" }, { "kind": "rise", "adj": "none" }, { "kind": "fall", "adj": "none" } ], "pattern_phrase": "fluctuating stable, then rapid fall, then rise, then fall", "rank_target_idx": 0, "rank_kind": "stable", "rank_adj": "fluctuating", "rank_axis": "std", "rank_direction": "DESC", "rank_extreme": "most fluctuating", "top_segments_meta": [ { "adj": "fluctuating", "kind": "stable", "label": "fluctuating stable", "start_idx": 0, "end_idx": 31 }, { "adj": "rapid", "kind": "fall", "label": "rapid fall", "start_idx": 31, "end_idx": 43 }, { "adj": "none", "kind": "rise", "label": "none rise", "start_idx": 43, "end_idx": 69 }, { "adj": "none", "kind": "fall", "label": "none fall", "start_idx": 69, "end_idx": 98 } ], "points_per_day": 96, "pattern": "fluctuating stable, then rapid fall, then rise, then fall", "year": 2017, "top_k": [ "2017-09-11", "2017-06-10", "2017-09-26", "2017-04-13", "2017-02-02" ], "all_injections": [ { "rank": 1, "intensity": 3.0, "date": "2017-09-11" }, { "rank": 2, "intensity": 2.9693877551020407, "date": "2017-06-10" }, { "rank": 3, "intensity": 2.938775510204082, "date": "2017-09-26" }, { "rank": 4, "intensity": 2.9081632653061225, "date": "2017-04-13" }, { "rank": 5, "intensity": 2.877551020408163, "date": "2017-02-02" }, { "rank": 6, "intensity": 2.8469387755102042, "date": "2017-06-23" }, { "rank": 7, "intensity": 2.816326530612245, "date": "2017-03-14" }, { "rank": 8, "intensity": 2.7857142857142856, "date": "2017-05-30" }, { "rank": 9, "intensity": 2.7551020408163267, "date": "2017-06-25" }, { "rank": 10, "intensity": 2.7244897959183674, "date": "2017-11-13" }, { "rank": 11, "intensity": 2.693877551020408, "date": "2017-09-23" }, { "rank": 12, "intensity": 2.663265306122449, "date": "2017-01-11" }, { "rank": 13, "intensity": 2.63265306122449, "date": "2017-09-02" }, { "rank": 14, "intensity": 2.6020408163265305, "date": "2017-05-27" }, { "rank": 15, "intensity": 2.5714285714285716, "date": "2017-02-11" }, { "rank": 16, "intensity": 2.5408163265306123, "date": "2017-09-28" }, { "rank": 17, "intensity": 2.510204081632653, "date": "2017-08-19" }, { "rank": 18, "intensity": 2.479591836734694, "date": "2017-05-14" }, { "rank": 19, "intensity": 2.4489795918367347, "date": "2017-08-06" }, { "rank": 20, "intensity": 2.4183673469387754, "date": "2017-08-24" }, { "rank": 21, "intensity": 2.387755102040816, "date": "2017-07-04" }, { "rank": 22, "intensity": 2.357142857142857, "date": "2017-09-04" }, { "rank": 23, "intensity": 2.326530612244898, "date": "2017-10-03" }, { "rank": 24, "intensity": 2.295918367346939, "date": "2017-03-12" }, { "rank": 25, "intensity": 2.2653061224489797, "date": "2017-11-15" }, { "rank": 26, "intensity": 2.2346938775510203, "date": "2017-08-04" }, { "rank": 27, "intensity": 2.204081632653061, "date": "2017-11-03" }, { "rank": 28, "intensity": 2.173469387755102, "date": "2017-04-09" }, { "rank": 29, "intensity": 2.142857142857143, "date": "2017-04-28" }, { "rank": 30, "intensity": 2.112244897959184, "date": "2017-06-07" }, { "rank": 31, "intensity": 2.0816326530612246, "date": "2017-03-17" }, { "rank": 32, "intensity": 2.0510204081632653, "date": "2017-08-31" }, { "rank": 33, "intensity": 2.020408163265306, "date": "2017-11-26" }, { "rank": 34, "intensity": 1.989795918367347, "date": "2017-10-17" }, { "rank": 35, "intensity": 1.9591836734693877, "date": "2017-09-16" }, { "rank": 36, "intensity": 1.9285714285714286, "date": "2017-08-27" }, { "rank": 37, "intensity": 1.8979591836734695, "date": "2017-08-08" }, { "rank": 38, "intensity": 1.8673469387755102, "date": "2017-01-16" }, { "rank": 39, "intensity": 1.836734693877551, "date": "2017-09-30" }, { "rank": 40, "intensity": 1.806122448979592, "date": "2017-01-06" }, { "rank": 41, "intensity": 1.7755102040816326, "date": "2017-12-27" }, { "rank": 42, "intensity": 1.7448979591836735, "date": "2017-12-24" }, { "rank": 43, "intensity": 1.7142857142857144, "date": "2017-10-23" }, { "rank": 44, "intensity": 1.683673469387755, "date": "2017-06-02" }, { "rank": 45, "intensity": 1.653061224489796, "date": "2017-12-13" }, { "rank": 46, "intensity": 1.6224489795918369, "date": "2017-11-22" }, { "rank": 47, "intensity": 1.5918367346938775, "date": "2017-03-28" }, { "rank": 48, "intensity": 1.5612244897959184, "date": "2017-02-07" }, { "rank": 49, "intensity": 1.5306122448979593, "date": "2017-11-28" }, { "rank": 50, "intensity": 1.5, "date": "2017-12-07" } ], "source": "ettm1" } }, { "id": "L3_T1_Composite_Trend_00111", "level": 3, "level_name": "Semantic Reasoning", "category": "Composite Trend", "subtask": "Composite Trend", "question": "Among days in channel 67 during 2020 that exhibit the trend pattern 'rapid rise, then slow fall, then rapid fall, then fall', identify the top-5 days where the rapid fall segment is the fastest. (Output format: a ranked list of dates, e.g., ['YYYY-MM-DD', ...])", "answer": "['2020-09-28', '2020-05-09', '2020-04-02', '2020-08-01', '2020-04-04']", "ground_truth": [ "2020-09-28", "2020-05-09", "2020-04-02", "2020-08-01", "2020-04-04" ], "eval_metric": "set_f1", "channel": "67", "ts_data_path": "ts_data/L3_T1_Composite_Trend_00111.csv", "meta": { "segments": [ { "kind": "rise", "adj": "rapid" }, { "kind": "fall", "adj": "slow" }, { "kind": "fall", "adj": "rapid" }, { "kind": "fall", "adj": "none" } ], "pattern_phrase": "rapid rise, then slow fall, then rapid fall, then fall", "rank_target_idx": 2, "rank_kind": "fall", "rank_adj": "rapid", "rank_axis": "slope", "rank_direction": "DESC", "rank_extreme": "fastest", "top_segments_meta": [ { "adj": "rapid", "kind": "rise", "label": "rapid rise", "start_idx": 0, "end_idx": 14 }, { "adj": "slow", "kind": "fall", "label": "slow fall", "start_idx": 14, "end_idx": 57 }, { "adj": "rapid", "kind": "fall", "label": "rapid fall", "start_idx": 57, "end_idx": 71 }, { "adj": "none", "kind": "fall", "label": "none fall", "start_idx": 71, "end_idx": 98 } ], "points_per_day": 96, "pattern": "rapid rise, then slow fall, then rapid fall, then fall", "year": 2020, "top_k": [ "2020-09-28", "2020-05-09", "2020-04-02", "2020-08-01", "2020-04-04" ], "all_injections": [ { "rank": 1, "intensity": 3.0, "date": "2020-09-28" }, { "rank": 2, "intensity": 2.9693877551020407, "date": "2020-05-09" }, { "rank": 3, "intensity": 2.938775510204082, "date": "2020-04-02" }, { "rank": 4, "intensity": 2.9081632653061225, "date": "2020-08-01" }, { "rank": 5, "intensity": 2.877551020408163, "date": "2020-04-04" }, { "rank": 6, "intensity": 2.8469387755102042, "date": "2020-01-27" }, { "rank": 7, "intensity": 2.816326530612245, "date": "2020-11-22" }, { "rank": 8, "intensity": 2.7857142857142856, "date": "2020-07-13" }, { "rank": 9, "intensity": 2.7551020408163267, "date": "2020-10-01" }, { "rank": 10, "intensity": 2.7244897959183674, "date": "2020-04-18" }, { "rank": 11, "intensity": 2.693877551020408, "date": "2020-12-25" }, { "rank": 12, "intensity": 2.663265306122449, "date": "2020-09-09" }, { "rank": 13, "intensity": 2.63265306122449, "date": "2020-09-05" }, { "rank": 14, "intensity": 2.6020408163265305, "date": "2020-10-10" }, { "rank": 15, "intensity": 2.5714285714285716, "date": "2020-01-06" }, { "rank": 16, "intensity": 2.5408163265306123, "date": "2020-03-07" }, { "rank": 17, "intensity": 2.510204081632653, "date": "2020-07-02" }, { "rank": 18, "intensity": 2.479591836734694, "date": "2020-05-24" }, { "rank": 19, "intensity": 2.4489795918367347, "date": "2020-07-29" }, { "rank": 20, "intensity": 2.4183673469387754, "date": "2020-04-11" }, { "rank": 21, "intensity": 2.387755102040816, "date": "2020-05-07" }, { "rank": 22, "intensity": 2.357142857142857, "date": "2020-05-27" }, { "rank": 23, "intensity": 2.326530612244898, "date": "2020-04-21" }, { "rank": 24, "intensity": 2.295918367346939, "date": "2020-04-16" }, { "rank": 25, "intensity": 2.2653061224489797, "date": "2020-10-21" }, { "rank": 26, "intensity": 2.2346938775510203, "date": "2020-06-02" }, { "rank": 27, "intensity": 2.204081632653061, "date": "2020-08-25" }, { "rank": 28, "intensity": 2.173469387755102, "date": "2020-04-25" }, { "rank": 29, "intensity": 2.142857142857143, "date": "2020-05-18" }, { "rank": 30, "intensity": 2.112244897959184, "date": "2020-02-28" }, { "rank": 31, "intensity": 2.0816326530612246, "date": "2020-01-12" }, { "rank": 32, "intensity": 2.0510204081632653, "date": "2020-01-01" }, { "rank": 33, "intensity": 2.020408163265306, "date": "2020-09-19" }, { "rank": 34, "intensity": 1.989795918367347, "date": "2020-07-06" }, { "rank": 35, "intensity": 1.9591836734693877, "date": "2020-07-16" }, { "rank": 36, "intensity": 1.9285714285714286, "date": "2020-03-14" }, { "rank": 37, "intensity": 1.8979591836734695, "date": "2020-03-24" }, { "rank": 38, "intensity": 1.8673469387755102, "date": "2020-04-07" }, { "rank": 39, "intensity": 1.836734693877551, "date": "2020-05-29" }, { "rank": 40, "intensity": 1.806122448979592, "date": "2020-11-18" }, { "rank": 41, "intensity": 1.7755102040816326, "date": "2020-01-20" }, { "rank": 42, "intensity": 1.7448979591836735, "date": "2020-12-27" }, { "rank": 43, "intensity": 1.7142857142857144, "date": "2020-11-15" }, { "rank": 44, "intensity": 1.683673469387755, "date": "2020-12-22" }, { "rank": 45, "intensity": 1.653061224489796, "date": "2020-07-08" }, { "rank": 46, "intensity": 1.6224489795918369, "date": "2020-06-29" }, { "rank": 47, "intensity": 1.5918367346938775, "date": "2020-04-30" }, { "rank": 48, "intensity": 1.5612244897959184, "date": "2020-08-12" }, { "rank": 49, "intensity": 1.5306122448979593, "date": "2020-03-17" }, { "rank": 50, "intensity": 1.5, "date": "2020-12-01" } ], "source": "causal_rivers" } }, { "id": "L3_T1_Composite_Trend_00112", "level": 3, "level_name": "Semantic Reasoning", "category": "Composite Trend", "subtask": "Composite Trend", "question": "Among days in channel 71 during 2020 that exhibit the trend pattern 'rise, then fall, then rapid fall', identify the top-5 days where the rapid fall segment is the fastest. (Output format: a ranked list of dates, e.g., ['YYYY-MM-DD', ...])", "answer": "['2020-08-30', '2020-05-31', '2020-04-11', '2020-02-20', '2020-10-26']", "ground_truth": [ "2020-08-30", "2020-05-31", "2020-04-11", "2020-02-20", "2020-10-26" ], "eval_metric": "set_f1", "channel": "71", "ts_data_path": "ts_data/L3_T1_Composite_Trend_00112.csv", "meta": { "segments": [ { "kind": "rise", "adj": "none" }, { "kind": "fall", "adj": "none" }, { "kind": "fall", "adj": "rapid" } ], "pattern_phrase": "rise, then fall, then rapid fall", "rank_target_idx": 2, "rank_kind": "fall", "rank_adj": "rapid", "rank_axis": "slope", "rank_direction": "DESC", "rank_extreme": "fastest", "top_segments_meta": [ { "adj": "none", "kind": "rise", "label": "none rise", "start_idx": 0, "end_idx": 35 }, { "adj": "none", "kind": "fall", "label": "none fall", "start_idx": 35, "end_idx": 77 }, { "adj": "rapid", "kind": "fall", "label": "rapid fall", "start_idx": 77, "end_idx": 98 } ], "points_per_day": 96, "pattern": "rise, then fall, then rapid fall", "year": 2020, "top_k": [ "2020-08-30", "2020-05-31", "2020-04-11", "2020-02-20", "2020-10-26" ], "all_injections": [ { "rank": 1, "intensity": 3.0, "date": "2020-08-30" }, { "rank": 2, "intensity": 2.9693877551020407, "date": "2020-05-31" }, { "rank": 3, "intensity": 2.938775510204082, "date": "2020-04-11" }, { "rank": 4, "intensity": 2.9081632653061225, "date": "2020-02-20" }, { "rank": 5, "intensity": 2.877551020408163, "date": "2020-10-26" }, { "rank": 6, "intensity": 2.8469387755102042, "date": "2020-05-28" }, { "rank": 7, "intensity": 2.816326530612245, "date": "2020-05-12" }, { "rank": 8, "intensity": 2.7857142857142856, "date": "2020-08-05" }, { "rank": 9, "intensity": 2.7551020408163267, "date": "2020-12-16" }, { "rank": 10, "intensity": 2.7244897959183674, "date": "2020-07-24" }, { "rank": 11, "intensity": 2.693877551020408, "date": "2020-01-05" }, { "rank": 12, "intensity": 2.663265306122449, "date": "2020-01-17" }, { "rank": 13, "intensity": 2.63265306122449, "date": "2020-03-08" }, { "rank": 14, "intensity": 2.6020408163265305, "date": "2020-01-22" }, { "rank": 15, "intensity": 2.5714285714285716, "date": "2020-10-15" }, { "rank": 16, "intensity": 2.5408163265306123, "date": "2020-08-19" }, { "rank": 17, "intensity": 2.510204081632653, "date": "2020-10-11" }, { "rank": 18, "intensity": 2.479591836734694, "date": "2020-12-25" }, { "rank": 19, "intensity": 2.4489795918367347, "date": "2020-12-07" }, { "rank": 20, "intensity": 2.4183673469387754, "date": "2020-09-04" }, { "rank": 21, "intensity": 2.387755102040816, "date": "2020-03-03" }, { "rank": 22, "intensity": 2.357142857142857, "date": "2020-01-11" }, { "rank": 23, "intensity": 2.326530612244898, "date": "2020-08-25" }, { "rank": 24, "intensity": 2.295918367346939, "date": "2020-04-15" }, { "rank": 25, "intensity": 2.2653061224489797, "date": "2020-07-21" }, { "rank": 26, "intensity": 2.2346938775510203, "date": "2020-08-03" }, { "rank": 27, "intensity": 2.204081632653061, "date": "2020-09-28" }, { "rank": 28, "intensity": 2.173469387755102, "date": "2020-09-01" }, { "rank": 29, "intensity": 2.142857142857143, "date": "2020-09-12" }, { "rank": 30, "intensity": 2.112244897959184, "date": "2020-10-03" }, { "rank": 31, "intensity": 2.0816326530612246, "date": "2020-09-23" }, { "rank": 32, "intensity": 2.0510204081632653, "date": "2020-11-19" }, { "rank": 33, "intensity": 2.020408163265306, "date": "2020-03-19" }, { "rank": 34, "intensity": 1.989795918367347, "date": "2020-07-19" }, { "rank": 35, "intensity": 1.9591836734693877, "date": "2020-07-03" }, { "rank": 36, "intensity": 1.9285714285714286, "date": "2020-06-19" }, { "rank": 37, "intensity": 1.8979591836734695, "date": "2020-01-08" }, { "rank": 38, "intensity": 1.8673469387755102, "date": "2020-03-16" }, { "rank": 39, "intensity": 1.836734693877551, "date": "2020-02-03" }, { "rank": 40, "intensity": 1.806122448979592, "date": "2020-11-11" }, { "rank": 41, "intensity": 1.7755102040816326, "date": "2020-06-30" }, { "rank": 42, "intensity": 1.7448979591836735, "date": "2020-05-19" }, { "rank": 43, "intensity": 1.7142857142857144, "date": "2020-04-23" }, { "rank": 44, "intensity": 1.683673469387755, "date": "2020-07-15" }, { "rank": 45, "intensity": 1.653061224489796, "date": "2020-06-27" }, { "rank": 46, "intensity": 1.6224489795918369, "date": "2020-06-06" }, { "rank": 47, "intensity": 1.5918367346938775, "date": "2020-05-01" }, { "rank": 48, "intensity": 1.5612244897959184, "date": "2020-08-07" }, { "rank": 49, "intensity": 1.5306122448979593, "date": "2020-08-01" }, { "rank": 50, "intensity": 1.5, "date": "2020-09-17" } ], "source": "causal_rivers" } }, { "id": "L3_T1_Composite_Trend_00113", "level": 3, "level_name": "Semantic Reasoning", "category": "Composite Trend", "subtask": "Composite Trend", "question": "Among days in channel 99 during 2022 that exhibit the trend pattern 'rapid fall, then steady stable, then fall, then steady stable', identify the top-5 days where the rapid fall segment is the fastest. (Output format: a ranked list of dates, e.g., ['YYYY-MM-DD', ...])", "answer": "['2022-04-13', '2022-10-20', '2022-07-12', '2022-05-20', '2022-05-22']", "ground_truth": [ "2022-04-13", "2022-10-20", "2022-07-12", "2022-05-20", "2022-05-22" ], "eval_metric": "set_f1", "channel": "99", "ts_data_path": "ts_data/L3_T1_Composite_Trend_00113.csv", "meta": { "segments": [ { "kind": "fall", "adj": "rapid" }, { "kind": "stable", "adj": "steady" }, { "kind": "fall", "adj": "none" }, { "kind": "stable", "adj": "steady" } ], "pattern_phrase": "rapid fall, then steady stable, then fall, then steady stable", "rank_target_idx": 0, "rank_kind": "fall", "rank_adj": "rapid", "rank_axis": "slope", "rank_direction": "DESC", "rank_extreme": "fastest", "top_segments_meta": [ { "adj": "rapid", "kind": "fall", "label": "rapid fall", "start_idx": 0, "end_idx": 8 }, { "adj": "steady", "kind": "stable", "label": "steady stable", "start_idx": 8, "end_idx": 43 }, { "adj": "none", "kind": "fall", "label": "none fall", "start_idx": 43, "end_idx": 62 }, { "adj": "steady", "kind": "stable", "label": "steady stable", "start_idx": 62, "end_idx": 98 } ], "points_per_day": 96, "pattern": "rapid fall, then steady stable, then fall, then steady stable", "year": 2022, "top_k": [ "2022-04-13", "2022-10-20", "2022-07-12", "2022-05-20", "2022-05-22" ], "all_injections": [ { "rank": 1, "intensity": 3.0, "date": "2022-04-13" }, { "rank": 2, "intensity": 2.9693877551020407, "date": "2022-10-20" }, { "rank": 3, "intensity": 2.938775510204082, "date": "2022-07-12" }, { "rank": 4, "intensity": 2.9081632653061225, "date": "2022-05-20" }, { "rank": 5, "intensity": 2.877551020408163, "date": "2022-05-22" }, { "rank": 6, "intensity": 2.8469387755102042, "date": "2022-06-15" }, { "rank": 7, "intensity": 2.816326530612245, "date": "2022-05-09" }, { "rank": 8, "intensity": 2.7857142857142856, "date": "2022-11-21" }, { "rank": 9, "intensity": 2.7551020408163267, "date": "2022-04-27" }, { "rank": 10, "intensity": 2.7244897959183674, "date": "2022-12-16" }, { "rank": 11, "intensity": 2.693877551020408, "date": "2022-01-10" }, { "rank": 12, "intensity": 2.663265306122449, "date": "2022-11-01" }, { "rank": 13, "intensity": 2.63265306122449, "date": "2022-04-15" }, { "rank": 14, "intensity": 2.6020408163265305, "date": "2022-08-03" }, { "rank": 15, "intensity": 2.5714285714285716, "date": "2022-03-14" }, { "rank": 16, "intensity": 2.5408163265306123, "date": "2022-07-04" }, { "rank": 17, "intensity": 2.510204081632653, "date": "2022-02-13" }, { "rank": 18, "intensity": 2.479591836734694, "date": "2022-08-24" }, { "rank": 19, "intensity": 2.4489795918367347, "date": "2022-02-17" }, { "rank": 20, "intensity": 2.4183673469387754, "date": "2022-12-25" }, { "rank": 21, "intensity": 2.387755102040816, "date": "2022-05-26" }, { "rank": 22, "intensity": 2.357142857142857, "date": "2022-05-12" }, { "rank": 23, "intensity": 2.326530612244898, "date": "2022-06-04" }, { "rank": 24, "intensity": 2.295918367346939, "date": "2022-11-24" }, { "rank": 25, "intensity": 2.2653061224489797, "date": "2022-10-13" }, { "rank": 26, "intensity": 2.2346938775510203, "date": "2022-06-12" }, { "rank": 27, "intensity": 2.204081632653061, "date": "2022-02-02" }, { "rank": 28, "intensity": 2.173469387755102, "date": "2022-06-25" }, { "rank": 29, "intensity": 2.142857142857143, "date": "2022-03-24" }, { "rank": 30, "intensity": 2.112244897959184, "date": "2022-04-23" }, { "rank": 31, "intensity": 2.0816326530612246, "date": "2022-03-06" }, { "rank": 32, "intensity": 2.0510204081632653, "date": "2022-01-31" }, { "rank": 33, "intensity": 2.020408163265306, "date": "2022-08-20" }, { "rank": 34, "intensity": 1.989795918367347, "date": "2022-02-25" }, { "rank": 35, "intensity": 1.9591836734693877, "date": "2022-06-22" }, { "rank": 36, "intensity": 1.9285714285714286, "date": "2022-11-27" }, { "rank": 37, "intensity": 1.8979591836734695, "date": "2022-11-18" }, { "rank": 38, "intensity": 1.8673469387755102, "date": "2022-08-09" }, { "rank": 39, "intensity": 1.836734693877551, "date": "2022-08-16" }, { "rank": 40, "intensity": 1.806122448979592, "date": "2022-11-30" }, { "rank": 41, "intensity": 1.7755102040816326, "date": "2022-12-27" }, { "rank": 42, "intensity": 1.7448979591836735, "date": "2022-05-31" }, { "rank": 43, "intensity": 1.7142857142857144, "date": "2022-12-02" }, { "rank": 44, "intensity": 1.683673469387755, "date": "2022-08-14" }, { "rank": 45, "intensity": 1.653061224489796, "date": "2022-04-05" }, { "rank": 46, "intensity": 1.6224489795918369, "date": "2022-12-19" }, { "rank": 47, "intensity": 1.5918367346938775, "date": "2022-03-28" }, { "rank": 48, "intensity": 1.5612244897959184, "date": "2022-12-22" }, { "rank": 49, "intensity": 1.5306122448979593, "date": "2022-11-04" }, { "rank": 50, "intensity": 1.5, "date": "2022-11-07" } ], "source": "causal_rivers" } }, { "id": "L3_T1_Composite_Trend_00114", "level": 3, "level_name": "Semantic Reasoning", "category": "Composite Trend", "subtask": "Composite Trend", "question": "Among days in channel 123 during 2022 that exhibit the trend pattern 'fluctuating stable, then rapid rise, then slow rise', identify the top-5 days where the fluctuating stable segment is the most fluctuating. (Output format: a ranked list of dates, e.g., ['YYYY-MM-DD', ...])", "answer": "['2022-11-15', '2022-06-11', '2022-06-28', '2022-04-21', '2022-02-13']", "ground_truth": [ "2022-11-15", "2022-06-11", "2022-06-28", "2022-04-21", "2022-02-13" ], "eval_metric": "set_f1", "channel": "123", "ts_data_path": "ts_data/L3_T1_Composite_Trend_00114.csv", "meta": { "segments": [ { "kind": "stable", "adj": "fluctuating" }, { "kind": "rise", "adj": "rapid" }, { "kind": "rise", "adj": "slow" } ], "pattern_phrase": "fluctuating stable, then rapid rise, then slow rise", "rank_target_idx": 0, "rank_kind": "stable", "rank_adj": "fluctuating", "rank_axis": "std", "rank_direction": "DESC", "rank_extreme": "most fluctuating", "top_segments_meta": [ { "adj": "fluctuating", "kind": "stable", "label": "fluctuating stable", "start_idx": 0, "end_idx": 38 }, { "adj": "rapid", "kind": "rise", "label": "rapid rise", "start_idx": 38, "end_idx": 52 }, { "adj": "slow", "kind": "rise", "label": "slow rise", "start_idx": 52, "end_idx": 98 } ], "points_per_day": 96, "pattern": "fluctuating stable, then rapid rise, then slow rise", "year": 2022, "top_k": [ "2022-11-15", "2022-06-11", "2022-06-28", "2022-04-21", "2022-02-13" ], "all_injections": [ { "rank": 1, "intensity": 3.0, "date": "2022-11-15" }, { "rank": 2, "intensity": 2.9693877551020407, "date": "2022-06-11" }, { "rank": 3, "intensity": 2.938775510204082, "date": "2022-06-28" }, { "rank": 4, "intensity": 2.9081632653061225, "date": "2022-04-21" }, { "rank": 5, "intensity": 2.877551020408163, "date": "2022-02-13" }, { "rank": 6, "intensity": 2.8469387755102042, "date": "2022-07-04" }, { "rank": 7, "intensity": 2.816326530612245, "date": "2022-09-14" }, { "rank": 8, "intensity": 2.7857142857142856, "date": "2022-07-30" }, { "rank": 9, "intensity": 2.7551020408163267, "date": "2022-03-21" }, { "rank": 10, "intensity": 2.7244897959183674, "date": "2022-10-26" }, { "rank": 11, "intensity": 2.693877551020408, "date": "2022-03-17" }, { "rank": 12, "intensity": 2.663265306122449, "date": "2022-09-17" }, { "rank": 13, "intensity": 2.63265306122449, "date": "2022-06-23" }, { "rank": 14, "intensity": 2.6020408163265305, "date": "2022-01-24" }, { "rank": 15, "intensity": 2.5714285714285716, "date": "2022-04-06" }, { "rank": 16, "intensity": 2.5408163265306123, "date": "2022-05-20" }, { "rank": 17, "intensity": 2.510204081632653, "date": "2022-08-28" }, { "rank": 18, "intensity": 2.479591836734694, "date": "2022-11-18" }, { "rank": 19, "intensity": 2.4489795918367347, "date": "2022-01-28" }, { "rank": 20, "intensity": 2.4183673469387754, "date": "2022-09-04" }, { "rank": 21, "intensity": 2.387755102040816, "date": "2022-07-25" }, { "rank": 22, "intensity": 2.357142857142857, "date": "2022-05-10" }, { "rank": 23, "intensity": 2.326530612244898, "date": "2022-07-13" }, { "rank": 24, "intensity": 2.295918367346939, "date": "2022-03-02" }, { "rank": 25, "intensity": 2.2653061224489797, "date": "2022-08-01" }, { "rank": 26, "intensity": 2.2346938775510203, "date": "2022-10-14" }, { "rank": 27, "intensity": 2.204081632653061, "date": "2022-09-20" }, { "rank": 28, "intensity": 2.173469387755102, "date": "2022-11-05" }, { "rank": 29, "intensity": 2.142857142857143, "date": "2022-11-10" }, { "rank": 30, "intensity": 2.112244897959184, "date": "2022-06-08" }, { "rank": 31, "intensity": 2.0816326530612246, "date": "2022-11-08" }, { "rank": 32, "intensity": 2.0510204081632653, "date": "2022-03-28" }, { "rank": 33, "intensity": 2.020408163265306, "date": "2022-06-19" }, { "rank": 34, "intensity": 1.989795918367347, "date": "2022-06-16" }, { "rank": 35, "intensity": 1.9591836734693877, "date": "2022-04-28" }, { "rank": 36, "intensity": 1.9285714285714286, "date": "2022-10-11" }, { "rank": 37, "intensity": 1.8979591836734695, "date": "2022-12-12" }, { "rank": 38, "intensity": 1.8673469387755102, "date": "2022-04-30" }, { "rank": 39, "intensity": 1.836734693877551, "date": "2022-07-06" }, { "rank": 40, "intensity": 1.806122448979592, "date": "2022-06-21" }, { "rank": 41, "intensity": 1.7755102040816326, "date": "2022-08-12" }, { "rank": 42, "intensity": 1.7448979591836735, "date": "2022-08-07" }, { "rank": 43, "intensity": 1.7142857142857144, "date": "2022-01-30" }, { "rank": 44, "intensity": 1.683673469387755, "date": "2022-12-28" }, { "rank": 45, "intensity": 1.653061224489796, "date": "2022-09-01" }, { "rank": 46, "intensity": 1.6224489795918369, "date": "2022-05-15" }, { "rank": 47, "intensity": 1.5918367346938775, "date": "2022-04-15" }, { "rank": 48, "intensity": 1.5612244897959184, "date": "2022-04-09" }, { "rank": 49, "intensity": 1.5306122448979593, "date": "2022-02-25" }, { "rank": 50, "intensity": 1.5, "date": "2022-12-19" } ], "source": "causal_rivers" } }, { "id": "L3_T1_Composite_Trend_00115", "level": 3, "level_name": "Semantic Reasoning", "category": "Composite Trend", "subtask": "Composite Trend", "question": "Among days in channel 124 during 2020 that exhibit the trend pattern 'fluctuating stable, then slow rise', identify the top-5 days where the fluctuating stable segment is the most fluctuating. (Output format: a ranked list of dates, e.g., ['YYYY-MM-DD', ...])", "answer": "['2020-01-03', '2020-05-02', '2020-08-20', '2020-06-11', '2020-06-06']", "ground_truth": [ "2020-01-03", "2020-05-02", "2020-08-20", "2020-06-11", "2020-06-06" ], "eval_metric": "set_f1", "channel": "124", "ts_data_path": "ts_data/L3_T1_Composite_Trend_00115.csv", "meta": { "segments": [ { "kind": "stable", "adj": "fluctuating" }, { "kind": "rise", "adj": "slow" } ], "pattern_phrase": "fluctuating stable, then slow rise", "rank_target_idx": 0, "rank_kind": "stable", "rank_adj": "fluctuating", "rank_axis": "std", "rank_direction": "DESC", "rank_extreme": "most fluctuating", "top_segments_meta": [ { "adj": "fluctuating", "kind": "stable", "label": "fluctuating stable", "start_idx": 0, "end_idx": 40 }, { "adj": "slow", "kind": "rise", "label": "slow rise", "start_idx": 40, "end_idx": 98 } ], "points_per_day": 96, "pattern": "fluctuating stable, then slow rise", "year": 2020, "top_k": [ "2020-01-03", "2020-05-02", "2020-08-20", "2020-06-11", "2020-06-06" ], "all_injections": [ { "rank": 1, "intensity": 3.0, "date": "2020-01-03" }, { "rank": 2, "intensity": 2.9693877551020407, "date": "2020-05-02" }, { "rank": 3, "intensity": 2.938775510204082, "date": "2020-08-20" }, { "rank": 4, "intensity": 2.9081632653061225, "date": "2020-06-11" }, { "rank": 5, "intensity": 2.877551020408163, "date": "2020-06-06" }, { "rank": 6, "intensity": 2.8469387755102042, "date": "2020-06-19" }, { "rank": 7, "intensity": 2.816326530612245, "date": "2020-07-06" }, { "rank": 8, "intensity": 2.7857142857142856, "date": "2020-09-13" }, { "rank": 9, "intensity": 2.7551020408163267, "date": "2020-08-16" }, { "rank": 10, "intensity": 2.7244897959183674, "date": "2020-02-06" }, { "rank": 11, "intensity": 2.693877551020408, "date": "2020-07-14" }, { "rank": 12, "intensity": 2.663265306122449, "date": "2020-01-17" }, { "rank": 13, "intensity": 2.63265306122449, "date": "2020-08-06" }, { "rank": 14, "intensity": 2.6020408163265305, "date": "2020-04-22" }, { "rank": 15, "intensity": 2.5714285714285716, "date": "2020-10-09" }, { "rank": 16, "intensity": 2.5408163265306123, "date": "2020-06-23" }, { "rank": 17, "intensity": 2.510204081632653, "date": "2020-02-14" }, { "rank": 18, "intensity": 2.479591836734694, "date": "2020-03-21" }, { "rank": 19, "intensity": 2.4489795918367347, "date": "2020-09-23" }, { "rank": 20, "intensity": 2.4183673469387754, "date": "2020-02-22" }, { "rank": 21, "intensity": 2.387755102040816, "date": "2020-03-29" }, { "rank": 22, "intensity": 2.357142857142857, "date": "2020-02-16" }, { "rank": 23, "intensity": 2.326530612244898, "date": "2020-05-15" }, { "rank": 24, "intensity": 2.295918367346939, "date": "2020-12-02" }, { "rank": 25, "intensity": 2.2653061224489797, "date": "2020-04-17" }, { "rank": 26, "intensity": 2.2346938775510203, "date": "2020-03-03" }, { "rank": 27, "intensity": 2.204081632653061, "date": "2020-01-09" }, { "rank": 28, "intensity": 2.173469387755102, "date": "2020-07-02" }, { "rank": 29, "intensity": 2.142857142857143, "date": "2020-04-15" }, { "rank": 30, "intensity": 2.112244897959184, "date": "2020-07-11" }, { "rank": 31, "intensity": 2.0816326530612246, "date": "2020-12-06" }, { "rank": 32, "intensity": 2.0510204081632653, "date": "2020-01-27" }, { "rank": 33, "intensity": 2.020408163265306, "date": "2020-05-20" }, { "rank": 34, "intensity": 1.989795918367347, "date": "2020-05-12" }, { "rank": 35, "intensity": 1.9591836734693877, "date": "2020-06-27" }, { "rank": 36, "intensity": 1.9285714285714286, "date": "2020-12-15" }, { "rank": 37, "intensity": 1.8979591836734695, "date": "2020-07-18" }, { "rank": 38, "intensity": 1.8673469387755102, "date": "2020-07-30" }, { "rank": 39, "intensity": 1.836734693877551, "date": "2020-09-26" }, { "rank": 40, "intensity": 1.806122448979592, "date": "2020-08-22" }, { "rank": 41, "intensity": 1.7755102040816326, "date": "2020-09-03" }, { "rank": 42, "intensity": 1.7448979591836735, "date": "2020-08-18" }, { "rank": 43, "intensity": 1.7142857142857144, "date": "2020-04-28" }, { "rank": 44, "intensity": 1.683673469387755, "date": "2020-10-03" }, { "rank": 45, "intensity": 1.653061224489796, "date": "2020-12-20" }, { "rank": 46, "intensity": 1.6224489795918369, "date": "2020-10-11" }, { "rank": 47, "intensity": 1.5918367346938775, "date": "2020-11-11" }, { "rank": 48, "intensity": 1.5612244897959184, "date": "2020-11-02" }, { "rank": 49, "intensity": 1.5306122448979593, "date": "2020-04-26" }, { "rank": 50, "intensity": 1.5, "date": "2020-08-24" } ], "source": "causal_rivers" } }, { "id": "L3_T1_Composite_Trend_00116", "level": 3, "level_name": "Semantic Reasoning", "category": "Composite Trend", "subtask": "Composite Trend", "question": "Among days in channel 146 during 2022 that exhibit the trend pattern 'fall, then rapid rise, then slow fall', identify the top-5 days where the slow fall segment is the slowest. (Output format: a ranked list of dates, e.g., ['YYYY-MM-DD', ...])", "answer": "['2022-08-01', '2022-12-21', '2022-02-22', '2022-01-08', '2022-05-18']", "ground_truth": [ "2022-08-01", "2022-12-21", "2022-02-22", "2022-01-08", "2022-05-18" ], "eval_metric": "set_f1", "channel": "146", "ts_data_path": "ts_data/L3_T1_Composite_Trend_00116.csv", "meta": { "segments": [ { "kind": "fall", "adj": "none" }, { "kind": "rise", "adj": "rapid" }, { "kind": "fall", "adj": "slow" } ], "pattern_phrase": "fall, then rapid rise, then slow fall", "rank_target_idx": 2, "rank_kind": "fall", "rank_adj": "slow", "rank_axis": "slope", "rank_direction": "ASC", "rank_extreme": "slowest", "top_segments_meta": [ { "adj": "none", "kind": "fall", "label": "none fall", "start_idx": 0, "end_idx": 29 }, { "adj": "rapid", "kind": "rise", "label": "rapid rise", "start_idx": 29, "end_idx": 44 }, { "adj": "slow", "kind": "fall", "label": "slow fall", "start_idx": 44, "end_idx": 98 } ], "points_per_day": 96, "pattern": "fall, then rapid rise, then slow fall", "year": 2022, "top_k": [ "2022-08-01", "2022-12-21", "2022-02-22", "2022-01-08", "2022-05-18" ], "all_injections": [ { "rank": 1, "intensity": 1.5, "date": "2022-08-01" }, { "rank": 2, "intensity": 1.530612244897959, "date": "2022-12-21" }, { "rank": 3, "intensity": 1.5612244897959184, "date": "2022-02-22" }, { "rank": 4, "intensity": 1.5918367346938775, "date": "2022-01-08" }, { "rank": 5, "intensity": 1.6224489795918366, "date": "2022-05-18" }, { "rank": 6, "intensity": 1.653061224489796, "date": "2022-03-03" }, { "rank": 7, "intensity": 1.683673469387755, "date": "2022-09-13" }, { "rank": 8, "intensity": 1.7142857142857142, "date": "2022-03-26" }, { "rank": 9, "intensity": 1.7448979591836735, "date": "2022-01-17" }, { "rank": 10, "intensity": 1.7755102040816326, "date": "2022-06-24" }, { "rank": 11, "intensity": 1.806122448979592, "date": "2022-12-26" }, { "rank": 12, "intensity": 1.836734693877551, "date": "2022-09-23" }, { "rank": 13, "intensity": 1.8673469387755102, "date": "2022-02-08" }, { "rank": 14, "intensity": 1.8979591836734695, "date": "2022-04-10" }, { "rank": 15, "intensity": 1.9285714285714286, "date": "2022-11-30" }, { "rank": 16, "intensity": 1.9591836734693877, "date": "2022-12-03" }, { "rank": 17, "intensity": 1.989795918367347, "date": "2022-06-13" }, { "rank": 18, "intensity": 2.020408163265306, "date": "2022-11-16" }, { "rank": 19, "intensity": 2.0510204081632653, "date": "2022-09-27" }, { "rank": 20, "intensity": 2.0816326530612246, "date": "2022-11-01" }, { "rank": 21, "intensity": 2.112244897959184, "date": "2022-06-07" }, { "rank": 22, "intensity": 2.142857142857143, "date": "2022-08-30" }, { "rank": 23, "intensity": 2.173469387755102, "date": "2022-08-28" }, { "rank": 24, "intensity": 2.204081632653061, "date": "2022-10-26" }, { "rank": 25, "intensity": 2.2346938775510203, "date": "2022-10-01" }, { "rank": 26, "intensity": 2.2653061224489797, "date": "2022-04-21" }, { "rank": 27, "intensity": 2.295918367346939, "date": "2022-05-15" }, { "rank": 28, "intensity": 2.326530612244898, "date": "2022-07-19" }, { "rank": 29, "intensity": 2.357142857142857, "date": "2022-01-23" }, { "rank": 30, "intensity": 2.387755102040816, "date": "2022-01-14" }, { "rank": 31, "intensity": 2.4183673469387754, "date": "2022-04-04" }, { "rank": 32, "intensity": 2.4489795918367347, "date": "2022-03-17" }, { "rank": 33, "intensity": 2.479591836734694, "date": "2022-08-17" }, { "rank": 34, "intensity": 2.510204081632653, "date": "2022-12-30" }, { "rank": 35, "intensity": 2.5408163265306123, "date": "2022-02-12" }, { "rank": 36, "intensity": 2.571428571428571, "date": "2022-11-28" }, { "rank": 37, "intensity": 2.6020408163265305, "date": "2022-10-11" }, { "rank": 38, "intensity": 2.63265306122449, "date": "2022-07-01" }, { "rank": 39, "intensity": 2.663265306122449, "date": "2022-11-19" }, { "rank": 40, "intensity": 2.693877551020408, "date": "2022-05-20" }, { "rank": 41, "intensity": 2.7244897959183674, "date": "2022-10-23" }, { "rank": 42, "intensity": 2.7551020408163263, "date": "2022-09-04" }, { "rank": 43, "intensity": 2.7857142857142856, "date": "2022-07-09" }, { "rank": 44, "intensity": 2.816326530612245, "date": "2022-12-15" }, { "rank": 45, "intensity": 2.8469387755102042, "date": "2022-06-26" }, { "rank": 46, "intensity": 2.877551020408163, "date": "2022-06-10" }, { "rank": 47, "intensity": 2.9081632653061225, "date": "2022-04-30" }, { "rank": 48, "intensity": 2.9387755102040813, "date": "2022-06-19" }, { "rank": 49, "intensity": 2.9693877551020407, "date": "2022-09-08" }, { "rank": 50, "intensity": 3.0, "date": "2022-02-01" } ], "source": "causal_rivers" } }, { "id": "L3_T1_Composite_Trend_00117", "level": 3, "level_name": "Semantic Reasoning", "category": "Composite Trend", "subtask": "Composite Trend", "question": "Among days in channel 147 during 2021 that exhibit the trend pattern 'slow fall, then fall', identify the top-5 days where the slow fall segment is the slowest. (Output format: a ranked list of dates, e.g., ['YYYY-MM-DD', ...])", "answer": "['2021-01-02', '2021-11-20', '2021-10-29', '2021-08-12', '2021-06-14']", "ground_truth": [ "2021-01-02", "2021-11-20", "2021-10-29", "2021-08-12", "2021-06-14" ], "eval_metric": "set_f1", "channel": "147", "ts_data_path": "ts_data/L3_T1_Composite_Trend_00117.csv", "meta": { "segments": [ { "kind": "fall", "adj": "slow" }, { "kind": "fall", "adj": "none" } ], "pattern_phrase": "slow fall, then fall", "rank_target_idx": 0, "rank_kind": "fall", "rank_adj": "slow", "rank_axis": "slope", "rank_direction": "ASC", "rank_extreme": "slowest", "top_segments_meta": [ { "adj": "slow", "kind": "fall", "label": "slow fall", "start_idx": 0, "end_idx": 61 }, { "adj": "none", "kind": "fall", "label": "none fall", "start_idx": 61, "end_idx": 98 } ], "points_per_day": 96, "pattern": "slow fall, then fall", "year": 2021, "top_k": [ "2021-01-02", "2021-11-20", "2021-10-29", "2021-08-12", "2021-06-14" ], "all_injections": [ { "rank": 1, "intensity": 1.5, "date": "2021-01-02" }, { "rank": 2, "intensity": 1.530612244897959, "date": "2021-11-20" }, { "rank": 3, "intensity": 1.5612244897959184, "date": "2021-10-29" }, { "rank": 4, "intensity": 1.5918367346938775, "date": "2021-08-12" }, { "rank": 5, "intensity": 1.6224489795918366, "date": "2021-06-14" }, { "rank": 6, "intensity": 1.653061224489796, "date": "2021-08-14" }, { "rank": 7, "intensity": 1.683673469387755, "date": "2021-05-31" }, { "rank": 8, "intensity": 1.7142857142857142, "date": "2021-03-24" }, { "rank": 9, "intensity": 1.7448979591836735, "date": "2021-12-22" }, { "rank": 10, "intensity": 1.7755102040816326, "date": "2021-05-14" }, { "rank": 11, "intensity": 1.806122448979592, "date": "2021-05-24" }, { "rank": 12, "intensity": 1.836734693877551, "date": "2021-02-22" }, { "rank": 13, "intensity": 1.8673469387755102, "date": "2021-10-13" }, { "rank": 14, "intensity": 1.8979591836734695, "date": "2021-08-18" }, { "rank": 15, "intensity": 1.9285714285714286, "date": "2021-01-22" }, { "rank": 16, "intensity": 1.9591836734693877, "date": "2021-12-14" }, { "rank": 17, "intensity": 1.989795918367347, "date": "2021-02-16" }, { "rank": 18, "intensity": 2.020408163265306, "date": "2021-12-12" }, { "rank": 19, "intensity": 2.0510204081632653, "date": "2021-01-30" }, { "rank": 20, "intensity": 2.0816326530612246, "date": "2021-06-24" }, { "rank": 21, "intensity": 2.112244897959184, "date": "2021-07-22" }, { "rank": 22, "intensity": 2.142857142857143, "date": "2021-10-17" }, { "rank": 23, "intensity": 2.173469387755102, "date": "2021-06-03" }, { "rank": 24, "intensity": 2.204081632653061, "date": "2021-04-21" }, { "rank": 25, "intensity": 2.2346938775510203, "date": "2021-11-09" }, { "rank": 26, "intensity": 2.2653061224489797, "date": "2021-10-04" }, { "rank": 27, "intensity": 2.295918367346939, "date": "2021-03-28" }, { "rank": 28, "intensity": 2.326530612244898, "date": "2021-08-30" }, { "rank": 29, "intensity": 2.357142857142857, "date": "2021-05-05" }, { "rank": 30, "intensity": 2.387755102040816, "date": "2021-01-27" }, { "rank": 31, "intensity": 2.4183673469387754, "date": "2021-12-29" }, { "rank": 32, "intensity": 2.4489795918367347, "date": "2021-08-16" }, { "rank": 33, "intensity": 2.479591836734694, "date": "2021-09-01" }, { "rank": 34, "intensity": 2.510204081632653, "date": "2021-06-07" }, { "rank": 35, "intensity": 2.5408163265306123, "date": "2021-08-28" }, { "rank": 36, "intensity": 2.571428571428571, "date": "2021-02-06" }, { "rank": 37, "intensity": 2.6020408163265305, "date": "2021-05-09" }, { "rank": 38, "intensity": 2.63265306122449, "date": "2021-10-01" }, { "rank": 39, "intensity": 2.663265306122449, "date": "2021-06-12" }, { "rank": 40, "intensity": 2.693877551020408, "date": "2021-12-17" }, { "rank": 41, "intensity": 2.7244897959183674, "date": "2021-03-16" }, { "rank": 42, "intensity": 2.7551020408163263, "date": "2021-03-18" }, { "rank": 43, "intensity": 2.7857142857142856, "date": "2021-08-03" }, { "rank": 44, "intensity": 2.816326530612245, "date": "2021-03-11" }, { "rank": 45, "intensity": 2.8469387755102042, "date": "2021-04-16" }, { "rank": 46, "intensity": 2.877551020408163, "date": "2021-06-17" }, { "rank": 47, "intensity": 2.9081632653061225, "date": "2021-05-18" }, { "rank": 48, "intensity": 2.9387755102040813, "date": "2021-11-15" }, { "rank": 49, "intensity": 2.9693877551020407, "date": "2021-09-16" }, { "rank": 50, "intensity": 3.0, "date": "2021-08-08" } ], "source": "causal_rivers" } }, { "id": "L3_T1_Composite_Trend_00118", "level": 3, "level_name": "Semantic Reasoning", "category": "Composite Trend", "subtask": "Composite Trend", "question": "Among days in channel 151 during 2020 that exhibit the trend pattern 'rapid rise, then fluctuating stable', identify the top-5 days where the rapid rise segment is the fastest. (Output format: a ranked list of dates, e.g., ['YYYY-MM-DD', ...])", "answer": "['2020-06-18', '2020-01-14', '2020-07-02', '2020-05-04', '2020-12-13']", "ground_truth": [ "2020-06-18", "2020-01-14", "2020-07-02", "2020-05-04", "2020-12-13" ], "eval_metric": "set_f1", "channel": "151", "ts_data_path": "ts_data/L3_T1_Composite_Trend_00118.csv", "meta": { "segments": [ { "kind": "rise", "adj": "rapid" }, { "kind": "stable", "adj": "fluctuating" } ], "pattern_phrase": "rapid rise, then fluctuating stable", "rank_target_idx": 0, "rank_kind": "rise", "rank_adj": "rapid", "rank_axis": "slope", "rank_direction": "DESC", "rank_extreme": "fastest", "top_segments_meta": [ { "adj": "rapid", "kind": "rise", "label": "rapid rise", "start_idx": 0, "end_idx": 28 }, { "adj": "fluctuating", "kind": "stable", "label": "fluctuating stable", "start_idx": 28, "end_idx": 98 } ], "points_per_day": 96, "pattern": "rapid rise, then fluctuating stable", "year": 2020, "top_k": [ "2020-06-18", "2020-01-14", "2020-07-02", "2020-05-04", "2020-12-13" ], "all_injections": [ { "rank": 1, "intensity": 3.0, "date": "2020-06-18" }, { "rank": 2, "intensity": 2.9693877551020407, "date": "2020-01-14" }, { "rank": 3, "intensity": 2.938775510204082, "date": "2020-07-02" }, { "rank": 4, "intensity": 2.9081632653061225, "date": "2020-05-04" }, { "rank": 5, "intensity": 2.877551020408163, "date": "2020-12-13" }, { "rank": 6, "intensity": 2.8469387755102042, "date": "2020-03-05" }, { "rank": 7, "intensity": 2.816326530612245, "date": "2020-11-30" }, { "rank": 8, "intensity": 2.7857142857142856, "date": "2020-02-29" }, { "rank": 9, "intensity": 2.7551020408163267, "date": "2020-01-06" }, { "rank": 10, "intensity": 2.7244897959183674, "date": "2020-01-11" }, { "rank": 11, "intensity": 2.693877551020408, "date": "2020-03-14" }, { "rank": 12, "intensity": 2.663265306122449, "date": "2020-03-23" }, { "rank": 13, "intensity": 2.63265306122449, "date": "2020-05-27" }, { "rank": 14, "intensity": 2.6020408163265305, "date": "2020-03-07" }, { "rank": 15, "intensity": 2.5714285714285716, "date": "2020-06-11" }, { "rank": 16, "intensity": 2.5408163265306123, "date": "2020-12-08" }, { "rank": 17, "intensity": 2.510204081632653, "date": "2020-04-22" }, { "rank": 18, "intensity": 2.479591836734694, "date": "2020-04-13" }, { "rank": 19, "intensity": 2.4489795918367347, "date": "2020-03-30" }, { "rank": 20, "intensity": 2.4183673469387754, "date": "2020-02-12" }, { "rank": 21, "intensity": 2.387755102040816, "date": "2020-01-28" }, { "rank": 22, "intensity": 2.357142857142857, "date": "2020-09-28" }, { "rank": 23, "intensity": 2.326530612244898, "date": "2020-05-19" }, { "rank": 24, "intensity": 2.295918367346939, "date": "2020-09-18" }, { "rank": 25, "intensity": 2.2653061224489797, "date": "2020-02-14" }, { "rank": 26, "intensity": 2.2346938775510203, "date": "2020-04-07" }, { "rank": 27, "intensity": 2.204081632653061, "date": "2020-03-17" }, { "rank": 28, "intensity": 2.173469387755102, "date": "2020-02-27" }, { "rank": 29, "intensity": 2.142857142857143, "date": "2020-09-13" }, { "rank": 30, "intensity": 2.112244897959184, "date": "2020-12-19" }, { "rank": 31, "intensity": 2.0816326530612246, "date": "2020-06-26" }, { "rank": 32, "intensity": 2.0510204081632653, "date": "2020-11-01" }, { "rank": 33, "intensity": 2.020408163265306, "date": "2020-11-08" }, { "rank": 34, "intensity": 1.989795918367347, "date": "2020-08-24" }, { "rank": 35, "intensity": 1.9591836734693877, "date": "2020-06-24" }, { "rank": 36, "intensity": 1.9285714285714286, "date": "2020-10-18" }, { "rank": 37, "intensity": 1.8979591836734695, "date": "2020-01-20" }, { "rank": 38, "intensity": 1.8673469387755102, "date": "2020-10-30" }, { "rank": 39, "intensity": 1.836734693877551, "date": "2020-05-14" }, { "rank": 40, "intensity": 1.806122448979592, "date": "2020-07-04" }, { "rank": 41, "intensity": 1.7755102040816326, "date": "2020-01-01" }, { "rank": 42, "intensity": 1.7448979591836735, "date": "2020-07-31" }, { "rank": 43, "intensity": 1.7142857142857144, "date": "2020-08-04" }, { "rank": 44, "intensity": 1.683673469387755, "date": "2020-05-24" }, { "rank": 45, "intensity": 1.653061224489796, "date": "2020-04-25" }, { "rank": 46, "intensity": 1.6224489795918369, "date": "2020-05-08" }, { "rank": 47, "intensity": 1.5918367346938775, "date": "2020-04-19" }, { "rank": 48, "intensity": 1.5612244897959184, "date": "2020-11-12" }, { "rank": 49, "intensity": 1.5306122448979593, "date": "2020-01-22" }, { "rank": 50, "intensity": 1.5, "date": "2020-04-02" } ], "source": "causal_rivers" } }, { "id": "L3_T1_Composite_Trend_00119", "level": 3, "level_name": "Semantic Reasoning", "category": "Composite Trend", "subtask": "Composite Trend", "question": "Among days in channel 154 during 2020 that exhibit the trend pattern 'slow fall, then rapid fall', identify the top-5 days where the rapid fall segment is the fastest. (Output format: a ranked list of dates, e.g., ['YYYY-MM-DD', ...])", "answer": "['2020-08-20', '2020-05-18', '2020-04-01', '2020-01-22', '2020-09-04']", "ground_truth": [ "2020-08-20", "2020-05-18", "2020-04-01", "2020-01-22", "2020-09-04" ], "eval_metric": "set_f1", "channel": "154", "ts_data_path": "ts_data/L3_T1_Composite_Trend_00119.csv", "meta": { "segments": [ { "kind": "fall", "adj": "slow" }, { "kind": "fall", "adj": "rapid" } ], "pattern_phrase": "slow fall, then rapid fall", "rank_target_idx": 1, "rank_kind": "fall", "rank_adj": "rapid", "rank_axis": "slope", "rank_direction": "DESC", "rank_extreme": "fastest", "top_segments_meta": [ { "adj": "slow", "kind": "fall", "label": "slow fall", "start_idx": 0, "end_idx": 76 }, { "adj": "rapid", "kind": "fall", "label": "rapid fall", "start_idx": 76, "end_idx": 98 } ], "points_per_day": 96, "pattern": "slow fall, then rapid fall", "year": 2020, "top_k": [ "2020-08-20", "2020-05-18", "2020-04-01", "2020-01-22", "2020-09-04" ], "all_injections": [ { "rank": 1, "intensity": 3.0, "date": "2020-08-20" }, { "rank": 2, "intensity": 2.9693877551020407, "date": "2020-05-18" }, { "rank": 3, "intensity": 2.938775510204082, "date": "2020-04-01" }, { "rank": 4, "intensity": 2.9081632653061225, "date": "2020-01-22" }, { "rank": 5, "intensity": 2.877551020408163, "date": "2020-09-04" }, { "rank": 6, "intensity": 2.8469387755102042, "date": "2020-11-14" }, { "rank": 7, "intensity": 2.816326530612245, "date": "2020-10-07" }, { "rank": 8, "intensity": 2.7857142857142856, "date": "2020-03-10" }, { "rank": 9, "intensity": 2.7551020408163267, "date": "2020-12-24" }, { "rank": 10, "intensity": 2.7244897959183674, "date": "2020-07-21" }, { "rank": 11, "intensity": 2.693877551020408, "date": "2020-11-16" }, { "rank": 12, "intensity": 2.663265306122449, "date": "2020-07-25" }, { "rank": 13, "intensity": 2.63265306122449, "date": "2020-03-20" }, { "rank": 14, "intensity": 2.6020408163265305, "date": "2020-06-14" }, { "rank": 15, "intensity": 2.5714285714285716, "date": "2020-11-21" }, { "rank": 16, "intensity": 2.5408163265306123, "date": "2020-01-14" }, { "rank": 17, "intensity": 2.510204081632653, "date": "2020-03-08" }, { "rank": 18, "intensity": 2.479591836734694, "date": "2020-07-17" }, { "rank": 19, "intensity": 2.4489795918367347, "date": "2020-12-05" }, { "rank": 20, "intensity": 2.4183673469387754, "date": "2020-11-03" }, { "rank": 21, "intensity": 2.387755102040816, "date": "2020-06-10" }, { "rank": 22, "intensity": 2.357142857142857, "date": "2020-02-14" }, { "rank": 23, "intensity": 2.326530612244898, "date": "2020-08-15" }, { "rank": 24, "intensity": 2.295918367346939, "date": "2020-10-22" }, { "rank": 25, "intensity": 2.2653061224489797, "date": "2020-06-26" }, { "rank": 26, "intensity": 2.2346938775510203, "date": "2020-08-12" }, { "rank": 27, "intensity": 2.204081632653061, "date": "2020-07-19" }, { "rank": 28, "intensity": 2.173469387755102, "date": "2020-01-24" }, { "rank": 29, "intensity": 2.142857142857143, "date": "2020-09-06" }, { "rank": 30, "intensity": 2.112244897959184, "date": "2020-11-30" }, { "rank": 31, "intensity": 2.0816326530612246, "date": "2020-07-06" }, { "rank": 32, "intensity": 2.0510204081632653, "date": "2020-04-18" }, { "rank": 33, "intensity": 2.020408163265306, "date": "2020-05-23" }, { "rank": 34, "intensity": 1.989795918367347, "date": "2020-07-02" }, { "rank": 35, "intensity": 1.9591836734693877, "date": "2020-11-24" }, { "rank": 36, "intensity": 1.9285714285714286, "date": "2020-04-11" }, { "rank": 37, "intensity": 1.8979591836734695, "date": "2020-03-04" }, { "rank": 38, "intensity": 1.8673469387755102, "date": "2020-10-02" }, { "rank": 39, "intensity": 1.836734693877551, "date": "2020-11-11" }, { "rank": 40, "intensity": 1.806122448979592, "date": "2020-10-30" }, { "rank": 41, "intensity": 1.7755102040816326, "date": "2020-06-21" }, { "rank": 42, "intensity": 1.7448979591836735, "date": "2020-05-14" }, { "rank": 43, "intensity": 1.7142857142857144, "date": "2020-04-13" }, { "rank": 44, "intensity": 1.683673469387755, "date": "2020-06-29" }, { "rank": 45, "intensity": 1.653061224489796, "date": "2020-05-28" }, { "rank": 46, "intensity": 1.6224489795918369, "date": "2020-09-25" }, { "rank": 47, "intensity": 1.5918367346938775, "date": "2020-11-08" }, { "rank": 48, "intensity": 1.5612244897959184, "date": "2020-02-22" }, { "rank": 49, "intensity": 1.5306122448979593, "date": "2020-12-12" }, { "rank": 50, "intensity": 1.5, "date": "2020-05-09" } ], "source": "causal_rivers" } }, { "id": "L3_T1_Composite_Trend_00120", "level": 3, "level_name": "Semantic Reasoning", "category": "Composite Trend", "subtask": "Composite Trend", "question": "Among days in channel 155 during 2020 that exhibit the trend pattern 'fluctuating stable, then rise, then slow rise, then fluctuating stable', identify the top-5 days where the slow rise segment is the slowest. (Output format: a ranked list of dates, e.g., ['YYYY-MM-DD', ...])", "answer": "['2020-02-27', '2020-03-11', '2020-02-18', '2020-03-04', '2020-02-03']", "ground_truth": [ "2020-02-27", "2020-03-11", "2020-02-18", "2020-03-04", "2020-02-03" ], "eval_metric": "set_f1", "channel": "155", "ts_data_path": "ts_data/L3_T1_Composite_Trend_00120.csv", "meta": { "segments": [ { "kind": "stable", "adj": "fluctuating" }, { "kind": "rise", "adj": "none" }, { "kind": "rise", "adj": "slow" }, { "kind": "stable", "adj": "fluctuating" } ], "pattern_phrase": "fluctuating stable, then rise, then slow rise, then fluctuating stable", "rank_target_idx": 2, "rank_kind": "rise", "rank_adj": "slow", "rank_axis": "slope", "rank_direction": "ASC", "rank_extreme": "slowest", "top_segments_meta": [ { "adj": "fluctuating", "kind": "stable", "label": "fluctuating stable", "start_idx": 0, "end_idx": 21 }, { "adj": "none", "kind": "rise", "label": "none rise", "start_idx": 21, "end_idx": 39 }, { "adj": "slow", "kind": "rise", "label": "slow rise", "start_idx": 39, "end_idx": 73 }, { "adj": "fluctuating", "kind": "stable", "label": "fluctuating stable", "start_idx": 73, "end_idx": 98 } ], "points_per_day": 96, "pattern": "fluctuating stable, then rise, then slow rise, then fluctuating stable", "year": 2020, "top_k": [ "2020-02-27", "2020-03-11", "2020-02-18", "2020-03-04", "2020-02-03" ], "all_injections": [ { "rank": 1, "intensity": 1.5, "date": "2020-02-27" }, { "rank": 2, "intensity": 1.530612244897959, "date": "2020-03-11" }, { "rank": 3, "intensity": 1.5612244897959184, "date": "2020-02-18" }, { "rank": 4, "intensity": 1.5918367346938775, "date": "2020-03-04" }, { "rank": 5, "intensity": 1.6224489795918366, "date": "2020-02-03" }, { "rank": 6, "intensity": 1.653061224489796, "date": "2020-09-24" }, { "rank": 7, "intensity": 1.683673469387755, "date": "2020-11-27" }, { "rank": 8, "intensity": 1.7142857142857142, "date": "2020-06-14" }, { "rank": 9, "intensity": 1.7448979591836735, "date": "2020-07-28" }, { "rank": 10, "intensity": 1.7755102040816326, "date": "2020-07-05" }, { "rank": 11, "intensity": 1.806122448979592, "date": "2020-05-28" }, { "rank": 12, "intensity": 1.836734693877551, "date": "2020-02-12" }, { "rank": 13, "intensity": 1.8673469387755102, "date": "2020-12-28" }, { "rank": 14, "intensity": 1.8979591836734695, "date": "2020-08-15" }, { "rank": 15, "intensity": 1.9285714285714286, "date": "2020-03-23" }, { "rank": 16, "intensity": 1.9591836734693877, "date": "2020-08-23" }, { "rank": 17, "intensity": 1.989795918367347, "date": "2020-06-24" }, { "rank": 18, "intensity": 2.020408163265306, "date": "2020-05-23" }, { "rank": 19, "intensity": 2.0510204081632653, "date": "2020-01-06" }, { "rank": 20, "intensity": 2.0816326530612246, "date": "2020-09-29" }, { "rank": 21, "intensity": 2.112244897959184, "date": "2020-10-31" }, { "rank": 22, "intensity": 2.142857142857143, "date": "2020-04-08" }, { "rank": 23, "intensity": 2.173469387755102, "date": "2020-03-15" }, { "rank": 24, "intensity": 2.204081632653061, "date": "2020-07-25" }, { "rank": 25, "intensity": 2.2346938775510203, "date": "2020-03-01" }, { "rank": 26, "intensity": 2.2653061224489797, "date": "2020-04-23" }, { "rank": 27, "intensity": 2.295918367346939, "date": "2020-06-03" }, { "rank": 28, "intensity": 2.326530612244898, "date": "2020-04-21" }, { "rank": 29, "intensity": 2.357142857142857, "date": "2020-09-13" }, { "rank": 30, "intensity": 2.387755102040816, "date": "2020-05-21" }, { "rank": 31, "intensity": 2.4183673469387754, "date": "2020-05-09" }, { "rank": 32, "intensity": 2.4489795918367347, "date": "2020-03-29" }, { "rank": 33, "intensity": 2.479591836734694, "date": "2020-12-05" }, { "rank": 34, "intensity": 2.510204081632653, "date": "2020-02-10" }, { "rank": 35, "intensity": 2.5408163265306123, "date": "2020-08-07" }, { "rank": 36, "intensity": 2.571428571428571, "date": "2020-08-10" }, { "rank": 37, "intensity": 2.6020408163265305, "date": "2020-08-12" }, { "rank": 38, "intensity": 2.63265306122449, "date": "2020-10-01" }, { "rank": 39, "intensity": 2.663265306122449, "date": "2020-03-08" }, { "rank": 40, "intensity": 2.693877551020408, "date": "2020-05-05" }, { "rank": 41, "intensity": 2.7244897959183674, "date": "2020-06-18" }, { "rank": 42, "intensity": 2.7551020408163263, "date": "2020-04-29" }, { "rank": 43, "intensity": 2.7857142857142856, "date": "2020-06-20" }, { "rank": 44, "intensity": 2.816326530612245, "date": "2020-01-28" }, { "rank": 45, "intensity": 2.8469387755102042, "date": "2020-02-23" }, { "rank": 46, "intensity": 2.877551020408163, "date": "2020-08-29" }, { "rank": 47, "intensity": 2.9081632653061225, "date": "2020-06-28" }, { "rank": 48, "intensity": 2.9387755102040813, "date": "2020-04-19" }, { "rank": 49, "intensity": 2.9693877551020407, "date": "2020-01-17" }, { "rank": 50, "intensity": 3.0, "date": "2020-05-15" } ], "source": "causal_rivers" } }, { "id": "L3_T1_Composite_Trend_00121", "level": 3, "level_name": "Semantic Reasoning", "category": "Composite Trend", "subtask": "Composite Trend", "question": "Among days in channel 166 during 2021 that exhibit the trend pattern 'slow fall, then rise, then fall', identify the top-5 days where the slow fall segment is the slowest. (Output format: a ranked list of dates, e.g., ['YYYY-MM-DD', ...])", "answer": "['2021-10-03', '2021-12-01', '2021-07-19', '2021-10-15', '2021-04-13']", "ground_truth": [ "2021-10-03", "2021-12-01", "2021-07-19", "2021-10-15", "2021-04-13" ], "eval_metric": "set_f1", "channel": "166", "ts_data_path": "ts_data/L3_T1_Composite_Trend_00121.csv", "meta": { "segments": [ { "kind": "fall", "adj": "slow" }, { "kind": "rise", "adj": "none" }, { "kind": "fall", "adj": "none" } ], "pattern_phrase": "slow fall, then rise, then fall", "rank_target_idx": 0, "rank_kind": "fall", "rank_adj": "slow", "rank_axis": "slope", "rank_direction": "ASC", "rank_extreme": "slowest", "top_segments_meta": [ { "adj": "slow", "kind": "fall", "label": "slow fall", "start_idx": 0, "end_idx": 48 }, { "adj": "none", "kind": "rise", "label": "none rise", "start_idx": 48, "end_idx": 73 }, { "adj": "none", "kind": "fall", "label": "none fall", "start_idx": 73, "end_idx": 98 } ], "points_per_day": 96, "pattern": "slow fall, then rise, then fall", "year": 2021, "top_k": [ "2021-10-03", "2021-12-01", "2021-07-19", "2021-10-15", "2021-04-13" ], "all_injections": [ { "rank": 1, "intensity": 1.5, "date": "2021-10-03" }, { "rank": 2, "intensity": 1.530612244897959, "date": "2021-12-01" }, { "rank": 3, "intensity": 1.5612244897959184, "date": "2021-07-19" }, { "rank": 4, "intensity": 1.5918367346938775, "date": "2021-10-15" }, { "rank": 5, "intensity": 1.6224489795918366, "date": "2021-04-13" }, { "rank": 6, "intensity": 1.653061224489796, "date": "2021-07-23" }, { "rank": 7, "intensity": 1.683673469387755, "date": "2021-01-23" }, { "rank": 8, "intensity": 1.7142857142857142, "date": "2021-02-05" }, { "rank": 9, "intensity": 1.7448979591836735, "date": "2021-09-16" }, { "rank": 10, "intensity": 1.7755102040816326, "date": "2021-08-20" }, { "rank": 11, "intensity": 1.806122448979592, "date": "2021-09-04" }, { "rank": 12, "intensity": 1.836734693877551, "date": "2021-02-20" }, { "rank": 13, "intensity": 1.8673469387755102, "date": "2021-04-26" }, { "rank": 14, "intensity": 1.8979591836734695, "date": "2021-09-27" }, { "rank": 15, "intensity": 1.9285714285714286, "date": "2021-03-14" }, { "rank": 16, "intensity": 1.9591836734693877, "date": "2021-10-25" }, { "rank": 17, "intensity": 1.989795918367347, "date": "2021-03-24" }, { "rank": 18, "intensity": 2.020408163265306, "date": "2021-08-16" }, { "rank": 19, "intensity": 2.0510204081632653, "date": "2021-12-08" }, { "rank": 20, "intensity": 2.0816326530612246, "date": "2021-05-13" }, { "rank": 21, "intensity": 2.112244897959184, "date": "2021-03-01" }, { "rank": 22, "intensity": 2.142857142857143, "date": "2021-03-18" }, { "rank": 23, "intensity": 2.173469387755102, "date": "2021-11-24" }, { "rank": 24, "intensity": 2.204081632653061, "date": "2021-06-13" }, { "rank": 25, "intensity": 2.2346938775510203, "date": "2021-03-11" }, { "rank": 26, "intensity": 2.2653061224489797, "date": "2021-12-04" }, { "rank": 27, "intensity": 2.295918367346939, "date": "2021-03-26" }, { "rank": 28, "intensity": 2.326530612244898, "date": "2021-02-13" }, { "rank": 29, "intensity": 2.357142857142857, "date": "2021-03-31" }, { "rank": 30, "intensity": 2.387755102040816, "date": "2021-05-20" }, { "rank": 31, "intensity": 2.4183673469387754, "date": "2021-04-06" }, { "rank": 32, "intensity": 2.4489795918367347, "date": "2021-11-01" }, { "rank": 33, "intensity": 2.479591836734694, "date": "2021-12-23" }, { "rank": 34, "intensity": 2.510204081632653, "date": "2021-02-23" }, { "rank": 35, "intensity": 2.5408163265306123, "date": "2021-12-17" }, { "rank": 36, "intensity": 2.571428571428571, "date": "2021-01-31" }, { "rank": 37, "intensity": 2.6020408163265305, "date": "2021-12-13" }, { "rank": 38, "intensity": 2.63265306122449, "date": "2021-05-16" }, { "rank": 39, "intensity": 2.663265306122449, "date": "2021-07-12" }, { "rank": 40, "intensity": 2.693877551020408, "date": "2021-01-11" }, { "rank": 41, "intensity": 2.7244897959183674, "date": "2021-09-14" }, { "rank": 42, "intensity": 2.7551020408163263, "date": "2021-03-16" }, { "rank": 43, "intensity": 2.7857142857142856, "date": "2021-11-07" }, { "rank": 44, "intensity": 2.816326530612245, "date": "2021-08-24" }, { "rank": 45, "intensity": 2.8469387755102042, "date": "2021-05-09" }, { "rank": 46, "intensity": 2.877551020408163, "date": "2021-11-19" }, { "rank": 47, "intensity": 2.9081632653061225, "date": "2021-12-27" }, { "rank": 48, "intensity": 2.9387755102040813, "date": "2021-06-17" }, { "rank": 49, "intensity": 2.9693877551020407, "date": "2021-02-26" }, { "rank": 50, "intensity": 3.0, "date": "2021-05-03" } ], "source": "causal_rivers" } }, { "id": "L3_T1_Composite_Trend_00122", "level": 3, "level_name": "Semantic Reasoning", "category": "Composite Trend", "subtask": "Composite Trend", "question": "Among days in channel 169 during 2022 that exhibit the trend pattern 'rise, then fluctuating stable, then rapid fall, then fluctuating stable', identify the top-5 days where the rapid fall segment is the fastest. (Output format: a ranked list of dates, e.g., ['YYYY-MM-DD', ...])", "answer": "['2022-05-23', '2022-10-16', '2022-10-12', '2022-03-24', '2022-05-16']", "ground_truth": [ "2022-05-23", "2022-10-16", "2022-10-12", "2022-03-24", "2022-05-16" ], "eval_metric": "set_f1", "channel": "169", "ts_data_path": "ts_data/L3_T1_Composite_Trend_00122.csv", "meta": { "segments": [ { "kind": "rise", "adj": "none" }, { "kind": "stable", "adj": "fluctuating" }, { "kind": "fall", "adj": "rapid" }, { "kind": "stable", "adj": "fluctuating" } ], "pattern_phrase": "rise, then fluctuating stable, then rapid fall, then fluctuating stable", "rank_target_idx": 2, "rank_kind": "fall", "rank_adj": "rapid", "rank_axis": "slope", "rank_direction": "DESC", "rank_extreme": "fastest", "top_segments_meta": [ { "adj": "none", "kind": "rise", "label": "none rise", "start_idx": 0, "end_idx": 25 }, { "adj": "fluctuating", "kind": "stable", "label": "fluctuating stable", "start_idx": 25, "end_idx": 55 }, { "adj": "rapid", "kind": "fall", "label": "rapid fall", "start_idx": 55, "end_idx": 67 }, { "adj": "fluctuating", "kind": "stable", "label": "fluctuating stable", "start_idx": 67, "end_idx": 98 } ], "points_per_day": 96, "pattern": "rise, then fluctuating stable, then rapid fall, then fluctuating stable", "year": 2022, "top_k": [ "2022-05-23", "2022-10-16", "2022-10-12", "2022-03-24", "2022-05-16" ], "all_injections": [ { "rank": 1, "intensity": 3.0, "date": "2022-05-23" }, { "rank": 2, "intensity": 2.9693877551020407, "date": "2022-10-16" }, { "rank": 3, "intensity": 2.938775510204082, "date": "2022-10-12" }, { "rank": 4, "intensity": 2.9081632653061225, "date": "2022-03-24" }, { "rank": 5, "intensity": 2.877551020408163, "date": "2022-05-16" }, { "rank": 6, "intensity": 2.8469387755102042, "date": "2022-10-04" }, { "rank": 7, "intensity": 2.816326530612245, "date": "2022-09-11" }, { "rank": 8, "intensity": 2.7857142857142856, "date": "2022-06-27" }, { "rank": 9, "intensity": 2.7551020408163267, "date": "2022-02-19" }, { "rank": 10, "intensity": 2.7244897959183674, "date": "2022-05-07" }, { "rank": 11, "intensity": 2.693877551020408, "date": "2022-02-05" }, { "rank": 12, "intensity": 2.663265306122449, "date": "2022-05-10" }, { "rank": 13, "intensity": 2.63265306122449, "date": "2022-01-21" }, { "rank": 14, "intensity": 2.6020408163265305, "date": "2022-09-09" }, { "rank": 15, "intensity": 2.5714285714285716, "date": "2022-04-13" }, { "rank": 16, "intensity": 2.5408163265306123, "date": "2022-07-17" }, { "rank": 17, "intensity": 2.510204081632653, "date": "2022-08-14" }, { "rank": 18, "intensity": 2.479591836734694, "date": "2022-06-10" }, { "rank": 19, "intensity": 2.4489795918367347, "date": "2022-02-02" }, { "rank": 20, "intensity": 2.4183673469387754, "date": "2022-06-29" }, { "rank": 21, "intensity": 2.387755102040816, "date": "2022-04-08" }, { "rank": 22, "intensity": 2.357142857142857, "date": "2022-08-20" }, { "rank": 23, "intensity": 2.326530612244898, "date": "2022-07-05" }, { "rank": 24, "intensity": 2.295918367346939, "date": "2022-02-10" }, { "rank": 25, "intensity": 2.2653061224489797, "date": "2022-02-21" }, { "rank": 26, "intensity": 2.2346938775510203, "date": "2022-01-30" }, { "rank": 27, "intensity": 2.204081632653061, "date": "2022-07-15" }, { "rank": 28, "intensity": 2.173469387755102, "date": "2022-07-26" }, { "rank": 29, "intensity": 2.142857142857143, "date": "2022-07-31" }, { "rank": 30, "intensity": 2.112244897959184, "date": "2022-11-01" }, { "rank": 31, "intensity": 2.0816326530612246, "date": "2022-08-10" }, { "rank": 32, "intensity": 2.0510204081632653, "date": "2022-06-18" }, { "rank": 33, "intensity": 2.020408163265306, "date": "2022-09-24" }, { "rank": 34, "intensity": 1.989795918367347, "date": "2022-11-05" }, { "rank": 35, "intensity": 1.9591836734693877, "date": "2022-09-01" }, { "rank": 36, "intensity": 1.9285714285714286, "date": "2022-04-25" }, { "rank": 37, "intensity": 1.8979591836734695, "date": "2022-01-06" }, { "rank": 38, "intensity": 1.8673469387755102, "date": "2022-07-24" }, { "rank": 39, "intensity": 1.836734693877551, "date": "2022-07-12" }, { "rank": 40, "intensity": 1.806122448979592, "date": "2022-12-13" }, { "rank": 41, "intensity": 1.7755102040816326, "date": "2022-03-19" }, { "rank": 42, "intensity": 1.7448979591836735, "date": "2022-01-24" }, { "rank": 43, "intensity": 1.7142857142857144, "date": "2022-03-02" }, { "rank": 44, "intensity": 1.683673469387755, "date": "2022-11-15" }, { "rank": 45, "intensity": 1.653061224489796, "date": "2022-05-13" }, { "rank": 46, "intensity": 1.6224489795918369, "date": "2022-04-17" }, { "rank": 47, "intensity": 1.5918367346938775, "date": "2022-11-19" }, { "rank": 48, "intensity": 1.5612244897959184, "date": "2022-03-17" }, { "rank": 49, "intensity": 1.5306122448979593, "date": "2022-12-29" }, { "rank": 50, "intensity": 1.5, "date": "2022-08-29" } ], "source": "causal_rivers" } }, { "id": "L3_T1_Composite_Trend_00123", "level": 3, "level_name": "Semantic Reasoning", "category": "Composite Trend", "subtask": "Composite Trend", "question": "Among days in channel 170 during 2020 that exhibit the trend pattern 'rise, then fall, then rapid rise', identify the top-5 days where the rapid rise segment is the fastest. (Output format: a ranked list of dates, e.g., ['YYYY-MM-DD', ...])", "answer": "['2020-06-03', '2020-10-26', '2020-04-19', '2020-08-01', '2020-05-03']", "ground_truth": [ "2020-06-03", "2020-10-26", "2020-04-19", "2020-08-01", "2020-05-03" ], "eval_metric": "set_f1", "channel": "170", "ts_data_path": "ts_data/L3_T1_Composite_Trend_00123.csv", "meta": { "segments": [ { "kind": "rise", "adj": "none" }, { "kind": "fall", "adj": "none" }, { "kind": "rise", "adj": "rapid" } ], "pattern_phrase": "rise, then fall, then rapid rise", "rank_target_idx": 2, "rank_kind": "rise", "rank_adj": "rapid", "rank_axis": "slope", "rank_direction": "DESC", "rank_extreme": "fastest", "top_segments_meta": [ { "adj": "none", "kind": "rise", "label": "none rise", "start_idx": 0, "end_idx": 36 }, { "adj": "none", "kind": "fall", "label": "none fall", "start_idx": 36, "end_idx": 79 }, { "adj": "rapid", "kind": "rise", "label": "rapid rise", "start_idx": 79, "end_idx": 98 } ], "points_per_day": 96, "pattern": "rise, then fall, then rapid rise", "year": 2020, "top_k": [ "2020-06-03", "2020-10-26", "2020-04-19", "2020-08-01", "2020-05-03" ], "all_injections": [ { "rank": 1, "intensity": 3.0, "date": "2020-06-03" }, { "rank": 2, "intensity": 2.9693877551020407, "date": "2020-10-26" }, { "rank": 3, "intensity": 2.938775510204082, "date": "2020-04-19" }, { "rank": 4, "intensity": 2.9081632653061225, "date": "2020-08-01" }, { "rank": 5, "intensity": 2.877551020408163, "date": "2020-05-03" }, { "rank": 6, "intensity": 2.8469387755102042, "date": "2020-01-06" }, { "rank": 7, "intensity": 2.816326530612245, "date": "2020-04-05" }, { "rank": 8, "intensity": 2.7857142857142856, "date": "2020-04-16" }, { "rank": 9, "intensity": 2.7551020408163267, "date": "2020-04-24" }, { "rank": 10, "intensity": 2.7244897959183674, "date": "2020-10-29" }, { "rank": 11, "intensity": 2.693877551020408, "date": "2020-03-12" }, { "rank": 12, "intensity": 2.663265306122449, "date": "2020-05-16" }, { "rank": 13, "intensity": 2.63265306122449, "date": "2020-05-30" }, { "rank": 14, "intensity": 2.6020408163265305, "date": "2020-04-03" }, { "rank": 15, "intensity": 2.5714285714285716, "date": "2020-11-30" }, { "rank": 16, "intensity": 2.5408163265306123, "date": "2020-05-09" }, { "rank": 17, "intensity": 2.510204081632653, "date": "2020-10-20" }, { "rank": 18, "intensity": 2.479591836734694, "date": "2020-11-15" }, { "rank": 19, "intensity": 2.4489795918367347, "date": "2020-01-09" }, { "rank": 20, "intensity": 2.4183673469387754, "date": "2020-03-21" }, { "rank": 21, "intensity": 2.387755102040816, "date": "2020-01-16" }, { "rank": 22, "intensity": 2.357142857142857, "date": "2020-04-08" }, { "rank": 23, "intensity": 2.326530612244898, "date": "2020-02-23" }, { "rank": 24, "intensity": 2.295918367346939, "date": "2020-07-22" }, { "rank": 25, "intensity": 2.2653061224489797, "date": "2020-12-06" }, { "rank": 26, "intensity": 2.2346938775510203, "date": "2020-07-15" }, { "rank": 27, "intensity": 2.204081632653061, "date": "2020-09-25" }, { "rank": 28, "intensity": 2.173469387755102, "date": "2020-02-06" }, { "rank": 29, "intensity": 2.142857142857143, "date": "2020-11-20" }, { "rank": 30, "intensity": 2.112244897959184, "date": "2020-07-08" }, { "rank": 31, "intensity": 2.0816326530612246, "date": "2020-02-21" }, { "rank": 32, "intensity": 2.0510204081632653, "date": "2020-10-22" }, { "rank": 33, "intensity": 2.020408163265306, "date": "2020-05-23" }, { "rank": 34, "intensity": 1.989795918367347, "date": "2020-02-14" }, { "rank": 35, "intensity": 1.9591836734693877, "date": "2020-06-28" }, { "rank": 36, "intensity": 1.9285714285714286, "date": "2020-08-26" }, { "rank": 37, "intensity": 1.8979591836734695, "date": "2020-03-02" }, { "rank": 38, "intensity": 1.8673469387755102, "date": "2020-08-08" }, { "rank": 39, "intensity": 1.836734693877551, "date": "2020-12-24" }, { "rank": 40, "intensity": 1.806122448979592, "date": "2020-02-18" }, { "rank": 41, "intensity": 1.7755102040816326, "date": "2020-06-12" }, { "rank": 42, "intensity": 1.7448979591836735, "date": "2020-02-11" }, { "rank": 43, "intensity": 1.7142857142857144, "date": "2020-07-12" }, { "rank": 44, "intensity": 1.683673469387755, "date": "2020-04-27" }, { "rank": 45, "intensity": 1.653061224489796, "date": "2020-03-07" }, { "rank": 46, "intensity": 1.6224489795918369, "date": "2020-06-25" }, { "rank": 47, "intensity": 1.5918367346938775, "date": "2020-10-01" }, { "rank": 48, "intensity": 1.5612244897959184, "date": "2020-04-13" }, { "rank": 49, "intensity": 1.5306122448979593, "date": "2020-04-10" }, { "rank": 50, "intensity": 1.5, "date": "2020-05-13" } ], "source": "causal_rivers" } }, { "id": "L3_T1_Composite_Trend_00124", "level": 3, "level_name": "Semantic Reasoning", "category": "Composite Trend", "subtask": "Composite Trend", "question": "Among days in channel 172 during 2022 that exhibit the trend pattern 'steady stable, then fluctuating stable', identify the top-5 days where the fluctuating stable segment is the most fluctuating. (Output format: a ranked list of dates, e.g., ['YYYY-MM-DD', ...])", "answer": "['2022-12-01', '2022-10-01', '2022-11-14', '2022-09-02', '2022-05-04']", "ground_truth": [ "2022-12-01", "2022-10-01", "2022-11-14", "2022-09-02", "2022-05-04" ], "eval_metric": "set_f1", "channel": "172", "ts_data_path": "ts_data/L3_T1_Composite_Trend_00124.csv", "meta": { "segments": [ { "kind": "stable", "adj": "steady" }, { "kind": "stable", "adj": "fluctuating" } ], "pattern_phrase": "steady stable, then fluctuating stable", "rank_target_idx": 1, "rank_kind": "stable", "rank_adj": "fluctuating", "rank_axis": "std", "rank_direction": "DESC", "rank_extreme": "most fluctuating", "top_segments_meta": [ { "adj": "steady", "kind": "stable", "label": "steady stable", "start_idx": 0, "end_idx": 60 }, { "adj": "fluctuating", "kind": "stable", "label": "fluctuating stable", "start_idx": 60, "end_idx": 98 } ], "points_per_day": 96, "pattern": "steady stable, then fluctuating stable", "year": 2022, "top_k": [ "2022-12-01", "2022-10-01", "2022-11-14", "2022-09-02", "2022-05-04" ], "all_injections": [ { "rank": 1, "intensity": 3.0, "date": "2022-12-01" }, { "rank": 2, "intensity": 2.9693877551020407, "date": "2022-10-01" }, { "rank": 3, "intensity": 2.938775510204082, "date": "2022-11-14" }, { "rank": 4, "intensity": 2.9081632653061225, "date": "2022-09-02" }, { "rank": 5, "intensity": 2.877551020408163, "date": "2022-05-04" }, { "rank": 6, "intensity": 2.8469387755102042, "date": "2022-02-19" }, { "rank": 7, "intensity": 2.816326530612245, "date": "2022-01-06" }, { "rank": 8, "intensity": 2.7857142857142856, "date": "2022-11-04" }, { "rank": 9, "intensity": 2.7551020408163267, "date": "2022-09-20" }, { "rank": 10, "intensity": 2.7244897959183674, "date": "2022-03-27" }, { "rank": 11, "intensity": 2.693877551020408, "date": "2022-11-23" }, { "rank": 12, "intensity": 2.663265306122449, "date": "2022-05-23" }, { "rank": 13, "intensity": 2.63265306122449, "date": "2022-08-20" }, { "rank": 14, "intensity": 2.6020408163265305, "date": "2022-08-22" }, { "rank": 15, "intensity": 2.5714285714285716, "date": "2022-03-08" }, { "rank": 16, "intensity": 2.5408163265306123, "date": "2022-06-28" }, { "rank": 17, "intensity": 2.510204081632653, "date": "2022-12-27" }, { "rank": 18, "intensity": 2.479591836734694, "date": "2022-04-22" }, { "rank": 19, "intensity": 2.4489795918367347, "date": "2022-05-16" }, { "rank": 20, "intensity": 2.4183673469387754, "date": "2022-11-28" }, { "rank": 21, "intensity": 2.387755102040816, "date": "2022-10-16" }, { "rank": 22, "intensity": 2.357142857142857, "date": "2022-05-13" }, { "rank": 23, "intensity": 2.326530612244898, "date": "2022-06-14" }, { "rank": 24, "intensity": 2.295918367346939, "date": "2022-10-18" }, { "rank": 25, "intensity": 2.2653061224489797, "date": "2022-12-13" }, { "rank": 26, "intensity": 2.2346938775510203, "date": "2022-07-21" }, { "rank": 27, "intensity": 2.204081632653061, "date": "2022-07-23" }, { "rank": 28, "intensity": 2.173469387755102, "date": "2022-03-22" }, { "rank": 29, "intensity": 2.142857142857143, "date": "2022-10-09" }, { "rank": 30, "intensity": 2.112244897959184, "date": "2022-10-27" }, { "rank": 31, "intensity": 2.0816326530612246, "date": "2022-09-05" }, { "rank": 32, "intensity": 2.0510204081632653, "date": "2022-04-30" }, { "rank": 33, "intensity": 2.020408163265306, "date": "2022-07-30" }, { "rank": 34, "intensity": 1.989795918367347, "date": "2022-05-08" }, { "rank": 35, "intensity": 1.9591836734693877, "date": "2022-12-10" }, { "rank": 36, "intensity": 1.9285714285714286, "date": "2022-12-06" }, { "rank": 37, "intensity": 1.8979591836734695, "date": "2022-02-12" }, { "rank": 38, "intensity": 1.8673469387755102, "date": "2022-01-26" }, { "rank": 39, "intensity": 1.836734693877551, "date": "2022-07-11" }, { "rank": 40, "intensity": 1.806122448979592, "date": "2022-03-20" }, { "rank": 41, "intensity": 1.7755102040816326, "date": "2022-11-07" }, { "rank": 42, "intensity": 1.7448979591836735, "date": "2022-04-05" }, { "rank": 43, "intensity": 1.7142857142857144, "date": "2022-04-11" }, { "rank": 44, "intensity": 1.683673469387755, "date": "2022-08-03" }, { "rank": 45, "intensity": 1.653061224489796, "date": "2022-05-27" }, { "rank": 46, "intensity": 1.6224489795918369, "date": "2022-02-16" }, { "rank": 47, "intensity": 1.5918367346938775, "date": "2022-10-07" }, { "rank": 48, "intensity": 1.5612244897959184, "date": "2022-09-25" }, { "rank": 49, "intensity": 1.5306122448979593, "date": "2022-06-19" }, { "rank": 50, "intensity": 1.5, "date": "2022-11-26" } ], "source": "causal_rivers" } }, { "id": "L3_T1_Composite_Trend_00125", "level": 3, "level_name": "Semantic Reasoning", "category": "Composite Trend", "subtask": "Composite Trend", "question": "Among days in channel 173 during 2023 that exhibit the trend pattern 'slow fall, then fall, then steady stable', identify the top-5 days where the slow fall segment is the slowest. (Output format: a ranked list of dates, e.g., ['YYYY-MM-DD', ...])", "answer": "['2023-06-26', '2023-07-01', '2023-03-18', '2023-01-09', '2023-12-22']", "ground_truth": [ "2023-06-26", "2023-07-01", "2023-03-18", "2023-01-09", "2023-12-22" ], "eval_metric": "set_f1", "channel": "173", "ts_data_path": "ts_data/L3_T1_Composite_Trend_00125.csv", "meta": { "segments": [ { "kind": "fall", "adj": "slow" }, { "kind": "fall", "adj": "none" }, { "kind": "stable", "adj": "steady" } ], "pattern_phrase": "slow fall, then fall, then steady stable", "rank_target_idx": 0, "rank_kind": "fall", "rank_adj": "slow", "rank_axis": "slope", "rank_direction": "ASC", "rank_extreme": "slowest", "top_segments_meta": [ { "adj": "slow", "kind": "fall", "label": "slow fall", "start_idx": 0, "end_idx": 35 }, { "adj": "none", "kind": "fall", "label": "none fall", "start_idx": 35, "end_idx": 57 }, { "adj": "steady", "kind": "stable", "label": "steady stable", "start_idx": 57, "end_idx": 98 } ], "points_per_day": 96, "pattern": "slow fall, then fall, then steady stable", "year": 2023, "top_k": [ "2023-06-26", "2023-07-01", "2023-03-18", "2023-01-09", "2023-12-22" ], "all_injections": [ { "rank": 1, "intensity": 1.5, "date": "2023-06-26" }, { "rank": 2, "intensity": 1.530612244897959, "date": "2023-07-01" }, { "rank": 3, "intensity": 1.5612244897959184, "date": "2023-03-18" }, { "rank": 4, "intensity": 1.5918367346938775, "date": "2023-01-09" }, { "rank": 5, "intensity": 1.6224489795918366, "date": "2023-12-22" }, { "rank": 6, "intensity": 1.653061224489796, "date": "2023-07-21" }, { "rank": 7, "intensity": 1.683673469387755, "date": "2023-07-19" }, { "rank": 8, "intensity": 1.7142857142857142, "date": "2023-07-23" }, { "rank": 9, "intensity": 1.7448979591836735, "date": "2023-04-29" }, { "rank": 10, "intensity": 1.7755102040816326, "date": "2023-11-17" }, { "rank": 11, "intensity": 1.806122448979592, "date": "2023-07-30" }, { "rank": 12, "intensity": 1.836734693877551, "date": "2023-06-15" }, { "rank": 13, "intensity": 1.8673469387755102, "date": "2023-11-21" }, { "rank": 14, "intensity": 1.8979591836734695, "date": "2023-04-23" }, { "rank": 15, "intensity": 1.9285714285714286, "date": "2023-02-13" }, { "rank": 16, "intensity": 1.9591836734693877, "date": "2023-05-28" }, { "rank": 17, "intensity": 1.989795918367347, "date": "2023-01-22" }, { "rank": 18, "intensity": 2.020408163265306, "date": "2023-01-25" }, { "rank": 19, "intensity": 2.0510204081632653, "date": "2023-03-29" }, { "rank": 20, "intensity": 2.0816326530612246, "date": "2023-09-06" }, { "rank": 21, "intensity": 2.112244897959184, "date": "2023-02-09" }, { "rank": 22, "intensity": 2.142857142857143, "date": "2023-03-05" }, { "rank": 23, "intensity": 2.173469387755102, "date": "2023-10-24" }, { "rank": 24, "intensity": 2.204081632653061, "date": "2023-02-06" }, { "rank": 25, "intensity": 2.2346938775510203, "date": "2023-03-01" }, { "rank": 26, "intensity": 2.2653061224489797, "date": "2023-04-25" }, { "rank": 27, "intensity": 2.295918367346939, "date": "2023-09-12" }, { "rank": 28, "intensity": 2.326530612244898, "date": "2023-05-15" }, { "rank": 29, "intensity": 2.357142857142857, "date": "2023-04-04" }, { "rank": 30, "intensity": 2.387755102040816, "date": "2023-09-01" }, { "rank": 31, "intensity": 2.4183673469387754, "date": "2023-12-08" }, { "rank": 32, "intensity": 2.4489795918367347, "date": "2023-04-10" }, { "rank": 33, "intensity": 2.479591836734694, "date": "2023-11-15" }, { "rank": 34, "intensity": 2.510204081632653, "date": "2023-09-30" }, { "rank": 35, "intensity": 2.5408163265306123, "date": "2023-03-11" }, { "rank": 36, "intensity": 2.571428571428571, "date": "2023-10-20" }, { "rank": 37, "intensity": 2.6020408163265305, "date": "2023-02-27" }, { "rank": 38, "intensity": 2.63265306122449, "date": "2023-10-27" }, { "rank": 39, "intensity": 2.663265306122449, "date": "2023-05-09" }, { "rank": 40, "intensity": 2.693877551020408, "date": "2023-02-22" }, { "rank": 41, "intensity": 2.7244897959183674, "date": "2023-12-16" }, { "rank": 42, "intensity": 2.7551020408163263, "date": "2023-08-23" }, { "rank": 43, "intensity": 2.7857142857142856, "date": "2023-07-03" }, { "rank": 44, "intensity": 2.816326530612245, "date": "2023-09-28" }, { "rank": 45, "intensity": 2.8469387755102042, "date": "2023-12-14" }, { "rank": 46, "intensity": 2.877551020408163, "date": "2023-09-10" }, { "rank": 47, "intensity": 2.9081632653061225, "date": "2023-03-15" }, { "rank": 48, "intensity": 2.9387755102040813, "date": "2023-12-03" }, { "rank": 49, "intensity": 2.9693877551020407, "date": "2023-12-26" }, { "rank": 50, "intensity": 3.0, "date": "2023-09-16" } ], "source": "causal_rivers" } }, { "id": "L3_T1_Composite_Trend_00126", "level": 3, "level_name": "Semantic Reasoning", "category": "Composite Trend", "subtask": "Composite Trend", "question": "Among days in channel 177 during 2023 that exhibit the trend pattern 'fluctuating stable, then steady stable', identify the top-5 days where the fluctuating stable segment is the most fluctuating. (Output format: a ranked list of dates, e.g., ['YYYY-MM-DD', ...])", "answer": "['2023-08-09', '2023-06-30', '2023-10-18', '2023-04-26', '2023-12-10']", "ground_truth": [ "2023-08-09", "2023-06-30", "2023-10-18", "2023-04-26", "2023-12-10" ], "eval_metric": "set_f1", "channel": "177", "ts_data_path": "ts_data/L3_T1_Composite_Trend_00126.csv", "meta": { "segments": [ { "kind": "stable", "adj": "fluctuating" }, { "kind": "stable", "adj": "steady" } ], "pattern_phrase": "fluctuating stable, then steady stable", "rank_target_idx": 0, "rank_kind": "stable", "rank_adj": "fluctuating", "rank_axis": "std", "rank_direction": "DESC", "rank_extreme": "most fluctuating", "top_segments_meta": [ { "adj": "fluctuating", "kind": "stable", "label": "fluctuating stable", "start_idx": 0, "end_idx": 37 }, { "adj": "steady", "kind": "stable", "label": "steady stable", "start_idx": 37, "end_idx": 98 } ], "points_per_day": 96, "pattern": "fluctuating stable, then steady stable", "year": 2023, "top_k": [ "2023-08-09", "2023-06-30", "2023-10-18", "2023-04-26", "2023-12-10" ], "all_injections": [ { "rank": 1, "intensity": 3.0, "date": "2023-08-09" }, { "rank": 2, "intensity": 2.9693877551020407, "date": "2023-06-30" }, { "rank": 3, "intensity": 2.938775510204082, "date": "2023-10-18" }, { "rank": 4, "intensity": 2.9081632653061225, "date": "2023-04-26" }, { "rank": 5, "intensity": 2.877551020408163, "date": "2023-12-10" }, { "rank": 6, "intensity": 2.8469387755102042, "date": "2023-07-08" }, { "rank": 7, "intensity": 2.816326530612245, "date": "2023-08-23" }, { "rank": 8, "intensity": 2.7857142857142856, "date": "2023-05-10" }, { "rank": 9, "intensity": 2.7551020408163267, "date": "2023-11-10" }, { "rank": 10, "intensity": 2.7244897959183674, "date": "2023-07-05" }, { "rank": 11, "intensity": 2.693877551020408, "date": "2023-09-06" }, { "rank": 12, "intensity": 2.663265306122449, "date": "2023-05-03" }, { "rank": 13, "intensity": 2.63265306122449, "date": "2023-02-04" }, { "rank": 14, "intensity": 2.6020408163265305, "date": "2023-03-05" }, { "rank": 15, "intensity": 2.5714285714285716, "date": "2023-12-30" }, { "rank": 16, "intensity": 2.5408163265306123, "date": "2023-04-07" }, { "rank": 17, "intensity": 2.510204081632653, "date": "2023-03-13" }, { "rank": 18, "intensity": 2.479591836734694, "date": "2023-08-29" }, { "rank": 19, "intensity": 2.4489795918367347, "date": "2023-06-18" }, { "rank": 20, "intensity": 2.4183673469387754, "date": "2023-08-18" }, { "rank": 21, "intensity": 2.387755102040816, "date": "2023-02-07" }, { "rank": 22, "intensity": 2.357142857142857, "date": "2023-10-27" }, { "rank": 23, "intensity": 2.326530612244898, "date": "2023-05-14" }, { "rank": 24, "intensity": 2.295918367346939, "date": "2023-11-06" }, { "rank": 25, "intensity": 2.2653061224489797, "date": "2023-03-24" }, { "rank": 26, "intensity": 2.2346938775510203, "date": "2023-03-19" }, { "rank": 27, "intensity": 2.204081632653061, "date": "2023-06-10" }, { "rank": 28, "intensity": 2.173469387755102, "date": "2023-02-24" }, { "rank": 29, "intensity": 2.142857142857143, "date": "2023-03-01" }, { "rank": 30, "intensity": 2.112244897959184, "date": "2023-10-25" }, { "rank": 31, "intensity": 2.0816326530612246, "date": "2023-08-26" }, { "rank": 32, "intensity": 2.0510204081632653, "date": "2023-10-11" }, { "rank": 33, "intensity": 2.020408163265306, "date": "2023-08-01" }, { "rank": 34, "intensity": 1.989795918367347, "date": "2023-06-15" }, { "rank": 35, "intensity": 1.9591836734693877, "date": "2023-02-17" }, { "rank": 36, "intensity": 1.9285714285714286, "date": "2023-05-29" }, { "rank": 37, "intensity": 1.8979591836734695, "date": "2023-04-04" }, { "rank": 38, "intensity": 1.8673469387755102, "date": "2023-01-28" }, { "rank": 39, "intensity": 1.836734693877551, "date": "2023-05-23" }, { "rank": 40, "intensity": 1.806122448979592, "date": "2023-04-24" }, { "rank": 41, "intensity": 1.7755102040816326, "date": "2023-08-11" }, { "rank": 42, "intensity": 1.7448979591836735, "date": "2023-03-21" }, { "rank": 43, "intensity": 1.7142857142857144, "date": "2023-10-29" }, { "rank": 44, "intensity": 1.683673469387755, "date": "2023-12-06" }, { "rank": 45, "intensity": 1.653061224489796, "date": "2023-04-13" }, { "rank": 46, "intensity": 1.6224489795918369, "date": "2023-09-09" }, { "rank": 47, "intensity": 1.5918367346938775, "date": "2023-06-26" }, { "rank": 48, "intensity": 1.5612244897959184, "date": "2023-06-07" }, { "rank": 49, "intensity": 1.5306122448979593, "date": "2023-07-12" }, { "rank": 50, "intensity": 1.5, "date": "2023-05-20" } ], "source": "causal_rivers" } }, { "id": "L3_T1_Composite_Trend_00127", "level": 3, "level_name": "Semantic Reasoning", "category": "Composite Trend", "subtask": "Composite Trend", "question": "Among days in channel 237 during 2022 that exhibit the trend pattern 'rapid fall, then rapid rise', identify the top-5 days where the rapid fall segment is the fastest. (Output format: a ranked list of dates, e.g., ['YYYY-MM-DD', ...])", "answer": "['2022-07-21', '2022-11-14', '2022-04-19', '2022-06-27', '2022-12-27']", "ground_truth": [ "2022-07-21", "2022-11-14", "2022-04-19", "2022-06-27", "2022-12-27" ], "eval_metric": "set_f1", "channel": "237", "ts_data_path": "ts_data/L3_T1_Composite_Trend_00127.csv", "meta": { "segments": [ { "kind": "fall", "adj": "rapid" }, { "kind": "rise", "adj": "rapid" } ], "pattern_phrase": "rapid fall, then rapid rise", "rank_target_idx": 0, "rank_kind": "fall", "rank_adj": "rapid", "rank_axis": "slope", "rank_direction": "DESC", "rank_extreme": "fastest", "top_segments_meta": [ { "adj": "rapid", "kind": "fall", "label": "rapid fall", "start_idx": 0, "end_idx": 52 }, { "adj": "rapid", "kind": "rise", "label": "rapid rise", "start_idx": 52, "end_idx": 98 } ], "points_per_day": 96, "pattern": "rapid fall, then rapid rise", "year": 2022, "top_k": [ "2022-07-21", "2022-11-14", "2022-04-19", "2022-06-27", "2022-12-27" ], "all_injections": [ { "rank": 1, "intensity": 3.0, "date": "2022-07-21" }, { "rank": 2, "intensity": 2.9693877551020407, "date": "2022-11-14" }, { "rank": 3, "intensity": 2.938775510204082, "date": "2022-04-19" }, { "rank": 4, "intensity": 2.9081632653061225, "date": "2022-06-27" }, { "rank": 5, "intensity": 2.877551020408163, "date": "2022-12-27" }, { "rank": 6, "intensity": 2.8469387755102042, "date": "2022-12-10" }, { "rank": 7, "intensity": 2.816326530612245, "date": "2022-07-15" }, { "rank": 8, "intensity": 2.7857142857142856, "date": "2022-04-30" }, { "rank": 9, "intensity": 2.7551020408163267, "date": "2022-06-29" }, { "rank": 10, "intensity": 2.7244897959183674, "date": "2022-01-04" }, { "rank": 11, "intensity": 2.693877551020408, "date": "2022-09-20" }, { "rank": 12, "intensity": 2.663265306122449, "date": "2022-07-17" }, { "rank": 13, "intensity": 2.63265306122449, "date": "2022-10-07" }, { "rank": 14, "intensity": 2.6020408163265305, "date": "2022-09-25" }, { "rank": 15, "intensity": 2.5714285714285716, "date": "2022-06-13" }, { "rank": 16, "intensity": 2.5408163265306123, "date": "2022-09-01" }, { "rank": 17, "intensity": 2.510204081632653, "date": "2022-12-13" }, { "rank": 18, "intensity": 2.479591836734694, "date": "2022-12-15" }, { "rank": 19, "intensity": 2.4489795918367347, "date": "2022-03-31" }, { "rank": 20, "intensity": 2.4183673469387754, "date": "2022-04-16" }, { "rank": 21, "intensity": 2.387755102040816, "date": "2022-05-31" }, { "rank": 22, "intensity": 2.357142857142857, "date": "2022-06-08" }, { "rank": 23, "intensity": 2.326530612244898, "date": "2022-09-11" }, { "rank": 24, "intensity": 2.295918367346939, "date": "2022-01-02" }, { "rank": 25, "intensity": 2.2653061224489797, "date": "2022-12-19" }, { "rank": 26, "intensity": 2.2346938775510203, "date": "2022-01-06" }, { "rank": 27, "intensity": 2.204081632653061, "date": "2022-02-19" }, { "rank": 28, "intensity": 2.173469387755102, "date": "2022-11-17" }, { "rank": 29, "intensity": 2.142857142857143, "date": "2022-03-08" }, { "rank": 30, "intensity": 2.112244897959184, "date": "2022-12-08" }, { "rank": 31, "intensity": 2.0816326530612246, "date": "2022-01-16" }, { "rank": 32, "intensity": 2.0510204081632653, "date": "2022-09-14" }, { "rank": 33, "intensity": 2.020408163265306, "date": "2022-11-19" }, { "rank": 34, "intensity": 1.989795918367347, "date": "2022-08-17" }, { "rank": 35, "intensity": 1.9591836734693877, "date": "2022-10-11" }, { "rank": 36, "intensity": 1.9285714285714286, "date": "2022-04-22" }, { "rank": 37, "intensity": 1.8979591836734695, "date": "2022-06-02" }, { "rank": 38, "intensity": 1.8673469387755102, "date": "2022-11-11" }, { "rank": 39, "intensity": 1.836734693877551, "date": "2022-09-03" }, { "rank": 40, "intensity": 1.806122448979592, "date": "2022-06-19" }, { "rank": 41, "intensity": 1.7755102040816326, "date": "2022-09-22" }, { "rank": 42, "intensity": 1.7448979591836735, "date": "2022-11-22" }, { "rank": 43, "intensity": 1.7142857142857144, "date": "2022-10-05" }, { "rank": 44, "intensity": 1.683673469387755, "date": "2022-03-27" }, { "rank": 45, "intensity": 1.653061224489796, "date": "2022-09-05" }, { "rank": 46, "intensity": 1.6224489795918369, "date": "2022-07-07" }, { "rank": 47, "intensity": 1.5918367346938775, "date": "2022-12-30" }, { "rank": 48, "intensity": 1.5612244897959184, "date": "2022-05-24" }, { "rank": 49, "intensity": 1.5306122448979593, "date": "2022-10-27" }, { "rank": 50, "intensity": 1.5, "date": "2022-04-09" } ], "source": "causal_rivers" } }, { "id": "L3_T1_Composite_Trend_00128", "level": 3, "level_name": "Semantic Reasoning", "category": "Composite Trend", "subtask": "Composite Trend", "question": "Among days in channel 245 during 2022 that exhibit the trend pattern 'fall, then rapid fall', identify the top-5 days where the rapid fall segment is the fastest. (Output format: a ranked list of dates, e.g., ['YYYY-MM-DD', ...])", "answer": "['2022-05-02', '2022-11-22', '2022-12-16', '2022-06-03', '2022-08-05']", "ground_truth": [ "2022-05-02", "2022-11-22", "2022-12-16", "2022-06-03", "2022-08-05" ], "eval_metric": "set_f1", "channel": "245", "ts_data_path": "ts_data/L3_T1_Composite_Trend_00128.csv", "meta": { "segments": [ { "kind": "fall", "adj": "none" }, { "kind": "fall", "adj": "rapid" } ], "pattern_phrase": "fall, then rapid fall", "rank_target_idx": 1, "rank_kind": "fall", "rank_adj": "rapid", "rank_axis": "slope", "rank_direction": "DESC", "rank_extreme": "fastest", "top_segments_meta": [ { "adj": "none", "kind": "fall", "label": "none fall", "start_idx": 0, "end_idx": 66 }, { "adj": "rapid", "kind": "fall", "label": "rapid fall", "start_idx": 66, "end_idx": 98 } ], "points_per_day": 96, "pattern": "fall, then rapid fall", "year": 2022, "top_k": [ "2022-05-02", "2022-11-22", "2022-12-16", "2022-06-03", "2022-08-05" ], "all_injections": [ { "rank": 1, "intensity": 3.0, "date": "2022-05-02" }, { "rank": 2, "intensity": 2.9693877551020407, "date": "2022-11-22" }, { "rank": 3, "intensity": 2.938775510204082, "date": "2022-12-16" }, { "rank": 4, "intensity": 2.9081632653061225, "date": "2022-06-03" }, { "rank": 5, "intensity": 2.877551020408163, "date": "2022-08-05" }, { "rank": 6, "intensity": 2.8469387755102042, "date": "2022-10-26" }, { "rank": 7, "intensity": 2.816326530612245, "date": "2022-11-01" }, { "rank": 8, "intensity": 2.7857142857142856, "date": "2022-04-06" }, { "rank": 9, "intensity": 2.7551020408163267, "date": "2022-03-03" }, { "rank": 10, "intensity": 2.7244897959183674, "date": "2022-05-25" }, { "rank": 11, "intensity": 2.693877551020408, "date": "2022-09-28" }, { "rank": 12, "intensity": 2.663265306122449, "date": "2022-10-30" }, { "rank": 13, "intensity": 2.63265306122449, "date": "2022-09-04" }, { "rank": 14, "intensity": 2.6020408163265305, "date": "2022-06-15" }, { "rank": 15, "intensity": 2.5714285714285716, "date": "2022-06-22" }, { "rank": 16, "intensity": 2.5408163265306123, "date": "2022-05-13" }, { "rank": 17, "intensity": 2.510204081632653, "date": "2022-11-20" }, { "rank": 18, "intensity": 2.479591836734694, "date": "2022-01-06" }, { "rank": 19, "intensity": 2.4489795918367347, "date": "2022-09-07" }, { "rank": 20, "intensity": 2.4183673469387754, "date": "2022-03-13" }, { "rank": 21, "intensity": 2.387755102040816, "date": "2022-04-08" }, { "rank": 22, "intensity": 2.357142857142857, "date": "2022-11-16" }, { "rank": 23, "intensity": 2.326530612244898, "date": "2022-06-12" }, { "rank": 24, "intensity": 2.295918367346939, "date": "2022-07-07" }, { "rank": 25, "intensity": 2.2653061224489797, "date": "2022-02-23" }, { "rank": 26, "intensity": 2.2346938775510203, "date": "2022-12-01" }, { "rank": 27, "intensity": 2.204081632653061, "date": "2022-05-30" }, { "rank": 28, "intensity": 2.173469387755102, "date": "2022-07-12" }, { "rank": 29, "intensity": 2.142857142857143, "date": "2022-06-09" }, { "rank": 30, "intensity": 2.112244897959184, "date": "2022-12-10" }, { "rank": 31, "intensity": 2.0816326530612246, "date": "2022-05-18" }, { "rank": 32, "intensity": 2.0510204081632653, "date": "2022-06-26" }, { "rank": 33, "intensity": 2.020408163265306, "date": "2022-11-07" }, { "rank": 34, "intensity": 1.989795918367347, "date": "2022-02-08" }, { "rank": 35, "intensity": 1.9591836734693877, "date": "2022-05-21" }, { "rank": 36, "intensity": 1.9285714285714286, "date": "2022-07-21" }, { "rank": 37, "intensity": 1.8979591836734695, "date": "2022-10-04" }, { "rank": 38, "intensity": 1.8673469387755102, "date": "2022-09-20" }, { "rank": 39, "intensity": 1.836734693877551, "date": "2022-08-28" }, { "rank": 40, "intensity": 1.806122448979592, "date": "2022-09-24" }, { "rank": 41, "intensity": 1.7755102040816326, "date": "2022-01-25" }, { "rank": 42, "intensity": 1.7448979591836735, "date": "2022-03-24" }, { "rank": 43, "intensity": 1.7142857142857144, "date": "2022-04-26" }, { "rank": 44, "intensity": 1.683673469387755, "date": "2022-02-28" }, { "rank": 45, "intensity": 1.653061224489796, "date": "2022-07-04" }, { "rank": 46, "intensity": 1.6224489795918369, "date": "2022-11-04" }, { "rank": 47, "intensity": 1.5918367346938775, "date": "2022-08-31" }, { "rank": 48, "intensity": 1.5612244897959184, "date": "2022-04-01" }, { "rank": 49, "intensity": 1.5306122448979593, "date": "2022-05-04" }, { "rank": 50, "intensity": 1.5, "date": "2022-07-16" } ], "source": "causal_rivers" } }, { "id": "L3_T1_Composite_Trend_00129", "level": 3, "level_name": "Semantic Reasoning", "category": "Composite Trend", "subtask": "Composite Trend", "question": "Among days in channel 312 during 2022 that exhibit the trend pattern 'rise, then rapid rise, then rapid fall', identify the top-5 days where the rapid fall segment is the fastest. (Output format: a ranked list of dates, e.g., ['YYYY-MM-DD', ...])", "answer": "['2022-02-26', '2022-11-11', '2022-07-26', '2022-06-11', '2022-04-28']", "ground_truth": [ "2022-02-26", "2022-11-11", "2022-07-26", "2022-06-11", "2022-04-28" ], "eval_metric": "set_f1", "channel": "312", "ts_data_path": "ts_data/L3_T1_Composite_Trend_00129.csv", "meta": { "segments": [ { "kind": "rise", "adj": "none" }, { "kind": "rise", "adj": "rapid" }, { "kind": "fall", "adj": "rapid" } ], "pattern_phrase": "rise, then rapid rise, then rapid fall", "rank_target_idx": 2, "rank_kind": "fall", "rank_adj": "rapid", "rank_axis": "slope", "rank_direction": "DESC", "rank_extreme": "fastest", "top_segments_meta": [ { "adj": "none", "kind": "rise", "label": "none rise", "start_idx": 0, "end_idx": 52 }, { "adj": "rapid", "kind": "rise", "label": "rapid rise", "start_idx": 52, "end_idx": 75 }, { "adj": "rapid", "kind": "fall", "label": "rapid fall", "start_idx": 75, "end_idx": 98 } ], "points_per_day": 96, "pattern": "rise, then rapid rise, then rapid fall", "year": 2022, "top_k": [ "2022-02-26", "2022-11-11", "2022-07-26", "2022-06-11", "2022-04-28" ], "all_injections": [ { "rank": 1, "intensity": 3.0, "date": "2022-02-26" }, { "rank": 2, "intensity": 2.9693877551020407, "date": "2022-11-11" }, { "rank": 3, "intensity": 2.938775510204082, "date": "2022-07-26" }, { "rank": 4, "intensity": 2.9081632653061225, "date": "2022-06-11" }, { "rank": 5, "intensity": 2.877551020408163, "date": "2022-04-28" }, { "rank": 6, "intensity": 2.8469387755102042, "date": "2022-10-13" }, { "rank": 7, "intensity": 2.816326530612245, "date": "2022-08-10" }, { "rank": 8, "intensity": 2.7857142857142856, "date": "2022-03-22" }, { "rank": 9, "intensity": 2.7551020408163267, "date": "2022-01-18" }, { "rank": 10, "intensity": 2.7244897959183674, "date": "2022-08-21" }, { "rank": 11, "intensity": 2.693877551020408, "date": "2022-12-07" }, { "rank": 12, "intensity": 2.663265306122449, "date": "2022-03-13" }, { "rank": 13, "intensity": 2.63265306122449, "date": "2022-11-14" }, { "rank": 14, "intensity": 2.6020408163265305, "date": "2022-02-12" }, { "rank": 15, "intensity": 2.5714285714285716, "date": "2022-09-02" }, { "rank": 16, "intensity": 2.5408163265306123, "date": "2022-12-16" }, { "rank": 17, "intensity": 2.510204081632653, "date": "2022-05-29" }, { "rank": 18, "intensity": 2.479591836734694, "date": "2022-08-25" }, { "rank": 19, "intensity": 2.4489795918367347, "date": "2022-03-06" }, { "rank": 20, "intensity": 2.4183673469387754, "date": "2022-12-13" }, { "rank": 21, "intensity": 2.387755102040816, "date": "2022-07-07" }, { "rank": 22, "intensity": 2.357142857142857, "date": "2022-10-11" }, { "rank": 23, "intensity": 2.326530612244898, "date": "2022-03-15" }, { "rank": 24, "intensity": 2.295918367346939, "date": "2022-05-27" }, { "rank": 25, "intensity": 2.2653061224489797, "date": "2022-06-29" }, { "rank": 26, "intensity": 2.2346938775510203, "date": "2022-02-01" }, { "rank": 27, "intensity": 2.204081632653061, "date": "2022-07-24" }, { "rank": 28, "intensity": 2.173469387755102, "date": "2022-10-27" }, { "rank": 29, "intensity": 2.142857142857143, "date": "2022-02-06" }, { "rank": 30, "intensity": 2.112244897959184, "date": "2022-11-18" }, { "rank": 31, "intensity": 2.0816326530612246, "date": "2022-11-23" }, { "rank": 32, "intensity": 2.0510204081632653, "date": "2022-10-01" }, { "rank": 33, "intensity": 2.020408163265306, "date": "2022-12-04" }, { "rank": 34, "intensity": 1.989795918367347, "date": "2022-11-04" }, { "rank": 35, "intensity": 1.9591836734693877, "date": "2022-10-08" }, { "rank": 36, "intensity": 1.9285714285714286, "date": "2022-09-27" }, { "rank": 37, "intensity": 1.8979591836734695, "date": "2022-05-10" }, { "rank": 38, "intensity": 1.8673469387755102, "date": "2022-09-09" }, { "rank": 39, "intensity": 1.836734693877551, "date": "2022-04-26" }, { "rank": 40, "intensity": 1.806122448979592, "date": "2022-01-22" }, { "rank": 41, "intensity": 1.7755102040816326, "date": "2022-04-22" }, { "rank": 42, "intensity": 1.7448979591836735, "date": "2022-03-09" }, { "rank": 43, "intensity": 1.7142857142857144, "date": "2022-12-01" }, { "rank": 44, "intensity": 1.683673469387755, "date": "2022-04-15" }, { "rank": 45, "intensity": 1.653061224489796, "date": "2022-03-24" }, { "rank": 46, "intensity": 1.6224489795918369, "date": "2022-05-16" }, { "rank": 47, "intensity": 1.5918367346938775, "date": "2022-07-04" }, { "rank": 48, "intensity": 1.5612244897959184, "date": "2022-02-08" }, { "rank": 49, "intensity": 1.5306122448979593, "date": "2022-06-26" }, { "rank": 50, "intensity": 1.5, "date": "2022-09-23" } ], "source": "causal_rivers" } }, { "id": "L3_T1_Composite_Trend_00130", "level": 3, "level_name": "Semantic Reasoning", "category": "Composite Trend", "subtask": "Composite Trend", "question": "Among days in channel 430 during 2022 that exhibit the trend pattern 'slow fall, then rapid rise, then slow fall', identify the top-5 days where the rapid rise segment is the fastest. (Output format: a ranked list of dates, e.g., ['YYYY-MM-DD', ...])", "answer": "['2022-06-24', '2022-11-12', '2022-06-04', '2022-10-20', '2022-09-16']", "ground_truth": [ "2022-06-24", "2022-11-12", "2022-06-04", "2022-10-20", "2022-09-16" ], "eval_metric": "set_f1", "channel": "430", "ts_data_path": "ts_data/L3_T1_Composite_Trend_00130.csv", "meta": { "segments": [ { "kind": "fall", "adj": "slow" }, { "kind": "rise", "adj": "rapid" }, { "kind": "fall", "adj": "slow" } ], "pattern_phrase": "slow fall, then rapid rise, then slow fall", "rank_target_idx": 1, "rank_kind": "rise", "rank_adj": "rapid", "rank_axis": "slope", "rank_direction": "DESC", "rank_extreme": "fastest", "top_segments_meta": [ { "adj": "slow", "kind": "fall", "label": "slow fall", "start_idx": 0, "end_idx": 44 }, { "adj": "rapid", "kind": "rise", "label": "rapid rise", "start_idx": 44, "end_idx": 57 }, { "adj": "slow", "kind": "fall", "label": "slow fall", "start_idx": 57, "end_idx": 98 } ], "points_per_day": 96, "pattern": "slow fall, then rapid rise, then slow fall", "year": 2022, "top_k": [ "2022-06-24", "2022-11-12", "2022-06-04", "2022-10-20", "2022-09-16" ], "all_injections": [ { "rank": 1, "intensity": 3.0, "date": "2022-06-24" }, { "rank": 2, "intensity": 2.9693877551020407, "date": "2022-11-12" }, { "rank": 3, "intensity": 2.938775510204082, "date": "2022-06-04" }, { "rank": 4, "intensity": 2.9081632653061225, "date": "2022-10-20" }, { "rank": 5, "intensity": 2.877551020408163, "date": "2022-09-16" }, { "rank": 6, "intensity": 2.8469387755102042, "date": "2022-09-07" }, { "rank": 7, "intensity": 2.816326530612245, "date": "2022-09-02" }, { "rank": 8, "intensity": 2.7857142857142856, "date": "2022-09-11" }, { "rank": 9, "intensity": 2.7551020408163267, "date": "2022-04-30" }, { "rank": 10, "intensity": 2.7244897959183674, "date": "2022-10-18" }, { "rank": 11, "intensity": 2.693877551020408, "date": "2022-09-20" }, { "rank": 12, "intensity": 2.663265306122449, "date": "2022-05-30" }, { "rank": 13, "intensity": 2.63265306122449, "date": "2022-01-03" }, { "rank": 14, "intensity": 2.6020408163265305, "date": "2022-07-30" }, { "rank": 15, "intensity": 2.5714285714285716, "date": "2022-03-10" }, { "rank": 16, "intensity": 2.5408163265306123, "date": "2022-04-26" }, { "rank": 17, "intensity": 2.510204081632653, "date": "2022-10-14" }, { "rank": 18, "intensity": 2.479591836734694, "date": "2022-11-07" }, { "rank": 19, "intensity": 2.4489795918367347, "date": "2022-02-17" }, { "rank": 20, "intensity": 2.4183673469387754, "date": "2022-09-30" }, { "rank": 21, "intensity": 2.387755102040816, "date": "2022-06-01" }, { "rank": 22, "intensity": 2.357142857142857, "date": "2022-03-18" }, { "rank": 23, "intensity": 2.326530612244898, "date": "2022-08-08" }, { "rank": 24, "intensity": 2.295918367346939, "date": "2022-01-30" }, { "rank": 25, "intensity": 2.2653061224489797, "date": "2022-01-22" }, { "rank": 26, "intensity": 2.2346938775510203, "date": "2022-07-27" }, { "rank": 27, "intensity": 2.204081632653061, "date": "2022-04-06" }, { "rank": 28, "intensity": 2.173469387755102, "date": "2022-08-27" }, { "rank": 29, "intensity": 2.142857142857143, "date": "2022-02-25" }, { "rank": 30, "intensity": 2.112244897959184, "date": "2022-09-27" }, { "rank": 31, "intensity": 2.0816326530612246, "date": "2022-08-31" }, { "rank": 32, "intensity": 2.0510204081632653, "date": "2022-11-16" }, { "rank": 33, "intensity": 2.020408163265306, "date": "2022-05-11" }, { "rank": 34, "intensity": 1.989795918367347, "date": "2022-10-03" }, { "rank": 35, "intensity": 1.9591836734693877, "date": "2022-12-16" }, { "rank": 36, "intensity": 1.9285714285714286, "date": "2022-10-07" }, { "rank": 37, "intensity": 1.8979591836734695, "date": "2022-01-18" }, { "rank": 38, "intensity": 1.8673469387755102, "date": "2022-02-02" }, { "rank": 39, "intensity": 1.836734693877551, "date": "2022-05-22" }, { "rank": 40, "intensity": 1.806122448979592, "date": "2022-12-29" }, { "rank": 41, "intensity": 1.7755102040816326, "date": "2022-04-11" }, { "rank": 42, "intensity": 1.7448979591836735, "date": "2022-01-07" }, { "rank": 43, "intensity": 1.7142857142857144, "date": "2022-06-09" }, { "rank": 44, "intensity": 1.683673469387755, "date": "2022-08-17" }, { "rank": 45, "intensity": 1.653061224489796, "date": "2022-02-27" }, { "rank": 46, "intensity": 1.6224489795918369, "date": "2022-05-18" }, { "rank": 47, "intensity": 1.5918367346938775, "date": "2022-07-02" }, { "rank": 48, "intensity": 1.5612244897959184, "date": "2022-09-09" }, { "rank": 49, "intensity": 1.5306122448979593, "date": "2022-04-28" }, { "rank": 50, "intensity": 1.5, "date": "2022-07-14" } ], "source": "causal_rivers" } }, { "id": "L3_T1_Composite_Trend_00131", "level": 3, "level_name": "Semantic Reasoning", "category": "Composite Trend", "subtask": "Composite Trend", "question": "Among days in channel 441 during 2021 that exhibit the trend pattern 'fall, then rapid rise', identify the top-5 days where the rapid rise segment is the fastest. (Output format: a ranked list of dates, e.g., ['YYYY-MM-DD', ...])", "answer": "['2021-06-28', '2021-05-30', '2021-11-17', '2021-05-09', '2021-12-06']", "ground_truth": [ "2021-06-28", "2021-05-30", "2021-11-17", "2021-05-09", "2021-12-06" ], "eval_metric": "set_f1", "channel": "441", "ts_data_path": "ts_data/L3_T1_Composite_Trend_00131.csv", "meta": { "segments": [ { "kind": "fall", "adj": "none" }, { "kind": "rise", "adj": "rapid" } ], "pattern_phrase": "fall, then rapid rise", "rank_target_idx": 1, "rank_kind": "rise", "rank_adj": "rapid", "rank_axis": "slope", "rank_direction": "DESC", "rank_extreme": "fastest", "top_segments_meta": [ { "adj": "none", "kind": "fall", "label": "none fall", "start_idx": 0, "end_idx": 64 }, { "adj": "rapid", "kind": "rise", "label": "rapid rise", "start_idx": 64, "end_idx": 98 } ], "points_per_day": 96, "pattern": "fall, then rapid rise", "year": 2021, "top_k": [ "2021-06-28", "2021-05-30", "2021-11-17", "2021-05-09", "2021-12-06" ], "all_injections": [ { "rank": 1, "intensity": 3.0, "date": "2021-06-28" }, { "rank": 2, "intensity": 2.9693877551020407, "date": "2021-05-30" }, { "rank": 3, "intensity": 2.938775510204082, "date": "2021-11-17" }, { "rank": 4, "intensity": 2.9081632653061225, "date": "2021-05-09" }, { "rank": 5, "intensity": 2.877551020408163, "date": "2021-12-06" }, { "rank": 6, "intensity": 2.8469387755102042, "date": "2021-04-09" }, { "rank": 7, "intensity": 2.816326530612245, "date": "2021-12-27" }, { "rank": 8, "intensity": 2.7857142857142856, "date": "2021-05-04" }, { "rank": 9, "intensity": 2.7551020408163267, "date": "2021-07-26" }, { "rank": 10, "intensity": 2.7244897959183674, "date": "2021-03-01" }, { "rank": 11, "intensity": 2.693877551020408, "date": "2021-05-28" }, { "rank": 12, "intensity": 2.663265306122449, "date": "2021-12-17" }, { "rank": 13, "intensity": 2.63265306122449, "date": "2021-01-11" }, { "rank": 14, "intensity": 2.6020408163265305, "date": "2021-09-14" }, { "rank": 15, "intensity": 2.5714285714285716, "date": "2021-09-19" }, { "rank": 16, "intensity": 2.5408163265306123, "date": "2021-01-15" }, { "rank": 17, "intensity": 2.510204081632653, "date": "2021-10-01" }, { "rank": 18, "intensity": 2.479591836734694, "date": "2021-04-07" }, { "rank": 19, "intensity": 2.4489795918367347, "date": "2021-12-30" }, { "rank": 20, "intensity": 2.4183673469387754, "date": "2021-04-13" }, { "rank": 21, "intensity": 2.387755102040816, "date": "2021-08-25" }, { "rank": 22, "intensity": 2.357142857142857, "date": "2021-02-04" }, { "rank": 23, "intensity": 2.326530612244898, "date": "2021-10-16" }, { "rank": 24, "intensity": 2.295918367346939, "date": "2021-06-08" }, { "rank": 25, "intensity": 2.2653061224489797, "date": "2021-11-23" }, { "rank": 26, "intensity": 2.2346938775510203, "date": "2021-03-12" }, { "rank": 27, "intensity": 2.204081632653061, "date": "2021-09-23" }, { "rank": 28, "intensity": 2.173469387755102, "date": "2021-11-12" }, { "rank": 29, "intensity": 2.142857142857143, "date": "2021-09-12" }, { "rank": 30, "intensity": 2.112244897959184, "date": "2021-07-24" }, { "rank": 31, "intensity": 2.0816326530612246, "date": "2021-02-20" }, { "rank": 32, "intensity": 2.0510204081632653, "date": "2021-04-11" }, { "rank": 33, "intensity": 2.020408163265306, "date": "2021-07-07" }, { "rank": 34, "intensity": 1.989795918367347, "date": "2021-10-31" }, { "rank": 35, "intensity": 1.9591836734693877, "date": "2021-01-26" }, { "rank": 36, "intensity": 1.9285714285714286, "date": "2021-06-03" }, { "rank": 37, "intensity": 1.8979591836734695, "date": "2021-09-25" }, { "rank": 38, "intensity": 1.8673469387755102, "date": "2021-02-15" }, { "rank": 39, "intensity": 1.836734693877551, "date": "2021-04-15" }, { "rank": 40, "intensity": 1.806122448979592, "date": "2021-04-05" }, { "rank": 41, "intensity": 1.7755102040816326, "date": "2021-12-19" }, { "rank": 42, "intensity": 1.7448979591836735, "date": "2021-08-19" }, { "rank": 43, "intensity": 1.7142857142857144, "date": "2021-08-06" }, { "rank": 44, "intensity": 1.683673469387755, "date": "2021-08-08" }, { "rank": 45, "intensity": 1.653061224489796, "date": "2021-10-07" }, { "rank": 46, "intensity": 1.6224489795918369, "date": "2021-01-30" }, { "rank": 47, "intensity": 1.5918367346938775, "date": "2021-04-02" }, { "rank": 48, "intensity": 1.5612244897959184, "date": "2021-05-15" }, { "rank": 49, "intensity": 1.5306122448979593, "date": "2021-11-09" }, { "rank": 50, "intensity": 1.5, "date": "2021-12-08" } ], "source": "causal_rivers" } }, { "id": "L3_T1_Composite_Trend_00132", "level": 3, "level_name": "Semantic Reasoning", "category": "Composite Trend", "subtask": "Composite Trend", "question": "Among days in channel 495 during 2023 that exhibit the trend pattern 'rapid fall, then slow rise', identify the top-5 days where the rapid fall segment is the fastest. (Output format: a ranked list of dates, e.g., ['YYYY-MM-DD', ...])", "answer": "['2023-02-19', '2023-05-11', '2023-04-30', '2023-05-19', '2023-11-09']", "ground_truth": [ "2023-02-19", "2023-05-11", "2023-04-30", "2023-05-19", "2023-11-09" ], "eval_metric": "set_f1", "channel": "495", "ts_data_path": "ts_data/L3_T1_Composite_Trend_00132.csv", "meta": { "segments": [ { "kind": "fall", "adj": "rapid" }, { "kind": "rise", "adj": "slow" } ], "pattern_phrase": "rapid fall, then slow rise", "rank_target_idx": 0, "rank_kind": "fall", "rank_adj": "rapid", "rank_axis": "slope", "rank_direction": "DESC", "rank_extreme": "fastest", "top_segments_meta": [ { "adj": "rapid", "kind": "fall", "label": "rapid fall", "start_idx": 0, "end_idx": 20 }, { "adj": "slow", "kind": "rise", "label": "slow rise", "start_idx": 20, "end_idx": 98 } ], "points_per_day": 96, "pattern": "rapid fall, then slow rise", "year": 2023, "top_k": [ "2023-02-19", "2023-05-11", "2023-04-30", "2023-05-19", "2023-11-09" ], "all_injections": [ { "rank": 1, "intensity": 3.0, "date": "2023-02-19" }, { "rank": 2, "intensity": 2.9693877551020407, "date": "2023-05-11" }, { "rank": 3, "intensity": 2.938775510204082, "date": "2023-04-30" }, { "rank": 4, "intensity": 2.9081632653061225, "date": "2023-05-19" }, { "rank": 5, "intensity": 2.877551020408163, "date": "2023-11-09" }, { "rank": 6, "intensity": 2.8469387755102042, "date": "2023-04-20" }, { "rank": 7, "intensity": 2.816326530612245, "date": "2023-09-30" }, { "rank": 8, "intensity": 2.7857142857142856, "date": "2023-11-07" }, { "rank": 9, "intensity": 2.7551020408163267, "date": "2023-10-19" }, { "rank": 10, "intensity": 2.7244897959183674, "date": "2023-05-05" }, { "rank": 11, "intensity": 2.693877551020408, "date": "2023-07-31" }, { "rank": 12, "intensity": 2.663265306122449, "date": "2023-03-12" }, { "rank": 13, "intensity": 2.63265306122449, "date": "2023-07-15" }, { "rank": 14, "intensity": 2.6020408163265305, "date": "2023-02-04" }, { "rank": 15, "intensity": 2.5714285714285716, "date": "2023-04-26" }, { "rank": 16, "intensity": 2.5408163265306123, "date": "2023-11-11" }, { "rank": 17, "intensity": 2.510204081632653, "date": "2023-05-08" }, { "rank": 18, "intensity": 2.479591836734694, "date": "2023-11-24" }, { "rank": 19, "intensity": 2.4489795918367347, "date": "2023-11-14" }, { "rank": 20, "intensity": 2.4183673469387754, "date": "2023-07-05" }, { "rank": 21, "intensity": 2.387755102040816, "date": "2023-08-24" }, { "rank": 22, "intensity": 2.357142857142857, "date": "2023-04-04" }, { "rank": 23, "intensity": 2.326530612244898, "date": "2023-06-07" }, { "rank": 24, "intensity": 2.295918367346939, "date": "2023-10-30" }, { "rank": 25, "intensity": 2.2653061224489797, "date": "2023-02-02" }, { "rank": 26, "intensity": 2.2346938775510203, "date": "2023-03-18" }, { "rank": 27, "intensity": 2.204081632653061, "date": "2023-01-10" }, { "rank": 28, "intensity": 2.173469387755102, "date": "2023-07-13" }, { "rank": 29, "intensity": 2.142857142857143, "date": "2023-03-31" }, { "rank": 30, "intensity": 2.112244897959184, "date": "2023-07-02" }, { "rank": 31, "intensity": 2.0816326530612246, "date": "2023-10-23" }, { "rank": 32, "intensity": 2.0510204081632653, "date": "2023-01-25" }, { "rank": 33, "intensity": 2.020408163265306, "date": "2023-01-19" }, { "rank": 34, "intensity": 1.989795918367347, "date": "2023-11-28" }, { "rank": 35, "intensity": 1.9591836734693877, "date": "2023-06-02" }, { "rank": 36, "intensity": 1.9285714285714286, "date": "2023-11-05" }, { "rank": 37, "intensity": 1.8979591836734695, "date": "2023-09-07" }, { "rank": 38, "intensity": 1.8673469387755102, "date": "2023-08-03" }, { "rank": 39, "intensity": 1.836734693877551, "date": "2023-09-18" }, { "rank": 40, "intensity": 1.806122448979592, "date": "2023-12-09" }, { "rank": 41, "intensity": 1.7755102040816326, "date": "2023-02-06" }, { "rank": 42, "intensity": 1.7448979591836735, "date": "2023-04-10" }, { "rank": 43, "intensity": 1.7142857142857144, "date": "2023-04-13" }, { "rank": 44, "intensity": 1.683673469387755, "date": "2023-01-30" }, { "rank": 45, "intensity": 1.653061224489796, "date": "2023-04-07" }, { "rank": 46, "intensity": 1.6224489795918369, "date": "2023-09-10" }, { "rank": 47, "intensity": 1.5918367346938775, "date": "2023-03-03" }, { "rank": 48, "intensity": 1.5612244897959184, "date": "2023-12-28" }, { "rank": 49, "intensity": 1.5306122448979593, "date": "2023-08-06" }, { "rank": 50, "intensity": 1.5, "date": "2023-02-26" } ], "source": "causal_rivers" } }, { "id": "L3_T1_Composite_Trend_00133", "level": 3, "level_name": "Semantic Reasoning", "category": "Composite Trend", "subtask": "Composite Trend", "question": "Among days in channel 496 during 2022 that exhibit the trend pattern 'rapid fall, then rise, then steady stable, then fluctuating stable', identify the top-5 days where the fluctuating stable segment is the most fluctuating. (Output format: a ranked list of dates, e.g., ['YYYY-MM-DD', ...])", "answer": "['2022-05-11', '2022-07-21', '2022-01-17', '2022-08-10', '2022-01-25']", "ground_truth": [ "2022-05-11", "2022-07-21", "2022-01-17", "2022-08-10", "2022-01-25" ], "eval_metric": "set_f1", "channel": "496", "ts_data_path": "ts_data/L3_T1_Composite_Trend_00133.csv", "meta": { "segments": [ { "kind": "fall", "adj": "rapid" }, { "kind": "rise", "adj": "none" }, { "kind": "stable", "adj": "steady" }, { "kind": "stable", "adj": "fluctuating" } ], "pattern_phrase": "rapid fall, then rise, then steady stable, then fluctuating stable", "rank_target_idx": 3, "rank_kind": "stable", "rank_adj": "fluctuating", "rank_axis": "std", "rank_direction": "DESC", "rank_extreme": "most fluctuating", "top_segments_meta": [ { "adj": "rapid", "kind": "fall", "label": "rapid fall", "start_idx": 0, "end_idx": 12 }, { "adj": "none", "kind": "rise", "label": "none rise", "start_idx": 12, "end_idx": 32 }, { "adj": "steady", "kind": "stable", "label": "steady stable", "start_idx": 32, "end_idx": 71 }, { "adj": "fluctuating", "kind": "stable", "label": "fluctuating stable", "start_idx": 71, "end_idx": 98 } ], "points_per_day": 96, "pattern": "rapid fall, then rise, then steady stable, then fluctuating stable", "year": 2022, "top_k": [ "2022-05-11", "2022-07-21", "2022-01-17", "2022-08-10", "2022-01-25" ], "all_injections": [ { "rank": 1, "intensity": 3.0, "date": "2022-05-11" }, { "rank": 2, "intensity": 2.9693877551020407, "date": "2022-07-21" }, { "rank": 3, "intensity": 2.938775510204082, "date": "2022-01-17" }, { "rank": 4, "intensity": 2.9081632653061225, "date": "2022-08-10" }, { "rank": 5, "intensity": 2.877551020408163, "date": "2022-01-25" }, { "rank": 6, "intensity": 2.8469387755102042, "date": "2022-11-08" }, { "rank": 7, "intensity": 2.816326530612245, "date": "2022-04-26" }, { "rank": 8, "intensity": 2.7857142857142856, "date": "2022-10-29" }, { "rank": 9, "intensity": 2.7551020408163267, "date": "2022-04-15" }, { "rank": 10, "intensity": 2.7244897959183674, "date": "2022-05-24" }, { "rank": 11, "intensity": 2.693877551020408, "date": "2022-12-13" }, { "rank": 12, "intensity": 2.663265306122449, "date": "2022-09-03" }, { "rank": 13, "intensity": 2.63265306122449, "date": "2022-04-22" }, { "rank": 14, "intensity": 2.6020408163265305, "date": "2022-12-19" }, { "rank": 15, "intensity": 2.5714285714285716, "date": "2022-09-30" }, { "rank": 16, "intensity": 2.5408163265306123, "date": "2022-10-10" }, { "rank": 17, "intensity": 2.510204081632653, "date": "2022-01-27" }, { "rank": 18, "intensity": 2.479591836734694, "date": "2022-11-16" }, { "rank": 19, "intensity": 2.4489795918367347, "date": "2022-08-26" }, { "rank": 20, "intensity": 2.4183673469387754, "date": "2022-06-27" }, { "rank": 21, "intensity": 2.387755102040816, "date": "2022-12-06" }, { "rank": 22, "intensity": 2.357142857142857, "date": "2022-06-10" }, { "rank": 23, "intensity": 2.326530612244898, "date": "2022-02-03" }, { "rank": 24, "intensity": 2.295918367346939, "date": "2022-01-02" }, { "rank": 25, "intensity": 2.2653061224489797, "date": "2022-02-27" }, { "rank": 26, "intensity": 2.2346938775510203, "date": "2022-02-25" }, { "rank": 27, "intensity": 2.204081632653061, "date": "2022-07-06" }, { "rank": 28, "intensity": 2.173469387755102, "date": "2022-12-17" }, { "rank": 29, "intensity": 2.142857142857143, "date": "2022-10-24" }, { "rank": 30, "intensity": 2.112244897959184, "date": "2022-02-12" }, { "rank": 31, "intensity": 2.0816326530612246, "date": "2022-05-05" }, { "rank": 32, "intensity": 2.0510204081632653, "date": "2022-10-31" }, { "rank": 33, "intensity": 2.020408163265306, "date": "2022-09-15" }, { "rank": 34, "intensity": 1.989795918367347, "date": "2022-10-17" }, { "rank": 35, "intensity": 1.9591836734693877, "date": "2022-01-11" }, { "rank": 36, "intensity": 1.9285714285714286, "date": "2022-12-03" }, { "rank": 37, "intensity": 1.8979591836734695, "date": "2022-11-19" }, { "rank": 38, "intensity": 1.8673469387755102, "date": "2022-01-20" }, { "rank": 39, "intensity": 1.836734693877551, "date": "2022-03-08" }, { "rank": 40, "intensity": 1.806122448979592, "date": "2022-02-20" }, { "rank": 41, "intensity": 1.7755102040816326, "date": "2022-10-07" }, { "rank": 42, "intensity": 1.7448979591836735, "date": "2022-12-23" }, { "rank": 43, "intensity": 1.7142857142857144, "date": "2022-03-04" }, { "rank": 44, "intensity": 1.683673469387755, "date": "2022-11-05" }, { "rank": 45, "intensity": 1.653061224489796, "date": "2022-05-08" }, { "rank": 46, "intensity": 1.6224489795918369, "date": "2022-03-29" }, { "rank": 47, "intensity": 1.5918367346938775, "date": "2022-12-09" }, { "rank": 48, "intensity": 1.5612244897959184, "date": "2022-08-23" }, { "rank": 49, "intensity": 1.5306122448979593, "date": "2022-06-13" }, { "rank": 50, "intensity": 1.5, "date": "2022-06-15" } ], "source": "causal_rivers" } }, { "id": "L3_T1_Composite_Trend_00134", "level": 3, "level_name": "Semantic Reasoning", "category": "Composite Trend", "subtask": "Composite Trend", "question": "Among days in channel 501 during 2021 that exhibit the trend pattern 'fall, then rapid fall', identify the top-5 days where the rapid fall segment is the fastest. (Output format: a ranked list of dates, e.g., ['YYYY-MM-DD', ...])", "answer": "['2021-03-08', '2021-06-08', '2021-06-01', '2021-08-05', '2021-08-02']", "ground_truth": [ "2021-03-08", "2021-06-08", "2021-06-01", "2021-08-05", "2021-08-02" ], "eval_metric": "set_f1", "channel": "501", "ts_data_path": "ts_data/L3_T1_Composite_Trend_00134.csv", "meta": { "segments": [ { "kind": "fall", "adj": "none" }, { "kind": "fall", "adj": "rapid" } ], "pattern_phrase": "fall, then rapid fall", "rank_target_idx": 1, "rank_kind": "fall", "rank_adj": "rapid", "rank_axis": "slope", "rank_direction": "DESC", "rank_extreme": "fastest", "top_segments_meta": [ { "adj": "none", "kind": "fall", "label": "none fall", "start_idx": 0, "end_idx": 68 }, { "adj": "rapid", "kind": "fall", "label": "rapid fall", "start_idx": 68, "end_idx": 98 } ], "points_per_day": 96, "pattern": "fall, then rapid fall", "year": 2021, "top_k": [ "2021-03-08", "2021-06-08", "2021-06-01", "2021-08-05", "2021-08-02" ], "all_injections": [ { "rank": 1, "intensity": 3.0, "date": "2021-03-08" }, { "rank": 2, "intensity": 2.9693877551020407, "date": "2021-06-08" }, { "rank": 3, "intensity": 2.938775510204082, "date": "2021-06-01" }, { "rank": 4, "intensity": 2.9081632653061225, "date": "2021-08-05" }, { "rank": 5, "intensity": 2.877551020408163, "date": "2021-08-02" }, { "rank": 6, "intensity": 2.8469387755102042, "date": "2021-04-16" }, { "rank": 7, "intensity": 2.816326530612245, "date": "2021-01-09" }, { "rank": 8, "intensity": 2.7857142857142856, "date": "2021-10-12" }, { "rank": 9, "intensity": 2.7551020408163267, "date": "2021-07-29" }, { "rank": 10, "intensity": 2.7244897959183674, "date": "2021-11-15" }, { "rank": 11, "intensity": 2.693877551020408, "date": "2021-05-29" }, { "rank": 12, "intensity": 2.663265306122449, "date": "2021-05-19" }, { "rank": 13, "intensity": 2.63265306122449, "date": "2021-01-23" }, { "rank": 14, "intensity": 2.6020408163265305, "date": "2021-11-04" }, { "rank": 15, "intensity": 2.5714285714285716, "date": "2021-12-10" }, { "rank": 16, "intensity": 2.5408163265306123, "date": "2021-08-11" }, { "rank": 17, "intensity": 2.510204081632653, "date": "2021-12-15" }, { "rank": 18, "intensity": 2.479591836734694, "date": "2021-04-28" }, { "rank": 19, "intensity": 2.4489795918367347, "date": "2021-07-23" }, { "rank": 20, "intensity": 2.4183673469387754, "date": "2021-08-08" }, { "rank": 21, "intensity": 2.387755102040816, "date": "2021-12-05" }, { "rank": 22, "intensity": 2.357142857142857, "date": "2021-11-02" }, { "rank": 23, "intensity": 2.326530612244898, "date": "2021-06-06" }, { "rank": 24, "intensity": 2.295918367346939, "date": "2021-10-02" }, { "rank": 25, "intensity": 2.2653061224489797, "date": "2021-10-24" }, { "rank": 26, "intensity": 2.2346938775510203, "date": "2021-10-26" }, { "rank": 27, "intensity": 2.204081632653061, "date": "2021-09-24" }, { "rank": 28, "intensity": 2.173469387755102, "date": "2021-07-04" }, { "rank": 29, "intensity": 2.142857142857143, "date": "2021-03-29" }, { "rank": 30, "intensity": 2.112244897959184, "date": "2021-02-17" }, { "rank": 31, "intensity": 2.0816326530612246, "date": "2021-09-30" }, { "rank": 32, "intensity": 2.0510204081632653, "date": "2021-11-25" }, { "rank": 33, "intensity": 2.020408163265306, "date": "2021-03-05" }, { "rank": 34, "intensity": 1.989795918367347, "date": "2021-10-10" }, { "rank": 35, "intensity": 1.9591836734693877, "date": "2021-12-03" }, { "rank": 36, "intensity": 1.9285714285714286, "date": "2021-10-08" }, { "rank": 37, "intensity": 1.8979591836734695, "date": "2021-01-30" }, { "rank": 38, "intensity": 1.8673469387755102, "date": "2021-09-11" }, { "rank": 39, "intensity": 1.836734693877551, "date": "2021-08-20" }, { "rank": 40, "intensity": 1.806122448979592, "date": "2021-11-22" }, { "rank": 41, "intensity": 1.7755102040816326, "date": "2021-04-24" }, { "rank": 42, "intensity": 1.7448979591836735, "date": "2021-08-29" }, { "rank": 43, "intensity": 1.7142857142857144, "date": "2021-12-25" }, { "rank": 44, "intensity": 1.683673469387755, "date": "2021-02-09" }, { "rank": 45, "intensity": 1.653061224489796, "date": "2021-07-20" }, { "rank": 46, "intensity": 1.6224489795918369, "date": "2021-03-02" }, { "rank": 47, "intensity": 1.5918367346938775, "date": "2021-05-08" }, { "rank": 48, "intensity": 1.5612244897959184, "date": "2021-06-18" }, { "rank": 49, "intensity": 1.5306122448979593, "date": "2021-01-02" }, { "rank": 50, "intensity": 1.5, "date": "2021-02-20" } ], "source": "causal_rivers" } }, { "id": "L3_T1_Composite_Trend_00135", "level": 3, "level_name": "Semantic Reasoning", "category": "Composite Trend", "subtask": "Composite Trend", "question": "Among days in channel 578 during 2023 that exhibit the trend pattern 'fluctuating stable, then steady stable, then rise, then rapid fall', identify the top-5 days where the fluctuating stable segment is the most fluctuating. (Output format: a ranked list of dates, e.g., ['YYYY-MM-DD', ...])", "answer": "['2023-12-20', '2023-02-11', '2023-02-22', '2023-02-14', '2023-10-06']", "ground_truth": [ "2023-12-20", "2023-02-11", "2023-02-22", "2023-02-14", "2023-10-06" ], "eval_metric": "set_f1", "channel": "578", "ts_data_path": "ts_data/L3_T1_Composite_Trend_00135.csv", "meta": { "segments": [ { "kind": "stable", "adj": "fluctuating" }, { "kind": "stable", "adj": "steady" }, { "kind": "rise", "adj": "none" }, { "kind": "fall", "adj": "rapid" } ], "pattern_phrase": "fluctuating stable, then steady stable, then rise, then rapid fall", "rank_target_idx": 0, "rank_kind": "stable", "rank_adj": "fluctuating", "rank_axis": "std", "rank_direction": "DESC", "rank_extreme": "most fluctuating", "top_segments_meta": [ { "adj": "fluctuating", "kind": "stable", "label": "fluctuating stable", "start_idx": 0, "end_idx": 26 }, { "adj": "steady", "kind": "stable", "label": "steady stable", "start_idx": 26, "end_idx": 68 }, { "adj": "none", "kind": "rise", "label": "none rise", "start_idx": 68, "end_idx": 89 }, { "adj": "rapid", "kind": "fall", "label": "rapid fall", "start_idx": 89, "end_idx": 98 } ], "points_per_day": 96, "pattern": "fluctuating stable, then steady stable, then rise, then rapid fall", "year": 2023, "top_k": [ "2023-12-20", "2023-02-11", "2023-02-22", "2023-02-14", "2023-10-06" ], "all_injections": [ { "rank": 1, "intensity": 3.0, "date": "2023-12-20" }, { "rank": 2, "intensity": 2.9693877551020407, "date": "2023-02-11" }, { "rank": 3, "intensity": 2.938775510204082, "date": "2023-02-22" }, { "rank": 4, "intensity": 2.9081632653061225, "date": "2023-02-14" }, { "rank": 5, "intensity": 2.877551020408163, "date": "2023-10-06" }, { "rank": 6, "intensity": 2.8469387755102042, "date": "2023-05-24" }, { "rank": 7, "intensity": 2.816326530612245, "date": "2023-09-18" }, { "rank": 8, "intensity": 2.7857142857142856, "date": "2023-01-19" }, { "rank": 9, "intensity": 2.7551020408163267, "date": "2023-08-04" }, { "rank": 10, "intensity": 2.7244897959183674, "date": "2023-03-03" }, { "rank": 11, "intensity": 2.693877551020408, "date": "2023-03-06" }, { "rank": 12, "intensity": 2.663265306122449, "date": "2023-01-22" }, { "rank": 13, "intensity": 2.63265306122449, "date": "2023-06-18" }, { "rank": 14, "intensity": 2.6020408163265305, "date": "2023-03-22" }, { "rank": 15, "intensity": 2.5714285714285716, "date": "2023-12-09" }, { "rank": 16, "intensity": 2.5408163265306123, "date": "2023-09-16" }, { "rank": 17, "intensity": 2.510204081632653, "date": "2023-04-04" }, { "rank": 18, "intensity": 2.479591836734694, "date": "2023-07-18" }, { "rank": 19, "intensity": 2.4489795918367347, "date": "2023-06-30" }, { "rank": 20, "intensity": 2.4183673469387754, "date": "2023-03-20" }, { "rank": 21, "intensity": 2.387755102040816, "date": "2023-05-04" }, { "rank": 22, "intensity": 2.357142857142857, "date": "2023-04-15" }, { "rank": 23, "intensity": 2.326530612244898, "date": "2023-01-03" }, { "rank": 24, "intensity": 2.295918367346939, "date": "2023-11-15" }, { "rank": 25, "intensity": 2.2653061224489797, "date": "2023-04-02" }, { "rank": 26, "intensity": 2.2346938775510203, "date": "2023-06-15" }, { "rank": 27, "intensity": 2.204081632653061, "date": "2023-07-06" }, { "rank": 28, "intensity": 2.173469387755102, "date": "2023-10-29" }, { "rank": 29, "intensity": 2.142857142857143, "date": "2023-08-09" }, { "rank": 30, "intensity": 2.112244897959184, "date": "2023-09-29" }, { "rank": 31, "intensity": 2.0816326530612246, "date": "2023-04-06" }, { "rank": 32, "intensity": 2.0510204081632653, "date": "2023-04-29" }, { "rank": 33, "intensity": 2.020408163265306, "date": "2023-12-28" }, { "rank": 34, "intensity": 1.989795918367347, "date": "2023-05-30" }, { "rank": 35, "intensity": 1.9591836734693877, "date": "2023-05-28" }, { "rank": 36, "intensity": 1.9285714285714286, "date": "2023-03-11" }, { "rank": 37, "intensity": 1.8979591836734695, "date": "2023-09-09" }, { "rank": 38, "intensity": 1.8673469387755102, "date": "2023-07-31" }, { "rank": 39, "intensity": 1.836734693877551, "date": "2023-10-20" }, { "rank": 40, "intensity": 1.806122448979592, "date": "2023-02-25" }, { "rank": 41, "intensity": 1.7755102040816326, "date": "2023-08-14" }, { "rank": 42, "intensity": 1.7448979591836735, "date": "2023-02-19" }, { "rank": 43, "intensity": 1.7142857142857144, "date": "2023-11-08" }, { "rank": 44, "intensity": 1.683673469387755, "date": "2023-10-22" }, { "rank": 45, "intensity": 1.653061224489796, "date": "2023-02-08" }, { "rank": 46, "intensity": 1.6224489795918369, "date": "2023-06-07" }, { "rank": 47, "intensity": 1.5918367346938775, "date": "2023-04-13" }, { "rank": 48, "intensity": 1.5612244897959184, "date": "2023-12-30" }, { "rank": 49, "intensity": 1.5306122448979593, "date": "2023-04-19" }, { "rank": 50, "intensity": 1.5, "date": "2023-08-24" } ], "source": "causal_rivers" } }, { "id": "L3_T1_Composite_Trend_00136", "level": 3, "level_name": "Semantic Reasoning", "category": "Composite Trend", "subtask": "Composite Trend", "question": "Among days in channel 580 during 2023 that exhibit the trend pattern 'fall, then steady stable, then rapid rise, then steady stable', identify the top-5 days where the rapid rise segment is the fastest. (Output format: a ranked list of dates, e.g., ['YYYY-MM-DD', ...])", "answer": "['2023-04-13', '2023-10-07', '2023-09-15', '2023-05-14', '2023-08-24']", "ground_truth": [ "2023-04-13", "2023-10-07", "2023-09-15", "2023-05-14", "2023-08-24" ], "eval_metric": "set_f1", "channel": "580", "ts_data_path": "ts_data/L3_T1_Composite_Trend_00136.csv", "meta": { "segments": [ { "kind": "fall", "adj": "none" }, { "kind": "stable", "adj": "steady" }, { "kind": "rise", "adj": "rapid" }, { "kind": "stable", "adj": "steady" } ], "pattern_phrase": "fall, then steady stable, then rapid rise, then steady stable", "rank_target_idx": 2, "rank_kind": "rise", "rank_adj": "rapid", "rank_axis": "slope", "rank_direction": "DESC", "rank_extreme": "fastest", "top_segments_meta": [ { "adj": "none", "kind": "fall", "label": "none fall", "start_idx": 0, "end_idx": 19 }, { "adj": "steady", "kind": "stable", "label": "steady stable", "start_idx": 19, "end_idx": 54 }, { "adj": "rapid", "kind": "rise", "label": "rapid rise", "start_idx": 54, "end_idx": 64 }, { "adj": "steady", "kind": "stable", "label": "steady stable", "start_idx": 64, "end_idx": 98 } ], "points_per_day": 96, "pattern": "fall, then steady stable, then rapid rise, then steady stable", "year": 2023, "top_k": [ "2023-04-13", "2023-10-07", "2023-09-15", "2023-05-14", "2023-08-24" ], "all_injections": [ { "rank": 1, "intensity": 3.0, "date": "2023-04-13" }, { "rank": 2, "intensity": 2.9693877551020407, "date": "2023-10-07" }, { "rank": 3, "intensity": 2.938775510204082, "date": "2023-09-15" }, { "rank": 4, "intensity": 2.9081632653061225, "date": "2023-05-14" }, { "rank": 5, "intensity": 2.877551020408163, "date": "2023-08-24" }, { "rank": 6, "intensity": 2.8469387755102042, "date": "2023-06-16" }, { "rank": 7, "intensity": 2.816326530612245, "date": "2023-04-30" }, { "rank": 8, "intensity": 2.7857142857142856, "date": "2023-03-23" }, { "rank": 9, "intensity": 2.7551020408163267, "date": "2023-03-13" }, { "rank": 10, "intensity": 2.7244897959183674, "date": "2023-10-05" }, { "rank": 11, "intensity": 2.693877551020408, "date": "2023-08-18" }, { "rank": 12, "intensity": 2.663265306122449, "date": "2023-02-26" }, { "rank": 13, "intensity": 2.63265306122449, "date": "2023-02-01" }, { "rank": 14, "intensity": 2.6020408163265305, "date": "2023-01-25" }, { "rank": 15, "intensity": 2.5714285714285716, "date": "2023-09-22" }, { "rank": 16, "intensity": 2.5408163265306123, "date": "2023-05-08" }, { "rank": 17, "intensity": 2.510204081632653, "date": "2023-05-11" }, { "rank": 18, "intensity": 2.479591836734694, "date": "2023-08-08" }, { "rank": 19, "intensity": 2.4489795918367347, "date": "2023-01-12" }, { "rank": 20, "intensity": 2.4183673469387754, "date": "2023-06-04" }, { "rank": 21, "intensity": 2.387755102040816, "date": "2023-10-12" }, { "rank": 22, "intensity": 2.357142857142857, "date": "2023-12-18" }, { "rank": 23, "intensity": 2.326530612244898, "date": "2023-12-05" }, { "rank": 24, "intensity": 2.295918367346939, "date": "2023-05-31" }, { "rank": 25, "intensity": 2.2653061224489797, "date": "2023-03-04" }, { "rank": 26, "intensity": 2.2346938775510203, "date": "2023-09-12" }, { "rank": 27, "intensity": 2.204081632653061, "date": "2023-12-25" }, { "rank": 28, "intensity": 2.173469387755102, "date": "2023-05-16" }, { "rank": 29, "intensity": 2.142857142857143, "date": "2023-12-07" }, { "rank": 30, "intensity": 2.112244897959184, "date": "2023-09-26" }, { "rank": 31, "intensity": 2.0816326530612246, "date": "2023-12-30" }, { "rank": 32, "intensity": 2.0510204081632653, "date": "2023-12-20" }, { "rank": 33, "intensity": 2.020408163265306, "date": "2023-12-22" }, { "rank": 34, "intensity": 1.989795918367347, "date": "2023-04-25" }, { "rank": 35, "intensity": 1.9591836734693877, "date": "2023-09-20" }, { "rank": 36, "intensity": 1.9285714285714286, "date": "2023-11-14" }, { "rank": 37, "intensity": 1.8979591836734695, "date": "2023-02-23" }, { "rank": 38, "intensity": 1.8673469387755102, "date": "2023-04-04" }, { "rank": 39, "intensity": 1.836734693877551, "date": "2023-08-22" }, { "rank": 40, "intensity": 1.806122448979592, "date": "2023-10-21" }, { "rank": 41, "intensity": 1.7755102040816326, "date": "2023-06-08" }, { "rank": 42, "intensity": 1.7448979591836735, "date": "2023-05-19" }, { "rank": 43, "intensity": 1.7142857142857144, "date": "2023-06-27" }, { "rank": 44, "intensity": 1.683673469387755, "date": "2023-08-15" }, { "rank": 45, "intensity": 1.653061224489796, "date": "2023-06-10" }, { "rank": 46, "intensity": 1.6224489795918369, "date": "2023-01-05" }, { "rank": 47, "intensity": 1.5918367346938775, "date": "2023-07-28" }, { "rank": 48, "intensity": 1.5612244897959184, "date": "2023-03-07" }, { "rank": 49, "intensity": 1.5306122448979593, "date": "2023-08-04" }, { "rank": 50, "intensity": 1.5, "date": "2023-04-21" } ], "source": "causal_rivers" } }, { "id": "L3_T1_Composite_Trend_00137", "level": 3, "level_name": "Semantic Reasoning", "category": "Composite Trend", "subtask": "Composite Trend", "question": "Among days in channel 589 during 2022 that exhibit the trend pattern 'slow rise, then rapid fall, then fluctuating stable', identify the top-5 days where the rapid fall segment is the fastest. (Output format: a ranked list of dates, e.g., ['YYYY-MM-DD', ...])", "answer": "['2022-04-01', '2022-12-10', '2022-02-05', '2022-09-02', '2022-09-12']", "ground_truth": [ "2022-04-01", "2022-12-10", "2022-02-05", "2022-09-02", "2022-09-12" ], "eval_metric": "set_f1", "channel": "589", "ts_data_path": "ts_data/L3_T1_Composite_Trend_00137.csv", "meta": { "segments": [ { "kind": "rise", "adj": "slow" }, { "kind": "fall", "adj": "rapid" }, { "kind": "stable", "adj": "fluctuating" } ], "pattern_phrase": "slow rise, then rapid fall, then fluctuating stable", "rank_target_idx": 1, "rank_kind": "fall", "rank_adj": "rapid", "rank_axis": "slope", "rank_direction": "DESC", "rank_extreme": "fastest", "top_segments_meta": [ { "adj": "slow", "kind": "rise", "label": "slow rise", "start_idx": 0, "end_idx": 51 }, { "adj": "rapid", "kind": "fall", "label": "rapid fall", "start_idx": 51, "end_idx": 65 }, { "adj": "fluctuating", "kind": "stable", "label": "fluctuating stable", "start_idx": 65, "end_idx": 98 } ], "points_per_day": 96, "pattern": "slow rise, then rapid fall, then fluctuating stable", "year": 2022, "top_k": [ "2022-04-01", "2022-12-10", "2022-02-05", "2022-09-02", "2022-09-12" ], "all_injections": [ { "rank": 1, "intensity": 3.0, "date": "2022-04-01" }, { "rank": 2, "intensity": 2.9693877551020407, "date": "2022-12-10" }, { "rank": 3, "intensity": 2.938775510204082, "date": "2022-02-05" }, { "rank": 4, "intensity": 2.9081632653061225, "date": "2022-09-02" }, { "rank": 5, "intensity": 2.877551020408163, "date": "2022-09-12" }, { "rank": 6, "intensity": 2.8469387755102042, "date": "2022-10-01" }, { "rank": 7, "intensity": 2.816326530612245, "date": "2022-10-26" }, { "rank": 8, "intensity": 2.7857142857142856, "date": "2022-08-21" }, { "rank": 9, "intensity": 2.7551020408163267, "date": "2022-07-25" }, { "rank": 10, "intensity": 2.7244897959183674, "date": "2022-03-05" }, { "rank": 11, "intensity": 2.693877551020408, "date": "2022-12-23" }, { "rank": 12, "intensity": 2.663265306122449, "date": "2022-12-30" }, { "rank": 13, "intensity": 2.63265306122449, "date": "2022-05-18" }, { "rank": 14, "intensity": 2.6020408163265305, "date": "2022-05-30" }, { "rank": 15, "intensity": 2.5714285714285716, "date": "2022-08-10" }, { "rank": 16, "intensity": 2.5408163265306123, "date": "2022-04-07" }, { "rank": 17, "intensity": 2.510204081632653, "date": "2022-11-18" }, { "rank": 18, "intensity": 2.479591836734694, "date": "2022-06-02" }, { "rank": 19, "intensity": 2.4489795918367347, "date": "2022-11-03" }, { "rank": 20, "intensity": 2.4183673469387754, "date": "2022-03-11" }, { "rank": 21, "intensity": 2.387755102040816, "date": "2022-03-27" }, { "rank": 22, "intensity": 2.357142857142857, "date": "2022-06-05" }, { "rank": 23, "intensity": 2.326530612244898, "date": "2022-08-15" }, { "rank": 24, "intensity": 2.295918367346939, "date": "2022-01-29" }, { "rank": 25, "intensity": 2.2653061224489797, "date": "2022-03-22" }, { "rank": 26, "intensity": 2.2346938775510203, "date": "2022-07-15" }, { "rank": 27, "intensity": 2.204081632653061, "date": "2022-08-07" }, { "rank": 28, "intensity": 2.173469387755102, "date": "2022-11-11" }, { "rank": 29, "intensity": 2.142857142857143, "date": "2022-01-05" }, { "rank": 30, "intensity": 2.112244897959184, "date": "2022-09-15" }, { "rank": 31, "intensity": 2.0816326530612246, "date": "2022-04-29" }, { "rank": 32, "intensity": 2.0510204081632653, "date": "2022-01-12" }, { "rank": 33, "intensity": 2.020408163265306, "date": "2022-02-24" }, { "rank": 34, "intensity": 1.989795918367347, "date": "2022-03-01" }, { "rank": 35, "intensity": 1.9591836734693877, "date": "2022-08-28" }, { "rank": 36, "intensity": 1.9285714285714286, "date": "2022-04-09" }, { "rank": 37, "intensity": 1.8979591836734695, "date": "2022-12-12" }, { "rank": 38, "intensity": 1.8673469387755102, "date": "2022-10-31" }, { "rank": 39, "intensity": 1.836734693877551, "date": "2022-12-03" }, { "rank": 40, "intensity": 1.806122448979592, "date": "2022-07-02" }, { "rank": 41, "intensity": 1.7755102040816326, "date": "2022-02-10" }, { "rank": 42, "intensity": 1.7448979591836735, "date": "2022-03-14" }, { "rank": 43, "intensity": 1.7142857142857144, "date": "2022-06-23" }, { "rank": 44, "intensity": 1.683673469387755, "date": "2022-09-05" }, { "rank": 45, "intensity": 1.653061224489796, "date": "2022-05-13" }, { "rank": 46, "intensity": 1.6224489795918369, "date": "2022-10-24" }, { "rank": 47, "intensity": 1.5918367346938775, "date": "2022-06-29" }, { "rank": 48, "intensity": 1.5612244897959184, "date": "2022-12-25" }, { "rank": 49, "intensity": 1.5306122448979593, "date": "2022-03-07" }, { "rank": 50, "intensity": 1.5, "date": "2022-12-07" } ], "source": "causal_rivers" } }, { "id": "L3_T1_Composite_Trend_00138", "level": 3, "level_name": "Semantic Reasoning", "category": "Composite Trend", "subtask": "Composite Trend", "question": "Among days in channel 591 during 2019 that exhibit the trend pattern 'slow rise, then slow fall, then fall, then slow fall', identify the top-5 days where the slow rise segment is the slowest. (Output format: a ranked list of dates, e.g., ['YYYY-MM-DD', ...])", "answer": "['2019-02-07', '2019-10-26', '2019-08-08', '2019-11-04', '2019-05-18']", "ground_truth": [ "2019-02-07", "2019-10-26", "2019-08-08", "2019-11-04", "2019-05-18" ], "eval_metric": "set_f1", "channel": "591", "ts_data_path": "ts_data/L3_T1_Composite_Trend_00138.csv", "meta": { "segments": [ { "kind": "rise", "adj": "slow" }, { "kind": "fall", "adj": "slow" }, { "kind": "fall", "adj": "none" }, { "kind": "fall", "adj": "slow" } ], "pattern_phrase": "slow rise, then slow fall, then fall, then slow fall", "rank_target_idx": 0, "rank_kind": "rise", "rank_adj": "slow", "rank_axis": "slope", "rank_direction": "ASC", "rank_extreme": "slowest", "top_segments_meta": [ { "adj": "slow", "kind": "rise", "label": "slow rise", "start_idx": 0, "end_idx": 26 }, { "adj": "slow", "kind": "fall", "label": "slow fall", "start_idx": 26, "end_idx": 55 }, { "adj": "none", "kind": "fall", "label": "none fall", "start_idx": 55, "end_idx": 71 }, { "adj": "slow", "kind": "fall", "label": "slow fall", "start_idx": 71, "end_idx": 98 } ], "points_per_day": 96, "pattern": "slow rise, then slow fall, then fall, then slow fall", "year": 2019, "top_k": [ "2019-02-07", "2019-10-26", "2019-08-08", "2019-11-04", "2019-05-18" ], "all_injections": [ { "rank": 1, "intensity": 1.5, "date": "2019-02-07" }, { "rank": 2, "intensity": 1.530612244897959, "date": "2019-10-26" }, { "rank": 3, "intensity": 1.5612244897959184, "date": "2019-08-08" }, { "rank": 4, "intensity": 1.5918367346938775, "date": "2019-11-04" }, { "rank": 5, "intensity": 1.6224489795918366, "date": "2019-05-18" }, { "rank": 6, "intensity": 1.653061224489796, "date": "2019-07-16" }, { "rank": 7, "intensity": 1.683673469387755, "date": "2019-10-14" }, { "rank": 8, "intensity": 1.7142857142857142, "date": "2019-12-07" }, { "rank": 9, "intensity": 1.7448979591836735, "date": "2019-09-07" }, { "rank": 10, "intensity": 1.7755102040816326, "date": "2019-07-04" }, { "rank": 11, "intensity": 1.806122448979592, "date": "2019-12-30" }, { "rank": 12, "intensity": 1.836734693877551, "date": "2019-04-29" }, { "rank": 13, "intensity": 1.8673469387755102, "date": "2019-04-10" }, { "rank": 14, "intensity": 1.8979591836734695, "date": "2019-01-25" }, { "rank": 15, "intensity": 1.9285714285714286, "date": "2019-02-02" }, { "rank": 16, "intensity": 1.9591836734693877, "date": "2019-11-19" }, { "rank": 17, "intensity": 1.989795918367347, "date": "2019-02-17" }, { "rank": 18, "intensity": 2.020408163265306, "date": "2019-03-17" }, { "rank": 19, "intensity": 2.0510204081632653, "date": "2019-05-31" }, { "rank": 20, "intensity": 2.0816326530612246, "date": "2019-09-12" }, { "rank": 21, "intensity": 2.112244897959184, "date": "2019-05-29" }, { "rank": 22, "intensity": 2.142857142857143, "date": "2019-02-23" }, { "rank": 23, "intensity": 2.173469387755102, "date": "2019-05-05" }, { "rank": 24, "intensity": 2.204081632653061, "date": "2019-10-05" }, { "rank": 25, "intensity": 2.2346938775510203, "date": "2019-02-20" }, { "rank": 26, "intensity": 2.2653061224489797, "date": "2019-02-15" }, { "rank": 27, "intensity": 2.295918367346939, "date": "2019-03-20" }, { "rank": 28, "intensity": 2.326530612244898, "date": "2019-08-03" }, { "rank": 29, "intensity": 2.357142857142857, "date": "2019-07-07" }, { "rank": 30, "intensity": 2.387755102040816, "date": "2019-04-18" }, { "rank": 31, "intensity": 2.4183673469387754, "date": "2019-12-21" }, { "rank": 32, "intensity": 2.4489795918367347, "date": "2019-08-11" }, { "rank": 33, "intensity": 2.479591836734694, "date": "2019-06-18" }, { "rank": 34, "intensity": 2.510204081632653, "date": "2019-07-25" }, { "rank": 35, "intensity": 2.5408163265306123, "date": "2019-04-01" }, { "rank": 36, "intensity": 2.571428571428571, "date": "2019-11-27" }, { "rank": 37, "intensity": 2.6020408163265305, "date": "2019-04-21" }, { "rank": 38, "intensity": 2.63265306122449, "date": "2019-04-26" }, { "rank": 39, "intensity": 2.663265306122449, "date": "2019-10-22" }, { "rank": 40, "intensity": 2.693877551020408, "date": "2019-01-08" }, { "rank": 41, "intensity": 2.7244897959183674, "date": "2019-05-26" }, { "rank": 42, "intensity": 2.7551020408163263, "date": "2019-12-16" }, { "rank": 43, "intensity": 2.7857142857142856, "date": "2019-04-12" }, { "rank": 44, "intensity": 2.816326530612245, "date": "2019-05-24" }, { "rank": 45, "intensity": 2.8469387755102042, "date": "2019-01-28" }, { "rank": 46, "intensity": 2.877551020408163, "date": "2019-09-03" }, { "rank": 47, "intensity": 2.9081632653061225, "date": "2019-08-22" }, { "rank": 48, "intensity": 2.9387755102040813, "date": "2019-05-07" }, { "rank": 49, "intensity": 2.9693877551020407, "date": "2019-01-30" }, { "rank": 50, "intensity": 3.0, "date": "2019-06-10" } ], "source": "causal_rivers" } }, { "id": "L3_T1_Composite_Trend_00139", "level": 3, "level_name": "Semantic Reasoning", "category": "Composite Trend", "subtask": "Composite Trend", "question": "Among days in channel 595 during 2019 that exhibit the trend pattern 'steady stable, then slow fall', identify the top-5 days where the slow fall segment is the slowest. (Output format: a ranked list of dates, e.g., ['YYYY-MM-DD', ...])", "answer": "['2019-12-01', '2019-04-04', '2019-07-18', '2019-11-02', '2019-03-19']", "ground_truth": [ "2019-12-01", "2019-04-04", "2019-07-18", "2019-11-02", "2019-03-19" ], "eval_metric": "set_f1", "channel": "595", "ts_data_path": "ts_data/L3_T1_Composite_Trend_00139.csv", "meta": { "segments": [ { "kind": "stable", "adj": "steady" }, { "kind": "fall", "adj": "slow" } ], "pattern_phrase": "steady stable, then slow fall", "rank_target_idx": 1, "rank_kind": "fall", "rank_adj": "slow", "rank_axis": "slope", "rank_direction": "ASC", "rank_extreme": "slowest", "top_segments_meta": [ { "adj": "steady", "kind": "stable", "label": "steady stable", "start_idx": 0, "end_idx": 52 }, { "adj": "slow", "kind": "fall", "label": "slow fall", "start_idx": 52, "end_idx": 98 } ], "points_per_day": 96, "pattern": "steady stable, then slow fall", "year": 2019, "top_k": [ "2019-12-01", "2019-04-04", "2019-07-18", "2019-11-02", "2019-03-19" ], "all_injections": [ { "rank": 1, "intensity": 1.5, "date": "2019-12-01" }, { "rank": 2, "intensity": 1.530612244897959, "date": "2019-04-04" }, { "rank": 3, "intensity": 1.5612244897959184, "date": "2019-07-18" }, { "rank": 4, "intensity": 1.5918367346938775, "date": "2019-11-02" }, { "rank": 5, "intensity": 1.6224489795918366, "date": "2019-03-19" }, { "rank": 6, "intensity": 1.653061224489796, "date": "2019-05-23" }, { "rank": 7, "intensity": 1.683673469387755, "date": "2019-03-17" }, { "rank": 8, "intensity": 1.7142857142857142, "date": "2019-08-18" }, { "rank": 9, "intensity": 1.7448979591836735, "date": "2019-08-14" }, { "rank": 10, "intensity": 1.7755102040816326, "date": "2019-07-07" }, { "rank": 11, "intensity": 1.806122448979592, "date": "2019-03-01" }, { "rank": 12, "intensity": 1.836734693877551, "date": "2019-06-08" }, { "rank": 13, "intensity": 1.8673469387755102, "date": "2019-01-02" }, { "rank": 14, "intensity": 1.8979591836734695, "date": "2019-10-30" }, { "rank": 15, "intensity": 1.9285714285714286, "date": "2019-02-26" }, { "rank": 16, "intensity": 1.9591836734693877, "date": "2019-08-03" }, { "rank": 17, "intensity": 1.989795918367347, "date": "2019-09-01" }, { "rank": 18, "intensity": 2.020408163265306, "date": "2019-09-15" }, { "rank": 19, "intensity": 2.0510204081632653, "date": "2019-08-09" }, { "rank": 20, "intensity": 2.0816326530612246, "date": "2019-03-06" }, { "rank": 21, "intensity": 2.112244897959184, "date": "2019-12-14" }, { "rank": 22, "intensity": 2.142857142857143, "date": "2019-09-28" }, { "rank": 23, "intensity": 2.173469387755102, "date": "2019-04-21" }, { "rank": 24, "intensity": 2.204081632653061, "date": "2019-07-11" }, { "rank": 25, "intensity": 2.2346938775510203, "date": "2019-04-12" }, { "rank": 26, "intensity": 2.2653061224489797, "date": "2019-01-13" }, { "rank": 27, "intensity": 2.295918367346939, "date": "2019-03-26" }, { "rank": 28, "intensity": 2.326530612244898, "date": "2019-01-20" }, { "rank": 29, "intensity": 2.357142857142857, "date": "2019-12-24" }, { "rank": 30, "intensity": 2.387755102040816, "date": "2019-09-18" }, { "rank": 31, "intensity": 2.4183673469387754, "date": "2019-06-12" }, { "rank": 32, "intensity": 2.4489795918367347, "date": "2019-01-29" }, { "rank": 33, "intensity": 2.479591836734694, "date": "2019-05-25" }, { "rank": 34, "intensity": 2.510204081632653, "date": "2019-05-01" }, { "rank": 35, "intensity": 2.5408163265306123, "date": "2019-06-05" }, { "rank": 36, "intensity": 2.571428571428571, "date": "2019-06-20" }, { "rank": 37, "intensity": 2.6020408163265305, "date": "2019-05-20" }, { "rank": 38, "intensity": 2.63265306122449, "date": "2019-09-07" }, { "rank": 39, "intensity": 2.663265306122449, "date": "2019-02-04" }, { "rank": 40, "intensity": 2.693877551020408, "date": "2019-01-16" }, { "rank": 41, "intensity": 2.7244897959183674, "date": "2019-02-14" }, { "rank": 42, "intensity": 2.7551020408163263, "date": "2019-09-09" }, { "rank": 43, "intensity": 2.7857142857142856, "date": "2019-07-27" }, { "rank": 44, "intensity": 2.816326530612245, "date": "2019-03-21" }, { "rank": 45, "intensity": 2.8469387755102042, "date": "2019-12-09" }, { "rank": 46, "intensity": 2.877551020408163, "date": "2019-05-30" }, { "rank": 47, "intensity": 2.9081632653061225, "date": "2019-07-03" }, { "rank": 48, "intensity": 2.9387755102040813, "date": "2019-09-11" }, { "rank": 49, "intensity": 2.9693877551020407, "date": "2019-05-03" }, { "rank": 50, "intensity": 3.0, "date": "2019-12-04" } ], "source": "causal_rivers" } }, { "id": "L3_T1_Composite_Trend_00140", "level": 3, "level_name": "Semantic Reasoning", "category": "Composite Trend", "subtask": "Composite Trend", "question": "Among days in channel 625 during 2019 that exhibit the trend pattern 'fluctuating stable, then rapid fall, then slow fall', identify the top-5 days where the rapid fall segment is the fastest. (Output format: a ranked list of dates, e.g., ['YYYY-MM-DD', ...])", "answer": "['2019-06-23', '2019-03-12', '2019-02-18', '2019-10-05', '2019-03-07']", "ground_truth": [ "2019-06-23", "2019-03-12", "2019-02-18", "2019-10-05", "2019-03-07" ], "eval_metric": "set_f1", "channel": "625", "ts_data_path": "ts_data/L3_T1_Composite_Trend_00140.csv", "meta": { "segments": [ { "kind": "stable", "adj": "fluctuating" }, { "kind": "fall", "adj": "rapid" }, { "kind": "fall", "adj": "slow" } ], "pattern_phrase": "fluctuating stable, then rapid fall, then slow fall", "rank_target_idx": 1, "rank_kind": "fall", "rank_adj": "rapid", "rank_axis": "slope", "rank_direction": "DESC", "rank_extreme": "fastest", "top_segments_meta": [ { "adj": "fluctuating", "kind": "stable", "label": "fluctuating stable", "start_idx": 0, "end_idx": 38 }, { "adj": "rapid", "kind": "fall", "label": "rapid fall", "start_idx": 38, "end_idx": 52 }, { "adj": "slow", "kind": "fall", "label": "slow fall", "start_idx": 52, "end_idx": 98 } ], "points_per_day": 96, "pattern": "fluctuating stable, then rapid fall, then slow fall", "year": 2019, "top_k": [ "2019-06-23", "2019-03-12", "2019-02-18", "2019-10-05", "2019-03-07" ], "all_injections": [ { "rank": 1, "intensity": 3.0, "date": "2019-06-23" }, { "rank": 2, "intensity": 2.9693877551020407, "date": "2019-03-12" }, { "rank": 3, "intensity": 2.938775510204082, "date": "2019-02-18" }, { "rank": 4, "intensity": 2.9081632653061225, "date": "2019-10-05" }, { "rank": 5, "intensity": 2.877551020408163, "date": "2019-03-07" }, { "rank": 6, "intensity": 2.8469387755102042, "date": "2019-11-05" }, { "rank": 7, "intensity": 2.816326530612245, "date": "2019-02-09" }, { "rank": 8, "intensity": 2.7857142857142856, "date": "2019-10-23" }, { "rank": 9, "intensity": 2.7551020408163267, "date": "2019-07-17" }, { "rank": 10, "intensity": 2.7244897959183674, "date": "2019-09-01" }, { "rank": 11, "intensity": 2.693877551020408, "date": "2019-07-02" }, { "rank": 12, "intensity": 2.663265306122449, "date": "2019-07-19" }, { "rank": 13, "intensity": 2.63265306122449, "date": "2019-08-17" }, { "rank": 14, "intensity": 2.6020408163265305, "date": "2019-01-15" }, { "rank": 15, "intensity": 2.5714285714285716, "date": "2019-04-05" }, { "rank": 16, "intensity": 2.5408163265306123, "date": "2019-01-27" }, { "rank": 17, "intensity": 2.510204081632653, "date": "2019-10-11" }, { "rank": 18, "intensity": 2.479591836734694, "date": "2019-05-13" }, { "rank": 19, "intensity": 2.4489795918367347, "date": "2019-03-02" }, { "rank": 20, "intensity": 2.4183673469387754, "date": "2019-10-20" }, { "rank": 21, "intensity": 2.387755102040816, "date": "2019-09-25" }, { "rank": 22, "intensity": 2.357142857142857, "date": "2019-01-23" }, { "rank": 23, "intensity": 2.326530612244898, "date": "2019-07-23" }, { "rank": 24, "intensity": 2.295918367346939, "date": "2019-09-27" }, { "rank": 25, "intensity": 2.2653061224489797, "date": "2019-12-20" }, { "rank": 26, "intensity": 2.2346938775510203, "date": "2019-03-16" }, { "rank": 27, "intensity": 2.204081632653061, "date": "2019-06-19" }, { "rank": 28, "intensity": 2.173469387755102, "date": "2019-08-03" }, { "rank": 29, "intensity": 2.142857142857143, "date": "2019-06-27" }, { "rank": 30, "intensity": 2.112244897959184, "date": "2019-05-16" }, { "rank": 31, "intensity": 2.0816326530612246, "date": "2019-12-13" }, { "rank": 32, "intensity": 2.0510204081632653, "date": "2019-06-30" }, { "rank": 33, "intensity": 2.020408163265306, "date": "2019-10-25" }, { "rank": 34, "intensity": 1.989795918367347, "date": "2019-02-21" }, { "rank": 35, "intensity": 1.9591836734693877, "date": "2019-04-15" }, { "rank": 36, "intensity": 1.9285714285714286, "date": "2019-05-20" }, { "rank": 37, "intensity": 1.8979591836734695, "date": "2019-02-28" }, { "rank": 38, "intensity": 1.8673469387755102, "date": "2019-12-06" }, { "rank": 39, "intensity": 1.836734693877551, "date": "2019-12-11" }, { "rank": 40, "intensity": 1.806122448979592, "date": "2019-08-20" }, { "rank": 41, "intensity": 1.7755102040816326, "date": "2019-04-11" }, { "rank": 42, "intensity": 1.7448979591836735, "date": "2019-01-25" }, { "rank": 43, "intensity": 1.7142857142857144, "date": "2019-01-29" }, { "rank": 44, "intensity": 1.683673469387755, "date": "2019-08-12" }, { "rank": 45, "intensity": 1.653061224489796, "date": "2019-11-07" }, { "rank": 46, "intensity": 1.6224489795918369, "date": "2019-11-13" }, { "rank": 47, "intensity": 1.5918367346938775, "date": "2019-02-14" }, { "rank": 48, "intensity": 1.5612244897959184, "date": "2019-11-10" }, { "rank": 49, "intensity": 1.5306122448979593, "date": "2019-10-31" }, { "rank": 50, "intensity": 1.5, "date": "2019-06-25" } ], "source": "causal_rivers" } }, { "id": "L3_T1_Composite_Trend_00141", "level": 3, "level_name": "Semantic Reasoning", "category": "Composite Trend", "subtask": "Composite Trend", "question": "Among days in channel 626 during 2019 that exhibit the trend pattern 'rise, then rapid fall', identify the top-5 days where the rapid fall segment is the fastest. (Output format: a ranked list of dates, e.g., ['YYYY-MM-DD', ...])", "answer": "['2019-08-13', '2019-03-02', '2019-08-11', '2019-03-23', '2019-04-07']", "ground_truth": [ "2019-08-13", "2019-03-02", "2019-08-11", "2019-03-23", "2019-04-07" ], "eval_metric": "set_f1", "channel": "626", "ts_data_path": "ts_data/L3_T1_Composite_Trend_00141.csv", "meta": { "segments": [ { "kind": "rise", "adj": "none" }, { "kind": "fall", "adj": "rapid" } ], "pattern_phrase": "rise, then rapid fall", "rank_target_idx": 1, "rank_kind": "fall", "rank_adj": "rapid", "rank_axis": "slope", "rank_direction": "DESC", "rank_extreme": "fastest", "top_segments_meta": [ { "adj": "none", "kind": "rise", "label": "none rise", "start_idx": 0, "end_idx": 65 }, { "adj": "rapid", "kind": "fall", "label": "rapid fall", "start_idx": 65, "end_idx": 98 } ], "points_per_day": 96, "pattern": "rise, then rapid fall", "year": 2019, "top_k": [ "2019-08-13", "2019-03-02", "2019-08-11", "2019-03-23", "2019-04-07" ], "all_injections": [ { "rank": 1, "intensity": 3.0, "date": "2019-08-13" }, { "rank": 2, "intensity": 2.9693877551020407, "date": "2019-03-02" }, { "rank": 3, "intensity": 2.938775510204082, "date": "2019-08-11" }, { "rank": 4, "intensity": 2.9081632653061225, "date": "2019-03-23" }, { "rank": 5, "intensity": 2.877551020408163, "date": "2019-04-07" }, { "rank": 6, "intensity": 2.8469387755102042, "date": "2019-10-18" }, { "rank": 7, "intensity": 2.816326530612245, "date": "2019-07-14" }, { "rank": 8, "intensity": 2.7857142857142856, "date": "2019-10-27" }, { "rank": 9, "intensity": 2.7551020408163267, "date": "2019-11-14" }, { "rank": 10, "intensity": 2.7244897959183674, "date": "2019-09-10" }, { "rank": 11, "intensity": 2.693877551020408, "date": "2019-03-18" }, { "rank": 12, "intensity": 2.663265306122449, "date": "2019-11-20" }, { "rank": 13, "intensity": 2.63265306122449, "date": "2019-10-11" }, { "rank": 14, "intensity": 2.6020408163265305, "date": "2019-10-24" }, { "rank": 15, "intensity": 2.5714285714285716, "date": "2019-05-13" }, { "rank": 16, "intensity": 2.5408163265306123, "date": "2019-08-01" }, { "rank": 17, "intensity": 2.510204081632653, "date": "2019-10-13" }, { "rank": 18, "intensity": 2.479591836734694, "date": "2019-05-16" }, { "rank": 19, "intensity": 2.4489795918367347, "date": "2019-01-05" }, { "rank": 20, "intensity": 2.4183673469387754, "date": "2019-03-05" }, { "rank": 21, "intensity": 2.387755102040816, "date": "2019-04-15" }, { "rank": 22, "intensity": 2.357142857142857, "date": "2019-10-08" }, { "rank": 23, "intensity": 2.326530612244898, "date": "2019-10-01" }, { "rank": 24, "intensity": 2.295918367346939, "date": "2019-09-24" }, { "rank": 25, "intensity": 2.2653061224489797, "date": "2019-04-19" }, { "rank": 26, "intensity": 2.2346938775510203, "date": "2019-09-14" }, { "rank": 27, "intensity": 2.204081632653061, "date": "2019-01-07" }, { "rank": 28, "intensity": 2.173469387755102, "date": "2019-09-02" }, { "rank": 29, "intensity": 2.142857142857143, "date": "2019-11-03" }, { "rank": 30, "intensity": 2.112244897959184, "date": "2019-06-23" }, { "rank": 31, "intensity": 2.0816326530612246, "date": "2019-12-12" }, { "rank": 32, "intensity": 2.0510204081632653, "date": "2019-03-08" }, { "rank": 33, "intensity": 2.020408163265306, "date": "2019-02-21" }, { "rank": 34, "intensity": 1.989795918367347, "date": "2019-06-12" }, { "rank": 35, "intensity": 1.9591836734693877, "date": "2019-06-05" }, { "rank": 36, "intensity": 1.9285714285714286, "date": "2019-07-19" }, { "rank": 37, "intensity": 1.8979591836734695, "date": "2019-02-24" }, { "rank": 38, "intensity": 1.8673469387755102, "date": "2019-01-03" }, { "rank": 39, "intensity": 1.836734693877551, "date": "2019-03-26" }, { "rank": 40, "intensity": 1.806122448979592, "date": "2019-02-07" }, { "rank": 41, "intensity": 1.7755102040816326, "date": "2019-03-29" }, { "rank": 42, "intensity": 1.7448979591836735, "date": "2019-06-27" }, { "rank": 43, "intensity": 1.7142857142857144, "date": "2019-04-05" }, { "rank": 44, "intensity": 1.683673469387755, "date": "2019-01-23" }, { "rank": 45, "intensity": 1.653061224489796, "date": "2019-08-25" }, { "rank": 46, "intensity": 1.6224489795918369, "date": "2019-09-28" }, { "rank": 47, "intensity": 1.5918367346938775, "date": "2019-09-07" }, { "rank": 48, "intensity": 1.5612244897959184, "date": "2019-12-29" }, { "rank": 49, "intensity": 1.5306122448979593, "date": "2019-11-08" }, { "rank": 50, "intensity": 1.5, "date": "2019-08-27" } ], "source": "causal_rivers" } }, { "id": "L3_T1_Composite_Trend_00142", "level": 3, "level_name": "Semantic Reasoning", "category": "Composite Trend", "subtask": "Composite Trend", "question": "Among days in channel 627 during 2020 that exhibit the trend pattern 'rapid rise, then steady stable', identify the top-5 days where the rapid rise segment is the fastest. (Output format: a ranked list of dates, e.g., ['YYYY-MM-DD', ...])", "answer": "['2020-10-21', '2020-10-23', '2020-02-01', '2020-05-07', '2020-08-31']", "ground_truth": [ "2020-10-21", "2020-10-23", "2020-02-01", "2020-05-07", "2020-08-31" ], "eval_metric": "set_f1", "channel": "627", "ts_data_path": "ts_data/L3_T1_Composite_Trend_00142.csv", "meta": { "segments": [ { "kind": "rise", "adj": "rapid" }, { "kind": "stable", "adj": "steady" } ], "pattern_phrase": "rapid rise, then steady stable", "rank_target_idx": 0, "rank_kind": "rise", "rank_adj": "rapid", "rank_axis": "slope", "rank_direction": "DESC", "rank_extreme": "fastest", "top_segments_meta": [ { "adj": "rapid", "kind": "rise", "label": "rapid rise", "start_idx": 0, "end_idx": 21 }, { "adj": "steady", "kind": "stable", "label": "steady stable", "start_idx": 21, "end_idx": 98 } ], "points_per_day": 96, "pattern": "rapid rise, then steady stable", "year": 2020, "top_k": [ "2020-10-21", "2020-10-23", "2020-02-01", "2020-05-07", "2020-08-31" ], "all_injections": [ { "rank": 1, "intensity": 3.0, "date": "2020-10-21" }, { "rank": 2, "intensity": 2.9693877551020407, "date": "2020-10-23" }, { "rank": 3, "intensity": 2.938775510204082, "date": "2020-02-01" }, { "rank": 4, "intensity": 2.9081632653061225, "date": "2020-05-07" }, { "rank": 5, "intensity": 2.877551020408163, "date": "2020-08-31" }, { "rank": 6, "intensity": 2.8469387755102042, "date": "2020-03-05" }, { "rank": 7, "intensity": 2.816326530612245, "date": "2020-12-27" }, { "rank": 8, "intensity": 2.7857142857142856, "date": "2020-05-30" }, { "rank": 9, "intensity": 2.7551020408163267, "date": "2020-03-25" }, { "rank": 10, "intensity": 2.7244897959183674, "date": "2020-06-24" }, { "rank": 11, "intensity": 2.693877551020408, "date": "2020-09-18" }, { "rank": 12, "intensity": 2.663265306122449, "date": "2020-03-18" }, { "rank": 13, "intensity": 2.63265306122449, "date": "2020-01-16" }, { "rank": 14, "intensity": 2.6020408163265305, "date": "2020-12-19" }, { "rank": 15, "intensity": 2.5714285714285716, "date": "2020-01-04" }, { "rank": 16, "intensity": 2.5408163265306123, "date": "2020-02-10" }, { "rank": 17, "intensity": 2.510204081632653, "date": "2020-07-20" }, { "rank": 18, "intensity": 2.479591836734694, "date": "2020-09-12" }, { "rank": 19, "intensity": 2.4489795918367347, "date": "2020-01-01" }, { "rank": 20, "intensity": 2.4183673469387754, "date": "2020-01-11" }, { "rank": 21, "intensity": 2.387755102040816, "date": "2020-11-19" }, { "rank": 22, "intensity": 2.357142857142857, "date": "2020-03-20" }, { "rank": 23, "intensity": 2.326530612244898, "date": "2020-10-16" }, { "rank": 24, "intensity": 2.295918367346939, "date": "2020-06-01" }, { "rank": 25, "intensity": 2.2653061224489797, "date": "2020-06-28" }, { "rank": 26, "intensity": 2.2346938775510203, "date": "2020-07-07" }, { "rank": 27, "intensity": 2.204081632653061, "date": "2020-11-30" }, { "rank": 28, "intensity": 2.173469387755102, "date": "2020-02-29" }, { "rank": 29, "intensity": 2.142857142857143, "date": "2020-04-01" }, { "rank": 30, "intensity": 2.112244897959184, "date": "2020-07-22" }, { "rank": 31, "intensity": 2.0816326530612246, "date": "2020-03-13" }, { "rank": 32, "intensity": 2.0510204081632653, "date": "2020-05-14" }, { "rank": 33, "intensity": 2.020408163265306, "date": "2020-11-01" }, { "rank": 34, "intensity": 1.989795918367347, "date": "2020-04-10" }, { "rank": 35, "intensity": 1.9591836734693877, "date": "2020-01-19" }, { "rank": 36, "intensity": 1.9285714285714286, "date": "2020-04-03" }, { "rank": 37, "intensity": 1.8979591836734695, "date": "2020-10-06" }, { "rank": 38, "intensity": 1.8673469387755102, "date": "2020-02-21" }, { "rank": 39, "intensity": 1.836734693877551, "date": "2020-05-03" }, { "rank": 40, "intensity": 1.806122448979592, "date": "2020-01-30" }, { "rank": 41, "intensity": 1.7755102040816326, "date": "2020-11-07" }, { "rank": 42, "intensity": 1.7448979591836735, "date": "2020-06-17" }, { "rank": 43, "intensity": 1.7142857142857144, "date": "2020-06-07" }, { "rank": 44, "intensity": 1.683673469387755, "date": "2020-01-14" }, { "rank": 45, "intensity": 1.653061224489796, "date": "2020-10-25" }, { "rank": 46, "intensity": 1.6224489795918369, "date": "2020-07-01" }, { "rank": 47, "intensity": 1.5918367346938775, "date": "2020-02-24" }, { "rank": 48, "intensity": 1.5612244897959184, "date": "2020-12-16" }, { "rank": 49, "intensity": 1.5306122448979593, "date": "2020-08-07" }, { "rank": 50, "intensity": 1.5, "date": "2020-04-27" } ], "source": "causal_rivers" } }, { "id": "L3_T1_Composite_Trend_00143", "level": 3, "level_name": "Semantic Reasoning", "category": "Composite Trend", "subtask": "Composite Trend", "question": "Among days in channel 647 during 2023 that exhibit the trend pattern 'rise, then fluctuating stable, then fall', identify the top-5 days where the fluctuating stable segment is the most fluctuating. (Output format: a ranked list of dates, e.g., ['YYYY-MM-DD', ...])", "answer": "['2023-10-01', '2023-05-29', '2023-06-14', '2023-11-09', '2023-05-26']", "ground_truth": [ "2023-10-01", "2023-05-29", "2023-06-14", "2023-11-09", "2023-05-26" ], "eval_metric": "set_f1", "channel": "647", "ts_data_path": "ts_data/L3_T1_Composite_Trend_00143.csv", "meta": { "segments": [ { "kind": "rise", "adj": "none" }, { "kind": "stable", "adj": "fluctuating" }, { "kind": "fall", "adj": "none" } ], "pattern_phrase": "rise, then fluctuating stable, then fall", "rank_target_idx": 1, "rank_kind": "stable", "rank_adj": "fluctuating", "rank_axis": "std", "rank_direction": "DESC", "rank_extreme": "most fluctuating", "top_segments_meta": [ { "adj": "none", "kind": "rise", "label": "none rise", "start_idx": 0, "end_idx": 30 }, { "adj": "fluctuating", "kind": "stable", "label": "fluctuating stable", "start_idx": 30, "end_idx": 66 }, { "adj": "none", "kind": "fall", "label": "none fall", "start_idx": 66, "end_idx": 98 } ], "points_per_day": 96, "pattern": "rise, then fluctuating stable, then fall", "year": 2023, "top_k": [ "2023-10-01", "2023-05-29", "2023-06-14", "2023-11-09", "2023-05-26" ], "all_injections": [ { "rank": 1, "intensity": 3.0, "date": "2023-10-01" }, { "rank": 2, "intensity": 2.9693877551020407, "date": "2023-05-29" }, { "rank": 3, "intensity": 2.938775510204082, "date": "2023-06-14" }, { "rank": 4, "intensity": 2.9081632653061225, "date": "2023-11-09" }, { "rank": 5, "intensity": 2.877551020408163, "date": "2023-05-26" }, { "rank": 6, "intensity": 2.8469387755102042, "date": "2023-10-10" }, { "rank": 7, "intensity": 2.816326530612245, "date": "2023-07-05" }, { "rank": 8, "intensity": 2.7857142857142856, "date": "2023-07-08" }, { "rank": 9, "intensity": 2.7551020408163267, "date": "2023-01-30" }, { "rank": 10, "intensity": 2.7244897959183674, "date": "2023-05-07" }, { "rank": 11, "intensity": 2.693877551020408, "date": "2023-05-20" }, { "rank": 12, "intensity": 2.663265306122449, "date": "2023-02-05" }, { "rank": 13, "intensity": 2.63265306122449, "date": "2023-02-01" }, { "rank": 14, "intensity": 2.6020408163265305, "date": "2023-03-24" }, { "rank": 15, "intensity": 2.5714285714285716, "date": "2023-07-03" }, { "rank": 16, "intensity": 2.5408163265306123, "date": "2023-09-27" }, { "rank": 17, "intensity": 2.510204081632653, "date": "2023-09-07" }, { "rank": 18, "intensity": 2.479591836734694, "date": "2023-11-12" }, { "rank": 19, "intensity": 2.4489795918367347, "date": "2023-01-07" }, { "rank": 20, "intensity": 2.4183673469387754, "date": "2023-11-15" }, { "rank": 21, "intensity": 2.387755102040816, "date": "2023-06-09" }, { "rank": 22, "intensity": 2.357142857142857, "date": "2023-09-09" }, { "rank": 23, "intensity": 2.326530612244898, "date": "2023-11-27" }, { "rank": 24, "intensity": 2.295918367346939, "date": "2023-03-31" }, { "rank": 25, "intensity": 2.2653061224489797, "date": "2023-03-28" }, { "rank": 26, "intensity": 2.2346938775510203, "date": "2023-06-01" }, { "rank": 27, "intensity": 2.204081632653061, "date": "2023-06-06" }, { "rank": 28, "intensity": 2.173469387755102, "date": "2023-05-17" }, { "rank": 29, "intensity": 2.142857142857143, "date": "2023-04-12" }, { "rank": 30, "intensity": 2.112244897959184, "date": "2023-10-13" }, { "rank": 31, "intensity": 2.0816326530612246, "date": "2023-12-02" }, { "rank": 32, "intensity": 2.0510204081632653, "date": "2023-06-26" }, { "rank": 33, "intensity": 2.020408163265306, "date": "2023-05-15" }, { "rank": 34, "intensity": 1.989795918367347, "date": "2023-01-09" }, { "rank": 35, "intensity": 1.9591836734693877, "date": "2023-08-21" }, { "rank": 36, "intensity": 1.9285714285714286, "date": "2023-06-12" }, { "rank": 37, "intensity": 1.8979591836734695, "date": "2023-09-01" }, { "rank": 38, "intensity": 1.8673469387755102, "date": "2023-01-14" }, { "rank": 39, "intensity": 1.836734693877551, "date": "2023-07-25" }, { "rank": 40, "intensity": 1.806122448979592, "date": "2023-03-16" }, { "rank": 41, "intensity": 1.7755102040816326, "date": "2023-02-07" }, { "rank": 42, "intensity": 1.7448979591836735, "date": "2023-04-14" }, { "rank": 43, "intensity": 1.7142857142857144, "date": "2023-06-20" }, { "rank": 44, "intensity": 1.683673469387755, "date": "2023-03-03" }, { "rank": 45, "intensity": 1.653061224489796, "date": "2023-04-03" }, { "rank": 46, "intensity": 1.6224489795918369, "date": "2023-04-19" }, { "rank": 47, "intensity": 1.5918367346938775, "date": "2023-08-16" }, { "rank": 48, "intensity": 1.5612244897959184, "date": "2023-06-03" }, { "rank": 49, "intensity": 1.5306122448979593, "date": "2023-02-14" }, { "rank": 50, "intensity": 1.5, "date": "2023-02-18" } ], "source": "causal_rivers" } }, { "id": "L3_T1_Composite_Trend_00144", "level": 3, "level_name": "Semantic Reasoning", "category": "Composite Trend", "subtask": "Composite Trend", "question": "Among days in channel 680 during 2022 that exhibit the trend pattern 'rise, then slow fall', identify the top-5 days where the slow fall segment is the slowest. (Output format: a ranked list of dates, e.g., ['YYYY-MM-DD', ...])", "answer": "['2022-01-14', '2022-08-07', '2022-06-10', '2022-07-07', '2022-05-30']", "ground_truth": [ "2022-01-14", "2022-08-07", "2022-06-10", "2022-07-07", "2022-05-30" ], "eval_metric": "set_f1", "channel": "680", "ts_data_path": "ts_data/L3_T1_Composite_Trend_00144.csv", "meta": { "segments": [ { "kind": "rise", "adj": "none" }, { "kind": "fall", "adj": "slow" } ], "pattern_phrase": "rise, then slow fall", "rank_target_idx": 1, "rank_kind": "fall", "rank_adj": "slow", "rank_axis": "slope", "rank_direction": "ASC", "rank_extreme": "slowest", "top_segments_meta": [ { "adj": "none", "kind": "rise", "label": "none rise", "start_idx": 0, "end_idx": 34 }, { "adj": "slow", "kind": "fall", "label": "slow fall", "start_idx": 34, "end_idx": 98 } ], "points_per_day": 96, "pattern": "rise, then slow fall", "year": 2022, "top_k": [ "2022-01-14", "2022-08-07", "2022-06-10", "2022-07-07", "2022-05-30" ], "all_injections": [ { "rank": 1, "intensity": 1.5, "date": "2022-01-14" }, { "rank": 2, "intensity": 1.530612244897959, "date": "2022-08-07" }, { "rank": 3, "intensity": 1.5612244897959184, "date": "2022-06-10" }, { "rank": 4, "intensity": 1.5918367346938775, "date": "2022-07-07" }, { "rank": 5, "intensity": 1.6224489795918366, "date": "2022-05-30" }, { "rank": 6, "intensity": 1.653061224489796, "date": "2022-07-25" }, { "rank": 7, "intensity": 1.683673469387755, "date": "2022-03-22" }, { "rank": 8, "intensity": 1.7142857142857142, "date": "2022-07-22" }, { "rank": 9, "intensity": 1.7448979591836735, "date": "2022-07-15" }, { "rank": 10, "intensity": 1.7755102040816326, "date": "2022-12-24" }, { "rank": 11, "intensity": 1.806122448979592, "date": "2022-09-28" }, { "rank": 12, "intensity": 1.836734693877551, "date": "2022-12-13" }, { "rank": 13, "intensity": 1.8673469387755102, "date": "2022-04-21" }, { "rank": 14, "intensity": 1.8979591836734695, "date": "2022-05-26" }, { "rank": 15, "intensity": 1.9285714285714286, "date": "2022-06-22" }, { "rank": 16, "intensity": 1.9591836734693877, "date": "2022-02-18" }, { "rank": 17, "intensity": 1.989795918367347, "date": "2022-01-12" }, { "rank": 18, "intensity": 2.020408163265306, "date": "2022-01-05" }, { "rank": 19, "intensity": 2.0510204081632653, "date": "2022-04-10" }, { "rank": 20, "intensity": 2.0816326530612246, "date": "2022-05-13" }, { "rank": 21, "intensity": 2.112244897959184, "date": "2022-11-09" }, { "rank": 22, "intensity": 2.142857142857143, "date": "2022-04-19" }, { "rank": 23, "intensity": 2.173469387755102, "date": "2022-07-02" }, { "rank": 24, "intensity": 2.204081632653061, "date": "2022-03-05" }, { "rank": 25, "intensity": 2.2346938775510203, "date": "2022-12-01" }, { "rank": 26, "intensity": 2.2653061224489797, "date": "2022-05-23" }, { "rank": 27, "intensity": 2.295918367346939, "date": "2022-03-14" }, { "rank": 28, "intensity": 2.326530612244898, "date": "2022-03-31" }, { "rank": 29, "intensity": 2.357142857142857, "date": "2022-08-20" }, { "rank": 30, "intensity": 2.387755102040816, "date": "2022-08-09" }, { "rank": 31, "intensity": 2.4183673469387754, "date": "2022-10-20" }, { "rank": 32, "intensity": 2.4489795918367347, "date": "2022-01-23" }, { "rank": 33, "intensity": 2.479591836734694, "date": "2022-03-17" }, { "rank": 34, "intensity": 2.510204081632653, "date": "2022-08-03" }, { "rank": 35, "intensity": 2.5408163265306123, "date": "2022-02-12" }, { "rank": 36, "intensity": 2.571428571428571, "date": "2022-10-17" }, { "rank": 37, "intensity": 2.6020408163265305, "date": "2022-11-16" }, { "rank": 38, "intensity": 2.63265306122449, "date": "2022-12-26" }, { "rank": 39, "intensity": 2.663265306122449, "date": "2022-11-21" }, { "rank": 40, "intensity": 2.693877551020408, "date": "2022-10-01" }, { "rank": 41, "intensity": 2.7244897959183674, "date": "2022-01-18" }, { "rank": 42, "intensity": 2.7551020408163263, "date": "2022-01-10" }, { "rank": 43, "intensity": 2.7857142857142856, "date": "2022-06-15" }, { "rank": 44, "intensity": 2.816326530612245, "date": "2022-11-12" }, { "rank": 45, "intensity": 2.8469387755102042, "date": "2022-04-06" }, { "rank": 46, "intensity": 2.877551020408163, "date": "2022-08-31" }, { "rank": 47, "intensity": 2.9081632653061225, "date": "2022-08-27" }, { "rank": 48, "intensity": 2.9387755102040813, "date": "2022-02-27" }, { "rank": 49, "intensity": 2.9693877551020407, "date": "2022-02-24" }, { "rank": 50, "intensity": 3.0, "date": "2022-06-12" } ], "source": "causal_rivers" } }, { "id": "L3_T1_Composite_Trend_00145", "level": 3, "level_name": "Semantic Reasoning", "category": "Composite Trend", "subtask": "Composite Trend", "question": "Among days in channel 683 during 2023 that exhibit the trend pattern 'slow rise, then rapid rise, then steady stable', identify the top-5 days where the slow rise segment is the slowest. (Output format: a ranked list of dates, e.g., ['YYYY-MM-DD', ...])", "answer": "['2023-04-25', '2023-09-02', '2023-08-13', '2023-04-14', '2023-07-30']", "ground_truth": [ "2023-04-25", "2023-09-02", "2023-08-13", "2023-04-14", "2023-07-30" ], "eval_metric": "set_f1", "channel": "683", "ts_data_path": "ts_data/L3_T1_Composite_Trend_00145.csv", "meta": { "segments": [ { "kind": "rise", "adj": "slow" }, { "kind": "rise", "adj": "rapid" }, { "kind": "stable", "adj": "steady" } ], "pattern_phrase": "slow rise, then rapid rise, then steady stable", "rank_target_idx": 0, "rank_kind": "rise", "rank_adj": "slow", "rank_axis": "slope", "rank_direction": "ASC", "rank_extreme": "slowest", "top_segments_meta": [ { "adj": "slow", "kind": "rise", "label": "slow rise", "start_idx": 0, "end_idx": 38 }, { "adj": "rapid", "kind": "rise", "label": "rapid rise", "start_idx": 38, "end_idx": 49 }, { "adj": "steady", "kind": "stable", "label": "steady stable", "start_idx": 49, "end_idx": 98 } ], "points_per_day": 96, "pattern": "slow rise, then rapid rise, then steady stable", "year": 2023, "top_k": [ "2023-04-25", "2023-09-02", "2023-08-13", "2023-04-14", "2023-07-30" ], "all_injections": [ { "rank": 1, "intensity": 1.5, "date": "2023-04-25" }, { "rank": 2, "intensity": 1.530612244897959, "date": "2023-09-02" }, { "rank": 3, "intensity": 1.5612244897959184, "date": "2023-08-13" }, { "rank": 4, "intensity": 1.5918367346938775, "date": "2023-04-14" }, { "rank": 5, "intensity": 1.6224489795918366, "date": "2023-07-30" }, { "rank": 6, "intensity": 1.653061224489796, "date": "2023-08-20" }, { "rank": 7, "intensity": 1.683673469387755, "date": "2023-02-06" }, { "rank": 8, "intensity": 1.7142857142857142, "date": "2023-12-15" }, { "rank": 9, "intensity": 1.7448979591836735, "date": "2023-03-31" }, { "rank": 10, "intensity": 1.7755102040816326, "date": "2023-09-23" }, { "rank": 11, "intensity": 1.806122448979592, "date": "2023-11-25" }, { "rank": 12, "intensity": 1.836734693877551, "date": "2023-09-05" }, { "rank": 13, "intensity": 1.8673469387755102, "date": "2023-01-05" }, { "rank": 14, "intensity": 1.8979591836734695, "date": "2023-08-22" }, { "rank": 15, "intensity": 1.9285714285714286, "date": "2023-04-29" }, { "rank": 16, "intensity": 1.9591836734693877, "date": "2023-01-21" }, { "rank": 17, "intensity": 1.989795918367347, "date": "2023-09-15" }, { "rank": 18, "intensity": 2.020408163265306, "date": "2023-05-06" }, { "rank": 19, "intensity": 2.0510204081632653, "date": "2023-06-01" }, { "rank": 20, "intensity": 2.0816326530612246, "date": "2023-05-17" }, { "rank": 21, "intensity": 2.112244897959184, "date": "2023-11-12" }, { "rank": 22, "intensity": 2.142857142857143, "date": "2023-05-04" }, { "rank": 23, "intensity": 2.173469387755102, "date": "2023-05-01" }, { "rank": 24, "intensity": 2.204081632653061, "date": "2023-11-08" }, { "rank": 25, "intensity": 2.2346938775510203, "date": "2023-05-25" }, { "rank": 26, "intensity": 2.2653061224489797, "date": "2023-04-11" }, { "rank": 27, "intensity": 2.295918367346939, "date": "2023-10-21" }, { "rank": 28, "intensity": 2.326530612244898, "date": "2023-12-24" }, { "rank": 29, "intensity": 2.357142857142857, "date": "2023-12-22" }, { "rank": 30, "intensity": 2.387755102040816, "date": "2023-09-09" }, { "rank": 31, "intensity": 2.4183673469387754, "date": "2023-10-09" }, { "rank": 32, "intensity": 2.4489795918367347, "date": "2023-10-06" }, { "rank": 33, "intensity": 2.479591836734694, "date": "2023-01-31" }, { "rank": 34, "intensity": 2.510204081632653, "date": "2023-10-19" }, { "rank": 35, "intensity": 2.5408163265306123, "date": "2023-10-29" }, { "rank": 36, "intensity": 2.571428571428571, "date": "2023-01-02" }, { "rank": 37, "intensity": 2.6020408163265305, "date": "2023-06-11" }, { "rank": 38, "intensity": 2.63265306122449, "date": "2023-04-17" }, { "rank": 39, "intensity": 2.663265306122449, "date": "2023-03-26" }, { "rank": 40, "intensity": 2.693877551020408, "date": "2023-03-01" }, { "rank": 41, "intensity": 2.7244897959183674, "date": "2023-07-16" }, { "rank": 42, "intensity": 2.7551020408163263, "date": "2023-07-20" }, { "rank": 43, "intensity": 2.7857142857142856, "date": "2023-06-06" }, { "rank": 44, "intensity": 2.816326530612245, "date": "2023-12-03" }, { "rank": 45, "intensity": 2.8469387755102042, "date": "2023-08-09" }, { "rank": 46, "intensity": 2.877551020408163, "date": "2023-07-26" }, { "rank": 47, "intensity": 2.9081632653061225, "date": "2023-02-12" }, { "rank": 48, "intensity": 2.9387755102040813, "date": "2023-01-12" }, { "rank": 49, "intensity": 2.9693877551020407, "date": "2023-03-08" }, { "rank": 50, "intensity": 3.0, "date": "2023-10-23" } ], "source": "causal_rivers" } }, { "id": "L3_T1_Composite_Trend_00146", "level": 3, "level_name": "Semantic Reasoning", "category": "Composite Trend", "subtask": "Composite Trend", "question": "Among days in channel 727 during 2019 that exhibit the trend pattern 'steady stable, then rapid rise', identify the top-5 days where the rapid rise segment is the fastest. (Output format: a ranked list of dates, e.g., ['YYYY-MM-DD', ...])", "answer": "['2019-10-14', '2019-05-27', '2019-11-25', '2019-08-27', '2019-09-21']", "ground_truth": [ "2019-10-14", "2019-05-27", "2019-11-25", "2019-08-27", "2019-09-21" ], "eval_metric": "set_f1", "channel": "727", "ts_data_path": "ts_data/L3_T1_Composite_Trend_00146.csv", "meta": { "segments": [ { "kind": "stable", "adj": "steady" }, { "kind": "rise", "adj": "rapid" } ], "pattern_phrase": "steady stable, then rapid rise", "rank_target_idx": 1, "rank_kind": "rise", "rank_adj": "rapid", "rank_axis": "slope", "rank_direction": "DESC", "rank_extreme": "fastest", "top_segments_meta": [ { "adj": "steady", "kind": "stable", "label": "steady stable", "start_idx": 0, "end_idx": 79 }, { "adj": "rapid", "kind": "rise", "label": "rapid rise", "start_idx": 79, "end_idx": 98 } ], "points_per_day": 96, "pattern": "steady stable, then rapid rise", "year": 2019, "top_k": [ "2019-10-14", "2019-05-27", "2019-11-25", "2019-08-27", "2019-09-21" ], "all_injections": [ { "rank": 1, "intensity": 3.0, "date": "2019-10-14" }, { "rank": 2, "intensity": 2.9693877551020407, "date": "2019-05-27" }, { "rank": 3, "intensity": 2.938775510204082, "date": "2019-11-25" }, { "rank": 4, "intensity": 2.9081632653061225, "date": "2019-08-27" }, { "rank": 5, "intensity": 2.877551020408163, "date": "2019-09-21" }, { "rank": 6, "intensity": 2.8469387755102042, "date": "2019-10-02" }, { "rank": 7, "intensity": 2.816326530612245, "date": "2019-07-28" }, { "rank": 8, "intensity": 2.7857142857142856, "date": "2019-03-21" }, { "rank": 9, "intensity": 2.7551020408163267, "date": "2019-04-08" }, { "rank": 10, "intensity": 2.7244897959183674, "date": "2019-10-31" }, { "rank": 11, "intensity": 2.693877551020408, "date": "2019-04-10" }, { "rank": 12, "intensity": 2.663265306122449, "date": "2019-09-01" }, { "rank": 13, "intensity": 2.63265306122449, "date": "2019-04-14" }, { "rank": 14, "intensity": 2.6020408163265305, "date": "2019-02-01" }, { "rank": 15, "intensity": 2.5714285714285716, "date": "2019-06-19" }, { "rank": 16, "intensity": 2.5408163265306123, "date": "2019-11-23" }, { "rank": 17, "intensity": 2.510204081632653, "date": "2019-06-22" }, { "rank": 18, "intensity": 2.479591836734694, "date": "2019-09-05" }, { "rank": 19, "intensity": 2.4489795918367347, "date": "2019-05-20" }, { "rank": 20, "intensity": 2.4183673469387754, "date": "2019-07-24" }, { "rank": 21, "intensity": 2.387755102040816, "date": "2019-06-16" }, { "rank": 22, "intensity": 2.357142857142857, "date": "2019-01-30" }, { "rank": 23, "intensity": 2.326530612244898, "date": "2019-08-22" }, { "rank": 24, "intensity": 2.295918367346939, "date": "2019-09-09" }, { "rank": 25, "intensity": 2.2653061224489797, "date": "2019-06-09" }, { "rank": 26, "intensity": 2.2346938775510203, "date": "2019-04-12" }, { "rank": 27, "intensity": 2.204081632653061, "date": "2019-08-25" }, { "rank": 28, "intensity": 2.173469387755102, "date": "2019-05-03" }, { "rank": 29, "intensity": 2.142857142857143, "date": "2019-10-23" }, { "rank": 30, "intensity": 2.112244897959184, "date": "2019-07-30" }, { "rank": 31, "intensity": 2.0816326530612246, "date": "2019-07-07" }, { "rank": 32, "intensity": 2.0510204081632653, "date": "2019-09-15" }, { "rank": 33, "intensity": 2.020408163265306, "date": "2019-07-15" }, { "rank": 34, "intensity": 1.989795918367347, "date": "2019-12-05" }, { "rank": 35, "intensity": 1.9591836734693877, "date": "2019-12-25" }, { "rank": 36, "intensity": 1.9285714285714286, "date": "2019-02-13" }, { "rank": 37, "intensity": 1.8979591836734695, "date": "2019-02-18" }, { "rank": 38, "intensity": 1.8673469387755102, "date": "2019-01-16" }, { "rank": 39, "intensity": 1.836734693877551, "date": "2019-06-07" }, { "rank": 40, "intensity": 1.806122448979592, "date": "2019-02-16" }, { "rank": 41, "intensity": 1.7755102040816326, "date": "2019-06-27" }, { "rank": 42, "intensity": 1.7448979591836735, "date": "2019-11-06" }, { "rank": 43, "intensity": 1.7142857142857144, "date": "2019-03-15" }, { "rank": 44, "intensity": 1.683673469387755, "date": "2019-12-17" }, { "rank": 45, "intensity": 1.653061224489796, "date": "2019-08-18" }, { "rank": 46, "intensity": 1.6224489795918369, "date": "2019-05-05" }, { "rank": 47, "intensity": 1.5918367346938775, "date": "2019-05-24" }, { "rank": 48, "intensity": 1.5612244897959184, "date": "2019-08-04" }, { "rank": 49, "intensity": 1.5306122448979593, "date": "2019-07-12" }, { "rank": 50, "intensity": 1.5, "date": "2019-08-20" } ], "source": "causal_rivers" } }, { "id": "L3_T1_Composite_Trend_00147", "level": 3, "level_name": "Semantic Reasoning", "category": "Composite Trend", "subtask": "Composite Trend", "question": "Among days in channel 728 during 2022 that exhibit the trend pattern 'fluctuating stable, then steady stable', identify the top-5 days where the fluctuating stable segment is the most fluctuating. (Output format: a ranked list of dates, e.g., ['YYYY-MM-DD', ...])", "answer": "['2022-06-21', '2022-08-04', '2022-12-16', '2022-11-08', '2022-06-24']", "ground_truth": [ "2022-06-21", "2022-08-04", "2022-12-16", "2022-11-08", "2022-06-24" ], "eval_metric": "set_f1", "channel": "728", "ts_data_path": "ts_data/L3_T1_Composite_Trend_00147.csv", "meta": { "segments": [ { "kind": "stable", "adj": "fluctuating" }, { "kind": "stable", "adj": "steady" } ], "pattern_phrase": "fluctuating stable, then steady stable", "rank_target_idx": 0, "rank_kind": "stable", "rank_adj": "fluctuating", "rank_axis": "std", "rank_direction": "DESC", "rank_extreme": "most fluctuating", "top_segments_meta": [ { "adj": "fluctuating", "kind": "stable", "label": "fluctuating stable", "start_idx": 0, "end_idx": 35 }, { "adj": "steady", "kind": "stable", "label": "steady stable", "start_idx": 35, "end_idx": 98 } ], "points_per_day": 96, "pattern": "fluctuating stable, then steady stable", "year": 2022, "top_k": [ "2022-06-21", "2022-08-04", "2022-12-16", "2022-11-08", "2022-06-24" ], "all_injections": [ { "rank": 1, "intensity": 3.0, "date": "2022-06-21" }, { "rank": 2, "intensity": 2.9693877551020407, "date": "2022-08-04" }, { "rank": 3, "intensity": 2.938775510204082, "date": "2022-12-16" }, { "rank": 4, "intensity": 2.9081632653061225, "date": "2022-11-08" }, { "rank": 5, "intensity": 2.877551020408163, "date": "2022-06-24" }, { "rank": 6, "intensity": 2.8469387755102042, "date": "2022-09-17" }, { "rank": 7, "intensity": 2.816326530612245, "date": "2022-05-26" }, { "rank": 8, "intensity": 2.7857142857142856, "date": "2022-09-20" }, { "rank": 9, "intensity": 2.7551020408163267, "date": "2022-04-25" }, { "rank": 10, "intensity": 2.7244897959183674, "date": "2022-11-05" }, { "rank": 11, "intensity": 2.693877551020408, "date": "2022-08-26" }, { "rank": 12, "intensity": 2.663265306122449, "date": "2022-01-27" }, { "rank": 13, "intensity": 2.63265306122449, "date": "2022-09-05" }, { "rank": 14, "intensity": 2.6020408163265305, "date": "2022-07-25" }, { "rank": 15, "intensity": 2.5714285714285716, "date": "2022-02-11" }, { "rank": 16, "intensity": 2.5408163265306123, "date": "2022-11-16" }, { "rank": 17, "intensity": 2.510204081632653, "date": "2022-08-17" }, { "rank": 18, "intensity": 2.479591836734694, "date": "2022-10-03" }, { "rank": 19, "intensity": 2.4489795918367347, "date": "2022-11-03" }, { "rank": 20, "intensity": 2.4183673469387754, "date": "2022-07-22" }, { "rank": 21, "intensity": 2.387755102040816, "date": "2022-10-22" }, { "rank": 22, "intensity": 2.357142857142857, "date": "2022-06-01" }, { "rank": 23, "intensity": 2.326530612244898, "date": "2022-04-17" }, { "rank": 24, "intensity": 2.295918367346939, "date": "2022-10-10" }, { "rank": 25, "intensity": 2.2653061224489797, "date": "2022-04-23" }, { "rank": 26, "intensity": 2.2346938775510203, "date": "2022-06-12" }, { "rank": 27, "intensity": 2.204081632653061, "date": "2022-09-26" }, { "rank": 28, "intensity": 2.173469387755102, "date": "2022-04-07" }, { "rank": 29, "intensity": 2.142857142857143, "date": "2022-08-08" }, { "rank": 30, "intensity": 2.112244897959184, "date": "2022-12-25" }, { "rank": 31, "intensity": 2.0816326530612246, "date": "2022-03-28" }, { "rank": 32, "intensity": 2.0510204081632653, "date": "2022-05-30" }, { "rank": 33, "intensity": 2.020408163265306, "date": "2022-09-12" }, { "rank": 34, "intensity": 1.989795918367347, "date": "2022-04-30" }, { "rank": 35, "intensity": 1.9591836734693877, "date": "2022-12-03" }, { "rank": 36, "intensity": 1.9285714285714286, "date": "2022-03-17" }, { "rank": 37, "intensity": 1.8979591836734695, "date": "2022-09-24" }, { "rank": 38, "intensity": 1.8673469387755102, "date": "2022-10-13" }, { "rank": 39, "intensity": 1.836734693877551, "date": "2022-07-31" }, { "rank": 40, "intensity": 1.806122448979592, "date": "2022-03-05" }, { "rank": 41, "intensity": 1.7755102040816326, "date": "2022-02-20" }, { "rank": 42, "intensity": 1.7448979591836735, "date": "2022-09-10" }, { "rank": 43, "intensity": 1.7142857142857144, "date": "2022-01-03" }, { "rank": 44, "intensity": 1.683673469387755, "date": "2022-03-31" }, { "rank": 45, "intensity": 1.653061224489796, "date": "2022-02-02" }, { "rank": 46, "intensity": 1.6224489795918369, "date": "2022-06-03" }, { "rank": 47, "intensity": 1.5918367346938775, "date": "2022-08-19" }, { "rank": 48, "intensity": 1.5612244897959184, "date": "2022-02-16" }, { "rank": 49, "intensity": 1.5306122448979593, "date": "2022-07-10" }, { "rank": 50, "intensity": 1.5, "date": "2022-07-04" } ], "source": "causal_rivers" } }, { "id": "L3_T1_Composite_Trend_00148", "level": 3, "level_name": "Semantic Reasoning", "category": "Composite Trend", "subtask": "Composite Trend", "question": "Among days in channel 729 during 2020 that exhibit the trend pattern 'fall, then rapid rise', identify the top-5 days where the rapid rise segment is the fastest. (Output format: a ranked list of dates, e.g., ['YYYY-MM-DD', ...])", "answer": "['2020-07-07', '2020-12-21', '2020-01-06', '2020-07-26', '2020-02-11']", "ground_truth": [ "2020-07-07", "2020-12-21", "2020-01-06", "2020-07-26", "2020-02-11" ], "eval_metric": "set_f1", "channel": "729", "ts_data_path": "ts_data/L3_T1_Composite_Trend_00148.csv", "meta": { "segments": [ { "kind": "fall", "adj": "none" }, { "kind": "rise", "adj": "rapid" } ], "pattern_phrase": "fall, then rapid rise", "rank_target_idx": 1, "rank_kind": "rise", "rank_adj": "rapid", "rank_axis": "slope", "rank_direction": "DESC", "rank_extreme": "fastest", "top_segments_meta": [ { "adj": "none", "kind": "fall", "label": "none fall", "start_idx": 0, "end_idx": 64 }, { "adj": "rapid", "kind": "rise", "label": "rapid rise", "start_idx": 64, "end_idx": 98 } ], "points_per_day": 96, "pattern": "fall, then rapid rise", "year": 2020, "top_k": [ "2020-07-07", "2020-12-21", "2020-01-06", "2020-07-26", "2020-02-11" ], "all_injections": [ { "rank": 1, "intensity": 3.0, "date": "2020-07-07" }, { "rank": 2, "intensity": 2.9693877551020407, "date": "2020-12-21" }, { "rank": 3, "intensity": 2.938775510204082, "date": "2020-01-06" }, { "rank": 4, "intensity": 2.9081632653061225, "date": "2020-07-26" }, { "rank": 5, "intensity": 2.877551020408163, "date": "2020-02-11" }, { "rank": 6, "intensity": 2.8469387755102042, "date": "2020-12-26" }, { "rank": 7, "intensity": 2.816326530612245, "date": "2020-06-20" }, { "rank": 8, "intensity": 2.7857142857142856, "date": "2020-09-13" }, { "rank": 9, "intensity": 2.7551020408163267, "date": "2020-09-02" }, { "rank": 10, "intensity": 2.7244897959183674, "date": "2020-02-17" }, { "rank": 11, "intensity": 2.693877551020408, "date": "2020-12-01" }, { "rank": 12, "intensity": 2.663265306122449, "date": "2020-06-22" }, { "rank": 13, "intensity": 2.63265306122449, "date": "2020-04-26" }, { "rank": 14, "intensity": 2.6020408163265305, "date": "2020-01-18" }, { "rank": 15, "intensity": 2.5714285714285716, "date": "2020-08-02" }, { "rank": 16, "intensity": 2.5408163265306123, "date": "2020-09-20" }, { "rank": 17, "intensity": 2.510204081632653, "date": "2020-10-02" }, { "rank": 18, "intensity": 2.479591836734694, "date": "2020-02-28" }, { "rank": 19, "intensity": 2.4489795918367347, "date": "2020-09-16" }, { "rank": 20, "intensity": 2.4183673469387754, "date": "2020-04-29" }, { "rank": 21, "intensity": 2.387755102040816, "date": "2020-05-09" }, { "rank": 22, "intensity": 2.357142857142857, "date": "2020-04-23" }, { "rank": 23, "intensity": 2.326530612244898, "date": "2020-11-29" }, { "rank": 24, "intensity": 2.295918367346939, "date": "2020-07-30" }, { "rank": 25, "intensity": 2.2653061224489797, "date": "2020-08-12" }, { "rank": 26, "intensity": 2.2346938775510203, "date": "2020-08-22" }, { "rank": 27, "intensity": 2.204081632653061, "date": "2020-02-24" }, { "rank": 28, "intensity": 2.173469387755102, "date": "2020-03-14" }, { "rank": 29, "intensity": 2.142857142857143, "date": "2020-01-26" }, { "rank": 30, "intensity": 2.112244897959184, "date": "2020-06-28" }, { "rank": 31, "intensity": 2.0816326530612246, "date": "2020-04-20" }, { "rank": 32, "intensity": 2.0510204081632653, "date": "2020-02-13" }, { "rank": 33, "intensity": 2.020408163265306, "date": "2020-03-20" }, { "rank": 34, "intensity": 1.989795918367347, "date": "2020-11-02" }, { "rank": 35, "intensity": 1.9591836734693877, "date": "2020-07-01" }, { "rank": 36, "intensity": 1.9285714285714286, "date": "2020-03-17" }, { "rank": 37, "intensity": 1.8979591836734695, "date": "2020-12-05" }, { "rank": 38, "intensity": 1.8673469387755102, "date": "2020-02-15" }, { "rank": 39, "intensity": 1.836734693877551, "date": "2020-05-15" }, { "rank": 40, "intensity": 1.806122448979592, "date": "2020-04-01" }, { "rank": 41, "intensity": 1.7755102040816326, "date": "2020-04-08" }, { "rank": 42, "intensity": 1.7448979591836735, "date": "2020-11-10" }, { "rank": 43, "intensity": 1.7142857142857144, "date": "2020-12-30" }, { "rank": 44, "intensity": 1.683673469387755, "date": "2020-10-16" }, { "rank": 45, "intensity": 1.653061224489796, "date": "2020-06-17" }, { "rank": 46, "intensity": 1.6224489795918369, "date": "2020-07-10" }, { "rank": 47, "intensity": 1.5918367346938775, "date": "2020-10-18" }, { "rank": 48, "intensity": 1.5612244897959184, "date": "2020-07-03" }, { "rank": 49, "intensity": 1.5306122448979593, "date": "2020-09-23" }, { "rank": 50, "intensity": 1.5, "date": "2020-12-28" } ], "source": "causal_rivers" } }, { "id": "L3_T1_Composite_Trend_00149", "level": 3, "level_name": "Semantic Reasoning", "category": "Composite Trend", "subtask": "Composite Trend", "question": "Among days in channel 754 during 2020 that exhibit the trend pattern 'rapid rise, then rise', identify the top-5 days where the rapid rise segment is the fastest. (Output format: a ranked list of dates, e.g., ['YYYY-MM-DD', ...])", "answer": "['2020-08-24', '2020-07-05', '2020-01-08', '2020-01-26', '2020-12-01']", "ground_truth": [ "2020-08-24", "2020-07-05", "2020-01-08", "2020-01-26", "2020-12-01" ], "eval_metric": "set_f1", "channel": "754", "ts_data_path": "ts_data/L3_T1_Composite_Trend_00149.csv", "meta": { "segments": [ { "kind": "rise", "adj": "rapid" }, { "kind": "rise", "adj": "none" } ], "pattern_phrase": "rapid rise, then rise", "rank_target_idx": 0, "rank_kind": "rise", "rank_adj": "rapid", "rank_axis": "slope", "rank_direction": "DESC", "rank_extreme": "fastest", "top_segments_meta": [ { "adj": "rapid", "kind": "rise", "label": "rapid rise", "start_idx": 0, "end_idx": 32 }, { "adj": "none", "kind": "rise", "label": "none rise", "start_idx": 32, "end_idx": 98 } ], "points_per_day": 96, "pattern": "rapid rise, then rise", "year": 2020, "top_k": [ "2020-08-24", "2020-07-05", "2020-01-08", "2020-01-26", "2020-12-01" ], "all_injections": [ { "rank": 1, "intensity": 3.0, "date": "2020-08-24" }, { "rank": 2, "intensity": 2.9693877551020407, "date": "2020-07-05" }, { "rank": 3, "intensity": 2.938775510204082, "date": "2020-01-08" }, { "rank": 4, "intensity": 2.9081632653061225, "date": "2020-01-26" }, { "rank": 5, "intensity": 2.877551020408163, "date": "2020-12-01" }, { "rank": 6, "intensity": 2.8469387755102042, "date": "2020-10-24" }, { "rank": 7, "intensity": 2.816326530612245, "date": "2020-05-12" }, { "rank": 8, "intensity": 2.7857142857142856, "date": "2020-02-22" }, { "rank": 9, "intensity": 2.7551020408163267, "date": "2020-11-16" }, { "rank": 10, "intensity": 2.7244897959183674, "date": "2020-08-07" }, { "rank": 11, "intensity": 2.693877551020408, "date": "2020-04-16" }, { "rank": 12, "intensity": 2.663265306122449, "date": "2020-11-03" }, { "rank": 13, "intensity": 2.63265306122449, "date": "2020-12-18" }, { "rank": 14, "intensity": 2.6020408163265305, "date": "2020-12-21" }, { "rank": 15, "intensity": 2.5714285714285716, "date": "2020-05-05" }, { "rank": 16, "intensity": 2.5408163265306123, "date": "2020-07-08" }, { "rank": 17, "intensity": 2.510204081632653, "date": "2020-06-01" }, { "rank": 18, "intensity": 2.479591836734694, "date": "2020-10-10" }, { "rank": 19, "intensity": 2.4489795918367347, "date": "2020-01-15" }, { "rank": 20, "intensity": 2.4183673469387754, "date": "2020-09-29" }, { "rank": 21, "intensity": 2.387755102040816, "date": "2020-04-11" }, { "rank": 22, "intensity": 2.357142857142857, "date": "2020-08-21" }, { "rank": 23, "intensity": 2.326530612244898, "date": "2020-03-31" }, { "rank": 24, "intensity": 2.295918367346939, "date": "2020-12-04" }, { "rank": 25, "intensity": 2.2653061224489797, "date": "2020-02-18" }, { "rank": 26, "intensity": 2.2346938775510203, "date": "2020-09-13" }, { "rank": 27, "intensity": 2.204081632653061, "date": "2020-01-10" }, { "rank": 28, "intensity": 2.173469387755102, "date": "2020-08-13" }, { "rank": 29, "intensity": 2.142857142857143, "date": "2020-12-16" }, { "rank": 30, "intensity": 2.112244897959184, "date": "2020-10-03" }, { "rank": 31, "intensity": 2.0816326530612246, "date": "2020-07-15" }, { "rank": 32, "intensity": 2.0510204081632653, "date": "2020-10-22" }, { "rank": 33, "intensity": 2.020408163265306, "date": "2020-12-29" }, { "rank": 34, "intensity": 1.989795918367347, "date": "2020-05-09" }, { "rank": 35, "intensity": 1.9591836734693877, "date": "2020-11-20" }, { "rank": 36, "intensity": 1.9285714285714286, "date": "2020-09-16" }, { "rank": 37, "intensity": 1.8979591836734695, "date": "2020-04-29" }, { "rank": 38, "intensity": 1.8673469387755102, "date": "2020-08-16" }, { "rank": 39, "intensity": 1.836734693877551, "date": "2020-10-28" }, { "rank": 40, "intensity": 1.806122448979592, "date": "2020-06-26" }, { "rank": 41, "intensity": 1.7755102040816326, "date": "2020-03-08" }, { "rank": 42, "intensity": 1.7448979591836735, "date": "2020-11-05" }, { "rank": 43, "intensity": 1.7142857142857144, "date": "2020-03-16" }, { "rank": 44, "intensity": 1.683673469387755, "date": "2020-07-26" }, { "rank": 45, "intensity": 1.653061224489796, "date": "2020-02-15" }, { "rank": 46, "intensity": 1.6224489795918369, "date": "2020-05-24" }, { "rank": 47, "intensity": 1.5918367346938775, "date": "2020-03-18" }, { "rank": 48, "intensity": 1.5612244897959184, "date": "2020-10-30" }, { "rank": 49, "intensity": 1.5306122448979593, "date": "2020-02-06" }, { "rank": 50, "intensity": 1.5, "date": "2020-03-10" } ], "source": "causal_rivers" } }, { "id": "L3_T1_Composite_Trend_00150", "level": 3, "level_name": "Semantic Reasoning", "category": "Composite Trend", "subtask": "Composite Trend", "question": "Among days in channel 762 during 2021 that exhibit the trend pattern 'steady stable, then rapid rise, then steady stable, then fluctuating stable', identify the top-5 days where the fluctuating stable segment is the most fluctuating. (Output format: a ranked list of dates, e.g., ['YYYY-MM-DD', ...])", "answer": "['2021-02-19', '2021-03-31', '2021-11-04', '2021-09-29', '2021-08-03']", "ground_truth": [ "2021-02-19", "2021-03-31", "2021-11-04", "2021-09-29", "2021-08-03" ], "eval_metric": "set_f1", "channel": "762", "ts_data_path": "ts_data/L3_T1_Composite_Trend_00150.csv", "meta": { "segments": [ { "kind": "stable", "adj": "steady" }, { "kind": "rise", "adj": "rapid" }, { "kind": "stable", "adj": "steady" }, { "kind": "stable", "adj": "fluctuating" } ], "pattern_phrase": "steady stable, then rapid rise, then steady stable, then fluctuating stable", "rank_target_idx": 3, "rank_kind": "stable", "rank_adj": "fluctuating", "rank_axis": "std", "rank_direction": "DESC", "rank_extreme": "most fluctuating", "top_segments_meta": [ { "adj": "steady", "kind": "stable", "label": "steady stable", "start_idx": 0, "end_idx": 36 }, { "adj": "rapid", "kind": "rise", "label": "rapid rise", "start_idx": 36, "end_idx": 44 }, { "adj": "steady", "kind": "stable", "label": "steady stable", "start_idx": 44, "end_idx": 77 }, { "adj": "fluctuating", "kind": "stable", "label": "fluctuating stable", "start_idx": 77, "end_idx": 98 } ], "points_per_day": 96, "pattern": "steady stable, then rapid rise, then steady stable, then fluctuating stable", "year": 2021, "top_k": [ "2021-02-19", "2021-03-31", "2021-11-04", "2021-09-29", "2021-08-03" ], "all_injections": [ { "rank": 1, "intensity": 3.0, "date": "2021-02-19" }, { "rank": 2, "intensity": 2.9693877551020407, "date": "2021-03-31" }, { "rank": 3, "intensity": 2.938775510204082, "date": "2021-11-04" }, { "rank": 4, "intensity": 2.9081632653061225, "date": "2021-09-29" }, { "rank": 5, "intensity": 2.877551020408163, "date": "2021-08-03" }, { "rank": 6, "intensity": 2.8469387755102042, "date": "2021-01-09" }, { "rank": 7, "intensity": 2.816326530612245, "date": "2021-07-16" }, { "rank": 8, "intensity": 2.7857142857142856, "date": "2021-03-11" }, { "rank": 9, "intensity": 2.7551020408163267, "date": "2021-07-29" }, { "rank": 10, "intensity": 2.7244897959183674, "date": "2021-04-23" }, { "rank": 11, "intensity": 2.693877551020408, "date": "2021-10-04" }, { "rank": 12, "intensity": 2.663265306122449, "date": "2021-08-27" }, { "rank": 13, "intensity": 2.63265306122449, "date": "2021-10-01" }, { "rank": 14, "intensity": 2.6020408163265305, "date": "2021-03-19" }, { "rank": 15, "intensity": 2.5714285714285716, "date": "2021-03-14" }, { "rank": 16, "intensity": 2.5408163265306123, "date": "2021-05-09" }, { "rank": 17, "intensity": 2.510204081632653, "date": "2021-01-14" }, { "rank": 18, "intensity": 2.479591836734694, "date": "2021-07-24" }, { "rank": 19, "intensity": 2.4489795918367347, "date": "2021-06-10" }, { "rank": 20, "intensity": 2.4183673469387754, "date": "2021-08-20" }, { "rank": 21, "intensity": 2.387755102040816, "date": "2021-02-26" }, { "rank": 22, "intensity": 2.357142857142857, "date": "2021-04-11" }, { "rank": 23, "intensity": 2.326530612244898, "date": "2021-09-05" }, { "rank": 24, "intensity": 2.295918367346939, "date": "2021-05-23" }, { "rank": 25, "intensity": 2.2653061224489797, "date": "2021-05-21" }, { "rank": 26, "intensity": 2.2346938775510203, "date": "2021-10-12" }, { "rank": 27, "intensity": 2.204081632653061, "date": "2021-01-17" }, { "rank": 28, "intensity": 2.173469387755102, "date": "2021-02-08" }, { "rank": 29, "intensity": 2.142857142857143, "date": "2021-10-20" }, { "rank": 30, "intensity": 2.112244897959184, "date": "2021-02-12" }, { "rank": 31, "intensity": 2.0816326530612246, "date": "2021-07-12" }, { "rank": 32, "intensity": 2.0510204081632653, "date": "2021-02-03" }, { "rank": 33, "intensity": 2.020408163265306, "date": "2021-07-09" }, { "rank": 34, "intensity": 1.989795918367347, "date": "2021-03-16" }, { "rank": 35, "intensity": 1.9591836734693877, "date": "2021-05-18" }, { "rank": 36, "intensity": 1.9285714285714286, "date": "2021-04-08" }, { "rank": 37, "intensity": 1.8979591836734695, "date": "2021-09-26" }, { "rank": 38, "intensity": 1.8673469387755102, "date": "2021-02-01" }, { "rank": 39, "intensity": 1.836734693877551, "date": "2021-06-01" }, { "rank": 40, "intensity": 1.806122448979592, "date": "2021-02-16" }, { "rank": 41, "intensity": 1.7755102040816326, "date": "2021-12-04" }, { "rank": 42, "intensity": 1.7448979591836735, "date": "2021-03-09" }, { "rank": 43, "intensity": 1.7142857142857144, "date": "2021-05-26" }, { "rank": 44, "intensity": 1.683673469387755, "date": "2021-04-27" }, { "rank": 45, "intensity": 1.653061224489796, "date": "2021-08-22" }, { "rank": 46, "intensity": 1.6224489795918369, "date": "2021-04-30" }, { "rank": 47, "intensity": 1.5918367346938775, "date": "2021-03-29" }, { "rank": 48, "intensity": 1.5612244897959184, "date": "2021-01-21" }, { "rank": 49, "intensity": 1.5306122448979593, "date": "2021-09-24" }, { "rank": 50, "intensity": 1.5, "date": "2021-11-17" } ], "source": "causal_rivers" } }, { "id": "L3_T1_Composite_Trend_00151", "level": 3, "level_name": "Semantic Reasoning", "category": "Composite Trend", "subtask": "Composite Trend", "question": "Among days in channel 811 during 2019 that exhibit the trend pattern 'fluctuating stable, then slow rise, then steady stable', identify the top-5 days where the slow rise segment is the slowest. (Output format: a ranked list of dates, e.g., ['YYYY-MM-DD', ...])", "answer": "['2019-01-02', '2019-06-14', '2019-11-07', '2019-03-09', '2019-10-25']", "ground_truth": [ "2019-01-02", "2019-06-14", "2019-11-07", "2019-03-09", "2019-10-25" ], "eval_metric": "set_f1", "channel": "811", "ts_data_path": "ts_data/L3_T1_Composite_Trend_00151.csv", "meta": { "segments": [ { "kind": "stable", "adj": "fluctuating" }, { "kind": "rise", "adj": "slow" }, { "kind": "stable", "adj": "steady" } ], "pattern_phrase": "fluctuating stable, then slow rise, then steady stable", "rank_target_idx": 1, "rank_kind": "rise", "rank_adj": "slow", "rank_axis": "slope", "rank_direction": "ASC", "rank_extreme": "slowest", "top_segments_meta": [ { "adj": "fluctuating", "kind": "stable", "label": "fluctuating stable", "start_idx": 0, "end_idx": 24 }, { "adj": "slow", "kind": "rise", "label": "slow rise", "start_idx": 24, "end_idx": 59 }, { "adj": "steady", "kind": "stable", "label": "steady stable", "start_idx": 59, "end_idx": 98 } ], "points_per_day": 96, "pattern": "fluctuating stable, then slow rise, then steady stable", "year": 2019, "top_k": [ "2019-01-02", "2019-06-14", "2019-11-07", "2019-03-09", "2019-10-25" ], "all_injections": [ { "rank": 1, "intensity": 1.5, "date": "2019-01-02" }, { "rank": 2, "intensity": 1.530612244897959, "date": "2019-06-14" }, { "rank": 3, "intensity": 1.5612244897959184, "date": "2019-11-07" }, { "rank": 4, "intensity": 1.5918367346938775, "date": "2019-03-09" }, { "rank": 5, "intensity": 1.6224489795918366, "date": "2019-10-25" }, { "rank": 6, "intensity": 1.653061224489796, "date": "2019-05-21" }, { "rank": 7, "intensity": 1.683673469387755, "date": "2019-09-25" }, { "rank": 8, "intensity": 1.7142857142857142, "date": "2019-06-04" }, { "rank": 9, "intensity": 1.7448979591836735, "date": "2019-11-30" }, { "rank": 10, "intensity": 1.7755102040816326, "date": "2019-04-24" }, { "rank": 11, "intensity": 1.806122448979592, "date": "2019-02-06" }, { "rank": 12, "intensity": 1.836734693877551, "date": "2019-06-29" }, { "rank": 13, "intensity": 1.8673469387755102, "date": "2019-01-13" }, { "rank": 14, "intensity": 1.8979591836734695, "date": "2019-08-06" }, { "rank": 15, "intensity": 1.9285714285714286, "date": "2019-09-10" }, { "rank": 16, "intensity": 1.9591836734693877, "date": "2019-02-20" }, { "rank": 17, "intensity": 1.989795918367347, "date": "2019-06-08" }, { "rank": 18, "intensity": 2.020408163265306, "date": "2019-07-27" }, { "rank": 19, "intensity": 2.0510204081632653, "date": "2019-10-27" }, { "rank": 20, "intensity": 2.0816326530612246, "date": "2019-08-11" }, { "rank": 21, "intensity": 2.112244897959184, "date": "2019-03-27" }, { "rank": 22, "intensity": 2.142857142857143, "date": "2019-05-26" }, { "rank": 23, "intensity": 2.173469387755102, "date": "2019-05-23" }, { "rank": 24, "intensity": 2.204081632653061, "date": "2019-12-18" }, { "rank": 25, "intensity": 2.2346938775510203, "date": "2019-07-15" }, { "rank": 26, "intensity": 2.2653061224489797, "date": "2019-12-26" }, { "rank": 27, "intensity": 2.295918367346939, "date": "2019-05-28" }, { "rank": 28, "intensity": 2.326530612244898, "date": "2019-04-10" }, { "rank": 29, "intensity": 2.357142857142857, "date": "2019-06-20" }, { "rank": 30, "intensity": 2.387755102040816, "date": "2019-02-04" }, { "rank": 31, "intensity": 2.4183673469387754, "date": "2019-07-21" }, { "rank": 32, "intensity": 2.4489795918367347, "date": "2019-01-30" }, { "rank": 33, "intensity": 2.479591836734694, "date": "2019-03-02" }, { "rank": 34, "intensity": 2.510204081632653, "date": "2019-08-13" }, { "rank": 35, "intensity": 2.5408163265306123, "date": "2019-09-19" }, { "rank": 36, "intensity": 2.571428571428571, "date": "2019-11-05" }, { "rank": 37, "intensity": 2.6020408163265305, "date": "2019-01-28" }, { "rank": 38, "intensity": 2.63265306122449, "date": "2019-03-13" }, { "rank": 39, "intensity": 2.663265306122449, "date": "2019-05-19" }, { "rank": 40, "intensity": 2.693877551020408, "date": "2019-08-24" }, { "rank": 41, "intensity": 2.7244897959183674, "date": "2019-08-21" }, { "rank": 42, "intensity": 2.7551020408163263, "date": "2019-04-02" }, { "rank": 43, "intensity": 2.7857142857142856, "date": "2019-02-08" }, { "rank": 44, "intensity": 2.816326530612245, "date": "2019-08-19" }, { "rank": 45, "intensity": 2.8469387755102042, "date": "2019-09-27" }, { "rank": 46, "intensity": 2.877551020408163, "date": "2019-06-12" }, { "rank": 47, "intensity": 2.9081632653061225, "date": "2019-09-02" }, { "rank": 48, "intensity": 2.9387755102040813, "date": "2019-11-14" }, { "rank": 49, "intensity": 2.9693877551020407, "date": "2019-07-12" }, { "rank": 50, "intensity": 3.0, "date": "2019-12-11" } ], "source": "causal_rivers" } }, { "id": "L3_T1_Composite_Trend_00152", "level": 3, "level_name": "Semantic Reasoning", "category": "Composite Trend", "subtask": "Composite Trend", "question": "Among days in channel 813 during 2021 that exhibit the trend pattern 'rise, then fluctuating stable', identify the top-5 days where the fluctuating stable segment is the most fluctuating. (Output format: a ranked list of dates, e.g., ['YYYY-MM-DD', ...])", "answer": "['2021-03-30', '2021-12-20', '2021-02-24', '2021-11-22', '2021-04-22']", "ground_truth": [ "2021-03-30", "2021-12-20", "2021-02-24", "2021-11-22", "2021-04-22" ], "eval_metric": "set_f1", "channel": "813", "ts_data_path": "ts_data/L3_T1_Composite_Trend_00152.csv", "meta": { "segments": [ { "kind": "rise", "adj": "none" }, { "kind": "stable", "adj": "fluctuating" } ], "pattern_phrase": "rise, then fluctuating stable", "rank_target_idx": 1, "rank_kind": "stable", "rank_adj": "fluctuating", "rank_axis": "std", "rank_direction": "DESC", "rank_extreme": "most fluctuating", "top_segments_meta": [ { "adj": "none", "kind": "rise", "label": "none rise", "start_idx": 0, "end_idx": 47 }, { "adj": "fluctuating", "kind": "stable", "label": "fluctuating stable", "start_idx": 47, "end_idx": 98 } ], "points_per_day": 96, "pattern": "rise, then fluctuating stable", "year": 2021, "top_k": [ "2021-03-30", "2021-12-20", "2021-02-24", "2021-11-22", "2021-04-22" ], "all_injections": [ { "rank": 1, "intensity": 3.0, "date": "2021-03-30" }, { "rank": 2, "intensity": 2.9693877551020407, "date": "2021-12-20" }, { "rank": 3, "intensity": 2.938775510204082, "date": "2021-02-24" }, { "rank": 4, "intensity": 2.9081632653061225, "date": "2021-11-22" }, { "rank": 5, "intensity": 2.877551020408163, "date": "2021-04-22" }, { "rank": 6, "intensity": 2.8469387755102042, "date": "2021-07-15" }, { "rank": 7, "intensity": 2.816326530612245, "date": "2021-02-26" }, { "rank": 8, "intensity": 2.7857142857142856, "date": "2021-05-24" }, { "rank": 9, "intensity": 2.7551020408163267, "date": "2021-01-15" }, { "rank": 10, "intensity": 2.7244897959183674, "date": "2021-05-21" }, { "rank": 11, "intensity": 2.693877551020408, "date": "2021-10-07" }, { "rank": 12, "intensity": 2.663265306122449, "date": "2021-06-13" }, { "rank": 13, "intensity": 2.63265306122449, "date": "2021-09-09" }, { "rank": 14, "intensity": 2.6020408163265305, "date": "2021-01-11" }, { "rank": 15, "intensity": 2.5714285714285716, "date": "2021-11-08" }, { "rank": 16, "intensity": 2.5408163265306123, "date": "2021-04-29" }, { "rank": 17, "intensity": 2.510204081632653, "date": "2021-12-10" }, { "rank": 18, "intensity": 2.479591836734694, "date": "2021-06-17" }, { "rank": 19, "intensity": 2.4489795918367347, "date": "2021-09-29" }, { "rank": 20, "intensity": 2.4183673469387754, "date": "2021-02-09" }, { "rank": 21, "intensity": 2.387755102040816, "date": "2021-10-26" }, { "rank": 22, "intensity": 2.357142857142857, "date": "2021-01-24" }, { "rank": 23, "intensity": 2.326530612244898, "date": "2021-07-23" }, { "rank": 24, "intensity": 2.295918367346939, "date": "2021-01-17" }, { "rank": 25, "intensity": 2.2653061224489797, "date": "2021-08-02" }, { "rank": 26, "intensity": 2.2346938775510203, "date": "2021-11-26" }, { "rank": 27, "intensity": 2.204081632653061, "date": "2021-05-27" }, { "rank": 28, "intensity": 2.173469387755102, "date": "2021-09-19" }, { "rank": 29, "intensity": 2.142857142857143, "date": "2021-12-24" }, { "rank": 30, "intensity": 2.112244897959184, "date": "2021-06-29" }, { "rank": 31, "intensity": 2.0816326530612246, "date": "2021-06-27" }, { "rank": 32, "intensity": 2.0510204081632653, "date": "2021-09-11" }, { "rank": 33, "intensity": 2.020408163265306, "date": "2021-04-11" }, { "rank": 34, "intensity": 1.989795918367347, "date": "2021-11-11" }, { "rank": 35, "intensity": 1.9591836734693877, "date": "2021-11-01" }, { "rank": 36, "intensity": 1.9285714285714286, "date": "2021-02-07" }, { "rank": 37, "intensity": 1.8979591836734695, "date": "2021-05-30" }, { "rank": 38, "intensity": 1.8673469387755102, "date": "2021-10-21" }, { "rank": 39, "intensity": 1.836734693877551, "date": "2021-07-08" }, { "rank": 40, "intensity": 1.806122448979592, "date": "2021-12-27" }, { "rank": 41, "intensity": 1.7755102040816326, "date": "2021-12-07" }, { "rank": 42, "intensity": 1.7448979591836735, "date": "2021-08-12" }, { "rank": 43, "intensity": 1.7142857142857144, "date": "2021-06-20" }, { "rank": 44, "intensity": 1.683673469387755, "date": "2021-04-17" }, { "rank": 45, "intensity": 1.653061224489796, "date": "2021-07-11" }, { "rank": 46, "intensity": 1.6224489795918369, "date": "2021-03-10" }, { "rank": 47, "intensity": 1.5918367346938775, "date": "2021-12-12" }, { "rank": 48, "intensity": 1.5612244897959184, "date": "2021-01-22" }, { "rank": 49, "intensity": 1.5306122448979593, "date": "2021-08-06" }, { "rank": 50, "intensity": 1.5, "date": "2021-06-24" } ], "source": "causal_rivers" } }, { "id": "L3_T1_Composite_Trend_00153", "level": 3, "level_name": "Semantic Reasoning", "category": "Composite Trend", "subtask": "Composite Trend", "question": "Among days in channel 865 during 2019 that exhibit the trend pattern 'fluctuating stable, then fall', identify the top-5 days where the fluctuating stable segment is the most fluctuating. (Output format: a ranked list of dates, e.g., ['YYYY-MM-DD', ...])", "answer": "['2019-11-22', '2019-09-21', '2019-09-15', '2019-12-16', '2019-12-12']", "ground_truth": [ "2019-11-22", "2019-09-21", "2019-09-15", "2019-12-16", "2019-12-12" ], "eval_metric": "set_f1", "channel": "865", "ts_data_path": "ts_data/L3_T1_Composite_Trend_00153.csv", "meta": { "segments": [ { "kind": "stable", "adj": "fluctuating" }, { "kind": "fall", "adj": "none" } ], "pattern_phrase": "fluctuating stable, then fall", "rank_target_idx": 0, "rank_kind": "stable", "rank_adj": "fluctuating", "rank_axis": "std", "rank_direction": "DESC", "rank_extreme": "most fluctuating", "top_segments_meta": [ { "adj": "fluctuating", "kind": "stable", "label": "fluctuating stable", "start_idx": 0, "end_idx": 53 }, { "adj": "none", "kind": "fall", "label": "none fall", "start_idx": 53, "end_idx": 98 } ], "points_per_day": 96, "pattern": "fluctuating stable, then fall", "year": 2019, "top_k": [ "2019-11-22", "2019-09-21", "2019-09-15", "2019-12-16", "2019-12-12" ], "all_injections": [ { "rank": 1, "intensity": 3.0, "date": "2019-11-22" }, { "rank": 2, "intensity": 2.9693877551020407, "date": "2019-09-21" }, { "rank": 3, "intensity": 2.938775510204082, "date": "2019-09-15" }, { "rank": 4, "intensity": 2.9081632653061225, "date": "2019-12-16" }, { "rank": 5, "intensity": 2.877551020408163, "date": "2019-12-12" }, { "rank": 6, "intensity": 2.8469387755102042, "date": "2019-07-24" }, { "rank": 7, "intensity": 2.816326530612245, "date": "2019-09-08" }, { "rank": 8, "intensity": 2.7857142857142856, "date": "2019-04-26" }, { "rank": 9, "intensity": 2.7551020408163267, "date": "2019-10-30" }, { "rank": 10, "intensity": 2.7244897959183674, "date": "2019-09-27" }, { "rank": 11, "intensity": 2.693877551020408, "date": "2019-08-06" }, { "rank": 12, "intensity": 2.663265306122449, "date": "2019-03-15" }, { "rank": 13, "intensity": 2.63265306122449, "date": "2019-05-18" }, { "rank": 14, "intensity": 2.6020408163265305, "date": "2019-04-19" }, { "rank": 15, "intensity": 2.5714285714285716, "date": "2019-07-22" }, { "rank": 16, "intensity": 2.5408163265306123, "date": "2019-02-24" }, { "rank": 17, "intensity": 2.510204081632653, "date": "2019-08-16" }, { "rank": 18, "intensity": 2.479591836734694, "date": "2019-05-12" }, { "rank": 19, "intensity": 2.4489795918367347, "date": "2019-02-08" }, { "rank": 20, "intensity": 2.4183673469387754, "date": "2019-12-26" }, { "rank": 21, "intensity": 2.387755102040816, "date": "2019-09-30" }, { "rank": 22, "intensity": 2.357142857142857, "date": "2019-02-06" }, { "rank": 23, "intensity": 2.326530612244898, "date": "2019-07-20" }, { "rank": 24, "intensity": 2.295918367346939, "date": "2019-06-18" }, { "rank": 25, "intensity": 2.2653061224489797, "date": "2019-10-15" }, { "rank": 26, "intensity": 2.2346938775510203, "date": "2019-06-01" }, { "rank": 27, "intensity": 2.204081632653061, "date": "2019-07-06" }, { "rank": 28, "intensity": 2.173469387755102, "date": "2019-12-28" }, { "rank": 29, "intensity": 2.142857142857143, "date": "2019-02-13" }, { "rank": 30, "intensity": 2.112244897959184, "date": "2019-10-24" }, { "rank": 31, "intensity": 2.0816326530612246, "date": "2019-07-30" }, { "rank": 32, "intensity": 2.0510204081632653, "date": "2019-06-03" }, { "rank": 33, "intensity": 2.020408163265306, "date": "2019-01-10" }, { "rank": 34, "intensity": 1.989795918367347, "date": "2019-05-22" }, { "rank": 35, "intensity": 1.9591836734693877, "date": "2019-06-10" }, { "rank": 36, "intensity": 1.9285714285714286, "date": "2019-03-31" }, { "rank": 37, "intensity": 1.8979591836734695, "date": "2019-01-25" }, { "rank": 38, "intensity": 1.8673469387755102, "date": "2019-02-10" }, { "rank": 39, "intensity": 1.836734693877551, "date": "2019-04-22" }, { "rank": 40, "intensity": 1.806122448979592, "date": "2019-11-28" }, { "rank": 41, "intensity": 1.7755102040816326, "date": "2019-04-09" }, { "rank": 42, "intensity": 1.7448979591836735, "date": "2019-05-25" }, { "rank": 43, "intensity": 1.7142857142857144, "date": "2019-09-18" }, { "rank": 44, "intensity": 1.683673469387755, "date": "2019-08-24" }, { "rank": 45, "intensity": 1.653061224489796, "date": "2019-11-15" }, { "rank": 46, "intensity": 1.6224489795918369, "date": "2019-04-06" }, { "rank": 47, "intensity": 1.5918367346938775, "date": "2019-04-30" }, { "rank": 48, "intensity": 1.5612244897959184, "date": "2019-02-19" }, { "rank": 49, "intensity": 1.5306122448979593, "date": "2019-01-15" }, { "rank": 50, "intensity": 1.5, "date": "2019-09-25" } ], "source": "causal_rivers" } }, { "id": "L3_T1_Composite_Trend_00154", "level": 3, "level_name": "Semantic Reasoning", "category": "Composite Trend", "subtask": "Composite Trend", "question": "Among days in channel 891 during 2022 that exhibit the trend pattern 'rise, then slow rise', identify the top-5 days where the slow rise segment is the slowest. (Output format: a ranked list of dates, e.g., ['YYYY-MM-DD', ...])", "answer": "['2022-03-26', '2022-05-04', '2022-04-09', '2022-12-25', '2022-12-03']", "ground_truth": [ "2022-03-26", "2022-05-04", "2022-04-09", "2022-12-25", "2022-12-03" ], "eval_metric": "set_f1", "channel": "891", "ts_data_path": "ts_data/L3_T1_Composite_Trend_00154.csv", "meta": { "segments": [ { "kind": "rise", "adj": "none" }, { "kind": "rise", "adj": "slow" } ], "pattern_phrase": "rise, then slow rise", "rank_target_idx": 1, "rank_kind": "rise", "rank_adj": "slow", "rank_axis": "slope", "rank_direction": "ASC", "rank_extreme": "slowest", "top_segments_meta": [ { "adj": "none", "kind": "rise", "label": "none rise", "start_idx": 0, "end_idx": 34 }, { "adj": "slow", "kind": "rise", "label": "slow rise", "start_idx": 34, "end_idx": 98 } ], "points_per_day": 96, "pattern": "rise, then slow rise", "year": 2022, "top_k": [ "2022-03-26", "2022-05-04", "2022-04-09", "2022-12-25", "2022-12-03" ], "all_injections": [ { "rank": 1, "intensity": 1.5, "date": "2022-03-26" }, { "rank": 2, "intensity": 1.530612244897959, "date": "2022-05-04" }, { "rank": 3, "intensity": 1.5612244897959184, "date": "2022-04-09" }, { "rank": 4, "intensity": 1.5918367346938775, "date": "2022-12-25" }, { "rank": 5, "intensity": 1.6224489795918366, "date": "2022-12-03" }, { "rank": 6, "intensity": 1.653061224489796, "date": "2022-06-06" }, { "rank": 7, "intensity": 1.683673469387755, "date": "2022-06-27" }, { "rank": 8, "intensity": 1.7142857142857142, "date": "2022-09-17" }, { "rank": 9, "intensity": 1.7448979591836735, "date": "2022-12-23" }, { "rank": 10, "intensity": 1.7755102040816326, "date": "2022-10-18" }, { "rank": 11, "intensity": 1.806122448979592, "date": "2022-03-17" }, { "rank": 12, "intensity": 1.836734693877551, "date": "2022-06-24" }, { "rank": 13, "intensity": 1.8673469387755102, "date": "2022-02-06" }, { "rank": 14, "intensity": 1.8979591836734695, "date": "2022-10-21" }, { "rank": 15, "intensity": 1.9285714285714286, "date": "2022-11-03" }, { "rank": 16, "intensity": 1.9591836734693877, "date": "2022-11-16" }, { "rank": 17, "intensity": 1.989795918367347, "date": "2022-04-20" }, { "rank": 18, "intensity": 2.020408163265306, "date": "2022-10-04" }, { "rank": 19, "intensity": 2.0510204081632653, "date": "2022-09-30" }, { "rank": 20, "intensity": 2.0816326530612246, "date": "2022-10-28" }, { "rank": 21, "intensity": 2.112244897959184, "date": "2022-05-25" }, { "rank": 22, "intensity": 2.142857142857143, "date": "2022-03-29" }, { "rank": 23, "intensity": 2.173469387755102, "date": "2022-04-22" }, { "rank": 24, "intensity": 2.204081632653061, "date": "2022-07-14" }, { "rank": 25, "intensity": 2.2346938775510203, "date": "2022-09-23" }, { "rank": 26, "intensity": 2.2653061224489797, "date": "2022-12-10" }, { "rank": 27, "intensity": 2.295918367346939, "date": "2022-11-13" }, { "rank": 28, "intensity": 2.326530612244898, "date": "2022-06-19" }, { "rank": 29, "intensity": 2.357142857142857, "date": "2022-07-18" }, { "rank": 30, "intensity": 2.387755102040816, "date": "2022-02-25" }, { "rank": 31, "intensity": 2.4183673469387754, "date": "2022-06-29" }, { "rank": 32, "intensity": 2.4489795918367347, "date": "2022-10-26" }, { "rank": 33, "intensity": 2.479591836734694, "date": "2022-09-26" }, { "rank": 34, "intensity": 2.510204081632653, "date": "2022-11-01" }, { "rank": 35, "intensity": 2.5408163265306123, "date": "2022-09-08" }, { "rank": 36, "intensity": 2.571428571428571, "date": "2022-02-13" }, { "rank": 37, "intensity": 2.6020408163265305, "date": "2022-03-04" }, { "rank": 38, "intensity": 2.63265306122449, "date": "2022-01-23" }, { "rank": 39, "intensity": 2.663265306122449, "date": "2022-03-10" }, { "rank": 40, "intensity": 2.693877551020408, "date": "2022-02-15" }, { "rank": 41, "intensity": 2.7244897959183674, "date": "2022-05-21" }, { "rank": 42, "intensity": 2.7551020408163263, "date": "2022-02-20" }, { "rank": 43, "intensity": 2.7857142857142856, "date": "2022-05-08" }, { "rank": 44, "intensity": 2.816326530612245, "date": "2022-01-28" }, { "rank": 45, "intensity": 2.8469387755102042, "date": "2022-09-03" }, { "rank": 46, "intensity": 2.877551020408163, "date": "2022-08-15" }, { "rank": 47, "intensity": 2.9081632653061225, "date": "2022-08-17" }, { "rank": 48, "intensity": 2.9387755102040813, "date": "2022-11-22" }, { "rank": 49, "intensity": 2.9693877551020407, "date": "2022-07-16" }, { "rank": 50, "intensity": 3.0, "date": "2022-05-10" } ], "source": "causal_rivers" } }, { "id": "L3_T1_Composite_Trend_00155", "level": 3, "level_name": "Semantic Reasoning", "category": "Composite Trend", "subtask": "Composite Trend", "question": "Among days in channel 894 during 2019 that exhibit the trend pattern 'fluctuating stable, then rapid fall, then slow fall', identify the top-5 days where the rapid fall segment is the fastest. (Output format: a ranked list of dates, e.g., ['YYYY-MM-DD', ...])", "answer": "['2019-10-16', '2019-05-15', '2019-05-21', '2019-11-06', '2019-09-02']", "ground_truth": [ "2019-10-16", "2019-05-15", "2019-05-21", "2019-11-06", "2019-09-02" ], "eval_metric": "set_f1", "channel": "894", "ts_data_path": "ts_data/L3_T1_Composite_Trend_00155.csv", "meta": { "segments": [ { "kind": "stable", "adj": "fluctuating" }, { "kind": "fall", "adj": "rapid" }, { "kind": "fall", "adj": "slow" } ], "pattern_phrase": "fluctuating stable, then rapid fall, then slow fall", "rank_target_idx": 1, "rank_kind": "fall", "rank_adj": "rapid", "rank_axis": "slope", "rank_direction": "DESC", "rank_extreme": "fastest", "top_segments_meta": [ { "adj": "fluctuating", "kind": "stable", "label": "fluctuating stable", "start_idx": 0, "end_idx": 37 }, { "adj": "rapid", "kind": "fall", "label": "rapid fall", "start_idx": 37, "end_idx": 51 }, { "adj": "slow", "kind": "fall", "label": "slow fall", "start_idx": 51, "end_idx": 98 } ], "points_per_day": 96, "pattern": "fluctuating stable, then rapid fall, then slow fall", "year": 2019, "top_k": [ "2019-10-16", "2019-05-15", "2019-05-21", "2019-11-06", "2019-09-02" ], "all_injections": [ { "rank": 1, "intensity": 3.0, "date": "2019-10-16" }, { "rank": 2, "intensity": 2.9693877551020407, "date": "2019-05-15" }, { "rank": 3, "intensity": 2.938775510204082, "date": "2019-05-21" }, { "rank": 4, "intensity": 2.9081632653061225, "date": "2019-11-06" }, { "rank": 5, "intensity": 2.877551020408163, "date": "2019-09-02" }, { "rank": 6, "intensity": 2.8469387755102042, "date": "2019-11-22" }, { "rank": 7, "intensity": 2.816326530612245, "date": "2019-11-29" }, { "rank": 8, "intensity": 2.7857142857142856, "date": "2019-05-13" }, { "rank": 9, "intensity": 2.7551020408163267, "date": "2019-07-09" }, { "rank": 10, "intensity": 2.7244897959183674, "date": "2019-09-10" }, { "rank": 11, "intensity": 2.693877551020408, "date": "2019-02-10" }, { "rank": 12, "intensity": 2.663265306122449, "date": "2019-12-12" }, { "rank": 13, "intensity": 2.63265306122449, "date": "2019-11-11" }, { "rank": 14, "intensity": 2.6020408163265305, "date": "2019-05-30" }, { "rank": 15, "intensity": 2.5714285714285716, "date": "2019-03-11" }, { "rank": 16, "intensity": 2.5408163265306123, "date": "2019-07-25" }, { "rank": 17, "intensity": 2.510204081632653, "date": "2019-01-29" }, { "rank": 18, "intensity": 2.479591836734694, "date": "2019-07-15" }, { "rank": 19, "intensity": 2.4489795918367347, "date": "2019-04-27" }, { "rank": 20, "intensity": 2.4183673469387754, "date": "2019-10-27" }, { "rank": 21, "intensity": 2.387755102040816, "date": "2019-05-26" }, { "rank": 22, "intensity": 2.357142857142857, "date": "2019-08-18" }, { "rank": 23, "intensity": 2.326530612244898, "date": "2019-04-10" }, { "rank": 24, "intensity": 2.295918367346939, "date": "2019-04-07" }, { "rank": 25, "intensity": 2.2653061224489797, "date": "2019-10-11" }, { "rank": 26, "intensity": 2.2346938775510203, "date": "2019-08-27" }, { "rank": 27, "intensity": 2.204081632653061, "date": "2019-01-18" }, { "rank": 28, "intensity": 2.173469387755102, "date": "2019-12-02" }, { "rank": 29, "intensity": 2.142857142857143, "date": "2019-05-01" }, { "rank": 30, "intensity": 2.112244897959184, "date": "2019-09-22" }, { "rank": 31, "intensity": 2.0816326530612246, "date": "2019-12-19" }, { "rank": 32, "intensity": 2.0510204081632653, "date": "2019-12-21" }, { "rank": 33, "intensity": 2.020408163265306, "date": "2019-06-18" }, { "rank": 34, "intensity": 1.989795918367347, "date": "2019-07-21" }, { "rank": 35, "intensity": 1.9591836734693877, "date": "2019-10-05" }, { "rank": 36, "intensity": 1.9285714285714286, "date": "2019-03-22" }, { "rank": 37, "intensity": 1.8979591836734695, "date": "2019-04-21" }, { "rank": 38, "intensity": 1.8673469387755102, "date": "2019-09-30" }, { "rank": 39, "intensity": 1.836734693877551, "date": "2019-07-19" }, { "rank": 40, "intensity": 1.806122448979592, "date": "2019-03-29" }, { "rank": 41, "intensity": 1.7755102040816326, "date": "2019-03-09" }, { "rank": 42, "intensity": 1.7448979591836735, "date": "2019-02-02" }, { "rank": 43, "intensity": 1.7142857142857144, "date": "2019-04-18" }, { "rank": 44, "intensity": 1.683673469387755, "date": "2019-08-08" }, { "rank": 45, "intensity": 1.653061224489796, "date": "2019-10-29" }, { "rank": 46, "intensity": 1.6224489795918369, "date": "2019-12-24" }, { "rank": 47, "intensity": 1.5918367346938775, "date": "2019-01-21" }, { "rank": 48, "intensity": 1.5612244897959184, "date": "2019-03-05" }, { "rank": 49, "intensity": 1.5306122448979593, "date": "2019-02-19" }, { "rank": 50, "intensity": 1.5, "date": "2019-07-04" } ], "source": "causal_rivers" } }, { "id": "L3_T1_Composite_Trend_00156", "level": 3, "level_name": "Semantic Reasoning", "category": "Composite Trend", "subtask": "Composite Trend", "question": "Among days in channel 895 during 2023 that exhibit the trend pattern 'rapid fall, then fluctuating stable', identify the top-5 days where the rapid fall segment is the fastest. (Output format: a ranked list of dates, e.g., ['YYYY-MM-DD', ...])", "answer": "['2023-09-30', '2023-04-21', '2023-04-18', '2023-10-18', '2023-04-15']", "ground_truth": [ "2023-09-30", "2023-04-21", "2023-04-18", "2023-10-18", "2023-04-15" ], "eval_metric": "set_f1", "channel": "895", "ts_data_path": "ts_data/L3_T1_Composite_Trend_00156.csv", "meta": { "segments": [ { "kind": "fall", "adj": "rapid" }, { "kind": "stable", "adj": "fluctuating" } ], "pattern_phrase": "rapid fall, then fluctuating stable", "rank_target_idx": 0, "rank_kind": "fall", "rank_adj": "rapid", "rank_axis": "slope", "rank_direction": "DESC", "rank_extreme": "fastest", "top_segments_meta": [ { "adj": "rapid", "kind": "fall", "label": "rapid fall", "start_idx": 0, "end_idx": 32 }, { "adj": "fluctuating", "kind": "stable", "label": "fluctuating stable", "start_idx": 32, "end_idx": 98 } ], "points_per_day": 96, "pattern": "rapid fall, then fluctuating stable", "year": 2023, "top_k": [ "2023-09-30", "2023-04-21", "2023-04-18", "2023-10-18", "2023-04-15" ], "all_injections": [ { "rank": 1, "intensity": 3.0, "date": "2023-09-30" }, { "rank": 2, "intensity": 2.9693877551020407, "date": "2023-04-21" }, { "rank": 3, "intensity": 2.938775510204082, "date": "2023-04-18" }, { "rank": 4, "intensity": 2.9081632653061225, "date": "2023-10-18" }, { "rank": 5, "intensity": 2.877551020408163, "date": "2023-04-15" }, { "rank": 6, "intensity": 2.8469387755102042, "date": "2023-10-04" }, { "rank": 7, "intensity": 2.816326530612245, "date": "2023-07-26" }, { "rank": 8, "intensity": 2.7857142857142856, "date": "2023-11-09" }, { "rank": 9, "intensity": 2.7551020408163267, "date": "2023-10-20" }, { "rank": 10, "intensity": 2.7244897959183674, "date": "2023-04-13" }, { "rank": 11, "intensity": 2.693877551020408, "date": "2023-06-16" }, { "rank": 12, "intensity": 2.663265306122449, "date": "2023-01-08" }, { "rank": 13, "intensity": 2.63265306122449, "date": "2023-04-25" }, { "rank": 14, "intensity": 2.6020408163265305, "date": "2023-08-12" }, { "rank": 15, "intensity": 2.5714285714285716, "date": "2023-05-24" }, { "rank": 16, "intensity": 2.5408163265306123, "date": "2023-01-05" }, { "rank": 17, "intensity": 2.510204081632653, "date": "2023-09-12" }, { "rank": 18, "intensity": 2.479591836734694, "date": "2023-12-07" }, { "rank": 19, "intensity": 2.4489795918367347, "date": "2023-12-30" }, { "rank": 20, "intensity": 2.4183673469387754, "date": "2023-07-03" }, { "rank": 21, "intensity": 2.387755102040816, "date": "2023-12-11" }, { "rank": 22, "intensity": 2.357142857142857, "date": "2023-09-17" }, { "rank": 23, "intensity": 2.326530612244898, "date": "2023-02-07" }, { "rank": 24, "intensity": 2.295918367346939, "date": "2023-07-10" }, { "rank": 25, "intensity": 2.2653061224489797, "date": "2023-02-03" }, { "rank": 26, "intensity": 2.2346938775510203, "date": "2023-03-17" }, { "rank": 27, "intensity": 2.204081632653061, "date": "2023-12-13" }, { "rank": 28, "intensity": 2.173469387755102, "date": "2023-01-11" }, { "rank": 29, "intensity": 2.142857142857143, "date": "2023-09-19" }, { "rank": 30, "intensity": 2.112244897959184, "date": "2023-09-07" }, { "rank": 31, "intensity": 2.0816326530612246, "date": "2023-11-18" }, { "rank": 32, "intensity": 2.0510204081632653, "date": "2023-04-02" }, { "rank": 33, "intensity": 2.020408163265306, "date": "2023-07-18" }, { "rank": 34, "intensity": 1.989795918367347, "date": "2023-02-01" }, { "rank": 35, "intensity": 1.9591836734693877, "date": "2023-07-23" }, { "rank": 36, "intensity": 1.9285714285714286, "date": "2023-11-07" }, { "rank": 37, "intensity": 1.8979591836734695, "date": "2023-03-29" }, { "rank": 38, "intensity": 1.8673469387755102, "date": "2023-07-13" }, { "rank": 39, "intensity": 1.836734693877551, "date": "2023-12-24" }, { "rank": 40, "intensity": 1.806122448979592, "date": "2023-01-26" }, { "rank": 41, "intensity": 1.7755102040816326, "date": "2023-08-04" }, { "rank": 42, "intensity": 1.7448979591836735, "date": "2023-06-04" }, { "rank": 43, "intensity": 1.7142857142857144, "date": "2023-02-21" }, { "rank": 44, "intensity": 1.683673469387755, "date": "2023-05-16" }, { "rank": 45, "intensity": 1.653061224489796, "date": "2023-09-21" }, { "rank": 46, "intensity": 1.6224489795918369, "date": "2023-01-01" }, { "rank": 47, "intensity": 1.5918367346938775, "date": "2023-05-31" }, { "rank": 48, "intensity": 1.5612244897959184, "date": "2023-08-29" }, { "rank": 49, "intensity": 1.5306122448979593, "date": "2023-10-13" }, { "rank": 50, "intensity": 1.5, "date": "2023-01-03" } ], "source": "causal_rivers" } }, { "id": "L3_T1_Composite_Trend_00157", "level": 3, "level_name": "Semantic Reasoning", "category": "Composite Trend", "subtask": "Composite Trend", "question": "Among days in channel 897 during 2022 that exhibit the trend pattern 'rapid fall, then steady stable', identify the top-5 days where the rapid fall segment is the fastest. (Output format: a ranked list of dates, e.g., ['YYYY-MM-DD', ...])", "answer": "['2022-11-02', '2022-03-30', '2022-08-19', '2022-03-25', '2022-07-11']", "ground_truth": [ "2022-11-02", "2022-03-30", "2022-08-19", "2022-03-25", "2022-07-11" ], "eval_metric": "set_f1", "channel": "897", "ts_data_path": "ts_data/L3_T1_Composite_Trend_00157.csv", "meta": { "segments": [ { "kind": "fall", "adj": "rapid" }, { "kind": "stable", "adj": "steady" } ], "pattern_phrase": "rapid fall, then steady stable", "rank_target_idx": 0, "rank_kind": "fall", "rank_adj": "rapid", "rank_axis": "slope", "rank_direction": "DESC", "rank_extreme": "fastest", "top_segments_meta": [ { "adj": "rapid", "kind": "fall", "label": "rapid fall", "start_idx": 0, "end_idx": 23 }, { "adj": "steady", "kind": "stable", "label": "steady stable", "start_idx": 23, "end_idx": 98 } ], "points_per_day": 96, "pattern": "rapid fall, then steady stable", "year": 2022, "top_k": [ "2022-11-02", "2022-03-30", "2022-08-19", "2022-03-25", "2022-07-11" ], "all_injections": [ { "rank": 1, "intensity": 3.0, "date": "2022-11-02" }, { "rank": 2, "intensity": 2.9693877551020407, "date": "2022-03-30" }, { "rank": 3, "intensity": 2.938775510204082, "date": "2022-08-19" }, { "rank": 4, "intensity": 2.9081632653061225, "date": "2022-03-25" }, { "rank": 5, "intensity": 2.877551020408163, "date": "2022-07-11" }, { "rank": 6, "intensity": 2.8469387755102042, "date": "2022-07-20" }, { "rank": 7, "intensity": 2.816326530612245, "date": "2022-09-16" }, { "rank": 8, "intensity": 2.7857142857142856, "date": "2022-06-18" }, { "rank": 9, "intensity": 2.7551020408163267, "date": "2022-04-22" }, { "rank": 10, "intensity": 2.7244897959183674, "date": "2022-08-10" }, { "rank": 11, "intensity": 2.693877551020408, "date": "2022-03-17" }, { "rank": 12, "intensity": 2.663265306122449, "date": "2022-02-25" }, { "rank": 13, "intensity": 2.63265306122449, "date": "2022-02-05" }, { "rank": 14, "intensity": 2.6020408163265305, "date": "2022-04-17" }, { "rank": 15, "intensity": 2.5714285714285716, "date": "2022-12-17" }, { "rank": 16, "intensity": 2.5408163265306123, "date": "2022-08-12" }, { "rank": 17, "intensity": 2.510204081632653, "date": "2022-10-26" }, { "rank": 18, "intensity": 2.479591836734694, "date": "2022-12-29" }, { "rank": 19, "intensity": 2.4489795918367347, "date": "2022-01-06" }, { "rank": 20, "intensity": 2.4183673469387754, "date": "2022-04-03" }, { "rank": 21, "intensity": 2.387755102040816, "date": "2022-05-11" }, { "rank": 22, "intensity": 2.357142857142857, "date": "2022-08-04" }, { "rank": 23, "intensity": 2.326530612244898, "date": "2022-01-20" }, { "rank": 24, "intensity": 2.295918367346939, "date": "2022-05-18" }, { "rank": 25, "intensity": 2.2653061224489797, "date": "2022-03-04" }, { "rank": 26, "intensity": 2.2346938775510203, "date": "2022-12-03" }, { "rank": 27, "intensity": 2.204081632653061, "date": "2022-11-06" }, { "rank": 28, "intensity": 2.173469387755102, "date": "2022-03-11" }, { "rank": 29, "intensity": 2.142857142857143, "date": "2022-05-09" }, { "rank": 30, "intensity": 2.112244897959184, "date": "2022-04-08" }, { "rank": 31, "intensity": 2.0816326530612246, "date": "2022-07-31" }, { "rank": 32, "intensity": 2.0510204081632653, "date": "2022-08-06" }, { "rank": 33, "intensity": 2.020408163265306, "date": "2022-07-06" }, { "rank": 34, "intensity": 1.989795918367347, "date": "2022-08-23" }, { "rank": 35, "intensity": 1.9591836734693877, "date": "2022-01-16" }, { "rank": 36, "intensity": 1.9285714285714286, "date": "2022-03-23" }, { "rank": 37, "intensity": 1.8979591836734695, "date": "2022-06-26" }, { "rank": 38, "intensity": 1.8673469387755102, "date": "2022-02-19" }, { "rank": 39, "intensity": 1.836734693877551, "date": "2022-02-08" }, { "rank": 40, "intensity": 1.806122448979592, "date": "2022-09-26" }, { "rank": 41, "intensity": 1.7755102040816326, "date": "2022-06-10" }, { "rank": 42, "intensity": 1.7448979591836735, "date": "2022-07-27" }, { "rank": 43, "intensity": 1.7142857142857144, "date": "2022-04-10" }, { "rank": 44, "intensity": 1.683673469387755, "date": "2022-06-06" }, { "rank": 45, "intensity": 1.653061224489796, "date": "2022-01-31" }, { "rank": 46, "intensity": 1.6224489795918369, "date": "2022-07-29" }, { "rank": 47, "intensity": 1.5918367346938775, "date": "2022-06-15" }, { "rank": 48, "intensity": 1.5612244897959184, "date": "2022-03-28" }, { "rank": 49, "intensity": 1.5306122448979593, "date": "2022-06-20" }, { "rank": 50, "intensity": 1.5, "date": "2022-04-28" } ], "source": "causal_rivers" } }, { "id": "L3_T1_Composite_Trend_00158", "level": 3, "level_name": "Semantic Reasoning", "category": "Composite Trend", "subtask": "Composite Trend", "question": "Among days in channel 933 during 2019 that exhibit the trend pattern 'steady stable, then rapid fall', identify the top-5 days where the rapid fall segment is the fastest. (Output format: a ranked list of dates, e.g., ['YYYY-MM-DD', ...])", "answer": "['2019-07-20', '2019-05-19', '2019-08-15', '2019-05-07', '2019-06-14']", "ground_truth": [ "2019-07-20", "2019-05-19", "2019-08-15", "2019-05-07", "2019-06-14" ], "eval_metric": "set_f1", "channel": "933", "ts_data_path": "ts_data/L3_T1_Composite_Trend_00158.csv", "meta": { "segments": [ { "kind": "stable", "adj": "steady" }, { "kind": "fall", "adj": "rapid" } ], "pattern_phrase": "steady stable, then rapid fall", "rank_target_idx": 1, "rank_kind": "fall", "rank_adj": "rapid", "rank_axis": "slope", "rank_direction": "DESC", "rank_extreme": "fastest", "top_segments_meta": [ { "adj": "steady", "kind": "stable", "label": "steady stable", "start_idx": 0, "end_idx": 79 }, { "adj": "rapid", "kind": "fall", "label": "rapid fall", "start_idx": 79, "end_idx": 98 } ], "points_per_day": 96, "pattern": "steady stable, then rapid fall", "year": 2019, "top_k": [ "2019-07-20", "2019-05-19", "2019-08-15", "2019-05-07", "2019-06-14" ], "all_injections": [ { "rank": 1, "intensity": 3.0, "date": "2019-07-20" }, { "rank": 2, "intensity": 2.9693877551020407, "date": "2019-05-19" }, { "rank": 3, "intensity": 2.938775510204082, "date": "2019-08-15" }, { "rank": 4, "intensity": 2.9081632653061225, "date": "2019-05-07" }, { "rank": 5, "intensity": 2.877551020408163, "date": "2019-06-14" }, { "rank": 6, "intensity": 2.8469387755102042, "date": "2019-03-06" }, { "rank": 7, "intensity": 2.816326530612245, "date": "2019-12-07" }, { "rank": 8, "intensity": 2.7857142857142856, "date": "2019-12-27" }, { "rank": 9, "intensity": 2.7551020408163267, "date": "2019-06-03" }, { "rank": 10, "intensity": 2.7244897959183674, "date": "2019-11-01" }, { "rank": 11, "intensity": 2.693877551020408, "date": "2019-11-06" }, { "rank": 12, "intensity": 2.663265306122449, "date": "2019-10-07" }, { "rank": 13, "intensity": 2.63265306122449, "date": "2019-05-13" }, { "rank": 14, "intensity": 2.6020408163265305, "date": "2019-10-01" }, { "rank": 15, "intensity": 2.5714285714285716, "date": "2019-06-27" }, { "rank": 16, "intensity": 2.5408163265306123, "date": "2019-03-27" }, { "rank": 17, "intensity": 2.510204081632653, "date": "2019-04-13" }, { "rank": 18, "intensity": 2.479591836734694, "date": "2019-04-08" }, { "rank": 19, "intensity": 2.4489795918367347, "date": "2019-11-28" }, { "rank": 20, "intensity": 2.4183673469387754, "date": "2019-08-04" }, { "rank": 21, "intensity": 2.387755102040816, "date": "2019-07-26" }, { "rank": 22, "intensity": 2.357142857142857, "date": "2019-09-28" }, { "rank": 23, "intensity": 2.326530612244898, "date": "2019-08-09" }, { "rank": 24, "intensity": 2.295918367346939, "date": "2019-08-30" }, { "rank": 25, "intensity": 2.2653061224489797, "date": "2019-06-01" }, { "rank": 26, "intensity": 2.2346938775510203, "date": "2019-09-12" }, { "rank": 27, "intensity": 2.204081632653061, "date": "2019-07-03" }, { "rank": 28, "intensity": 2.173469387755102, "date": "2019-08-23" }, { "rank": 29, "intensity": 2.142857142857143, "date": "2019-03-03" }, { "rank": 30, "intensity": 2.112244897959184, "date": "2019-07-08" }, { "rank": 31, "intensity": 2.0816326530612246, "date": "2019-09-16" }, { "rank": 32, "intensity": 2.0510204081632653, "date": "2019-02-19" }, { "rank": 33, "intensity": 2.020408163265306, "date": "2019-01-02" }, { "rank": 34, "intensity": 1.989795918367347, "date": "2019-10-28" }, { "rank": 35, "intensity": 1.9591836734693877, "date": "2019-06-07" }, { "rank": 36, "intensity": 1.9285714285714286, "date": "2019-11-25" }, { "rank": 37, "intensity": 1.8979591836734695, "date": "2019-09-03" }, { "rank": 38, "intensity": 1.8673469387755102, "date": "2019-02-13" }, { "rank": 39, "intensity": 1.836734693877551, "date": "2019-03-13" }, { "rank": 40, "intensity": 1.806122448979592, "date": "2019-03-20" }, { "rank": 41, "intensity": 1.7755102040816326, "date": "2019-08-11" }, { "rank": 42, "intensity": 1.7448979591836735, "date": "2019-08-18" }, { "rank": 43, "intensity": 1.7142857142857144, "date": "2019-10-22" }, { "rank": 44, "intensity": 1.683673469387755, "date": "2019-11-09" }, { "rank": 45, "intensity": 1.653061224489796, "date": "2019-04-27" }, { "rank": 46, "intensity": 1.6224489795918369, "date": "2019-06-30" }, { "rank": 47, "intensity": 1.5918367346938775, "date": "2019-05-16" }, { "rank": 48, "intensity": 1.5612244897959184, "date": "2019-05-02" }, { "rank": 49, "intensity": 1.5306122448979593, "date": "2019-11-22" }, { "rank": 50, "intensity": 1.5, "date": "2019-06-18" } ], "source": "causal_rivers" } }, { "id": "L3_T1_Composite_Trend_00159", "level": 3, "level_name": "Semantic Reasoning", "category": "Composite Trend", "subtask": "Composite Trend", "question": "Among days in channel HUFL during 2017 that exhibit the trend pattern 'slow fall, then fluctuating stable', identify the top-5 days where the slow fall segment is the slowest. (Output format: a ranked list of dates, e.g., ['YYYY-MM-DD', ...])", "answer": "['2017-12-18', '2017-08-06', '2017-01-17', '2017-10-23', '2017-07-14']", "ground_truth": [ "2017-12-18", "2017-08-06", "2017-01-17", "2017-10-23", "2017-07-14" ], "eval_metric": "set_f1", "channel": "HUFL", "ts_data_path": "ts_data/L3_T1_Composite_Trend_00159.csv", "meta": { "segments": [ { "kind": "fall", "adj": "slow" }, { "kind": "stable", "adj": "fluctuating" } ], "pattern_phrase": "slow fall, then fluctuating stable", "rank_target_idx": 0, "rank_kind": "fall", "rank_adj": "slow", "rank_axis": "slope", "rank_direction": "ASC", "rank_extreme": "slowest", "top_segments_meta": [ { "adj": "slow", "kind": "fall", "label": "slow fall", "start_idx": 0, "end_idx": 57 }, { "adj": "fluctuating", "kind": "stable", "label": "fluctuating stable", "start_idx": 57, "end_idx": 98 } ], "points_per_day": 96, "pattern": "slow fall, then fluctuating stable", "year": 2017, "top_k": [ "2017-12-18", "2017-08-06", "2017-01-17", "2017-10-23", "2017-07-14" ], "all_injections": [ { "rank": 1, "intensity": 1.5, "date": "2017-12-18" }, { "rank": 2, "intensity": 1.530612244897959, "date": "2017-08-06" }, { "rank": 3, "intensity": 1.5612244897959184, "date": "2017-01-17" }, { "rank": 4, "intensity": 1.5918367346938775, "date": "2017-10-23" }, { "rank": 5, "intensity": 1.6224489795918366, "date": "2017-07-14" }, { "rank": 6, "intensity": 1.653061224489796, "date": "2017-09-14" }, { "rank": 7, "intensity": 1.683673469387755, "date": "2017-08-08" }, { "rank": 8, "intensity": 1.7142857142857142, "date": "2017-01-26" }, { "rank": 9, "intensity": 1.7448979591836735, "date": "2017-02-16" }, { "rank": 10, "intensity": 1.7755102040816326, "date": "2017-11-22" }, { "rank": 11, "intensity": 1.806122448979592, "date": "2017-05-13" }, { "rank": 12, "intensity": 1.836734693877551, "date": "2017-11-19" }, { "rank": 13, "intensity": 1.8673469387755102, "date": "2017-08-30" }, { "rank": 14, "intensity": 1.8979591836734695, "date": "2017-06-19" }, { "rank": 15, "intensity": 1.9285714285714286, "date": "2017-02-07" }, { "rank": 16, "intensity": 1.9591836734693877, "date": "2017-09-09" }, { "rank": 17, "intensity": 1.989795918367347, "date": "2017-02-10" }, { "rank": 18, "intensity": 2.020408163265306, "date": "2017-04-14" }, { "rank": 19, "intensity": 2.0510204081632653, "date": "2017-03-09" }, { "rank": 20, "intensity": 2.0816326530612246, "date": "2017-08-02" }, { "rank": 21, "intensity": 2.112244897959184, "date": "2017-09-24" }, { "rank": 22, "intensity": 2.142857142857143, "date": "2017-11-12" }, { "rank": 23, "intensity": 2.173469387755102, "date": "2017-02-20" }, { "rank": 24, "intensity": 2.204081632653061, "date": "2017-10-05" }, { "rank": 25, "intensity": 2.2346938775510203, "date": "2017-12-13" }, { "rank": 26, "intensity": 2.2653061224489797, "date": "2017-06-25" }, { "rank": 27, "intensity": 2.295918367346939, "date": "2017-05-23" }, { "rank": 28, "intensity": 2.326530612244898, "date": "2017-03-23" }, { "rank": 29, "intensity": 2.357142857142857, "date": "2017-08-25" }, { "rank": 30, "intensity": 2.387755102040816, "date": "2017-05-10" }, { "rank": 31, "intensity": 2.4183673469387754, "date": "2017-02-27" }, { "rank": 32, "intensity": 2.4489795918367347, "date": "2017-03-07" }, { "rank": 33, "intensity": 2.479591836734694, "date": "2017-12-03" }, { "rank": 34, "intensity": 2.510204081632653, "date": "2017-08-22" }, { "rank": 35, "intensity": 2.5408163265306123, "date": "2017-10-28" }, { "rank": 36, "intensity": 2.571428571428571, "date": "2017-09-04" }, { "rank": 37, "intensity": 2.6020408163265305, "date": "2017-01-02" }, { "rank": 38, "intensity": 2.63265306122449, "date": "2017-09-17" }, { "rank": 39, "intensity": 2.663265306122449, "date": "2017-07-09" }, { "rank": 40, "intensity": 2.693877551020408, "date": "2017-12-07" }, { "rank": 41, "intensity": 2.7244897959183674, "date": "2017-03-18" }, { "rank": 42, "intensity": 2.7551020408163263, "date": "2017-03-20" }, { "rank": 43, "intensity": 2.7857142857142856, "date": "2017-01-08" }, { "rank": 44, "intensity": 2.816326530612245, "date": "2017-05-06" }, { "rank": 45, "intensity": 2.8469387755102042, "date": "2017-07-06" }, { "rank": 46, "intensity": 2.877551020408163, "date": "2017-07-11" }, { "rank": 47, "intensity": 2.9081632653061225, "date": "2017-12-16" }, { "rank": 48, "intensity": 2.9387755102040813, "date": "2017-03-25" }, { "rank": 49, "intensity": 2.9693877551020407, "date": "2017-05-30" }, { "rank": 50, "intensity": 3.0, "date": "2017-04-23" } ], "source": "ettm1" } }, { "id": "L3_T1_Composite_Trend_00160", "level": 3, "level_name": "Semantic Reasoning", "category": "Composite Trend", "subtask": "Composite Trend", "question": "Among days in channel HULL during 2017 that exhibit the trend pattern 'rapid fall, then slow fall, then fluctuating stable, then slow fall', identify the top-5 days where the fluctuating stable segment is the most fluctuating. (Output format: a ranked list of dates, e.g., ['YYYY-MM-DD', ...])", "answer": "['2017-08-04', '2017-08-26', '2017-09-24', '2017-06-11', '2017-03-11']", "ground_truth": [ "2017-08-04", "2017-08-26", "2017-09-24", "2017-06-11", "2017-03-11" ], "eval_metric": "set_f1", "channel": "HULL", "ts_data_path": "ts_data/L3_T1_Composite_Trend_00160.csv", "meta": { "segments": [ { "kind": "fall", "adj": "rapid" }, { "kind": "fall", "adj": "slow" }, { "kind": "stable", "adj": "fluctuating" }, { "kind": "fall", "adj": "slow" } ], "pattern_phrase": "rapid fall, then slow fall, then fluctuating stable, then slow fall", "rank_target_idx": 2, "rank_kind": "stable", "rank_adj": "fluctuating", "rank_axis": "std", "rank_direction": "DESC", "rank_extreme": "most fluctuating", "top_segments_meta": [ { "adj": "rapid", "kind": "fall", "label": "rapid fall", "start_idx": 0, "end_idx": 11 }, { "adj": "slow", "kind": "fall", "label": "slow fall", "start_idx": 11, "end_idx": 43 }, { "adj": "fluctuating", "kind": "stable", "label": "fluctuating stable", "start_idx": 43, "end_idx": 67 }, { "adj": "slow", "kind": "fall", "label": "slow fall", "start_idx": 67, "end_idx": 98 } ], "points_per_day": 96, "pattern": "rapid fall, then slow fall, then fluctuating stable, then slow fall", "year": 2017, "top_k": [ "2017-08-04", "2017-08-26", "2017-09-24", "2017-06-11", "2017-03-11" ], "all_injections": [ { "rank": 1, "intensity": 3.0, "date": "2017-08-04" }, { "rank": 2, "intensity": 2.9693877551020407, "date": "2017-08-26" }, { "rank": 3, "intensity": 2.938775510204082, "date": "2017-09-24" }, { "rank": 4, "intensity": 2.9081632653061225, "date": "2017-06-11" }, { "rank": 5, "intensity": 2.877551020408163, "date": "2017-03-11" }, { "rank": 6, "intensity": 2.8469387755102042, "date": "2017-08-17" }, { "rank": 7, "intensity": 2.816326530612245, "date": "2017-07-22" }, { "rank": 8, "intensity": 2.7857142857142856, "date": "2017-04-12" }, { "rank": 9, "intensity": 2.7551020408163267, "date": "2017-10-12" }, { "rank": 10, "intensity": 2.7244897959183674, "date": "2017-06-18" }, { "rank": 11, "intensity": 2.693877551020408, "date": "2017-11-28" }, { "rank": 12, "intensity": 2.663265306122449, "date": "2017-03-13" }, { "rank": 13, "intensity": 2.63265306122449, "date": "2017-01-17" }, { "rank": 14, "intensity": 2.6020408163265305, "date": "2017-10-29" }, { "rank": 15, "intensity": 2.5714285714285716, "date": "2017-10-21" }, { "rank": 16, "intensity": 2.5408163265306123, "date": "2017-04-21" }, { "rank": 17, "intensity": 2.510204081632653, "date": "2017-05-12" }, { "rank": 18, "intensity": 2.479591836734694, "date": "2017-09-05" }, { "rank": 19, "intensity": 2.4489795918367347, "date": "2017-10-10" }, { "rank": 20, "intensity": 2.4183673469387754, "date": "2017-12-07" }, { "rank": 21, "intensity": 2.387755102040816, "date": "2017-10-23" }, { "rank": 22, "intensity": 2.357142857142857, "date": "2017-01-15" }, { "rank": 23, "intensity": 2.326530612244898, "date": "2017-11-22" }, { "rank": 24, "intensity": 2.295918367346939, "date": "2017-11-02" }, { "rank": 25, "intensity": 2.2653061224489797, "date": "2017-01-27" }, { "rank": 26, "intensity": 2.2346938775510203, "date": "2017-12-03" }, { "rank": 27, "intensity": 2.204081632653061, "date": "2017-02-15" }, { "rank": 28, "intensity": 2.173469387755102, "date": "2017-01-31" }, { "rank": 29, "intensity": 2.142857142857143, "date": "2017-06-20" }, { "rank": 30, "intensity": 2.112244897959184, "date": "2017-05-22" }, { "rank": 31, "intensity": 2.0816326530612246, "date": "2017-03-21" }, { "rank": 32, "intensity": 2.0510204081632653, "date": "2017-12-01" }, { "rank": 33, "intensity": 2.020408163265306, "date": "2017-07-24" }, { "rank": 34, "intensity": 1.989795918367347, "date": "2017-11-17" }, { "rank": 35, "intensity": 1.9591836734693877, "date": "2017-07-09" }, { "rank": 36, "intensity": 1.9285714285714286, "date": "2017-07-05" }, { "rank": 37, "intensity": 1.8979591836734695, "date": "2017-03-23" }, { "rank": 38, "intensity": 1.8673469387755102, "date": "2017-10-17" }, { "rank": 39, "intensity": 1.836734693877551, "date": "2017-05-19" }, { "rank": 40, "intensity": 1.806122448979592, "date": "2017-05-06" }, { "rank": 41, "intensity": 1.7755102040816326, "date": "2017-11-09" }, { "rank": 42, "intensity": 1.7448979591836735, "date": "2017-08-02" }, { "rank": 43, "intensity": 1.7142857142857144, "date": "2017-04-04" }, { "rank": 44, "intensity": 1.683673469387755, "date": "2017-09-29" }, { "rank": 45, "intensity": 1.653061224489796, "date": "2017-08-11" }, { "rank": 46, "intensity": 1.6224489795918369, "date": "2017-08-29" }, { "rank": 47, "intensity": 1.5918367346938775, "date": "2017-07-28" }, { "rank": 48, "intensity": 1.5612244897959184, "date": "2017-11-14" }, { "rank": 49, "intensity": 1.5306122448979593, "date": "2017-11-26" }, { "rank": 50, "intensity": 1.5, "date": "2017-02-24" } ], "source": "ettm1" } }, { "id": "L3_T1_Composite_Trend_00161", "level": 3, "level_name": "Semantic Reasoning", "category": "Composite Trend", "subtask": "Composite Trend", "question": "Among days in channel MUFL during 2017 that exhibit the trend pattern 'rise, then slow fall', identify the top-5 days where the slow fall segment is the slowest. (Output format: a ranked list of dates, e.g., ['YYYY-MM-DD', ...])", "answer": "['2017-06-06', '2017-10-12', '2017-07-08', '2017-10-09', '2017-10-17']", "ground_truth": [ "2017-06-06", "2017-10-12", "2017-07-08", "2017-10-09", "2017-10-17" ], "eval_metric": "set_f1", "channel": "MUFL", "ts_data_path": "ts_data/L3_T1_Composite_Trend_00161.csv", "meta": { "segments": [ { "kind": "rise", "adj": "none" }, { "kind": "fall", "adj": "slow" } ], "pattern_phrase": "rise, then slow fall", "rank_target_idx": 1, "rank_kind": "fall", "rank_adj": "slow", "rank_axis": "slope", "rank_direction": "ASC", "rank_extreme": "slowest", "top_segments_meta": [ { "adj": "none", "kind": "rise", "label": "none rise", "start_idx": 0, "end_idx": 35 }, { "adj": "slow", "kind": "fall", "label": "slow fall", "start_idx": 35, "end_idx": 98 } ], "points_per_day": 96, "pattern": "rise, then slow fall", "year": 2017, "top_k": [ "2017-06-06", "2017-10-12", "2017-07-08", "2017-10-09", "2017-10-17" ], "all_injections": [ { "rank": 1, "intensity": 1.5, "date": "2017-06-06" }, { "rank": 2, "intensity": 1.530612244897959, "date": "2017-10-12" }, { "rank": 3, "intensity": 1.5612244897959184, "date": "2017-07-08" }, { "rank": 4, "intensity": 1.5918367346938775, "date": "2017-10-09" }, { "rank": 5, "intensity": 1.6224489795918366, "date": "2017-10-17" }, { "rank": 6, "intensity": 1.653061224489796, "date": "2017-12-19" }, { "rank": 7, "intensity": 1.683673469387755, "date": "2017-03-22" }, { "rank": 8, "intensity": 1.7142857142857142, "date": "2017-10-19" }, { "rank": 9, "intensity": 1.7448979591836735, "date": "2017-06-28" }, { "rank": 10, "intensity": 1.7755102040816326, "date": "2017-03-09" }, { "rank": 11, "intensity": 1.806122448979592, "date": "2017-10-29" }, { "rank": 12, "intensity": 1.836734693877551, "date": "2017-01-10" }, { "rank": 13, "intensity": 1.8673469387755102, "date": "2017-06-03" }, { "rank": 14, "intensity": 1.8979591836734695, "date": "2017-09-14" }, { "rank": 15, "intensity": 1.9285714285714286, "date": "2017-05-07" }, { "rank": 16, "intensity": 1.9591836734693877, "date": "2017-08-27" }, { "rank": 17, "intensity": 1.989795918367347, "date": "2017-01-29" }, { "rank": 18, "intensity": 2.020408163265306, "date": "2017-10-04" }, { "rank": 19, "intensity": 2.0510204081632653, "date": "2017-06-17" }, { "rank": 20, "intensity": 2.0816326530612246, "date": "2017-12-07" }, { "rank": 21, "intensity": 2.112244897959184, "date": "2017-04-21" }, { "rank": 22, "intensity": 2.142857142857143, "date": "2017-11-23" }, { "rank": 23, "intensity": 2.173469387755102, "date": "2017-06-15" }, { "rank": 24, "intensity": 2.204081632653061, "date": "2017-02-02" }, { "rank": 25, "intensity": 2.2346938775510203, "date": "2017-11-21" }, { "rank": 26, "intensity": 2.2653061224489797, "date": "2017-07-01" }, { "rank": 27, "intensity": 2.295918367346939, "date": "2017-09-09" }, { "rank": 28, "intensity": 2.326530612244898, "date": "2017-11-29" }, { "rank": 29, "intensity": 2.357142857142857, "date": "2017-08-10" }, { "rank": 30, "intensity": 2.387755102040816, "date": "2017-01-16" }, { "rank": 31, "intensity": 2.4183673469387754, "date": "2017-02-10" }, { "rank": 32, "intensity": 2.4489795918367347, "date": "2017-02-17" }, { "rank": 33, "intensity": 2.479591836734694, "date": "2017-08-29" }, { "rank": 34, "intensity": 2.510204081632653, "date": "2017-12-28" }, { "rank": 35, "intensity": 2.5408163265306123, "date": "2017-01-25" }, { "rank": 36, "intensity": 2.571428571428571, "date": "2017-11-02" }, { "rank": 37, "intensity": 2.6020408163265305, "date": "2017-03-03" }, { "rank": 38, "intensity": 2.63265306122449, "date": "2017-11-06" }, { "rank": 39, "intensity": 2.663265306122449, "date": "2017-11-13" }, { "rank": 40, "intensity": 2.693877551020408, "date": "2017-01-01" }, { "rank": 41, "intensity": 2.7244897959183674, "date": "2017-09-22" }, { "rank": 42, "intensity": 2.7551020408163263, "date": "2017-01-27" }, { "rank": 43, "intensity": 2.7857142857142856, "date": "2017-03-11" }, { "rank": 44, "intensity": 2.816326530612245, "date": "2017-07-30" }, { "rank": 45, "intensity": 2.8469387755102042, "date": "2017-09-18" }, { "rank": 46, "intensity": 2.877551020408163, "date": "2017-05-30" }, { "rank": 47, "intensity": 2.9081632653061225, "date": "2017-08-13" }, { "rank": 48, "intensity": 2.9387755102040813, "date": "2017-05-15" }, { "rank": 49, "intensity": 2.9693877551020407, "date": "2017-07-20" }, { "rank": 50, "intensity": 3.0, "date": "2017-12-26" } ], "source": "ettm1" } }, { "id": "L3_T1_Composite_Trend_00162", "level": 3, "level_name": "Semantic Reasoning", "category": "Composite Trend", "subtask": "Composite Trend", "question": "Among days in channel MULL during 2017 that exhibit the trend pattern 'steady stable, then rapid rise', identify the top-5 days where the rapid rise segment is the fastest. (Output format: a ranked list of dates, e.g., ['YYYY-MM-DD', ...])", "answer": "['2017-01-31', '2017-07-10', '2017-01-28', '2017-01-03', '2017-02-22']", "ground_truth": [ "2017-01-31", "2017-07-10", "2017-01-28", "2017-01-03", "2017-02-22" ], "eval_metric": "set_f1", "channel": "MULL", "ts_data_path": "ts_data/L3_T1_Composite_Trend_00162.csv", "meta": { "segments": [ { "kind": "stable", "adj": "steady" }, { "kind": "rise", "adj": "rapid" } ], "pattern_phrase": "steady stable, then rapid rise", "rank_target_idx": 1, "rank_kind": "rise", "rank_adj": "rapid", "rank_axis": "slope", "rank_direction": "DESC", "rank_extreme": "fastest", "top_segments_meta": [ { "adj": "steady", "kind": "stable", "label": "steady stable", "start_idx": 0, "end_idx": 79 }, { "adj": "rapid", "kind": "rise", "label": "rapid rise", "start_idx": 79, "end_idx": 98 } ], "points_per_day": 96, "pattern": "steady stable, then rapid rise", "year": 2017, "top_k": [ "2017-01-31", "2017-07-10", "2017-01-28", "2017-01-03", "2017-02-22" ], "all_injections": [ { "rank": 1, "intensity": 3.0, "date": "2017-01-31" }, { "rank": 2, "intensity": 2.9693877551020407, "date": "2017-07-10" }, { "rank": 3, "intensity": 2.938775510204082, "date": "2017-01-28" }, { "rank": 4, "intensity": 2.9081632653061225, "date": "2017-01-03" }, { "rank": 5, "intensity": 2.877551020408163, "date": "2017-02-22" }, { "rank": 6, "intensity": 2.8469387755102042, "date": "2017-09-23" }, { "rank": 7, "intensity": 2.816326530612245, "date": "2017-08-05" }, { "rank": 8, "intensity": 2.7857142857142856, "date": "2017-06-10" }, { "rank": 9, "intensity": 2.7551020408163267, "date": "2017-08-18" }, { "rank": 10, "intensity": 2.7244897959183674, "date": "2017-11-18" }, { "rank": 11, "intensity": 2.693877551020408, "date": "2017-05-25" }, { "rank": 12, "intensity": 2.663265306122449, "date": "2017-09-11" }, { "rank": 13, "intensity": 2.63265306122449, "date": "2017-06-15" }, { "rank": 14, "intensity": 2.6020408163265305, "date": "2017-01-13" }, { "rank": 15, "intensity": 2.5714285714285716, "date": "2017-08-07" }, { "rank": 16, "intensity": 2.5408163265306123, "date": "2017-06-17" }, { "rank": 17, "intensity": 2.510204081632653, "date": "2017-11-16" }, { "rank": 18, "intensity": 2.479591836734694, "date": "2017-09-02" }, { "rank": 19, "intensity": 2.4489795918367347, "date": "2017-03-16" }, { "rank": 20, "intensity": 2.4183673469387754, "date": "2017-07-04" }, { "rank": 21, "intensity": 2.387755102040816, "date": "2017-01-10" }, { "rank": 22, "intensity": 2.357142857142857, "date": "2017-06-22" }, { "rank": 23, "intensity": 2.326530612244898, "date": "2017-04-24" }, { "rank": 24, "intensity": 2.295918367346939, "date": "2017-08-20" }, { "rank": 25, "intensity": 2.2653061224489797, "date": "2017-02-10" }, { "rank": 26, "intensity": 2.2346938775510203, "date": "2017-08-10" }, { "rank": 27, "intensity": 2.204081632653061, "date": "2017-07-29" }, { "rank": 28, "intensity": 2.173469387755102, "date": "2017-03-18" }, { "rank": 29, "intensity": 2.142857142857143, "date": "2017-12-08" }, { "rank": 30, "intensity": 2.112244897959184, "date": "2017-04-09" }, { "rank": 31, "intensity": 2.0816326530612246, "date": "2017-08-16" }, { "rank": 32, "intensity": 2.0510204081632653, "date": "2017-05-30" }, { "rank": 33, "intensity": 2.020408163265306, "date": "2017-12-29" }, { "rank": 34, "intensity": 1.989795918367347, "date": "2017-02-28" }, { "rank": 35, "intensity": 1.9591836734693877, "date": "2017-04-04" }, { "rank": 36, "intensity": 1.9285714285714286, "date": "2017-02-05" }, { "rank": 37, "intensity": 1.8979591836734695, "date": "2017-08-23" }, { "rank": 38, "intensity": 1.8673469387755102, "date": "2017-02-18" }, { "rank": 39, "intensity": 1.836734693877551, "date": "2017-11-14" }, { "rank": 40, "intensity": 1.806122448979592, "date": "2017-11-08" }, { "rank": 41, "intensity": 1.7755102040816326, "date": "2017-05-15" }, { "rank": 42, "intensity": 1.7448979591836735, "date": "2017-10-23" }, { "rank": 43, "intensity": 1.7142857142857144, "date": "2017-05-22" }, { "rank": 44, "intensity": 1.683673469387755, "date": "2017-06-12" }, { "rank": 45, "intensity": 1.653061224489796, "date": "2017-03-03" }, { "rank": 46, "intensity": 1.6224489795918369, "date": "2017-08-25" }, { "rank": 47, "intensity": 1.5918367346938775, "date": "2017-03-31" }, { "rank": 48, "intensity": 1.5612244897959184, "date": "2017-09-26" }, { "rank": 49, "intensity": 1.5306122448979593, "date": "2017-03-26" }, { "rank": 50, "intensity": 1.5, "date": "2017-10-29" } ], "source": "ettm1" } }, { "id": "L3_T1_Composite_Trend_00163", "level": 3, "level_name": "Semantic Reasoning", "category": "Composite Trend", "subtask": "Composite Trend", "question": "Among days in channel LUFL during 2017 that exhibit the trend pattern 'slow fall, then steady stable, then fall', identify the top-5 days where the slow fall segment is the slowest. (Output format: a ranked list of dates, e.g., ['YYYY-MM-DD', ...])", "answer": "['2017-12-03', '2017-05-04', '2017-05-31', '2017-01-06', '2017-03-23']", "ground_truth": [ "2017-12-03", "2017-05-04", "2017-05-31", "2017-01-06", "2017-03-23" ], "eval_metric": "set_f1", "channel": "LUFL", "ts_data_path": "ts_data/L3_T1_Composite_Trend_00163.csv", "meta": { "segments": [ { "kind": "fall", "adj": "slow" }, { "kind": "stable", "adj": "steady" }, { "kind": "fall", "adj": "none" } ], "pattern_phrase": "slow fall, then steady stable, then fall", "rank_target_idx": 0, "rank_kind": "fall", "rank_adj": "slow", "rank_axis": "slope", "rank_direction": "ASC", "rank_extreme": "slowest", "top_segments_meta": [ { "adj": "slow", "kind": "fall", "label": "slow fall", "start_idx": 0, "end_idx": 37 }, { "adj": "steady", "kind": "stable", "label": "steady stable", "start_idx": 37, "end_idx": 78 }, { "adj": "none", "kind": "fall", "label": "none fall", "start_idx": 78, "end_idx": 98 } ], "points_per_day": 96, "pattern": "slow fall, then steady stable, then fall", "year": 2017, "top_k": [ "2017-12-03", "2017-05-04", "2017-05-31", "2017-01-06", "2017-03-23" ], "all_injections": [ { "rank": 1, "intensity": 1.5, "date": "2017-12-03" }, { "rank": 2, "intensity": 1.530612244897959, "date": "2017-05-04" }, { "rank": 3, "intensity": 1.5612244897959184, "date": "2017-05-31" }, { "rank": 4, "intensity": 1.5918367346938775, "date": "2017-01-06" }, { "rank": 5, "intensity": 1.6224489795918366, "date": "2017-03-23" }, { "rank": 6, "intensity": 1.653061224489796, "date": "2017-12-01" }, { "rank": 7, "intensity": 1.683673469387755, "date": "2017-02-19" }, { "rank": 8, "intensity": 1.7142857142857142, "date": "2017-01-23" }, { "rank": 9, "intensity": 1.7448979591836735, "date": "2017-08-31" }, { "rank": 10, "intensity": 1.7755102040816326, "date": "2017-03-16" }, { "rank": 11, "intensity": 1.806122448979592, "date": "2017-08-15" }, { "rank": 12, "intensity": 1.836734693877551, "date": "2017-05-21" }, { "rank": 13, "intensity": 1.8673469387755102, "date": "2017-11-27" }, { "rank": 14, "intensity": 1.8979591836734695, "date": "2017-04-10" }, { "rank": 15, "intensity": 1.9285714285714286, "date": "2017-02-16" }, { "rank": 16, "intensity": 1.9591836734693877, "date": "2017-09-18" }, { "rank": 17, "intensity": 1.989795918367347, "date": "2017-10-22" }, { "rank": 18, "intensity": 2.020408163265306, "date": "2017-11-24" }, { "rank": 19, "intensity": 2.0510204081632653, "date": "2017-09-26" }, { "rank": 20, "intensity": 2.0816326530612246, "date": "2017-12-21" }, { "rank": 21, "intensity": 2.112244897959184, "date": "2017-11-29" }, { "rank": 22, "intensity": 2.142857142857143, "date": "2017-12-19" }, { "rank": 23, "intensity": 2.173469387755102, "date": "2017-07-29" }, { "rank": 24, "intensity": 2.204081632653061, "date": "2017-06-25" }, { "rank": 25, "intensity": 2.2346938775510203, "date": "2017-02-21" }, { "rank": 26, "intensity": 2.2653061224489797, "date": "2017-10-14" }, { "rank": 27, "intensity": 2.295918367346939, "date": "2017-03-09" }, { "rank": 28, "intensity": 2.326530612244898, "date": "2017-02-11" }, { "rank": 29, "intensity": 2.357142857142857, "date": "2017-06-10" }, { "rank": 30, "intensity": 2.387755102040816, "date": "2017-11-03" }, { "rank": 31, "intensity": 2.4183673469387754, "date": "2017-10-12" }, { "rank": 32, "intensity": 2.4489795918367347, "date": "2017-03-02" }, { "rank": 33, "intensity": 2.479591836734694, "date": "2017-08-10" }, { "rank": 34, "intensity": 2.510204081632653, "date": "2017-06-02" }, { "rank": 35, "intensity": 2.5408163265306123, "date": "2017-07-13" }, { "rank": 36, "intensity": 2.571428571428571, "date": "2017-09-11" }, { "rank": 37, "intensity": 2.6020408163265305, "date": "2017-03-11" }, { "rank": 38, "intensity": 2.63265306122449, "date": "2017-05-25" }, { "rank": 39, "intensity": 2.663265306122449, "date": "2017-06-30" }, { "rank": 40, "intensity": 2.693877551020408, "date": "2017-04-14" }, { "rank": 41, "intensity": 2.7244897959183674, "date": "2017-11-12" }, { "rank": 42, "intensity": 2.7551020408163263, "date": "2017-07-18" }, { "rank": 43, "intensity": 2.7857142857142856, "date": "2017-01-15" }, { "rank": 44, "intensity": 2.816326530612245, "date": "2017-09-30" }, { "rank": 45, "intensity": 2.8469387755102042, "date": "2017-05-29" }, { "rank": 46, "intensity": 2.877551020408163, "date": "2017-10-02" }, { "rank": 47, "intensity": 2.9081632653061225, "date": "2017-01-02" }, { "rank": 48, "intensity": 2.9387755102040813, "date": "2017-07-11" }, { "rank": 49, "intensity": 2.9693877551020407, "date": "2017-04-17" }, { "rank": 50, "intensity": 3.0, "date": "2017-08-17" } ], "source": "ettm1" } }, { "id": "L3_T1_Composite_Trend_00164", "level": 3, "level_name": "Semantic Reasoning", "category": "Composite Trend", "subtask": "Composite Trend", "question": "Among days in channel LULL during 2017 that exhibit the trend pattern 'slow fall, then rise, then rapid fall', identify the top-5 days where the rapid fall segment is the fastest. (Output format: a ranked list of dates, e.g., ['YYYY-MM-DD', ...])", "answer": "['2017-08-03', '2017-03-01', '2017-02-12', '2017-12-18', '2017-08-22']", "ground_truth": [ "2017-08-03", "2017-03-01", "2017-02-12", "2017-12-18", "2017-08-22" ], "eval_metric": "set_f1", "channel": "LULL", "ts_data_path": "ts_data/L3_T1_Composite_Trend_00164.csv", "meta": { "segments": [ { "kind": "fall", "adj": "slow" }, { "kind": "rise", "adj": "none" }, { "kind": "fall", "adj": "rapid" } ], "pattern_phrase": "slow fall, then rise, then rapid fall", "rank_target_idx": 2, "rank_kind": "fall", "rank_adj": "rapid", "rank_axis": "slope", "rank_direction": "DESC", "rank_extreme": "fastest", "top_segments_meta": [ { "adj": "slow", "kind": "fall", "label": "slow fall", "start_idx": 0, "end_idx": 52 }, { "adj": "none", "kind": "rise", "label": "none rise", "start_idx": 52, "end_idx": 83 }, { "adj": "rapid", "kind": "fall", "label": "rapid fall", "start_idx": 83, "end_idx": 98 } ], "points_per_day": 96, "pattern": "slow fall, then rise, then rapid fall", "year": 2017, "top_k": [ "2017-08-03", "2017-03-01", "2017-02-12", "2017-12-18", "2017-08-22" ], "all_injections": [ { "rank": 1, "intensity": 3.0, "date": "2017-08-03" }, { "rank": 2, "intensity": 2.9693877551020407, "date": "2017-03-01" }, { "rank": 3, "intensity": 2.938775510204082, "date": "2017-02-12" }, { "rank": 4, "intensity": 2.9081632653061225, "date": "2017-12-18" }, { "rank": 5, "intensity": 2.877551020408163, "date": "2017-08-22" }, { "rank": 6, "intensity": 2.8469387755102042, "date": "2017-12-01" }, { "rank": 7, "intensity": 2.816326530612245, "date": "2017-04-07" }, { "rank": 8, "intensity": 2.7857142857142856, "date": "2017-01-02" }, { "rank": 9, "intensity": 2.7551020408163267, "date": "2017-01-29" }, { "rank": 10, "intensity": 2.7244897959183674, "date": "2017-12-05" }, { "rank": 11, "intensity": 2.693877551020408, "date": "2017-01-27" }, { "rank": 12, "intensity": 2.663265306122449, "date": "2017-04-09" }, { "rank": 13, "intensity": 2.63265306122449, "date": "2017-06-25" }, { "rank": 14, "intensity": 2.6020408163265305, "date": "2017-02-02" }, { "rank": 15, "intensity": 2.5714285714285716, "date": "2017-05-20" }, { "rank": 16, "intensity": 2.5408163265306123, "date": "2017-09-20" }, { "rank": 17, "intensity": 2.510204081632653, "date": "2017-05-25" }, { "rank": 18, "intensity": 2.479591836734694, "date": "2017-05-22" }, { "rank": 19, "intensity": 2.4489795918367347, "date": "2017-11-07" }, { "rank": 20, "intensity": 2.4183673469387754, "date": "2017-11-14" }, { "rank": 21, "intensity": 2.387755102040816, "date": "2017-06-20" }, { "rank": 22, "intensity": 2.357142857142857, "date": "2017-05-14" }, { "rank": 23, "intensity": 2.326530612244898, "date": "2017-12-20" }, { "rank": 24, "intensity": 2.295918367346939, "date": "2017-01-21" }, { "rank": 25, "intensity": 2.2653061224489797, "date": "2017-02-27" }, { "rank": 26, "intensity": 2.2346938775510203, "date": "2017-11-09" }, { "rank": 27, "intensity": 2.204081632653061, "date": "2017-05-07" }, { "rank": 28, "intensity": 2.173469387755102, "date": "2017-12-25" }, { "rank": 29, "intensity": 2.142857142857143, "date": "2017-08-10" }, { "rank": 30, "intensity": 2.112244897959184, "date": "2017-06-06" }, { "rank": 31, "intensity": 2.0816326530612246, "date": "2017-09-17" }, { "rank": 32, "intensity": 2.0510204081632653, "date": "2017-10-24" }, { "rank": 33, "intensity": 2.020408163265306, "date": "2017-11-03" }, { "rank": 34, "intensity": 1.989795918367347, "date": "2017-03-05" }, { "rank": 35, "intensity": 1.9591836734693877, "date": "2017-09-09" }, { "rank": 36, "intensity": 1.9285714285714286, "date": "2017-12-30" }, { "rank": 37, "intensity": 1.8979591836734695, "date": "2017-04-24" }, { "rank": 38, "intensity": 1.8673469387755102, "date": "2017-06-28" }, { "rank": 39, "intensity": 1.836734693877551, "date": "2017-06-14" }, { "rank": 40, "intensity": 1.806122448979592, "date": "2017-09-03" }, { "rank": 41, "intensity": 1.7755102040816326, "date": "2017-07-10" }, { "rank": 42, "intensity": 1.7448979591836735, "date": "2017-06-10" }, { "rank": 43, "intensity": 1.7142857142857144, "date": "2017-08-27" }, { "rank": 44, "intensity": 1.683673469387755, "date": "2017-06-03" }, { "rank": 45, "intensity": 1.653061224489796, "date": "2017-03-09" }, { "rank": 46, "intensity": 1.6224489795918369, "date": "2017-07-26" }, { "rank": 47, "intensity": 1.5918367346938775, "date": "2017-02-04" }, { "rank": 48, "intensity": 1.5612244897959184, "date": "2017-12-12" }, { "rank": 49, "intensity": 1.5306122448979593, "date": "2017-01-11" }, { "rank": 50, "intensity": 1.5, "date": "2017-09-14" } ], "source": "ettm1" } }, { "id": "L3_T1_Composite_Trend_00165", "level": 3, "level_name": "Semantic Reasoning", "category": "Composite Trend", "subtask": "Composite Trend", "question": "Among days in channel OT during 2017 that exhibit the trend pattern 'slow fall, then rise', identify the top-5 days where the slow fall segment is the slowest. (Output format: a ranked list of dates, e.g., ['YYYY-MM-DD', ...])", "answer": "['2017-08-02', '2017-08-07', '2017-11-06', '2017-09-15', '2017-01-01']", "ground_truth": [ "2017-08-02", "2017-08-07", "2017-11-06", "2017-09-15", "2017-01-01" ], "eval_metric": "set_f1", "channel": "OT", "ts_data_path": "ts_data/L3_T1_Composite_Trend_00165.csv", "meta": { "segments": [ { "kind": "fall", "adj": "slow" }, { "kind": "rise", "adj": "none" } ], "pattern_phrase": "slow fall, then rise", "rank_target_idx": 0, "rank_kind": "fall", "rank_adj": "slow", "rank_axis": "slope", "rank_direction": "ASC", "rank_extreme": "slowest", "top_segments_meta": [ { "adj": "slow", "kind": "fall", "label": "slow fall", "start_idx": 0, "end_idx": 64 }, { "adj": "none", "kind": "rise", "label": "none rise", "start_idx": 64, "end_idx": 98 } ], "points_per_day": 96, "pattern": "slow fall, then rise", "year": 2017, "top_k": [ "2017-08-02", "2017-08-07", "2017-11-06", "2017-09-15", "2017-01-01" ], "all_injections": [ { "rank": 1, "intensity": 1.5, "date": "2017-08-02" }, { "rank": 2, "intensity": 1.530612244897959, "date": "2017-08-07" }, { "rank": 3, "intensity": 1.5612244897959184, "date": "2017-11-06" }, { "rank": 4, "intensity": 1.5918367346938775, "date": "2017-09-15" }, { "rank": 5, "intensity": 1.6224489795918366, "date": "2017-01-01" }, { "rank": 6, "intensity": 1.653061224489796, "date": "2017-07-09" }, { "rank": 7, "intensity": 1.683673469387755, "date": "2017-03-14" }, { "rank": 8, "intensity": 1.7142857142857142, "date": "2017-08-20" }, { "rank": 9, "intensity": 1.7448979591836735, "date": "2017-06-21" }, { "rank": 10, "intensity": 1.7755102040816326, "date": "2017-09-05" }, { "rank": 11, "intensity": 1.806122448979592, "date": "2017-01-11" }, { "rank": 12, "intensity": 1.836734693877551, "date": "2017-04-16" }, { "rank": 13, "intensity": 1.8673469387755102, "date": "2017-07-27" }, { "rank": 14, "intensity": 1.8979591836734695, "date": "2017-03-26" }, { "rank": 15, "intensity": 1.9285714285714286, "date": "2017-04-30" }, { "rank": 16, "intensity": 1.9591836734693877, "date": "2017-10-25" }, { "rank": 17, "intensity": 1.989795918367347, "date": "2017-02-04" }, { "rank": 18, "intensity": 2.020408163265306, "date": "2017-08-23" }, { "rank": 19, "intensity": 2.0510204081632653, "date": "2017-08-11" }, { "rank": 20, "intensity": 2.0816326530612246, "date": "2017-07-01" }, { "rank": 21, "intensity": 2.112244897959184, "date": "2017-05-25" }, { "rank": 22, "intensity": 2.142857142857143, "date": "2017-03-06" }, { "rank": 23, "intensity": 2.173469387755102, "date": "2017-10-16" }, { "rank": 24, "intensity": 2.204081632653061, "date": "2017-10-22" }, { "rank": 25, "intensity": 2.2346938775510203, "date": "2017-10-07" }, { "rank": 26, "intensity": 2.2653061224489797, "date": "2017-06-11" }, { "rank": 27, "intensity": 2.295918367346939, "date": "2017-05-23" }, { "rank": 28, "intensity": 2.326530612244898, "date": "2017-12-20" }, { "rank": 29, "intensity": 2.357142857142857, "date": "2017-05-10" }, { "rank": 30, "intensity": 2.387755102040816, "date": "2017-10-03" }, { "rank": 31, "intensity": 2.4183673469387754, "date": "2017-05-04" }, { "rank": 32, "intensity": 2.4489795918367347, "date": "2017-01-31" }, { "rank": 33, "intensity": 2.479591836734694, "date": "2017-02-20" }, { "rank": 34, "intensity": 2.510204081632653, "date": "2017-04-03" }, { "rank": 35, "intensity": 2.5408163265306123, "date": "2017-11-25" }, { "rank": 36, "intensity": 2.571428571428571, "date": "2017-08-13" }, { "rank": 37, "intensity": 2.6020408163265305, "date": "2017-11-14" }, { "rank": 38, "intensity": 2.63265306122449, "date": "2017-04-07" }, { "rank": 39, "intensity": 2.663265306122449, "date": "2017-11-01" }, { "rank": 40, "intensity": 2.693877551020408, "date": "2017-09-20" }, { "rank": 41, "intensity": 2.7244897959183674, "date": "2017-09-30" }, { "rank": 42, "intensity": 2.7551020408163263, "date": "2017-09-02" }, { "rank": 43, "intensity": 2.7857142857142856, "date": "2017-08-26" }, { "rank": 44, "intensity": 2.816326530612245, "date": "2017-03-28" }, { "rank": 45, "intensity": 2.8469387755102042, "date": "2017-12-28" }, { "rank": 46, "intensity": 2.877551020408163, "date": "2017-11-09" }, { "rank": 47, "intensity": 2.9081632653061225, "date": "2017-05-16" }, { "rank": 48, "intensity": 2.9387755102040813, "date": "2017-02-28" }, { "rank": 49, "intensity": 2.9693877551020407, "date": "2017-04-11" }, { "rank": 50, "intensity": 3.0, "date": "2017-01-16" } ], "source": "ettm1" } }, { "id": "L3_T1_Composite_Trend_00166", "level": 3, "level_name": "Semantic Reasoning", "category": "Composite Trend", "subtask": "Composite Trend", "question": "Among days in channel 67 during 2022 that exhibit the trend pattern 'fall, then slow fall, then rapid rise', identify the top-5 days where the rapid rise segment is the fastest. (Output format: a ranked list of dates, e.g., ['YYYY-MM-DD', ...])", "answer": "['2022-11-27', '2022-10-02', '2022-09-20', '2022-01-13', '2022-12-19']", "ground_truth": [ "2022-11-27", "2022-10-02", "2022-09-20", "2022-01-13", "2022-12-19" ], "eval_metric": "set_f1", "channel": "67", "ts_data_path": "ts_data/L3_T1_Composite_Trend_00166.csv", "meta": { "segments": [ { "kind": "fall", "adj": "none" }, { "kind": "fall", "adj": "slow" }, { "kind": "rise", "adj": "rapid" } ], "pattern_phrase": "fall, then slow fall, then rapid rise", "rank_target_idx": 2, "rank_kind": "rise", "rank_adj": "rapid", "rank_axis": "slope", "rank_direction": "DESC", "rank_extreme": "fastest", "top_segments_meta": [ { "adj": "none", "kind": "fall", "label": "none fall", "start_idx": 0, "end_idx": 30 }, { "adj": "slow", "kind": "fall", "label": "slow fall", "start_idx": 30, "end_idx": 81 }, { "adj": "rapid", "kind": "rise", "label": "rapid rise", "start_idx": 81, "end_idx": 98 } ], "points_per_day": 96, "pattern": "fall, then slow fall, then rapid rise", "year": 2022, "top_k": [ "2022-11-27", "2022-10-02", "2022-09-20", "2022-01-13", "2022-12-19" ], "all_injections": [ { "rank": 1, "intensity": 3.0, "date": "2022-11-27" }, { "rank": 2, "intensity": 2.9693877551020407, "date": "2022-10-02" }, { "rank": 3, "intensity": 2.938775510204082, "date": "2022-09-20" }, { "rank": 4, "intensity": 2.9081632653061225, "date": "2022-01-13" }, { "rank": 5, "intensity": 2.877551020408163, "date": "2022-12-19" }, { "rank": 6, "intensity": 2.8469387755102042, "date": "2022-01-03" }, { "rank": 7, "intensity": 2.816326530612245, "date": "2022-06-14" }, { "rank": 8, "intensity": 2.7857142857142856, "date": "2022-10-22" }, { "rank": 9, "intensity": 2.7551020408163267, "date": "2022-11-04" }, { "rank": 10, "intensity": 2.7244897959183674, "date": "2022-01-22" }, { "rank": 11, "intensity": 2.693877551020408, "date": "2022-12-30" }, { "rank": 12, "intensity": 2.663265306122449, "date": "2022-07-28" }, { "rank": 13, "intensity": 2.63265306122449, "date": "2022-05-22" }, { "rank": 14, "intensity": 2.6020408163265305, "date": "2022-08-19" }, { "rank": 15, "intensity": 2.5714285714285716, "date": "2022-07-19" }, { "rank": 16, "intensity": 2.5408163265306123, "date": "2022-03-27" }, { "rank": 17, "intensity": 2.510204081632653, "date": "2022-11-17" }, { "rank": 18, "intensity": 2.479591836734694, "date": "2022-12-25" }, { "rank": 19, "intensity": 2.4489795918367347, "date": "2022-10-10" }, { "rank": 20, "intensity": 2.4183673469387754, "date": "2022-02-28" }, { "rank": 21, "intensity": 2.387755102040816, "date": "2022-08-31" }, { "rank": 22, "intensity": 2.357142857142857, "date": "2022-11-25" }, { "rank": 23, "intensity": 2.326530612244898, "date": "2022-08-11" }, { "rank": 24, "intensity": 2.295918367346939, "date": "2022-09-06" }, { "rank": 25, "intensity": 2.2653061224489797, "date": "2022-09-24" }, { "rank": 26, "intensity": 2.2346938775510203, "date": "2022-08-22" }, { "rank": 27, "intensity": 2.204081632653061, "date": "2022-07-30" }, { "rank": 28, "intensity": 2.173469387755102, "date": "2022-11-13" }, { "rank": 29, "intensity": 2.142857142857143, "date": "2022-10-16" }, { "rank": 30, "intensity": 2.112244897959184, "date": "2022-01-01" }, { "rank": 31, "intensity": 2.0816326530612246, "date": "2022-06-11" }, { "rank": 32, "intensity": 2.0510204081632653, "date": "2022-10-08" }, { "rank": 33, "intensity": 2.020408163265306, "date": "2022-08-01" }, { "rank": 34, "intensity": 1.989795918367347, "date": "2022-07-09" }, { "rank": 35, "intensity": 1.9591836734693877, "date": "2022-04-06" }, { "rank": 36, "intensity": 1.9285714285714286, "date": "2022-12-03" }, { "rank": 37, "intensity": 1.8979591836734695, "date": "2022-02-16" }, { "rank": 38, "intensity": 1.8673469387755102, "date": "2022-11-08" }, { "rank": 39, "intensity": 1.836734693877551, "date": "2022-05-09" }, { "rank": 40, "intensity": 1.806122448979592, "date": "2022-01-09" }, { "rank": 41, "intensity": 1.7755102040816326, "date": "2022-12-01" }, { "rank": 42, "intensity": 1.7448979591836735, "date": "2022-05-12" }, { "rank": 43, "intensity": 1.7142857142857144, "date": "2022-08-04" }, { "rank": 44, "intensity": 1.683673469387755, "date": "2022-03-17" }, { "rank": 45, "intensity": 1.653061224489796, "date": "2022-02-20" }, { "rank": 46, "intensity": 1.6224489795918369, "date": "2022-09-14" }, { "rank": 47, "intensity": 1.5918367346938775, "date": "2022-05-04" }, { "rank": 48, "intensity": 1.5612244897959184, "date": "2022-12-05" }, { "rank": 49, "intensity": 1.5306122448979593, "date": "2022-11-29" }, { "rank": 50, "intensity": 1.5, "date": "2022-09-27" } ], "source": "causal_rivers" } }, { "id": "L3_T1_Composite_Trend_00167", "level": 3, "level_name": "Semantic Reasoning", "category": "Composite Trend", "subtask": "Composite Trend", "question": "Among days in channel 71 during 2022 that exhibit the trend pattern 'steady stable, then fluctuating stable, then rapid fall, then slow fall', identify the top-5 days where the slow fall segment is the slowest. (Output format: a ranked list of dates, e.g., ['YYYY-MM-DD', ...])", "answer": "['2022-09-18', '2022-12-30', '2022-11-01', '2022-04-24', '2022-05-05']", "ground_truth": [ "2022-09-18", "2022-12-30", "2022-11-01", "2022-04-24", "2022-05-05" ], "eval_metric": "set_f1", "channel": "71", "ts_data_path": "ts_data/L3_T1_Composite_Trend_00167.csv", "meta": { "segments": [ { "kind": "stable", "adj": "steady" }, { "kind": "stable", "adj": "fluctuating" }, { "kind": "fall", "adj": "rapid" }, { "kind": "fall", "adj": "slow" } ], "pattern_phrase": "steady stable, then fluctuating stable, then rapid fall, then slow fall", "rank_target_idx": 3, "rank_kind": "fall", "rank_adj": "slow", "rank_axis": "slope", "rank_direction": "ASC", "rank_extreme": "slowest", "top_segments_meta": [ { "adj": "steady", "kind": "stable", "label": "steady stable", "start_idx": 0, "end_idx": 35 }, { "adj": "fluctuating", "kind": "stable", "label": "fluctuating stable", "start_idx": 35, "end_idx": 59 }, { "adj": "rapid", "kind": "fall", "label": "rapid fall", "start_idx": 59, "end_idx": 68 }, { "adj": "slow", "kind": "fall", "label": "slow fall", "start_idx": 68, "end_idx": 98 } ], "points_per_day": 96, "pattern": "steady stable, then fluctuating stable, then rapid fall, then slow fall", "year": 2022, "top_k": [ "2022-09-18", "2022-12-30", "2022-11-01", "2022-04-24", "2022-05-05" ], "all_injections": [ { "rank": 1, "intensity": 1.5, "date": "2022-09-18" }, { "rank": 2, "intensity": 1.530612244897959, "date": "2022-12-30" }, { "rank": 3, "intensity": 1.5612244897959184, "date": "2022-11-01" }, { "rank": 4, "intensity": 1.5918367346938775, "date": "2022-04-24" }, { "rank": 5, "intensity": 1.6224489795918366, "date": "2022-05-05" }, { "rank": 6, "intensity": 1.653061224489796, "date": "2022-08-02" }, { "rank": 7, "intensity": 1.683673469387755, "date": "2022-12-19" }, { "rank": 8, "intensity": 1.7142857142857142, "date": "2022-05-10" }, { "rank": 9, "intensity": 1.7448979591836735, "date": "2022-09-10" }, { "rank": 10, "intensity": 1.7755102040816326, "date": "2022-10-23" }, { "rank": 11, "intensity": 1.806122448979592, "date": "2022-08-23" }, { "rank": 12, "intensity": 1.836734693877551, "date": "2022-08-07" }, { "rank": 13, "intensity": 1.8673469387755102, "date": "2022-01-07" }, { "rank": 14, "intensity": 1.8979591836734695, "date": "2022-06-18" }, { "rank": 15, "intensity": 1.9285714285714286, "date": "2022-04-28" }, { "rank": 16, "intensity": 1.9591836734693877, "date": "2022-04-12" }, { "rank": 17, "intensity": 1.989795918367347, "date": "2022-06-09" }, { "rank": 18, "intensity": 2.020408163265306, "date": "2022-05-07" }, { "rank": 19, "intensity": 2.0510204081632653, "date": "2022-11-10" }, { "rank": 20, "intensity": 2.0816326530612246, "date": "2022-04-22" }, { "rank": 21, "intensity": 2.112244897959184, "date": "2022-01-04" }, { "rank": 22, "intensity": 2.142857142857143, "date": "2022-08-30" }, { "rank": 23, "intensity": 2.173469387755102, "date": "2022-03-11" }, { "rank": 24, "intensity": 2.204081632653061, "date": "2022-02-23" }, { "rank": 25, "intensity": 2.2346938775510203, "date": "2022-11-05" }, { "rank": 26, "intensity": 2.2653061224489797, "date": "2022-01-18" }, { "rank": 27, "intensity": 2.295918367346939, "date": "2022-07-17" }, { "rank": 28, "intensity": 2.326530612244898, "date": "2022-12-16" }, { "rank": 29, "intensity": 2.357142857142857, "date": "2022-10-19" }, { "rank": 30, "intensity": 2.387755102040816, "date": "2022-06-02" }, { "rank": 31, "intensity": 2.4183673469387754, "date": "2022-09-01" }, { "rank": 32, "intensity": 2.4489795918367347, "date": "2022-08-21" }, { "rank": 33, "intensity": 2.479591836734694, "date": "2022-05-16" }, { "rank": 34, "intensity": 2.510204081632653, "date": "2022-09-28" }, { "rank": 35, "intensity": 2.5408163265306123, "date": "2022-04-14" }, { "rank": 36, "intensity": 2.571428571428571, "date": "2022-10-29" }, { "rank": 37, "intensity": 2.6020408163265305, "date": "2022-03-15" }, { "rank": 38, "intensity": 2.63265306122449, "date": "2022-11-26" }, { "rank": 39, "intensity": 2.663265306122449, "date": "2022-03-03" }, { "rank": 40, "intensity": 2.693877551020408, "date": "2022-09-05" }, { "rank": 41, "intensity": 2.7244897959183674, "date": "2022-11-20" }, { "rank": 42, "intensity": 2.7551020408163263, "date": "2022-10-21" }, { "rank": 43, "intensity": 2.7857142857142856, "date": "2022-03-30" }, { "rank": 44, "intensity": 2.816326530612245, "date": "2022-10-15" }, { "rank": 45, "intensity": 2.8469387755102042, "date": "2022-04-03" }, { "rank": 46, "intensity": 2.877551020408163, "date": "2022-05-27" }, { "rank": 47, "intensity": 2.9081632653061225, "date": "2022-02-14" }, { "rank": 48, "intensity": 2.9387755102040813, "date": "2022-01-29" }, { "rank": 49, "intensity": 2.9693877551020407, "date": "2022-01-16" }, { "rank": 50, "intensity": 3.0, "date": "2022-01-20" } ], "source": "causal_rivers" } }, { "id": "L3_T1_Composite_Trend_00168", "level": 3, "level_name": "Semantic Reasoning", "category": "Composite Trend", "subtask": "Composite Trend", "question": "Among days in channel 99 during 2021 that exhibit the trend pattern 'slow fall, then rapid fall', identify the top-5 days where the rapid fall segment is the fastest. (Output format: a ranked list of dates, e.g., ['YYYY-MM-DD', ...])", "answer": "['2021-01-22', '2021-07-02', '2021-01-12', '2021-05-27', '2021-10-27']", "ground_truth": [ "2021-01-22", "2021-07-02", "2021-01-12", "2021-05-27", "2021-10-27" ], "eval_metric": "set_f1", "channel": "99", "ts_data_path": "ts_data/L3_T1_Composite_Trend_00168.csv", "meta": { "segments": [ { "kind": "fall", "adj": "slow" }, { "kind": "fall", "adj": "rapid" } ], "pattern_phrase": "slow fall, then rapid fall", "rank_target_idx": 1, "rank_kind": "fall", "rank_adj": "rapid", "rank_axis": "slope", "rank_direction": "DESC", "rank_extreme": "fastest", "top_segments_meta": [ { "adj": "slow", "kind": "fall", "label": "slow fall", "start_idx": 0, "end_idx": 76 }, { "adj": "rapid", "kind": "fall", "label": "rapid fall", "start_idx": 76, "end_idx": 98 } ], "points_per_day": 96, "pattern": "slow fall, then rapid fall", "year": 2021, "top_k": [ "2021-01-22", "2021-07-02", "2021-01-12", "2021-05-27", "2021-10-27" ], "all_injections": [ { "rank": 1, "intensity": 3.0, "date": "2021-01-22" }, { "rank": 2, "intensity": 2.9693877551020407, "date": "2021-07-02" }, { "rank": 3, "intensity": 2.938775510204082, "date": "2021-01-12" }, { "rank": 4, "intensity": 2.9081632653061225, "date": "2021-05-27" }, { "rank": 5, "intensity": 2.877551020408163, "date": "2021-10-27" }, { "rank": 6, "intensity": 2.8469387755102042, "date": "2021-06-08" }, { "rank": 7, "intensity": 2.816326530612245, "date": "2021-06-01" }, { "rank": 8, "intensity": 2.7857142857142856, "date": "2021-12-05" }, { "rank": 9, "intensity": 2.7551020408163267, "date": "2021-12-10" }, { "rank": 10, "intensity": 2.7244897959183674, "date": "2021-07-23" }, { "rank": 11, "intensity": 2.693877551020408, "date": "2021-05-15" }, { "rank": 12, "intensity": 2.663265306122449, "date": "2021-11-19" }, { "rank": 13, "intensity": 2.63265306122449, "date": "2021-08-01" }, { "rank": 14, "intensity": 2.6020408163265305, "date": "2021-04-20" }, { "rank": 15, "intensity": 2.5714285714285716, "date": "2021-11-06" }, { "rank": 16, "intensity": 2.5408163265306123, "date": "2021-01-18" }, { "rank": 17, "intensity": 2.510204081632653, "date": "2021-04-08" }, { "rank": 18, "intensity": 2.479591836734694, "date": "2021-11-27" }, { "rank": 19, "intensity": 2.4489795918367347, "date": "2021-06-23" }, { "rank": 20, "intensity": 2.4183673469387754, "date": "2021-07-27" }, { "rank": 21, "intensity": 2.387755102040816, "date": "2021-03-15" }, { "rank": 22, "intensity": 2.357142857142857, "date": "2021-10-03" }, { "rank": 23, "intensity": 2.326530612244898, "date": "2021-04-24" }, { "rank": 24, "intensity": 2.295918367346939, "date": "2021-02-27" }, { "rank": 25, "intensity": 2.2653061224489797, "date": "2021-11-03" }, { "rank": 26, "intensity": 2.2346938775510203, "date": "2021-11-30" }, { "rank": 27, "intensity": 2.204081632653061, "date": "2021-02-21" }, { "rank": 28, "intensity": 2.173469387755102, "date": "2021-04-27" }, { "rank": 29, "intensity": 2.142857142857143, "date": "2021-01-03" }, { "rank": 30, "intensity": 2.112244897959184, "date": "2021-10-22" }, { "rank": 31, "intensity": 2.0816326530612246, "date": "2021-01-27" }, { "rank": 32, "intensity": 2.0510204081632653, "date": "2021-12-22" }, { "rank": 33, "intensity": 2.020408163265306, "date": "2021-03-20" }, { "rank": 34, "intensity": 1.989795918367347, "date": "2021-04-29" }, { "rank": 35, "intensity": 1.9591836734693877, "date": "2021-09-18" }, { "rank": 36, "intensity": 1.9285714285714286, "date": "2021-09-25" }, { "rank": 37, "intensity": 1.8979591836734695, "date": "2021-09-12" }, { "rank": 38, "intensity": 1.8673469387755102, "date": "2021-11-24" }, { "rank": 39, "intensity": 1.836734693877551, "date": "2021-09-23" }, { "rank": 40, "intensity": 1.806122448979592, "date": "2021-10-20" }, { "rank": 41, "intensity": 1.7755102040816326, "date": "2021-09-16" }, { "rank": 42, "intensity": 1.7448979591836735, "date": "2021-09-03" }, { "rank": 43, "intensity": 1.7142857142857144, "date": "2021-09-01" }, { "rank": 44, "intensity": 1.683673469387755, "date": "2021-12-14" }, { "rank": 45, "intensity": 1.653061224489796, "date": "2021-05-23" }, { "rank": 46, "intensity": 1.6224489795918369, "date": "2021-06-29" }, { "rank": 47, "intensity": 1.5918367346938775, "date": "2021-06-19" }, { "rank": 48, "intensity": 1.5612244897959184, "date": "2021-03-11" }, { "rank": 49, "intensity": 1.5306122448979593, "date": "2021-12-24" }, { "rank": 50, "intensity": 1.5, "date": "2021-08-16" } ], "source": "causal_rivers" } }, { "id": "L3_T1_Composite_Trend_00169", "level": 3, "level_name": "Semantic Reasoning", "category": "Composite Trend", "subtask": "Composite Trend", "question": "Among days in channel 123 during 2020 that exhibit the trend pattern 'slow rise, then slow fall', identify the top-5 days where the slow fall segment is the slowest. (Output format: a ranked list of dates, e.g., ['YYYY-MM-DD', ...])", "answer": "['2020-08-17', '2020-08-03', '2020-08-27', '2020-03-23', '2020-01-19']", "ground_truth": [ "2020-08-17", "2020-08-03", "2020-08-27", "2020-03-23", "2020-01-19" ], "eval_metric": "set_f1", "channel": "123", "ts_data_path": "ts_data/L3_T1_Composite_Trend_00169.csv", "meta": { "segments": [ { "kind": "rise", "adj": "slow" }, { "kind": "fall", "adj": "slow" } ], "pattern_phrase": "slow rise, then slow fall", "rank_target_idx": 1, "rank_kind": "fall", "rank_adj": "slow", "rank_axis": "slope", "rank_direction": "ASC", "rank_extreme": "slowest", "top_segments_meta": [ { "adj": "slow", "kind": "rise", "label": "slow rise", "start_idx": 0, "end_idx": 49 }, { "adj": "slow", "kind": "fall", "label": "slow fall", "start_idx": 49, "end_idx": 98 } ], "points_per_day": 96, "pattern": "slow rise, then slow fall", "year": 2020, "top_k": [ "2020-08-17", "2020-08-03", "2020-08-27", "2020-03-23", "2020-01-19" ], "all_injections": [ { "rank": 1, "intensity": 1.5, "date": "2020-08-17" }, { "rank": 2, "intensity": 1.530612244897959, "date": "2020-08-03" }, { "rank": 3, "intensity": 1.5612244897959184, "date": "2020-08-27" }, { "rank": 4, "intensity": 1.5918367346938775, "date": "2020-03-23" }, { "rank": 5, "intensity": 1.6224489795918366, "date": "2020-01-19" }, { "rank": 6, "intensity": 1.653061224489796, "date": "2020-11-20" }, { "rank": 7, "intensity": 1.683673469387755, "date": "2020-11-22" }, { "rank": 8, "intensity": 1.7142857142857142, "date": "2020-01-02" }, { "rank": 9, "intensity": 1.7448979591836735, "date": "2020-10-31" }, { "rank": 10, "intensity": 1.7755102040816326, "date": "2020-06-23" }, { "rank": 11, "intensity": 1.806122448979592, "date": "2020-10-14" }, { "rank": 12, "intensity": 1.836734693877551, "date": "2020-03-20" }, { "rank": 13, "intensity": 1.8673469387755102, "date": "2020-06-27" }, { "rank": 14, "intensity": 1.8979591836734695, "date": "2020-04-05" }, { "rank": 15, "intensity": 1.9285714285714286, "date": "2020-02-23" }, { "rank": 16, "intensity": 1.9591836734693877, "date": "2020-01-22" }, { "rank": 17, "intensity": 1.989795918367347, "date": "2020-12-06" }, { "rank": 18, "intensity": 2.020408163265306, "date": "2020-08-13" }, { "rank": 19, "intensity": 2.0510204081632653, "date": "2020-02-20" }, { "rank": 20, "intensity": 2.0816326530612246, "date": "2020-04-16" }, { "rank": 21, "intensity": 2.112244897959184, "date": "2020-05-23" }, { "rank": 22, "intensity": 2.142857142857143, "date": "2020-02-02" }, { "rank": 23, "intensity": 2.173469387755102, "date": "2020-04-07" }, { "rank": 24, "intensity": 2.204081632653061, "date": "2020-04-13" }, { "rank": 25, "intensity": 2.2346938775510203, "date": "2020-12-19" }, { "rank": 26, "intensity": 2.2653061224489797, "date": "2020-06-08" }, { "rank": 27, "intensity": 2.295918367346939, "date": "2020-12-14" }, { "rank": 28, "intensity": 2.326530612244898, "date": "2020-04-27" }, { "rank": 29, "intensity": 2.357142857142857, "date": "2020-03-17" }, { "rank": 30, "intensity": 2.387755102040816, "date": "2020-09-18" }, { "rank": 31, "intensity": 2.4183673469387754, "date": "2020-04-18" }, { "rank": 32, "intensity": 2.4489795918367347, "date": "2020-01-15" }, { "rank": 33, "intensity": 2.479591836734694, "date": "2020-10-27" }, { "rank": 34, "intensity": 2.510204081632653, "date": "2020-02-26" }, { "rank": 35, "intensity": 2.5408163265306123, "date": "2020-02-28" }, { "rank": 36, "intensity": 2.571428571428571, "date": "2020-12-08" }, { "rank": 37, "intensity": 2.6020408163265305, "date": "2020-11-29" }, { "rank": 38, "intensity": 2.63265306122449, "date": "2020-09-05" }, { "rank": 39, "intensity": 2.663265306122449, "date": "2020-09-08" }, { "rank": 40, "intensity": 2.693877551020408, "date": "2020-12-22" }, { "rank": 41, "intensity": 2.7244897959183674, "date": "2020-08-09" }, { "rank": 42, "intensity": 2.7551020408163263, "date": "2020-03-12" }, { "rank": 43, "intensity": 2.7857142857142856, "date": "2020-11-09" }, { "rank": 44, "intensity": 2.816326530612245, "date": "2020-10-03" }, { "rank": 45, "intensity": 2.8469387755102042, "date": "2020-05-13" }, { "rank": 46, "intensity": 2.877551020408163, "date": "2020-06-15" }, { "rank": 47, "intensity": 2.9081632653061225, "date": "2020-12-29" }, { "rank": 48, "intensity": 2.9387755102040813, "date": "2020-04-22" }, { "rank": 49, "intensity": 2.9693877551020407, "date": "2020-02-07" }, { "rank": 50, "intensity": 3.0, "date": "2020-01-11" } ], "source": "causal_rivers" } }, { "id": "L3_T1_Composite_Trend_00170", "level": 3, "level_name": "Semantic Reasoning", "category": "Composite Trend", "subtask": "Composite Trend", "question": "Among days in channel 124 during 2023 that exhibit the trend pattern 'slow rise, then fall', identify the top-5 days where the slow rise segment is the slowest. (Output format: a ranked list of dates, e.g., ['YYYY-MM-DD', ...])", "answer": "['2023-05-27', '2023-07-10', '2023-03-06', '2023-01-24', '2023-12-10']", "ground_truth": [ "2023-05-27", "2023-07-10", "2023-03-06", "2023-01-24", "2023-12-10" ], "eval_metric": "set_f1", "channel": "124", "ts_data_path": "ts_data/L3_T1_Composite_Trend_00170.csv", "meta": { "segments": [ { "kind": "rise", "adj": "slow" }, { "kind": "fall", "adj": "none" } ], "pattern_phrase": "slow rise, then fall", "rank_target_idx": 0, "rank_kind": "rise", "rank_adj": "slow", "rank_axis": "slope", "rank_direction": "ASC", "rank_extreme": "slowest", "top_segments_meta": [ { "adj": "slow", "kind": "rise", "label": "slow rise", "start_idx": 0, "end_idx": 61 }, { "adj": "none", "kind": "fall", "label": "none fall", "start_idx": 61, "end_idx": 98 } ], "points_per_day": 96, "pattern": "slow rise, then fall", "year": 2023, "top_k": [ "2023-05-27", "2023-07-10", "2023-03-06", "2023-01-24", "2023-12-10" ], "all_injections": [ { "rank": 1, "intensity": 1.5, "date": "2023-05-27" }, { "rank": 2, "intensity": 1.530612244897959, "date": "2023-07-10" }, { "rank": 3, "intensity": 1.5612244897959184, "date": "2023-03-06" }, { "rank": 4, "intensity": 1.5918367346938775, "date": "2023-01-24" }, { "rank": 5, "intensity": 1.6224489795918366, "date": "2023-12-10" }, { "rank": 6, "intensity": 1.653061224489796, "date": "2023-07-15" }, { "rank": 7, "intensity": 1.683673469387755, "date": "2023-07-17" }, { "rank": 8, "intensity": 1.7142857142857142, "date": "2023-11-29" }, { "rank": 9, "intensity": 1.7448979591836735, "date": "2023-11-22" }, { "rank": 10, "intensity": 1.7755102040816326, "date": "2023-06-07" }, { "rank": 11, "intensity": 1.806122448979592, "date": "2023-11-27" }, { "rank": 12, "intensity": 1.836734693877551, "date": "2023-08-16" }, { "rank": 13, "intensity": 1.8673469387755102, "date": "2023-09-06" }, { "rank": 14, "intensity": 1.8979591836734695, "date": "2023-02-07" }, { "rank": 15, "intensity": 1.9285714285714286, "date": "2023-07-12" }, { "rank": 16, "intensity": 1.9591836734693877, "date": "2023-01-31" }, { "rank": 17, "intensity": 1.989795918367347, "date": "2023-05-16" }, { "rank": 18, "intensity": 2.020408163265306, "date": "2023-05-20" }, { "rank": 19, "intensity": 2.0510204081632653, "date": "2023-05-23" }, { "rank": 20, "intensity": 2.0816326530612246, "date": "2023-11-13" }, { "rank": 21, "intensity": 2.112244897959184, "date": "2023-12-12" }, { "rank": 22, "intensity": 2.142857142857143, "date": "2023-05-02" }, { "rank": 23, "intensity": 2.173469387755102, "date": "2023-07-04" }, { "rank": 24, "intensity": 2.204081632653061, "date": "2023-10-04" }, { "rank": 25, "intensity": 2.2346938775510203, "date": "2023-12-25" }, { "rank": 26, "intensity": 2.2653061224489797, "date": "2023-04-09" }, { "rank": 27, "intensity": 2.295918367346939, "date": "2023-09-30" }, { "rank": 28, "intensity": 2.326530612244898, "date": "2023-12-28" }, { "rank": 29, "intensity": 2.357142857142857, "date": "2023-01-04" }, { "rank": 30, "intensity": 2.387755102040816, "date": "2023-11-16" }, { "rank": 31, "intensity": 2.4183673469387754, "date": "2023-03-17" }, { "rank": 32, "intensity": 2.4489795918367347, "date": "2023-06-27" }, { "rank": 33, "intensity": 2.479591836734694, "date": "2023-05-05" }, { "rank": 34, "intensity": 2.510204081632653, "date": "2023-09-19" }, { "rank": 35, "intensity": 2.5408163265306123, "date": "2023-12-01" }, { "rank": 36, "intensity": 2.571428571428571, "date": "2023-04-03" }, { "rank": 37, "intensity": 2.6020408163265305, "date": "2023-09-25" }, { "rank": 38, "intensity": 2.63265306122449, "date": "2023-05-10" }, { "rank": 39, "intensity": 2.663265306122449, "date": "2023-10-09" }, { "rank": 40, "intensity": 2.693877551020408, "date": "2023-01-19" }, { "rank": 41, "intensity": 2.7244897959183674, "date": "2023-11-09" }, { "rank": 42, "intensity": 2.7551020408163263, "date": "2023-09-17" }, { "rank": 43, "intensity": 2.7857142857142856, "date": "2023-02-21" }, { "rank": 44, "intensity": 2.816326530612245, "date": "2023-10-24" }, { "rank": 45, "intensity": 2.8469387755102042, "date": "2023-07-08" }, { "rank": 46, "intensity": 2.877551020408163, "date": "2023-02-15" }, { "rank": 47, "intensity": 2.9081632653061225, "date": "2023-06-29" }, { "rank": 48, "intensity": 2.9387755102040813, "date": "2023-01-08" }, { "rank": 49, "intensity": 2.9693877551020407, "date": "2023-09-09" }, { "rank": 50, "intensity": 3.0, "date": "2023-10-17" } ], "source": "causal_rivers" } }, { "id": "L3_T1_Composite_Trend_00171", "level": 3, "level_name": "Semantic Reasoning", "category": "Composite Trend", "subtask": "Composite Trend", "question": "Among days in channel 146 during 2020 that exhibit the trend pattern 'slow rise, then slow fall, then rapid fall', identify the top-5 days where the rapid fall segment is the fastest. (Output format: a ranked list of dates, e.g., ['YYYY-MM-DD', ...])", "answer": "['2020-08-28', '2020-11-01', '2020-01-05', '2020-07-05', '2020-07-11']", "ground_truth": [ "2020-08-28", "2020-11-01", "2020-01-05", "2020-07-05", "2020-07-11" ], "eval_metric": "set_f1", "channel": "146", "ts_data_path": "ts_data/L3_T1_Composite_Trend_00171.csv", "meta": { "segments": [ { "kind": "rise", "adj": "slow" }, { "kind": "fall", "adj": "slow" }, { "kind": "fall", "adj": "rapid" } ], "pattern_phrase": "slow rise, then slow fall, then rapid fall", "rank_target_idx": 2, "rank_kind": "fall", "rank_adj": "rapid", "rank_axis": "slope", "rank_direction": "DESC", "rank_extreme": "fastest", "top_segments_meta": [ { "adj": "slow", "kind": "rise", "label": "slow rise", "start_idx": 0, "end_idx": 42 }, { "adj": "slow", "kind": "fall", "label": "slow fall", "start_idx": 42, "end_idx": 85 }, { "adj": "rapid", "kind": "fall", "label": "rapid fall", "start_idx": 85, "end_idx": 98 } ], "points_per_day": 96, "pattern": "slow rise, then slow fall, then rapid fall", "year": 2020, "top_k": [ "2020-08-28", "2020-11-01", "2020-01-05", "2020-07-05", "2020-07-11" ], "all_injections": [ { "rank": 1, "intensity": 3.0, "date": "2020-08-28" }, { "rank": 2, "intensity": 2.9693877551020407, "date": "2020-11-01" }, { "rank": 3, "intensity": 2.938775510204082, "date": "2020-01-05" }, { "rank": 4, "intensity": 2.9081632653061225, "date": "2020-07-05" }, { "rank": 5, "intensity": 2.877551020408163, "date": "2020-07-11" }, { "rank": 6, "intensity": 2.8469387755102042, "date": "2020-01-12" }, { "rank": 7, "intensity": 2.816326530612245, "date": "2020-11-05" }, { "rank": 8, "intensity": 2.7857142857142856, "date": "2020-04-18" }, { "rank": 9, "intensity": 2.7551020408163267, "date": "2020-09-15" }, { "rank": 10, "intensity": 2.7244897959183674, "date": "2020-11-29" }, { "rank": 11, "intensity": 2.693877551020408, "date": "2020-10-30" }, { "rank": 12, "intensity": 2.663265306122449, "date": "2020-03-15" }, { "rank": 13, "intensity": 2.63265306122449, "date": "2020-11-17" }, { "rank": 14, "intensity": 2.6020408163265305, "date": "2020-01-10" }, { "rank": 15, "intensity": 2.5714285714285716, "date": "2020-09-02" }, { "rank": 16, "intensity": 2.5408163265306123, "date": "2020-06-23" }, { "rank": 17, "intensity": 2.510204081632653, "date": "2020-04-29" }, { "rank": 18, "intensity": 2.479591836734694, "date": "2020-05-19" }, { "rank": 19, "intensity": 2.4489795918367347, "date": "2020-02-27" }, { "rank": 20, "intensity": 2.4183673469387754, "date": "2020-01-16" }, { "rank": 21, "intensity": 2.387755102040816, "date": "2020-01-01" }, { "rank": 22, "intensity": 2.357142857142857, "date": "2020-08-21" }, { "rank": 23, "intensity": 2.326530612244898, "date": "2020-10-12" }, { "rank": 24, "intensity": 2.295918367346939, "date": "2020-05-13" }, { "rank": 25, "intensity": 2.2653061224489797, "date": "2020-04-15" }, { "rank": 26, "intensity": 2.2346938775510203, "date": "2020-04-24" }, { "rank": 27, "intensity": 2.204081632653061, "date": "2020-06-08" }, { "rank": 28, "intensity": 2.173469387755102, "date": "2020-07-16" }, { "rank": 29, "intensity": 2.142857142857143, "date": "2020-04-07" }, { "rank": 30, "intensity": 2.112244897959184, "date": "2020-11-09" }, { "rank": 31, "intensity": 2.0816326530612246, "date": "2020-06-21" }, { "rank": 32, "intensity": 2.0510204081632653, "date": "2020-12-08" }, { "rank": 33, "intensity": 2.020408163265306, "date": "2020-07-08" }, { "rank": 34, "intensity": 1.989795918367347, "date": "2020-01-31" }, { "rank": 35, "intensity": 1.9591836734693877, "date": "2020-12-12" }, { "rank": 36, "intensity": 1.9285714285714286, "date": "2020-03-27" }, { "rank": 37, "intensity": 1.8979591836734695, "date": "2020-10-23" }, { "rank": 38, "intensity": 1.8673469387755102, "date": "2020-08-03" }, { "rank": 39, "intensity": 1.836734693877551, "date": "2020-08-11" }, { "rank": 40, "intensity": 1.806122448979592, "date": "2020-01-25" }, { "rank": 41, "intensity": 1.7755102040816326, "date": "2020-06-18" }, { "rank": 42, "intensity": 1.7448979591836735, "date": "2020-10-27" }, { "rank": 43, "intensity": 1.7142857142857144, "date": "2020-02-10" }, { "rank": 44, "intensity": 1.683673469387755, "date": "2020-11-13" }, { "rank": 45, "intensity": 1.653061224489796, "date": "2020-06-12" }, { "rank": 46, "intensity": 1.6224489795918369, "date": "2020-11-11" }, { "rank": 47, "intensity": 1.5918367346938775, "date": "2020-01-23" }, { "rank": 48, "intensity": 1.5612244897959184, "date": "2020-08-18" }, { "rank": 49, "intensity": 1.5306122448979593, "date": "2020-10-19" }, { "rank": 50, "intensity": 1.5, "date": "2020-07-28" } ], "source": "causal_rivers" } }, { "id": "L3_T1_Composite_Trend_00172", "level": 3, "level_name": "Semantic Reasoning", "category": "Composite Trend", "subtask": "Composite Trend", "question": "Among days in channel 147 during 2019 that exhibit the trend pattern 'fluctuating stable, then rapid rise, then fall', identify the top-5 days where the rapid rise segment is the fastest. (Output format: a ranked list of dates, e.g., ['YYYY-MM-DD', ...])", "answer": "['2019-03-01', '2019-08-25', '2019-07-12', '2019-07-30', '2019-10-29']", "ground_truth": [ "2019-03-01", "2019-08-25", "2019-07-12", "2019-07-30", "2019-10-29" ], "eval_metric": "set_f1", "channel": "147", "ts_data_path": "ts_data/L3_T1_Composite_Trend_00172.csv", "meta": { "segments": [ { "kind": "stable", "adj": "fluctuating" }, { "kind": "rise", "adj": "rapid" }, { "kind": "fall", "adj": "none" } ], "pattern_phrase": "fluctuating stable, then rapid rise, then fall", "rank_target_idx": 1, "rank_kind": "rise", "rank_adj": "rapid", "rank_axis": "slope", "rank_direction": "DESC", "rank_extreme": "fastest", "top_segments_meta": [ { "adj": "fluctuating", "kind": "stable", "label": "fluctuating stable", "start_idx": 0, "end_idx": 46 }, { "adj": "rapid", "kind": "rise", "label": "rapid rise", "start_idx": 46, "end_idx": 64 }, { "adj": "none", "kind": "fall", "label": "none fall", "start_idx": 64, "end_idx": 98 } ], "points_per_day": 96, "pattern": "fluctuating stable, then rapid rise, then fall", "year": 2019, "top_k": [ "2019-03-01", "2019-08-25", "2019-07-12", "2019-07-30", "2019-10-29" ], "all_injections": [ { "rank": 1, "intensity": 3.0, "date": "2019-03-01" }, { "rank": 2, "intensity": 2.9693877551020407, "date": "2019-08-25" }, { "rank": 3, "intensity": 2.938775510204082, "date": "2019-07-12" }, { "rank": 4, "intensity": 2.9081632653061225, "date": "2019-07-30" }, { "rank": 5, "intensity": 2.877551020408163, "date": "2019-10-29" }, { "rank": 6, "intensity": 2.8469387755102042, "date": "2019-08-15" }, { "rank": 7, "intensity": 2.816326530612245, "date": "2019-05-20" }, { "rank": 8, "intensity": 2.7857142857142856, "date": "2019-05-05" }, { "rank": 9, "intensity": 2.7551020408163267, "date": "2019-11-24" }, { "rank": 10, "intensity": 2.7244897959183674, "date": "2019-08-05" }, { "rank": 11, "intensity": 2.693877551020408, "date": "2019-09-09" }, { "rank": 12, "intensity": 2.663265306122449, "date": "2019-05-10" }, { "rank": 13, "intensity": 2.63265306122449, "date": "2019-12-03" }, { "rank": 14, "intensity": 2.6020408163265305, "date": "2019-02-14" }, { "rank": 15, "intensity": 2.5714285714285716, "date": "2019-03-23" }, { "rank": 16, "intensity": 2.5408163265306123, "date": "2019-11-13" }, { "rank": 17, "intensity": 2.510204081632653, "date": "2019-09-21" }, { "rank": 18, "intensity": 2.479591836734694, "date": "2019-12-15" }, { "rank": 19, "intensity": 2.4489795918367347, "date": "2019-09-24" }, { "rank": 20, "intensity": 2.4183673469387754, "date": "2019-03-11" }, { "rank": 21, "intensity": 2.387755102040816, "date": "2019-03-25" }, { "rank": 22, "intensity": 2.357142857142857, "date": "2019-08-23" }, { "rank": 23, "intensity": 2.326530612244898, "date": "2019-08-13" }, { "rank": 24, "intensity": 2.295918367346939, "date": "2019-04-28" }, { "rank": 25, "intensity": 2.2653061224489797, "date": "2019-12-25" }, { "rank": 26, "intensity": 2.2346938775510203, "date": "2019-09-28" }, { "rank": 27, "intensity": 2.204081632653061, "date": "2019-10-25" }, { "rank": 28, "intensity": 2.173469387755102, "date": "2019-08-21" }, { "rank": 29, "intensity": 2.142857142857143, "date": "2019-11-06" }, { "rank": 30, "intensity": 2.112244897959184, "date": "2019-11-21" }, { "rank": 31, "intensity": 2.0816326530612246, "date": "2019-04-11" }, { "rank": 32, "intensity": 2.0510204081632653, "date": "2019-06-12" }, { "rank": 33, "intensity": 2.020408163265306, "date": "2019-01-04" }, { "rank": 34, "intensity": 1.989795918367347, "date": "2019-10-15" }, { "rank": 35, "intensity": 1.9591836734693877, "date": "2019-04-20" }, { "rank": 36, "intensity": 1.9285714285714286, "date": "2019-04-07" }, { "rank": 37, "intensity": 1.8979591836734695, "date": "2019-08-10" }, { "rank": 38, "intensity": 1.8673469387755102, "date": "2019-04-15" }, { "rank": 39, "intensity": 1.836734693877551, "date": "2019-07-16" }, { "rank": 40, "intensity": 1.806122448979592, "date": "2019-06-17" }, { "rank": 41, "intensity": 1.7755102040816326, "date": "2019-07-04" }, { "rank": 42, "intensity": 1.7448979591836735, "date": "2019-01-18" }, { "rank": 43, "intensity": 1.7142857142857144, "date": "2019-02-21" }, { "rank": 44, "intensity": 1.683673469387755, "date": "2019-01-29" }, { "rank": 45, "intensity": 1.653061224489796, "date": "2019-12-10" }, { "rank": 46, "intensity": 1.6224489795918369, "date": "2019-02-18" }, { "rank": 47, "intensity": 1.5918367346938775, "date": "2019-02-12" }, { "rank": 48, "intensity": 1.5612244897959184, "date": "2019-07-01" }, { "rank": 49, "intensity": 1.5306122448979593, "date": "2019-08-08" }, { "rank": 50, "intensity": 1.5, "date": "2019-09-19" } ], "source": "causal_rivers" } }, { "id": "L3_T1_Composite_Trend_00173", "level": 3, "level_name": "Semantic Reasoning", "category": "Composite Trend", "subtask": "Composite Trend", "question": "Among days in channel 151 during 2022 that exhibit the trend pattern 'rapid fall, then steady stable, then slow fall', identify the top-5 days where the rapid fall segment is the fastest. (Output format: a ranked list of dates, e.g., ['YYYY-MM-DD', ...])", "answer": "['2022-07-24', '2022-09-19', '2022-03-22', '2022-06-11', '2022-02-01']", "ground_truth": [ "2022-07-24", "2022-09-19", "2022-03-22", "2022-06-11", "2022-02-01" ], "eval_metric": "set_f1", "channel": "151", "ts_data_path": "ts_data/L3_T1_Composite_Trend_00173.csv", "meta": { "segments": [ { "kind": "fall", "adj": "rapid" }, { "kind": "stable", "adj": "steady" }, { "kind": "fall", "adj": "slow" } ], "pattern_phrase": "rapid fall, then steady stable, then slow fall", "rank_target_idx": 0, "rank_kind": "fall", "rank_adj": "rapid", "rank_axis": "slope", "rank_direction": "DESC", "rank_extreme": "fastest", "top_segments_meta": [ { "adj": "rapid", "kind": "fall", "label": "rapid fall", "start_idx": 0, "end_idx": 12 }, { "adj": "steady", "kind": "stable", "label": "steady stable", "start_idx": 12, "end_idx": 61 }, { "adj": "slow", "kind": "fall", "label": "slow fall", "start_idx": 61, "end_idx": 98 } ], "points_per_day": 96, "pattern": "rapid fall, then steady stable, then slow fall", "year": 2022, "top_k": [ "2022-07-24", "2022-09-19", "2022-03-22", "2022-06-11", "2022-02-01" ], "all_injections": [ { "rank": 1, "intensity": 3.0, "date": "2022-07-24" }, { "rank": 2, "intensity": 2.9693877551020407, "date": "2022-09-19" }, { "rank": 3, "intensity": 2.938775510204082, "date": "2022-03-22" }, { "rank": 4, "intensity": 2.9081632653061225, "date": "2022-06-11" }, { "rank": 5, "intensity": 2.877551020408163, "date": "2022-02-01" }, { "rank": 6, "intensity": 2.8469387755102042, "date": "2022-11-26" }, { "rank": 7, "intensity": 2.816326530612245, "date": "2022-03-10" }, { "rank": 8, "intensity": 2.7857142857142856, "date": "2022-05-05" }, { "rank": 9, "intensity": 2.7551020408163267, "date": "2022-01-15" }, { "rank": 10, "intensity": 2.7244897959183674, "date": "2022-03-02" }, { "rank": 11, "intensity": 2.693877551020408, "date": "2022-11-03" }, { "rank": 12, "intensity": 2.663265306122449, "date": "2022-12-28" }, { "rank": 13, "intensity": 2.63265306122449, "date": "2022-06-20" }, { "rank": 14, "intensity": 2.6020408163265305, "date": "2022-09-16" }, { "rank": 15, "intensity": 2.5714285714285716, "date": "2022-01-11" }, { "rank": 16, "intensity": 2.5408163265306123, "date": "2022-09-25" }, { "rank": 17, "intensity": 2.510204081632653, "date": "2022-03-19" }, { "rank": 18, "intensity": 2.479591836734694, "date": "2022-07-10" }, { "rank": 19, "intensity": 2.4489795918367347, "date": "2022-10-10" }, { "rank": 20, "intensity": 2.4183673469387754, "date": "2022-04-05" }, { "rank": 21, "intensity": 2.387755102040816, "date": "2022-05-13" }, { "rank": 22, "intensity": 2.357142857142857, "date": "2022-09-10" }, { "rank": 23, "intensity": 2.326530612244898, "date": "2022-09-22" }, { "rank": 24, "intensity": 2.295918367346939, "date": "2022-11-12" }, { "rank": 25, "intensity": 2.2653061224489797, "date": "2022-05-11" }, { "rank": 26, "intensity": 2.2346938775510203, "date": "2022-03-16" }, { "rank": 27, "intensity": 2.204081632653061, "date": "2022-09-27" }, { "rank": 28, "intensity": 2.173469387755102, "date": "2022-01-28" }, { "rank": 29, "intensity": 2.142857142857143, "date": "2022-12-08" }, { "rank": 30, "intensity": 2.112244897959184, "date": "2022-10-28" }, { "rank": 31, "intensity": 2.0816326530612246, "date": "2022-11-09" }, { "rank": 32, "intensity": 2.0510204081632653, "date": "2022-08-18" }, { "rank": 33, "intensity": 2.020408163265306, "date": "2022-06-05" }, { "rank": 34, "intensity": 1.989795918367347, "date": "2022-03-14" }, { "rank": 35, "intensity": 1.9591836734693877, "date": "2022-07-28" }, { "rank": 36, "intensity": 1.9285714285714286, "date": "2022-10-23" }, { "rank": 37, "intensity": 1.8979591836734695, "date": "2022-02-11" }, { "rank": 38, "intensity": 1.8673469387755102, "date": "2022-05-15" }, { "rank": 39, "intensity": 1.836734693877551, "date": "2022-01-25" }, { "rank": 40, "intensity": 1.806122448979592, "date": "2022-04-12" }, { "rank": 41, "intensity": 1.7755102040816326, "date": "2022-02-24" }, { "rank": 42, "intensity": 1.7448979591836735, "date": "2022-02-26" }, { "rank": 43, "intensity": 1.7142857142857144, "date": "2022-12-03" }, { "rank": 44, "intensity": 1.683673469387755, "date": "2022-07-19" }, { "rank": 45, "intensity": 1.653061224489796, "date": "2022-03-30" }, { "rank": 46, "intensity": 1.6224489795918369, "date": "2022-01-22" }, { "rank": 47, "intensity": 1.5918367346938775, "date": "2022-04-18" }, { "rank": 48, "intensity": 1.5612244897959184, "date": "2022-01-04" }, { "rank": 49, "intensity": 1.5306122448979593, "date": "2022-10-30" }, { "rank": 50, "intensity": 1.5, "date": "2022-12-18" } ], "source": "causal_rivers" } }, { "id": "L3_T1_Composite_Trend_00174", "level": 3, "level_name": "Semantic Reasoning", "category": "Composite Trend", "subtask": "Composite Trend", "question": "Among days in channel 154 during 2020 that exhibit the trend pattern 'fall, then rapid rise, then rise, then fall', identify the top-5 days where the rapid rise segment is the fastest. (Output format: a ranked list of dates, e.g., ['YYYY-MM-DD', ...])", "answer": "['2020-09-21', '2020-05-23', '2020-01-25', '2020-12-30', '2020-11-28']", "ground_truth": [ "2020-09-21", "2020-05-23", "2020-01-25", "2020-12-30", "2020-11-28" ], "eval_metric": "set_f1", "channel": "154", "ts_data_path": "ts_data/L3_T1_Composite_Trend_00174.csv", "meta": { "segments": [ { "kind": "fall", "adj": "none" }, { "kind": "rise", "adj": "rapid" }, { "kind": "rise", "adj": "none" }, { "kind": "fall", "adj": "none" } ], "pattern_phrase": "fall, then rapid rise, then rise, then fall", "rank_target_idx": 1, "rank_kind": "rise", "rank_adj": "rapid", "rank_axis": "slope", "rank_direction": "DESC", "rank_extreme": "fastest", "top_segments_meta": [ { "adj": "none", "kind": "fall", "label": "none fall", "start_idx": 0, "end_idx": 28 }, { "adj": "rapid", "kind": "rise", "label": "rapid rise", "start_idx": 28, "end_idx": 41 }, { "adj": "none", "kind": "rise", "label": "none rise", "start_idx": 41, "end_idx": 69 }, { "adj": "none", "kind": "fall", "label": "none fall", "start_idx": 69, "end_idx": 98 } ], "points_per_day": 96, "pattern": "fall, then rapid rise, then rise, then fall", "year": 2020, "top_k": [ "2020-09-21", "2020-05-23", "2020-01-25", "2020-12-30", "2020-11-28" ], "all_injections": [ { "rank": 1, "intensity": 3.0, "date": "2020-09-21" }, { "rank": 2, "intensity": 2.9693877551020407, "date": "2020-05-23" }, { "rank": 3, "intensity": 2.938775510204082, "date": "2020-01-25" }, { "rank": 4, "intensity": 2.9081632653061225, "date": "2020-12-30" }, { "rank": 5, "intensity": 2.877551020408163, "date": "2020-11-28" }, { "rank": 6, "intensity": 2.8469387755102042, "date": "2020-06-24" }, { "rank": 7, "intensity": 2.816326530612245, "date": "2020-07-04" }, { "rank": 8, "intensity": 2.7857142857142856, "date": "2020-11-04" }, { "rank": 9, "intensity": 2.7551020408163267, "date": "2020-08-26" }, { "rank": 10, "intensity": 2.7244897959183674, "date": "2020-10-10" }, { "rank": 11, "intensity": 2.693877551020408, "date": "2020-03-19" }, { "rank": 12, "intensity": 2.663265306122449, "date": "2020-01-20" }, { "rank": 13, "intensity": 2.63265306122449, "date": "2020-01-31" }, { "rank": 14, "intensity": 2.6020408163265305, "date": "2020-08-11" }, { "rank": 15, "intensity": 2.5714285714285716, "date": "2020-06-02" }, { "rank": 16, "intensity": 2.5408163265306123, "date": "2020-10-19" }, { "rank": 17, "intensity": 2.510204081632653, "date": "2020-08-20" }, { "rank": 18, "intensity": 2.479591836734694, "date": "2020-12-24" }, { "rank": 19, "intensity": 2.4489795918367347, "date": "2020-04-18" }, { "rank": 20, "intensity": 2.4183673469387754, "date": "2020-08-17" }, { "rank": 21, "intensity": 2.387755102040816, "date": "2020-03-14" }, { "rank": 22, "intensity": 2.357142857142857, "date": "2020-11-25" }, { "rank": 23, "intensity": 2.326530612244898, "date": "2020-08-30" }, { "rank": 24, "intensity": 2.295918367346939, "date": "2020-02-04" }, { "rank": 25, "intensity": 2.2653061224489797, "date": "2020-12-26" }, { "rank": 26, "intensity": 2.2346938775510203, "date": "2020-04-24" }, { "rank": 27, "intensity": 2.204081632653061, "date": "2020-03-06" }, { "rank": 28, "intensity": 2.173469387755102, "date": "2020-04-07" }, { "rank": 29, "intensity": 2.142857142857143, "date": "2020-05-27" }, { "rank": 30, "intensity": 2.112244897959184, "date": "2020-12-10" }, { "rank": 31, "intensity": 2.0816326530612246, "date": "2020-10-25" }, { "rank": 32, "intensity": 2.0510204081632653, "date": "2020-05-02" }, { "rank": 33, "intensity": 2.020408163265306, "date": "2020-10-13" }, { "rank": 34, "intensity": 1.989795918367347, "date": "2020-02-20" }, { "rank": 35, "intensity": 1.9591836734693877, "date": "2020-02-10" }, { "rank": 36, "intensity": 1.9285714285714286, "date": "2020-09-16" }, { "rank": 37, "intensity": 1.8979591836734695, "date": "2020-12-07" }, { "rank": 38, "intensity": 1.8673469387755102, "date": "2020-01-23" }, { "rank": 39, "intensity": 1.836734693877551, "date": "2020-08-02" }, { "rank": 40, "intensity": 1.806122448979592, "date": "2020-07-20" }, { "rank": 41, "intensity": 1.7755102040816326, "date": "2020-07-01" }, { "rank": 42, "intensity": 1.7448979591836735, "date": "2020-10-17" }, { "rank": 43, "intensity": 1.7142857142857144, "date": "2020-07-13" }, { "rank": 44, "intensity": 1.683673469387755, "date": "2020-11-14" }, { "rank": 45, "intensity": 1.653061224489796, "date": "2020-04-04" }, { "rank": 46, "intensity": 1.6224489795918369, "date": "2020-05-17" }, { "rank": 47, "intensity": 1.5918367346938775, "date": "2020-07-18" }, { "rank": 48, "intensity": 1.5612244897959184, "date": "2020-05-25" }, { "rank": 49, "intensity": 1.5306122448979593, "date": "2020-05-12" }, { "rank": 50, "intensity": 1.5, "date": "2020-12-04" } ], "source": "causal_rivers" } }, { "id": "L3_T1_Composite_Trend_00175", "level": 3, "level_name": "Semantic Reasoning", "category": "Composite Trend", "subtask": "Composite Trend", "question": "Among days in channel 155 during 2023 that exhibit the trend pattern 'steady stable, then rise, then slow fall', identify the top-5 days where the slow fall segment is the slowest. (Output format: a ranked list of dates, e.g., ['YYYY-MM-DD', ...])", "answer": "['2023-10-08', '2023-11-21', '2023-09-30', '2023-01-05', '2023-01-15']", "ground_truth": [ "2023-10-08", "2023-11-21", "2023-09-30", "2023-01-05", "2023-01-15" ], "eval_metric": "set_f1", "channel": "155", "ts_data_path": "ts_data/L3_T1_Composite_Trend_00175.csv", "meta": { "segments": [ { "kind": "stable", "adj": "steady" }, { "kind": "rise", "adj": "none" }, { "kind": "fall", "adj": "slow" } ], "pattern_phrase": "steady stable, then rise, then slow fall", "rank_target_idx": 2, "rank_kind": "fall", "rank_adj": "slow", "rank_axis": "slope", "rank_direction": "ASC", "rank_extreme": "slowest", "top_segments_meta": [ { "adj": "steady", "kind": "stable", "label": "steady stable", "start_idx": 0, "end_idx": 42 }, { "adj": "none", "kind": "rise", "label": "none rise", "start_idx": 42, "end_idx": 62 }, { "adj": "slow", "kind": "fall", "label": "slow fall", "start_idx": 62, "end_idx": 98 } ], "points_per_day": 96, "pattern": "steady stable, then rise, then slow fall", "year": 2023, "top_k": [ "2023-10-08", "2023-11-21", "2023-09-30", "2023-01-05", "2023-01-15" ], "all_injections": [ { "rank": 1, "intensity": 1.5, "date": "2023-10-08" }, { "rank": 2, "intensity": 1.530612244897959, "date": "2023-11-21" }, { "rank": 3, "intensity": 1.5612244897959184, "date": "2023-09-30" }, { "rank": 4, "intensity": 1.5918367346938775, "date": "2023-01-05" }, { "rank": 5, "intensity": 1.6224489795918366, "date": "2023-01-15" }, { "rank": 6, "intensity": 1.653061224489796, "date": "2023-07-13" }, { "rank": 7, "intensity": 1.683673469387755, "date": "2023-01-11" }, { "rank": 8, "intensity": 1.7142857142857142, "date": "2023-11-01" }, { "rank": 9, "intensity": 1.7448979591836735, "date": "2023-08-25" }, { "rank": 10, "intensity": 1.7755102040816326, "date": "2023-02-04" }, { "rank": 11, "intensity": 1.806122448979592, "date": "2023-01-13" }, { "rank": 12, "intensity": 1.836734693877551, "date": "2023-11-26" }, { "rank": 13, "intensity": 1.8673469387755102, "date": "2023-11-15" }, { "rank": 14, "intensity": 1.8979591836734695, "date": "2023-08-04" }, { "rank": 15, "intensity": 1.9285714285714286, "date": "2023-12-14" }, { "rank": 16, "intensity": 1.9591836734693877, "date": "2023-12-25" }, { "rank": 17, "intensity": 1.989795918367347, "date": "2023-04-01" }, { "rank": 18, "intensity": 2.020408163265306, "date": "2023-01-29" }, { "rank": 19, "intensity": 2.0510204081632653, "date": "2023-09-25" }, { "rank": 20, "intensity": 2.0816326530612246, "date": "2023-08-15" }, { "rank": 21, "intensity": 2.112244897959184, "date": "2023-02-24" }, { "rank": 22, "intensity": 2.142857142857143, "date": "2023-06-15" }, { "rank": 23, "intensity": 2.173469387755102, "date": "2023-06-04" }, { "rank": 24, "intensity": 2.204081632653061, "date": "2023-08-17" }, { "rank": 25, "intensity": 2.2346938775510203, "date": "2023-10-18" }, { "rank": 26, "intensity": 2.2653061224489797, "date": "2023-07-27" }, { "rank": 27, "intensity": 2.295918367346939, "date": "2023-06-19" }, { "rank": 28, "intensity": 2.326530612244898, "date": "2023-04-28" }, { "rank": 29, "intensity": 2.357142857142857, "date": "2023-09-10" }, { "rank": 30, "intensity": 2.387755102040816, "date": "2023-11-18" }, { "rank": 31, "intensity": 2.4183673469387754, "date": "2023-07-04" }, { "rank": 32, "intensity": 2.4489795918367347, "date": "2023-12-22" }, { "rank": 33, "intensity": 2.479591836734694, "date": "2023-08-01" }, { "rank": 34, "intensity": 2.510204081632653, "date": "2023-10-02" }, { "rank": 35, "intensity": 2.5408163265306123, "date": "2023-05-08" }, { "rank": 36, "intensity": 2.571428571428571, "date": "2023-09-16" }, { "rank": 37, "intensity": 2.6020408163265305, "date": "2023-05-27" }, { "rank": 38, "intensity": 2.63265306122449, "date": "2023-10-24" }, { "rank": 39, "intensity": 2.663265306122449, "date": "2023-09-12" }, { "rank": 40, "intensity": 2.693877551020408, "date": "2023-01-25" }, { "rank": 41, "intensity": 2.7244897959183674, "date": "2023-10-21" }, { "rank": 42, "intensity": 2.7551020408163263, "date": "2023-01-03" }, { "rank": 43, "intensity": 2.7857142857142856, "date": "2023-10-05" }, { "rank": 44, "intensity": 2.816326530612245, "date": "2023-04-08" }, { "rank": 45, "intensity": 2.8469387755102042, "date": "2023-05-11" }, { "rank": 46, "intensity": 2.877551020408163, "date": "2023-03-05" }, { "rank": 47, "intensity": 2.9081632653061225, "date": "2023-06-12" }, { "rank": 48, "intensity": 2.9387755102040813, "date": "2023-03-21" }, { "rank": 49, "intensity": 2.9693877551020407, "date": "2023-07-16" }, { "rank": 50, "intensity": 3.0, "date": "2023-12-04" } ], "source": "causal_rivers" } }, { "id": "L3_T1_Composite_Trend_00176", "level": 3, "level_name": "Semantic Reasoning", "category": "Composite Trend", "subtask": "Composite Trend", "question": "Among days in channel 166 during 2021 that exhibit the trend pattern 'fluctuating stable, then slow fall, then fluctuating stable', identify the top-5 days where the slow fall segment is the slowest. (Output format: a ranked list of dates, e.g., ['YYYY-MM-DD', ...])", "answer": "['2021-05-11', '2021-03-17', '2021-06-13', '2021-07-21', '2021-03-31']", "ground_truth": [ "2021-05-11", "2021-03-17", "2021-06-13", "2021-07-21", "2021-03-31" ], "eval_metric": "set_f1", "channel": "166", "ts_data_path": "ts_data/L3_T1_Composite_Trend_00176.csv", "meta": { "segments": [ { "kind": "stable", "adj": "fluctuating" }, { "kind": "fall", "adj": "slow" }, { "kind": "stable", "adj": "fluctuating" } ], "pattern_phrase": "fluctuating stable, then slow fall, then fluctuating stable", "rank_target_idx": 1, "rank_kind": "fall", "rank_adj": "slow", "rank_axis": "slope", "rank_direction": "ASC", "rank_extreme": "slowest", "top_segments_meta": [ { "adj": "fluctuating", "kind": "stable", "label": "fluctuating stable", "start_idx": 0, "end_idx": 28 }, { "adj": "slow", "kind": "fall", "label": "slow fall", "start_idx": 28, "end_idx": 67 }, { "adj": "fluctuating", "kind": "stable", "label": "fluctuating stable", "start_idx": 67, "end_idx": 98 } ], "points_per_day": 96, "pattern": "fluctuating stable, then slow fall, then fluctuating stable", "year": 2021, "top_k": [ "2021-05-11", "2021-03-17", "2021-06-13", "2021-07-21", "2021-03-31" ], "all_injections": [ { "rank": 1, "intensity": 1.5, "date": "2021-05-11" }, { "rank": 2, "intensity": 1.530612244897959, "date": "2021-03-17" }, { "rank": 3, "intensity": 1.5612244897959184, "date": "2021-06-13" }, { "rank": 4, "intensity": 1.5918367346938775, "date": "2021-07-21" }, { "rank": 5, "intensity": 1.6224489795918366, "date": "2021-03-31" }, { "rank": 6, "intensity": 1.653061224489796, "date": "2021-12-15" }, { "rank": 7, "intensity": 1.683673469387755, "date": "2021-08-08" }, { "rank": 8, "intensity": 1.7142857142857142, "date": "2021-11-07" }, { "rank": 9, "intensity": 1.7448979591836735, "date": "2021-09-18" }, { "rank": 10, "intensity": 1.7755102040816326, "date": "2021-01-21" }, { "rank": 11, "intensity": 1.806122448979592, "date": "2021-08-01" }, { "rank": 12, "intensity": 1.836734693877551, "date": "2021-10-16" }, { "rank": 13, "intensity": 1.8673469387755102, "date": "2021-01-29" }, { "rank": 14, "intensity": 1.8979591836734695, "date": "2021-10-13" }, { "rank": 15, "intensity": 1.9285714285714286, "date": "2021-05-06" }, { "rank": 16, "intensity": 1.9591836734693877, "date": "2021-03-14" }, { "rank": 17, "intensity": 1.989795918367347, "date": "2021-09-08" }, { "rank": 18, "intensity": 2.020408163265306, "date": "2021-07-06" }, { "rank": 19, "intensity": 2.0510204081632653, "date": "2021-01-05" }, { "rank": 20, "intensity": 2.0816326530612246, "date": "2021-06-26" }, { "rank": 21, "intensity": 2.112244897959184, "date": "2021-04-02" }, { "rank": 22, "intensity": 2.142857142857143, "date": "2021-06-23" }, { "rank": 23, "intensity": 2.173469387755102, "date": "2021-11-19" }, { "rank": 24, "intensity": 2.204081632653061, "date": "2021-10-20" }, { "rank": 25, "intensity": 2.2346938775510203, "date": "2021-12-02" }, { "rank": 26, "intensity": 2.2653061224489797, "date": "2021-01-11" }, { "rank": 27, "intensity": 2.295918367346939, "date": "2021-05-31" }, { "rank": 28, "intensity": 2.326530612244898, "date": "2021-12-30" }, { "rank": 29, "intensity": 2.357142857142857, "date": "2021-05-01" }, { "rank": 30, "intensity": 2.387755102040816, "date": "2021-04-15" }, { "rank": 31, "intensity": 2.4183673469387754, "date": "2021-11-23" }, { "rank": 32, "intensity": 2.4489795918367347, "date": "2021-12-17" }, { "rank": 33, "intensity": 2.479591836734694, "date": "2021-03-07" }, { "rank": 34, "intensity": 2.510204081632653, "date": "2021-02-21" }, { "rank": 35, "intensity": 2.5408163265306123, "date": "2021-11-09" }, { "rank": 36, "intensity": 2.571428571428571, "date": "2021-09-02" }, { "rank": 37, "intensity": 2.6020408163265305, "date": "2021-04-07" }, { "rank": 38, "intensity": 2.63265306122449, "date": "2021-03-22" }, { "rank": 39, "intensity": 2.663265306122449, "date": "2021-02-24" }, { "rank": 40, "intensity": 2.693877551020408, "date": "2021-06-15" }, { "rank": 41, "intensity": 2.7244897959183674, "date": "2021-10-02" }, { "rank": 42, "intensity": 2.7551020408163263, "date": "2021-05-04" }, { "rank": 43, "intensity": 2.7857142857142856, "date": "2021-12-24" }, { "rank": 44, "intensity": 2.816326530612245, "date": "2021-08-03" }, { "rank": 45, "intensity": 2.8469387755102042, "date": "2021-04-18" }, { "rank": 46, "intensity": 2.877551020408163, "date": "2021-09-06" }, { "rank": 47, "intensity": 2.9081632653061225, "date": "2021-02-11" }, { "rank": 48, "intensity": 2.9387755102040813, "date": "2021-09-20" }, { "rank": 49, "intensity": 2.9693877551020407, "date": "2021-03-19" }, { "rank": 50, "intensity": 3.0, "date": "2021-03-11" } ], "source": "causal_rivers" } }, { "id": "L3_T1_Composite_Trend_00177", "level": 3, "level_name": "Semantic Reasoning", "category": "Composite Trend", "subtask": "Composite Trend", "question": "Among days in channel 169 during 2022 that exhibit the trend pattern 'rapid fall, then steady stable', identify the top-5 days where the rapid fall segment is the fastest. (Output format: a ranked list of dates, e.g., ['YYYY-MM-DD', ...])", "answer": "['2022-01-20', '2022-01-10', '2022-06-22', '2022-02-07', '2022-05-31']", "ground_truth": [ "2022-01-20", "2022-01-10", "2022-06-22", "2022-02-07", "2022-05-31" ], "eval_metric": "set_f1", "channel": "169", "ts_data_path": "ts_data/L3_T1_Composite_Trend_00177.csv", "meta": { "segments": [ { "kind": "fall", "adj": "rapid" }, { "kind": "stable", "adj": "steady" } ], "pattern_phrase": "rapid fall, then steady stable", "rank_target_idx": 0, "rank_kind": "fall", "rank_adj": "rapid", "rank_axis": "slope", "rank_direction": "DESC", "rank_extreme": "fastest", "top_segments_meta": [ { "adj": "rapid", "kind": "fall", "label": "rapid fall", "start_idx": 0, "end_idx": 19 }, { "adj": "steady", "kind": "stable", "label": "steady stable", "start_idx": 19, "end_idx": 98 } ], "points_per_day": 96, "pattern": "rapid fall, then steady stable", "year": 2022, "top_k": [ "2022-01-20", "2022-01-10", "2022-06-22", "2022-02-07", "2022-05-31" ], "all_injections": [ { "rank": 1, "intensity": 3.0, "date": "2022-01-20" }, { "rank": 2, "intensity": 2.9693877551020407, "date": "2022-01-10" }, { "rank": 3, "intensity": 2.938775510204082, "date": "2022-06-22" }, { "rank": 4, "intensity": 2.9081632653061225, "date": "2022-02-07" }, { "rank": 5, "intensity": 2.877551020408163, "date": "2022-05-31" }, { "rank": 6, "intensity": 2.8469387755102042, "date": "2022-03-17" }, { "rank": 7, "intensity": 2.816326530612245, "date": "2022-06-04" }, { "rank": 8, "intensity": 2.7857142857142856, "date": "2022-10-30" }, { "rank": 9, "intensity": 2.7551020408163267, "date": "2022-12-10" }, { "rank": 10, "intensity": 2.7244897959183674, "date": "2022-07-13" }, { "rank": 11, "intensity": 2.693877551020408, "date": "2022-10-20" }, { "rank": 12, "intensity": 2.663265306122449, "date": "2022-01-16" }, { "rank": 13, "intensity": 2.63265306122449, "date": "2022-11-13" }, { "rank": 14, "intensity": 2.6020408163265305, "date": "2022-07-21" }, { "rank": 15, "intensity": 2.5714285714285716, "date": "2022-03-20" }, { "rank": 16, "intensity": 2.5408163265306123, "date": "2022-05-02" }, { "rank": 17, "intensity": 2.510204081632653, "date": "2022-10-11" }, { "rank": 18, "intensity": 2.479591836734694, "date": "2022-03-29" }, { "rank": 19, "intensity": 2.4489795918367347, "date": "2022-09-01" }, { "rank": 20, "intensity": 2.4183673469387754, "date": "2022-08-17" }, { "rank": 21, "intensity": 2.387755102040816, "date": "2022-05-25" }, { "rank": 22, "intensity": 2.357142857142857, "date": "2022-12-14" }, { "rank": 23, "intensity": 2.326530612244898, "date": "2022-04-06" }, { "rank": 24, "intensity": 2.295918367346939, "date": "2022-08-24" }, { "rank": 25, "intensity": 2.2653061224489797, "date": "2022-09-30" }, { "rank": 26, "intensity": 2.2346938775510203, "date": "2022-11-30" }, { "rank": 27, "intensity": 2.204081632653061, "date": "2022-05-17" }, { "rank": 28, "intensity": 2.173469387755102, "date": "2022-04-02" }, { "rank": 29, "intensity": 2.142857142857143, "date": "2022-02-01" }, { "rank": 30, "intensity": 2.112244897959184, "date": "2022-08-20" }, { "rank": 31, "intensity": 2.0816326530612246, "date": "2022-01-04" }, { "rank": 32, "intensity": 2.0510204081632653, "date": "2022-06-07" }, { "rank": 33, "intensity": 2.020408163265306, "date": "2022-12-17" }, { "rank": 34, "intensity": 1.989795918367347, "date": "2022-08-10" }, { "rank": 35, "intensity": 1.9591836734693877, "date": "2022-07-10" }, { "rank": 36, "intensity": 1.9285714285714286, "date": "2022-02-12" }, { "rank": 37, "intensity": 1.8979591836734695, "date": "2022-03-13" }, { "rank": 38, "intensity": 1.8673469387755102, "date": "2022-06-18" }, { "rank": 39, "intensity": 1.836734693877551, "date": "2022-11-11" }, { "rank": 40, "intensity": 1.806122448979592, "date": "2022-02-19" }, { "rank": 41, "intensity": 1.7755102040816326, "date": "2022-01-06" }, { "rank": 42, "intensity": 1.7448979591836735, "date": "2022-01-08" }, { "rank": 43, "intensity": 1.7142857142857144, "date": "2022-03-09" }, { "rank": 44, "intensity": 1.683673469387755, "date": "2022-04-09" }, { "rank": 45, "intensity": 1.653061224489796, "date": "2022-11-27" }, { "rank": 46, "intensity": 1.6224489795918369, "date": "2022-07-29" }, { "rank": 47, "intensity": 1.5918367346938775, "date": "2022-05-23" }, { "rank": 48, "intensity": 1.5612244897959184, "date": "2022-06-26" }, { "rank": 49, "intensity": 1.5306122448979593, "date": "2022-05-12" }, { "rank": 50, "intensity": 1.5, "date": "2022-11-03" } ], "source": "causal_rivers" } }, { "id": "L3_T1_Composite_Trend_00178", "level": 3, "level_name": "Semantic Reasoning", "category": "Composite Trend", "subtask": "Composite Trend", "question": "Among days in channel 170 during 2020 that exhibit the trend pattern 'steady stable, then rapid fall', identify the top-5 days where the rapid fall segment is the fastest. (Output format: a ranked list of dates, e.g., ['YYYY-MM-DD', ...])", "answer": "['2020-03-26', '2020-06-25', '2020-12-15', '2020-10-26', '2020-03-11']", "ground_truth": [ "2020-03-26", "2020-06-25", "2020-12-15", "2020-10-26", "2020-03-11" ], "eval_metric": "set_f1", "channel": "170", "ts_data_path": "ts_data/L3_T1_Composite_Trend_00178.csv", "meta": { "segments": [ { "kind": "stable", "adj": "steady" }, { "kind": "fall", "adj": "rapid" } ], "pattern_phrase": "steady stable, then rapid fall", "rank_target_idx": 1, "rank_kind": "fall", "rank_adj": "rapid", "rank_axis": "slope", "rank_direction": "DESC", "rank_extreme": "fastest", "top_segments_meta": [ { "adj": "steady", "kind": "stable", "label": "steady stable", "start_idx": 0, "end_idx": 79 }, { "adj": "rapid", "kind": "fall", "label": "rapid fall", "start_idx": 79, "end_idx": 98 } ], "points_per_day": 96, "pattern": "steady stable, then rapid fall", "year": 2020, "top_k": [ "2020-03-26", "2020-06-25", "2020-12-15", "2020-10-26", "2020-03-11" ], "all_injections": [ { "rank": 1, "intensity": 3.0, "date": "2020-03-26" }, { "rank": 2, "intensity": 2.9693877551020407, "date": "2020-06-25" }, { "rank": 3, "intensity": 2.938775510204082, "date": "2020-12-15" }, { "rank": 4, "intensity": 2.9081632653061225, "date": "2020-10-26" }, { "rank": 5, "intensity": 2.877551020408163, "date": "2020-03-11" }, { "rank": 6, "intensity": 2.8469387755102042, "date": "2020-04-05" }, { "rank": 7, "intensity": 2.816326530612245, "date": "2020-06-07" }, { "rank": 8, "intensity": 2.7857142857142856, "date": "2020-03-29" }, { "rank": 9, "intensity": 2.7551020408163267, "date": "2020-09-14" }, { "rank": 10, "intensity": 2.7244897959183674, "date": "2020-02-04" }, { "rank": 11, "intensity": 2.693877551020408, "date": "2020-02-06" }, { "rank": 12, "intensity": 2.663265306122449, "date": "2020-02-28" }, { "rank": 13, "intensity": 2.63265306122449, "date": "2020-10-30" }, { "rank": 14, "intensity": 2.6020408163265305, "date": "2020-11-11" }, { "rank": 15, "intensity": 2.5714285714285716, "date": "2020-04-29" }, { "rank": 16, "intensity": 2.5408163265306123, "date": "2020-10-13" }, { "rank": 17, "intensity": 2.510204081632653, "date": "2020-04-09" }, { "rank": 18, "intensity": 2.479591836734694, "date": "2020-05-22" }, { "rank": 19, "intensity": 2.4489795918367347, "date": "2020-04-01" }, { "rank": 20, "intensity": 2.4183673469387754, "date": "2020-07-28" }, { "rank": 21, "intensity": 2.387755102040816, "date": "2020-12-26" }, { "rank": 22, "intensity": 2.357142857142857, "date": "2020-05-07" }, { "rank": 23, "intensity": 2.326530612244898, "date": "2020-08-21" }, { "rank": 24, "intensity": 2.295918367346939, "date": "2020-06-13" }, { "rank": 25, "intensity": 2.2653061224489797, "date": "2020-09-12" }, { "rank": 26, "intensity": 2.2346938775510203, "date": "2020-06-15" }, { "rank": 27, "intensity": 2.204081632653061, "date": "2020-09-06" }, { "rank": 28, "intensity": 2.173469387755102, "date": "2020-08-01" }, { "rank": 29, "intensity": 2.142857142857143, "date": "2020-04-23" }, { "rank": 30, "intensity": 2.112244897959184, "date": "2020-05-29" }, { "rank": 31, "intensity": 2.0816326530612246, "date": "2020-10-05" }, { "rank": 32, "intensity": 2.0510204081632653, "date": "2020-02-22" }, { "rank": 33, "intensity": 2.020408163265306, "date": "2020-12-11" }, { "rank": 34, "intensity": 1.989795918367347, "date": "2020-11-14" }, { "rank": 35, "intensity": 1.9591836734693877, "date": "2020-03-16" }, { "rank": 36, "intensity": 1.9285714285714286, "date": "2020-02-12" }, { "rank": 37, "intensity": 1.8979591836734695, "date": "2020-11-07" }, { "rank": 38, "intensity": 1.8673469387755102, "date": "2020-01-22" }, { "rank": 39, "intensity": 1.836734693877551, "date": "2020-04-18" }, { "rank": 40, "intensity": 1.806122448979592, "date": "2020-02-16" }, { "rank": 41, "intensity": 1.7755102040816326, "date": "2020-04-16" }, { "rank": 42, "intensity": 1.7448979591836735, "date": "2020-01-08" }, { "rank": 43, "intensity": 1.7142857142857144, "date": "2020-11-22" }, { "rank": 44, "intensity": 1.683673469387755, "date": "2020-12-20" }, { "rank": 45, "intensity": 1.653061224489796, "date": "2020-08-17" }, { "rank": 46, "intensity": 1.6224489795918369, "date": "2020-07-02" }, { "rank": 47, "intensity": 1.5918367346938775, "date": "2020-01-29" }, { "rank": 48, "intensity": 1.5612244897959184, "date": "2020-09-30" }, { "rank": 49, "intensity": 1.5306122448979593, "date": "2020-08-05" }, { "rank": 50, "intensity": 1.5, "date": "2020-06-22" } ], "source": "causal_rivers" } }, { "id": "L3_T1_Composite_Trend_00179", "level": 3, "level_name": "Semantic Reasoning", "category": "Composite Trend", "subtask": "Composite Trend", "question": "Among days in channel 172 during 2022 that exhibit the trend pattern 'rapid rise, then rapid fall', identify the top-5 days where the rapid fall segment is the fastest. (Output format: a ranked list of dates, e.g., ['YYYY-MM-DD', ...])", "answer": "['2022-07-14', '2022-07-21', '2022-06-11', '2022-02-22', '2022-10-09']", "ground_truth": [ "2022-07-14", "2022-07-21", "2022-06-11", "2022-02-22", "2022-10-09" ], "eval_metric": "set_f1", "channel": "172", "ts_data_path": "ts_data/L3_T1_Composite_Trend_00179.csv", "meta": { "segments": [ { "kind": "rise", "adj": "rapid" }, { "kind": "fall", "adj": "rapid" } ], "pattern_phrase": "rapid rise, then rapid fall", "rank_target_idx": 1, "rank_kind": "fall", "rank_adj": "rapid", "rank_axis": "slope", "rank_direction": "DESC", "rank_extreme": "fastest", "top_segments_meta": [ { "adj": "rapid", "kind": "rise", "label": "rapid rise", "start_idx": 0, "end_idx": 46 }, { "adj": "rapid", "kind": "fall", "label": "rapid fall", "start_idx": 46, "end_idx": 98 } ], "points_per_day": 96, "pattern": "rapid rise, then rapid fall", "year": 2022, "top_k": [ "2022-07-14", "2022-07-21", "2022-06-11", "2022-02-22", "2022-10-09" ], "all_injections": [ { "rank": 1, "intensity": 3.0, "date": "2022-07-14" }, { "rank": 2, "intensity": 2.9693877551020407, "date": "2022-07-21" }, { "rank": 3, "intensity": 2.938775510204082, "date": "2022-06-11" }, { "rank": 4, "intensity": 2.9081632653061225, "date": "2022-02-22" }, { "rank": 5, "intensity": 2.877551020408163, "date": "2022-10-09" }, { "rank": 6, "intensity": 2.8469387755102042, "date": "2022-08-27" }, { "rank": 7, "intensity": 2.816326530612245, "date": "2022-07-29" }, { "rank": 8, "intensity": 2.7857142857142856, "date": "2022-06-23" }, { "rank": 9, "intensity": 2.7551020408163267, "date": "2022-08-29" }, { "rank": 10, "intensity": 2.7244897959183674, "date": "2022-02-11" }, { "rank": 11, "intensity": 2.693877551020408, "date": "2022-02-01" }, { "rank": 12, "intensity": 2.663265306122449, "date": "2022-03-15" }, { "rank": 13, "intensity": 2.63265306122449, "date": "2022-08-01" }, { "rank": 14, "intensity": 2.6020408163265305, "date": "2022-01-17" }, { "rank": 15, "intensity": 2.5714285714285716, "date": "2022-04-01" }, { "rank": 16, "intensity": 2.5408163265306123, "date": "2022-06-06" }, { "rank": 17, "intensity": 2.510204081632653, "date": "2022-09-08" }, { "rank": 18, "intensity": 2.479591836734694, "date": "2022-02-09" }, { "rank": 19, "intensity": 2.4489795918367347, "date": "2022-04-10" }, { "rank": 20, "intensity": 2.4183673469387754, "date": "2022-09-30" }, { "rank": 21, "intensity": 2.387755102040816, "date": "2022-11-08" }, { "rank": 22, "intensity": 2.357142857142857, "date": "2022-01-05" }, { "rank": 23, "intensity": 2.326530612244898, "date": "2022-02-06" }, { "rank": 24, "intensity": 2.295918367346939, "date": "2022-11-26" }, { "rank": 25, "intensity": 2.2653061224489797, "date": "2022-04-15" }, { "rank": 26, "intensity": 2.2346938775510203, "date": "2022-08-10" }, { "rank": 27, "intensity": 2.204081632653061, "date": "2022-04-18" }, { "rank": 28, "intensity": 2.173469387755102, "date": "2022-05-01" }, { "rank": 29, "intensity": 2.142857142857143, "date": "2022-10-03" }, { "rank": 30, "intensity": 2.112244897959184, "date": "2022-03-07" }, { "rank": 31, "intensity": 2.0816326530612246, "date": "2022-01-14" }, { "rank": 32, "intensity": 2.0510204081632653, "date": "2022-09-02" }, { "rank": 33, "intensity": 2.020408163265306, "date": "2022-05-30" }, { "rank": 34, "intensity": 1.989795918367347, "date": "2022-06-16" }, { "rank": 35, "intensity": 1.9591836734693877, "date": "2022-12-11" }, { "rank": 36, "intensity": 1.9285714285714286, "date": "2022-04-26" }, { "rank": 37, "intensity": 1.8979591836734695, "date": "2022-06-08" }, { "rank": 38, "intensity": 1.8673469387755102, "date": "2022-08-14" }, { "rank": 39, "intensity": 1.836734693877551, "date": "2022-04-23" }, { "rank": 40, "intensity": 1.806122448979592, "date": "2022-02-26" }, { "rank": 41, "intensity": 1.7755102040816326, "date": "2022-11-13" }, { "rank": 42, "intensity": 1.7448979591836735, "date": "2022-02-03" }, { "rank": 43, "intensity": 1.7142857142857144, "date": "2022-10-11" }, { "rank": 44, "intensity": 1.683673469387755, "date": "2022-01-01" }, { "rank": 45, "intensity": 1.653061224489796, "date": "2022-01-03" }, { "rank": 46, "intensity": 1.6224489795918369, "date": "2022-12-06" }, { "rank": 47, "intensity": 1.5918367346938775, "date": "2022-03-01" }, { "rank": 48, "intensity": 1.5612244897959184, "date": "2022-04-08" }, { "rank": 49, "intensity": 1.5306122448979593, "date": "2022-05-05" }, { "rank": 50, "intensity": 1.5, "date": "2022-09-13" } ], "source": "causal_rivers" } }, { "id": "L3_T1_Composite_Trend_00180", "level": 3, "level_name": "Semantic Reasoning", "category": "Composite Trend", "subtask": "Composite Trend", "question": "Among days in channel 173 during 2019 that exhibit the trend pattern 'rise, then rapid rise', identify the top-5 days where the rapid rise segment is the fastest. (Output format: a ranked list of dates, e.g., ['YYYY-MM-DD', ...])", "answer": "['2019-12-26', '2019-08-13', '2019-08-09', '2019-09-12', '2019-06-26']", "ground_truth": [ "2019-12-26", "2019-08-13", "2019-08-09", "2019-09-12", "2019-06-26" ], "eval_metric": "set_f1", "channel": "173", "ts_data_path": "ts_data/L3_T1_Composite_Trend_00180.csv", "meta": { "segments": [ { "kind": "rise", "adj": "none" }, { "kind": "rise", "adj": "rapid" } ], "pattern_phrase": "rise, then rapid rise", "rank_target_idx": 1, "rank_kind": "rise", "rank_adj": "rapid", "rank_axis": "slope", "rank_direction": "DESC", "rank_extreme": "fastest", "top_segments_meta": [ { "adj": "none", "kind": "rise", "label": "none rise", "start_idx": 0, "end_idx": 68 }, { "adj": "rapid", "kind": "rise", "label": "rapid rise", "start_idx": 68, "end_idx": 98 } ], "points_per_day": 96, "pattern": "rise, then rapid rise", "year": 2019, "top_k": [ "2019-12-26", "2019-08-13", "2019-08-09", "2019-09-12", "2019-06-26" ], "all_injections": [ { "rank": 1, "intensity": 3.0, "date": "2019-12-26" }, { "rank": 2, "intensity": 2.9693877551020407, "date": "2019-08-13" }, { "rank": 3, "intensity": 2.938775510204082, "date": "2019-08-09" }, { "rank": 4, "intensity": 2.9081632653061225, "date": "2019-09-12" }, { "rank": 5, "intensity": 2.877551020408163, "date": "2019-06-26" }, { "rank": 6, "intensity": 2.8469387755102042, "date": "2019-01-02" }, { "rank": 7, "intensity": 2.816326530612245, "date": "2019-12-22" }, { "rank": 8, "intensity": 2.7857142857142856, "date": "2019-07-17" }, { "rank": 9, "intensity": 2.7551020408163267, "date": "2019-01-25" }, { "rank": 10, "intensity": 2.7244897959183674, "date": "2019-08-06" }, { "rank": 11, "intensity": 2.693877551020408, "date": "2019-12-06" }, { "rank": 12, "intensity": 2.663265306122449, "date": "2019-02-04" }, { "rank": 13, "intensity": 2.63265306122449, "date": "2019-12-19" }, { "rank": 14, "intensity": 2.6020408163265305, "date": "2019-11-15" }, { "rank": 15, "intensity": 2.5714285714285716, "date": "2019-10-11" }, { "rank": 16, "intensity": 2.5408163265306123, "date": "2019-09-21" }, { "rank": 17, "intensity": 2.510204081632653, "date": "2019-11-18" }, { "rank": 18, "intensity": 2.479591836734694, "date": "2019-01-17" }, { "rank": 19, "intensity": 2.4489795918367347, "date": "2019-09-29" }, { "rank": 20, "intensity": 2.4183673469387754, "date": "2019-02-01" }, { "rank": 21, "intensity": 2.387755102040816, "date": "2019-05-07" }, { "rank": 22, "intensity": 2.357142857142857, "date": "2019-07-10" }, { "rank": 23, "intensity": 2.326530612244898, "date": "2019-05-25" }, { "rank": 24, "intensity": 2.295918367346939, "date": "2019-03-14" }, { "rank": 25, "intensity": 2.2653061224489797, "date": "2019-10-19" }, { "rank": 26, "intensity": 2.2346938775510203, "date": "2019-07-15" }, { "rank": 27, "intensity": 2.204081632653061, "date": "2019-06-03" }, { "rank": 28, "intensity": 2.173469387755102, "date": "2019-03-10" }, { "rank": 29, "intensity": 2.142857142857143, "date": "2019-05-05" }, { "rank": 30, "intensity": 2.112244897959184, "date": "2019-08-30" }, { "rank": 31, "intensity": 2.0816326530612246, "date": "2019-04-17" }, { "rank": 32, "intensity": 2.0510204081632653, "date": "2019-12-24" }, { "rank": 33, "intensity": 2.020408163265306, "date": "2019-01-09" }, { "rank": 34, "intensity": 1.989795918367347, "date": "2019-03-20" }, { "rank": 35, "intensity": 1.9591836734693877, "date": "2019-01-15" }, { "rank": 36, "intensity": 1.9285714285714286, "date": "2019-09-10" }, { "rank": 37, "intensity": 1.8979591836734695, "date": "2019-05-15" }, { "rank": 38, "intensity": 1.8673469387755102, "date": "2019-02-17" }, { "rank": 39, "intensity": 1.836734693877551, "date": "2019-05-27" }, { "rank": 40, "intensity": 1.806122448979592, "date": "2019-08-01" }, { "rank": 41, "intensity": 1.7755102040816326, "date": "2019-08-28" }, { "rank": 42, "intensity": 1.7448979591836735, "date": "2019-10-21" }, { "rank": 43, "intensity": 1.7142857142857144, "date": "2019-06-16" }, { "rank": 44, "intensity": 1.683673469387755, "date": "2019-04-12" }, { "rank": 45, "intensity": 1.653061224489796, "date": "2019-06-12" }, { "rank": 46, "intensity": 1.6224489795918369, "date": "2019-05-22" }, { "rank": 47, "intensity": 1.5918367346938775, "date": "2019-07-12" }, { "rank": 48, "intensity": 1.5612244897959184, "date": "2019-03-28" }, { "rank": 49, "intensity": 1.5306122448979593, "date": "2019-07-05" }, { "rank": 50, "intensity": 1.5, "date": "2019-07-25" } ], "source": "causal_rivers" } }, { "id": "L3_T1_Composite_Trend_00181", "level": 3, "level_name": "Semantic Reasoning", "category": "Composite Trend", "subtask": "Composite Trend", "question": "Among days in channel 177 during 2019 that exhibit the trend pattern 'fluctuating stable, then steady stable, then slow rise, then rapid rise', identify the top-5 days where the slow rise segment is the slowest. (Output format: a ranked list of dates, e.g., ['YYYY-MM-DD', ...])", "answer": "['2019-10-16', '2019-06-15', '2019-05-28', '2019-07-13', '2019-01-26']", "ground_truth": [ "2019-10-16", "2019-06-15", "2019-05-28", "2019-07-13", "2019-01-26" ], "eval_metric": "set_f1", "channel": "177", "ts_data_path": "ts_data/L3_T1_Composite_Trend_00181.csv", "meta": { "segments": [ { "kind": "stable", "adj": "fluctuating" }, { "kind": "stable", "adj": "steady" }, { "kind": "rise", "adj": "slow" }, { "kind": "rise", "adj": "rapid" } ], "pattern_phrase": "fluctuating stable, then steady stable, then slow rise, then rapid rise", "rank_target_idx": 2, "rank_kind": "rise", "rank_adj": "slow", "rank_axis": "slope", "rank_direction": "ASC", "rank_extreme": "slowest", "top_segments_meta": [ { "adj": "fluctuating", "kind": "stable", "label": "fluctuating stable", "start_idx": 0, "end_idx": 21 }, { "adj": "steady", "kind": "stable", "label": "steady stable", "start_idx": 21, "end_idx": 59 }, { "adj": "slow", "kind": "rise", "label": "slow rise", "start_idx": 59, "end_idx": 90 }, { "adj": "rapid", "kind": "rise", "label": "rapid rise", "start_idx": 90, "end_idx": 98 } ], "points_per_day": 96, "pattern": "fluctuating stable, then steady stable, then slow rise, then rapid rise", "year": 2019, "top_k": [ "2019-10-16", "2019-06-15", "2019-05-28", "2019-07-13", "2019-01-26" ], "all_injections": [ { "rank": 1, "intensity": 1.5, "date": "2019-10-16" }, { "rank": 2, "intensity": 1.530612244897959, "date": "2019-06-15" }, { "rank": 3, "intensity": 1.5612244897959184, "date": "2019-05-28" }, { "rank": 4, "intensity": 1.5918367346938775, "date": "2019-07-13" }, { "rank": 5, "intensity": 1.6224489795918366, "date": "2019-01-26" }, { "rank": 6, "intensity": 1.653061224489796, "date": "2019-04-17" }, { "rank": 7, "intensity": 1.683673469387755, "date": "2019-04-09" }, { "rank": 8, "intensity": 1.7142857142857142, "date": "2019-01-18" }, { "rank": 9, "intensity": 1.7448979591836735, "date": "2019-01-05" }, { "rank": 10, "intensity": 1.7755102040816326, "date": "2019-03-14" }, { "rank": 11, "intensity": 1.806122448979592, "date": "2019-01-16" }, { "rank": 12, "intensity": 1.836734693877551, "date": "2019-12-23" }, { "rank": 13, "intensity": 1.8673469387755102, "date": "2019-05-26" }, { "rank": 14, "intensity": 1.8979591836734695, "date": "2019-06-03" }, { "rank": 15, "intensity": 1.9285714285714286, "date": "2019-12-03" }, { "rank": 16, "intensity": 1.9591836734693877, "date": "2019-05-20" }, { "rank": 17, "intensity": 1.989795918367347, "date": "2019-02-08" }, { "rank": 18, "intensity": 2.020408163265306, "date": "2019-11-10" }, { "rank": 19, "intensity": 2.0510204081632653, "date": "2019-06-18" }, { "rank": 20, "intensity": 2.0816326530612246, "date": "2019-03-06" }, { "rank": 21, "intensity": 2.112244897959184, "date": "2019-10-30" }, { "rank": 22, "intensity": 2.142857142857143, "date": "2019-08-02" }, { "rank": 23, "intensity": 2.173469387755102, "date": "2019-10-06" }, { "rank": 24, "intensity": 2.204081632653061, "date": "2019-01-14" }, { "rank": 25, "intensity": 2.2346938775510203, "date": "2019-09-29" }, { "rank": 26, "intensity": 2.2653061224489797, "date": "2019-03-20" }, { "rank": 27, "intensity": 2.295918367346939, "date": "2019-12-15" }, { "rank": 28, "intensity": 2.326530612244898, "date": "2019-11-03" }, { "rank": 29, "intensity": 2.357142857142857, "date": "2019-04-30" }, { "rank": 30, "intensity": 2.387755102040816, "date": "2019-06-07" }, { "rank": 31, "intensity": 2.4183673469387754, "date": "2019-08-16" }, { "rank": 32, "intensity": 2.4489795918367347, "date": "2019-05-24" }, { "rank": 33, "intensity": 2.479591836734694, "date": "2019-07-29" }, { "rank": 34, "intensity": 2.510204081632653, "date": "2019-10-10" }, { "rank": 35, "intensity": 2.5408163265306123, "date": "2019-07-27" }, { "rank": 36, "intensity": 2.571428571428571, "date": "2019-09-02" }, { "rank": 37, "intensity": 2.6020408163265305, "date": "2019-08-07" }, { "rank": 38, "intensity": 2.63265306122449, "date": "2019-03-25" }, { "rank": 39, "intensity": 2.663265306122449, "date": "2019-12-09" }, { "rank": 40, "intensity": 2.693877551020408, "date": "2019-05-22" }, { "rank": 41, "intensity": 2.7244897959183674, "date": "2019-07-31" }, { "rank": 42, "intensity": 2.7551020408163263, "date": "2019-06-21" }, { "rank": 43, "intensity": 2.7857142857142856, "date": "2019-11-13" }, { "rank": 44, "intensity": 2.816326530612245, "date": "2019-08-04" }, { "rank": 45, "intensity": 2.8469387755102042, "date": "2019-11-16" }, { "rank": 46, "intensity": 2.877551020408163, "date": "2019-07-11" }, { "rank": 47, "intensity": 2.9081632653061225, "date": "2019-02-20" }, { "rank": 48, "intensity": 2.9387755102040813, "date": "2019-06-01" }, { "rank": 49, "intensity": 2.9693877551020407, "date": "2019-06-11" }, { "rank": 50, "intensity": 3.0, "date": "2019-04-06" } ], "source": "causal_rivers" } }, { "id": "L3_T1_Composite_Trend_00182", "level": 3, "level_name": "Semantic Reasoning", "category": "Composite Trend", "subtask": "Composite Trend", "question": "Among days in channel 237 during 2023 that exhibit the trend pattern 'steady stable, then fall, then fluctuating stable, then rise', identify the top-5 days where the fluctuating stable segment is the most fluctuating. (Output format: a ranked list of dates, e.g., ['YYYY-MM-DD', ...])", "answer": "['2023-04-08', '2023-03-27', '2023-03-30', '2023-01-06', '2023-11-19']", "ground_truth": [ "2023-04-08", "2023-03-27", "2023-03-30", "2023-01-06", "2023-11-19" ], "eval_metric": "set_f1", "channel": "237", "ts_data_path": "ts_data/L3_T1_Composite_Trend_00182.csv", "meta": { "segments": [ { "kind": "stable", "adj": "steady" }, { "kind": "fall", "adj": "none" }, { "kind": "stable", "adj": "fluctuating" }, { "kind": "rise", "adj": "none" } ], "pattern_phrase": "steady stable, then fall, then fluctuating stable, then rise", "rank_target_idx": 2, "rank_kind": "stable", "rank_adj": "fluctuating", "rank_axis": "std", "rank_direction": "DESC", "rank_extreme": "most fluctuating", "top_segments_meta": [ { "adj": "steady", "kind": "stable", "label": "steady stable", "start_idx": 0, "end_idx": 38 }, { "adj": "none", "kind": "fall", "label": "none fall", "start_idx": 38, "end_idx": 56 }, { "adj": "fluctuating", "kind": "stable", "label": "fluctuating stable", "start_idx": 56, "end_idx": 80 }, { "adj": "none", "kind": "rise", "label": "none rise", "start_idx": 80, "end_idx": 98 } ], "points_per_day": 96, "pattern": "steady stable, then fall, then fluctuating stable, then rise", "year": 2023, "top_k": [ "2023-04-08", "2023-03-27", "2023-03-30", "2023-01-06", "2023-11-19" ], "all_injections": [ { "rank": 1, "intensity": 3.0, "date": "2023-04-08" }, { "rank": 2, "intensity": 2.9693877551020407, "date": "2023-03-27" }, { "rank": 3, "intensity": 2.938775510204082, "date": "2023-03-30" }, { "rank": 4, "intensity": 2.9081632653061225, "date": "2023-01-06" }, { "rank": 5, "intensity": 2.877551020408163, "date": "2023-11-19" }, { "rank": 6, "intensity": 2.8469387755102042, "date": "2023-01-03" }, { "rank": 7, "intensity": 2.816326530612245, "date": "2023-05-11" }, { "rank": 8, "intensity": 2.7857142857142856, "date": "2023-04-19" }, { "rank": 9, "intensity": 2.7551020408163267, "date": "2023-10-01" }, { "rank": 10, "intensity": 2.7244897959183674, "date": "2023-12-30" }, { "rank": 11, "intensity": 2.693877551020408, "date": "2023-02-04" }, { "rank": 12, "intensity": 2.663265306122449, "date": "2023-09-01" }, { "rank": 13, "intensity": 2.63265306122449, "date": "2023-03-09" }, { "rank": 14, "intensity": 2.6020408163265305, "date": "2023-09-11" }, { "rank": 15, "intensity": 2.5714285714285716, "date": "2023-12-13" }, { "rank": 16, "intensity": 2.5408163265306123, "date": "2023-11-30" }, { "rank": 17, "intensity": 2.510204081632653, "date": "2023-09-08" }, { "rank": 18, "intensity": 2.479591836734694, "date": "2023-04-27" }, { "rank": 19, "intensity": 2.4489795918367347, "date": "2023-09-16" }, { "rank": 20, "intensity": 2.4183673469387754, "date": "2023-02-23" }, { "rank": 21, "intensity": 2.387755102040816, "date": "2023-04-11" }, { "rank": 22, "intensity": 2.357142857142857, "date": "2023-06-28" }, { "rank": 23, "intensity": 2.326530612244898, "date": "2023-06-05" }, { "rank": 24, "intensity": 2.295918367346939, "date": "2023-04-23" }, { "rank": 25, "intensity": 2.2653061224489797, "date": "2023-08-10" }, { "rank": 26, "intensity": 2.2346938775510203, "date": "2023-08-04" }, { "rank": 27, "intensity": 2.204081632653061, "date": "2023-08-08" }, { "rank": 28, "intensity": 2.173469387755102, "date": "2023-08-01" }, { "rank": 29, "intensity": 2.142857142857143, "date": "2023-12-09" }, { "rank": 30, "intensity": 2.112244897959184, "date": "2023-12-11" }, { "rank": 31, "intensity": 2.0816326530612246, "date": "2023-01-31" }, { "rank": 32, "intensity": 2.0510204081632653, "date": "2023-05-04" }, { "rank": 33, "intensity": 2.020408163265306, "date": "2023-08-30" }, { "rank": 34, "intensity": 1.989795918367347, "date": "2023-01-19" }, { "rank": 35, "intensity": 1.9591836734693877, "date": "2023-03-20" }, { "rank": 36, "intensity": 1.9285714285714286, "date": "2023-07-22" }, { "rank": 37, "intensity": 1.8979591836734695, "date": "2023-04-02" }, { "rank": 38, "intensity": 1.8673469387755102, "date": "2023-07-17" }, { "rank": 39, "intensity": 1.836734693877551, "date": "2023-07-03" }, { "rank": 40, "intensity": 1.806122448979592, "date": "2023-07-24" }, { "rank": 41, "intensity": 1.7755102040816326, "date": "2023-08-21" }, { "rank": 42, "intensity": 1.7448979591836735, "date": "2023-08-18" }, { "rank": 43, "intensity": 1.7142857142857144, "date": "2023-12-24" }, { "rank": 44, "intensity": 1.683673469387755, "date": "2023-06-26" }, { "rank": 45, "intensity": 1.653061224489796, "date": "2023-04-15" }, { "rank": 46, "intensity": 1.6224489795918369, "date": "2023-07-10" }, { "rank": 47, "intensity": 1.5918367346938775, "date": "2023-06-24" }, { "rank": 48, "intensity": 1.5612244897959184, "date": "2023-01-09" }, { "rank": 49, "intensity": 1.5306122448979593, "date": "2023-05-20" }, { "rank": 50, "intensity": 1.5, "date": "2023-07-01" } ], "source": "causal_rivers" } }, { "id": "L3_T1_Composite_Trend_00183", "level": 3, "level_name": "Semantic Reasoning", "category": "Composite Trend", "subtask": "Composite Trend", "question": "Among days in channel 245 during 2020 that exhibit the trend pattern 'fluctuating stable, then rise', identify the top-5 days where the fluctuating stable segment is the most fluctuating. (Output format: a ranked list of dates, e.g., ['YYYY-MM-DD', ...])", "answer": "['2020-09-30', '2020-12-09', '2020-10-21', '2020-03-27', '2020-04-27']", "ground_truth": [ "2020-09-30", "2020-12-09", "2020-10-21", "2020-03-27", "2020-04-27" ], "eval_metric": "set_f1", "channel": "245", "ts_data_path": "ts_data/L3_T1_Composite_Trend_00183.csv", "meta": { "segments": [ { "kind": "stable", "adj": "fluctuating" }, { "kind": "rise", "adj": "none" } ], "pattern_phrase": "fluctuating stable, then rise", "rank_target_idx": 0, "rank_kind": "stable", "rank_adj": "fluctuating", "rank_axis": "std", "rank_direction": "DESC", "rank_extreme": "most fluctuating", "top_segments_meta": [ { "adj": "fluctuating", "kind": "stable", "label": "fluctuating stable", "start_idx": 0, "end_idx": 56 }, { "adj": "none", "kind": "rise", "label": "none rise", "start_idx": 56, "end_idx": 98 } ], "points_per_day": 96, "pattern": "fluctuating stable, then rise", "year": 2020, "top_k": [ "2020-09-30", "2020-12-09", "2020-10-21", "2020-03-27", "2020-04-27" ], "all_injections": [ { "rank": 1, "intensity": 3.0, "date": "2020-09-30" }, { "rank": 2, "intensity": 2.9693877551020407, "date": "2020-12-09" }, { "rank": 3, "intensity": 2.938775510204082, "date": "2020-10-21" }, { "rank": 4, "intensity": 2.9081632653061225, "date": "2020-03-27" }, { "rank": 5, "intensity": 2.877551020408163, "date": "2020-04-27" }, { "rank": 6, "intensity": 2.8469387755102042, "date": "2020-06-11" }, { "rank": 7, "intensity": 2.816326530612245, "date": "2020-05-03" }, { "rank": 8, "intensity": 2.7857142857142856, "date": "2020-12-30" }, { "rank": 9, "intensity": 2.7551020408163267, "date": "2020-10-14" }, { "rank": 10, "intensity": 2.7244897959183674, "date": "2020-01-24" }, { "rank": 11, "intensity": 2.693877551020408, "date": "2020-01-28" }, { "rank": 12, "intensity": 2.663265306122449, "date": "2020-04-30" }, { "rank": 13, "intensity": 2.63265306122449, "date": "2020-10-12" }, { "rank": 14, "intensity": 2.6020408163265305, "date": "2020-02-04" }, { "rank": 15, "intensity": 2.5714285714285716, "date": "2020-03-10" }, { "rank": 16, "intensity": 2.5408163265306123, "date": "2020-12-06" }, { "rank": 17, "intensity": 2.510204081632653, "date": "2020-12-01" }, { "rank": 18, "intensity": 2.479591836734694, "date": "2020-05-12" }, { "rank": 19, "intensity": 2.4489795918367347, "date": "2020-09-13" }, { "rank": 20, "intensity": 2.4183673469387754, "date": "2020-06-29" }, { "rank": 21, "intensity": 2.387755102040816, "date": "2020-05-27" }, { "rank": 22, "intensity": 2.357142857142857, "date": "2020-05-30" }, { "rank": 23, "intensity": 2.326530612244898, "date": "2020-04-09" }, { "rank": 24, "intensity": 2.295918367346939, "date": "2020-09-11" }, { "rank": 25, "intensity": 2.2653061224489797, "date": "2020-11-21" }, { "rank": 26, "intensity": 2.2346938775510203, "date": "2020-02-11" }, { "rank": 27, "intensity": 2.204081632653061, "date": "2020-11-19" }, { "rank": 28, "intensity": 2.173469387755102, "date": "2020-07-04" }, { "rank": 29, "intensity": 2.142857142857143, "date": "2020-06-02" }, { "rank": 30, "intensity": 2.112244897959184, "date": "2020-11-04" }, { "rank": 31, "intensity": 2.0816326530612246, "date": "2020-09-04" }, { "rank": 32, "intensity": 2.0510204081632653, "date": "2020-05-14" }, { "rank": 33, "intensity": 2.020408163265306, "date": "2020-01-04" }, { "rank": 34, "intensity": 1.989795918367347, "date": "2020-10-25" }, { "rank": 35, "intensity": 1.9591836734693877, "date": "2020-06-19" }, { "rank": 36, "intensity": 1.9285714285714286, "date": "2020-01-08" }, { "rank": 37, "intensity": 1.8979591836734695, "date": "2020-08-21" }, { "rank": 38, "intensity": 1.8673469387755102, "date": "2020-04-17" }, { "rank": 39, "intensity": 1.836734693877551, "date": "2020-05-17" }, { "rank": 40, "intensity": 1.806122448979592, "date": "2020-05-25" }, { "rank": 41, "intensity": 1.7755102040816326, "date": "2020-08-11" }, { "rank": 42, "intensity": 1.7448979591836735, "date": "2020-01-15" }, { "rank": 43, "intensity": 1.7142857142857144, "date": "2020-06-04" }, { "rank": 44, "intensity": 1.683673469387755, "date": "2020-02-08" }, { "rank": 45, "intensity": 1.653061224489796, "date": "2020-01-17" }, { "rank": 46, "intensity": 1.6224489795918369, "date": "2020-05-07" }, { "rank": 47, "intensity": 1.5918367346938775, "date": "2020-02-24" }, { "rank": 48, "intensity": 1.5612244897959184, "date": "2020-04-25" }, { "rank": 49, "intensity": 1.5306122448979593, "date": "2020-03-16" }, { "rank": 50, "intensity": 1.5, "date": "2020-06-22" } ], "source": "causal_rivers" } }, { "id": "L3_T1_Composite_Trend_00184", "level": 3, "level_name": "Semantic Reasoning", "category": "Composite Trend", "subtask": "Composite Trend", "question": "Among days in channel 312 during 2022 that exhibit the trend pattern 'slow fall, then fluctuating stable, then slow fall', identify the top-5 days where the fluctuating stable segment is the most fluctuating. (Output format: a ranked list of dates, e.g., ['YYYY-MM-DD', ...])", "answer": "['2022-07-12', '2022-02-25', '2022-10-16', '2022-06-21', '2022-02-04']", "ground_truth": [ "2022-07-12", "2022-02-25", "2022-10-16", "2022-06-21", "2022-02-04" ], "eval_metric": "set_f1", "channel": "312", "ts_data_path": "ts_data/L3_T1_Composite_Trend_00184.csv", "meta": { "segments": [ { "kind": "fall", "adj": "slow" }, { "kind": "stable", "adj": "fluctuating" }, { "kind": "fall", "adj": "slow" } ], "pattern_phrase": "slow fall, then fluctuating stable, then slow fall", "rank_target_idx": 1, "rank_kind": "stable", "rank_adj": "fluctuating", "rank_axis": "std", "rank_direction": "DESC", "rank_extreme": "most fluctuating", "top_segments_meta": [ { "adj": "slow", "kind": "fall", "label": "slow fall", "start_idx": 0, "end_idx": 34 }, { "adj": "fluctuating", "kind": "stable", "label": "fluctuating stable", "start_idx": 34, "end_idx": 58 }, { "adj": "slow", "kind": "fall", "label": "slow fall", "start_idx": 58, "end_idx": 98 } ], "points_per_day": 96, "pattern": "slow fall, then fluctuating stable, then slow fall", "year": 2022, "top_k": [ "2022-07-12", "2022-02-25", "2022-10-16", "2022-06-21", "2022-02-04" ], "all_injections": [ { "rank": 1, "intensity": 3.0, "date": "2022-07-12" }, { "rank": 2, "intensity": 2.9693877551020407, "date": "2022-02-25" }, { "rank": 3, "intensity": 2.938775510204082, "date": "2022-10-16" }, { "rank": 4, "intensity": 2.9081632653061225, "date": "2022-06-21" }, { "rank": 5, "intensity": 2.877551020408163, "date": "2022-02-04" }, { "rank": 6, "intensity": 2.8469387755102042, "date": "2022-06-09" }, { "rank": 7, "intensity": 2.816326530612245, "date": "2022-08-26" }, { "rank": 8, "intensity": 2.7857142857142856, "date": "2022-11-23" }, { "rank": 9, "intensity": 2.7551020408163267, "date": "2022-05-02" }, { "rank": 10, "intensity": 2.7244897959183674, "date": "2022-03-24" }, { "rank": 11, "intensity": 2.693877551020408, "date": "2022-04-04" }, { "rank": 12, "intensity": 2.663265306122449, "date": "2022-12-03" }, { "rank": 13, "intensity": 2.63265306122449, "date": "2022-04-01" }, { "rank": 14, "intensity": 2.6020408163265305, "date": "2022-04-29" }, { "rank": 15, "intensity": 2.5714285714285716, "date": "2022-05-20" }, { "rank": 16, "intensity": 2.5408163265306123, "date": "2022-11-15" }, { "rank": 17, "intensity": 2.510204081632653, "date": "2022-05-17" }, { "rank": 18, "intensity": 2.479591836734694, "date": "2022-02-10" }, { "rank": 19, "intensity": 2.4489795918367347, "date": "2022-06-07" }, { "rank": 20, "intensity": 2.4183673469387754, "date": "2022-11-03" }, { "rank": 21, "intensity": 2.387755102040816, "date": "2022-07-05" }, { "rank": 22, "intensity": 2.357142857142857, "date": "2022-11-19" }, { "rank": 23, "intensity": 2.326530612244898, "date": "2022-10-23" }, { "rank": 24, "intensity": 2.295918367346939, "date": "2022-01-13" }, { "rank": 25, "intensity": 2.2653061224489797, "date": "2022-04-19" }, { "rank": 26, "intensity": 2.2346938775510203, "date": "2022-01-28" }, { "rank": 27, "intensity": 2.204081632653061, "date": "2022-07-27" }, { "rank": 28, "intensity": 2.173469387755102, "date": "2022-10-10" }, { "rank": 29, "intensity": 2.142857142857143, "date": "2022-03-01" }, { "rank": 30, "intensity": 2.112244897959184, "date": "2022-08-02" }, { "rank": 31, "intensity": 2.0816326530612246, "date": "2022-05-14" }, { "rank": 32, "intensity": 2.0510204081632653, "date": "2022-07-23" }, { "rank": 33, "intensity": 2.020408163265306, "date": "2022-03-13" }, { "rank": 34, "intensity": 1.989795918367347, "date": "2022-05-23" }, { "rank": 35, "intensity": 1.9591836734693877, "date": "2022-01-09" }, { "rank": 36, "intensity": 1.9285714285714286, "date": "2022-03-28" }, { "rank": 37, "intensity": 1.8979591836734695, "date": "2022-09-15" }, { "rank": 38, "intensity": 1.8673469387755102, "date": "2022-01-17" }, { "rank": 39, "intensity": 1.836734693877551, "date": "2022-03-19" }, { "rank": 40, "intensity": 1.806122448979592, "date": "2022-11-01" }, { "rank": 41, "intensity": 1.7755102040816326, "date": "2022-01-30" }, { "rank": 42, "intensity": 1.7448979591836735, "date": "2022-12-21" }, { "rank": 43, "intensity": 1.7142857142857144, "date": "2022-07-08" }, { "rank": 44, "intensity": 1.683673469387755, "date": "2022-10-13" }, { "rank": 45, "intensity": 1.653061224489796, "date": "2022-02-12" }, { "rank": 46, "intensity": 1.6224489795918369, "date": "2022-12-23" }, { "rank": 47, "intensity": 1.5918367346938775, "date": "2022-06-26" }, { "rank": 48, "intensity": 1.5612244897959184, "date": "2022-08-05" }, { "rank": 49, "intensity": 1.5306122448979593, "date": "2022-04-14" }, { "rank": 50, "intensity": 1.5, "date": "2022-09-27" } ], "source": "causal_rivers" } }, { "id": "L3_T1_Composite_Trend_00185", "level": 3, "level_name": "Semantic Reasoning", "category": "Composite Trend", "subtask": "Composite Trend", "question": "Among days in channel 430 during 2021 that exhibit the trend pattern 'fluctuating stable, then rise', identify the top-5 days where the fluctuating stable segment is the most fluctuating. (Output format: a ranked list of dates, e.g., ['YYYY-MM-DD', ...])", "answer": "['2021-04-15', '2021-04-08', '2021-06-09', '2021-09-03', '2021-07-01']", "ground_truth": [ "2021-04-15", "2021-04-08", "2021-06-09", "2021-09-03", "2021-07-01" ], "eval_metric": "set_f1", "channel": "430", "ts_data_path": "ts_data/L3_T1_Composite_Trend_00185.csv", "meta": { "segments": [ { "kind": "stable", "adj": "fluctuating" }, { "kind": "rise", "adj": "none" } ], "pattern_phrase": "fluctuating stable, then rise", "rank_target_idx": 0, "rank_kind": "stable", "rank_adj": "fluctuating", "rank_axis": "std", "rank_direction": "DESC", "rank_extreme": "most fluctuating", "top_segments_meta": [ { "adj": "fluctuating", "kind": "stable", "label": "fluctuating stable", "start_idx": 0, "end_idx": 52 }, { "adj": "none", "kind": "rise", "label": "none rise", "start_idx": 52, "end_idx": 98 } ], "points_per_day": 96, "pattern": "fluctuating stable, then rise", "year": 2021, "top_k": [ "2021-04-15", "2021-04-08", "2021-06-09", "2021-09-03", "2021-07-01" ], "all_injections": [ { "rank": 1, "intensity": 3.0, "date": "2021-04-15" }, { "rank": 2, "intensity": 2.9693877551020407, "date": "2021-04-08" }, { "rank": 3, "intensity": 2.938775510204082, "date": "2021-06-09" }, { "rank": 4, "intensity": 2.9081632653061225, "date": "2021-09-03" }, { "rank": 5, "intensity": 2.877551020408163, "date": "2021-07-01" }, { "rank": 6, "intensity": 2.8469387755102042, "date": "2021-02-23" }, { "rank": 7, "intensity": 2.816326530612245, "date": "2021-06-14" }, { "rank": 8, "intensity": 2.7857142857142856, "date": "2021-06-22" }, { "rank": 9, "intensity": 2.7551020408163267, "date": "2021-09-10" }, { "rank": 10, "intensity": 2.7244897959183674, "date": "2021-11-11" }, { "rank": 11, "intensity": 2.693877551020408, "date": "2021-10-11" }, { "rank": 12, "intensity": 2.663265306122449, "date": "2021-06-07" }, { "rank": 13, "intensity": 2.63265306122449, "date": "2021-04-21" }, { "rank": 14, "intensity": 2.6020408163265305, "date": "2021-02-09" }, { "rank": 15, "intensity": 2.5714285714285716, "date": "2021-07-07" }, { "rank": 16, "intensity": 2.5408163265306123, "date": "2021-09-16" }, { "rank": 17, "intensity": 2.510204081632653, "date": "2021-02-07" }, { "rank": 18, "intensity": 2.479591836734694, "date": "2021-03-01" }, { "rank": 19, "intensity": 2.4489795918367347, "date": "2021-07-10" }, { "rank": 20, "intensity": 2.4183673469387754, "date": "2021-05-05" }, { "rank": 21, "intensity": 2.387755102040816, "date": "2021-05-18" }, { "rank": 22, "intensity": 2.357142857142857, "date": "2021-12-26" }, { "rank": 23, "intensity": 2.326530612244898, "date": "2021-01-15" }, { "rank": 24, "intensity": 2.295918367346939, "date": "2021-12-21" }, { "rank": 25, "intensity": 2.2653061224489797, "date": "2021-07-19" }, { "rank": 26, "intensity": 2.2346938775510203, "date": "2021-12-11" }, { "rank": 27, "intensity": 2.204081632653061, "date": "2021-04-13" }, { "rank": 28, "intensity": 2.173469387755102, "date": "2021-04-11" }, { "rank": 29, "intensity": 2.142857142857143, "date": "2021-11-20" }, { "rank": 30, "intensity": 2.112244897959184, "date": "2021-10-29" }, { "rank": 31, "intensity": 2.0816326530612246, "date": "2021-04-01" }, { "rank": 32, "intensity": 2.0510204081632653, "date": "2021-11-23" }, { "rank": 33, "intensity": 2.020408163265306, "date": "2021-01-09" }, { "rank": 34, "intensity": 1.989795918367347, "date": "2021-10-17" }, { "rank": 35, "intensity": 1.9591836734693877, "date": "2021-03-15" }, { "rank": 36, "intensity": 1.9285714285714286, "date": "2021-07-13" }, { "rank": 37, "intensity": 1.8979591836734695, "date": "2021-07-15" }, { "rank": 38, "intensity": 1.8673469387755102, "date": "2021-01-05" }, { "rank": 39, "intensity": 1.836734693877551, "date": "2021-08-29" }, { "rank": 40, "intensity": 1.806122448979592, "date": "2021-01-22" }, { "rank": 41, "intensity": 1.7755102040816326, "date": "2021-05-16" }, { "rank": 42, "intensity": 1.7448979591836735, "date": "2021-06-27" }, { "rank": 43, "intensity": 1.7142857142857144, "date": "2021-02-16" }, { "rank": 44, "intensity": 1.683673469387755, "date": "2021-10-06" }, { "rank": 45, "intensity": 1.653061224489796, "date": "2021-12-09" }, { "rank": 46, "intensity": 1.6224489795918369, "date": "2021-03-10" }, { "rank": 47, "intensity": 1.5918367346938775, "date": "2021-06-16" }, { "rank": 48, "intensity": 1.5612244897959184, "date": "2021-01-20" }, { "rank": 49, "intensity": 1.5306122448979593, "date": "2021-11-25" }, { "rank": 50, "intensity": 1.5, "date": "2021-06-01" } ], "source": "causal_rivers" } }, { "id": "L3_T1_Composite_Trend_00186", "level": 3, "level_name": "Semantic Reasoning", "category": "Composite Trend", "subtask": "Composite Trend", "question": "Among days in channel 441 during 2021 that exhibit the trend pattern 'steady stable, then rise, then fluctuating stable', identify the top-5 days where the fluctuating stable segment is the most fluctuating. (Output format: a ranked list of dates, e.g., ['YYYY-MM-DD', ...])", "answer": "['2021-05-07', '2021-04-14', '2021-01-12', '2021-03-21', '2021-10-30']", "ground_truth": [ "2021-05-07", "2021-04-14", "2021-01-12", "2021-03-21", "2021-10-30" ], "eval_metric": "set_f1", "channel": "441", "ts_data_path": "ts_data/L3_T1_Composite_Trend_00186.csv", "meta": { "segments": [ { "kind": "stable", "adj": "steady" }, { "kind": "rise", "adj": "none" }, { "kind": "stable", "adj": "fluctuating" } ], "pattern_phrase": "steady stable, then rise, then fluctuating stable", "rank_target_idx": 2, "rank_kind": "stable", "rank_adj": "fluctuating", "rank_axis": "std", "rank_direction": "DESC", "rank_extreme": "most fluctuating", "top_segments_meta": [ { "adj": "steady", "kind": "stable", "label": "steady stable", "start_idx": 0, "end_idx": 48 }, { "adj": "none", "kind": "rise", "label": "none rise", "start_idx": 48, "end_idx": 72 }, { "adj": "fluctuating", "kind": "stable", "label": "fluctuating stable", "start_idx": 72, "end_idx": 98 } ], "points_per_day": 96, "pattern": "steady stable, then rise, then fluctuating stable", "year": 2021, "top_k": [ "2021-05-07", "2021-04-14", "2021-01-12", "2021-03-21", "2021-10-30" ], "all_injections": [ { "rank": 1, "intensity": 3.0, "date": "2021-05-07" }, { "rank": 2, "intensity": 2.9693877551020407, "date": "2021-04-14" }, { "rank": 3, "intensity": 2.938775510204082, "date": "2021-01-12" }, { "rank": 4, "intensity": 2.9081632653061225, "date": "2021-03-21" }, { "rank": 5, "intensity": 2.877551020408163, "date": "2021-10-30" }, { "rank": 6, "intensity": 2.8469387755102042, "date": "2021-12-05" }, { "rank": 7, "intensity": 2.816326530612245, "date": "2021-10-05" }, { "rank": 8, "intensity": 2.7857142857142856, "date": "2021-12-20" }, { "rank": 9, "intensity": 2.7551020408163267, "date": "2021-06-18" }, { "rank": 10, "intensity": 2.7244897959183674, "date": "2021-02-07" }, { "rank": 11, "intensity": 2.693877551020408, "date": "2021-06-22" }, { "rank": 12, "intensity": 2.663265306122449, "date": "2021-05-13" }, { "rank": 13, "intensity": 2.63265306122449, "date": "2021-12-16" }, { "rank": 14, "intensity": 2.6020408163265305, "date": "2021-02-05" }, { "rank": 15, "intensity": 2.5714285714285716, "date": "2021-12-18" }, { "rank": 16, "intensity": 2.5408163265306123, "date": "2021-12-24" }, { "rank": 17, "intensity": 2.510204081632653, "date": "2021-09-30" }, { "rank": 18, "intensity": 2.479591836734694, "date": "2021-06-26" }, { "rank": 19, "intensity": 2.4489795918367347, "date": "2021-12-11" }, { "rank": 20, "intensity": 2.4183673469387754, "date": "2021-01-20" }, { "rank": 21, "intensity": 2.387755102040816, "date": "2021-06-20" }, { "rank": 22, "intensity": 2.357142857142857, "date": "2021-06-24" }, { "rank": 23, "intensity": 2.326530612244898, "date": "2021-08-16" }, { "rank": 24, "intensity": 2.295918367346939, "date": "2021-05-26" }, { "rank": 25, "intensity": 2.2653061224489797, "date": "2021-04-17" }, { "rank": 26, "intensity": 2.2346938775510203, "date": "2021-07-18" }, { "rank": 27, "intensity": 2.204081632653061, "date": "2021-03-28" }, { "rank": 28, "intensity": 2.173469387755102, "date": "2021-06-01" }, { "rank": 29, "intensity": 2.142857142857143, "date": "2021-02-27" }, { "rank": 30, "intensity": 2.112244897959184, "date": "2021-06-16" }, { "rank": 31, "intensity": 2.0816326530612246, "date": "2021-03-30" }, { "rank": 32, "intensity": 2.0510204081632653, "date": "2021-01-06" }, { "rank": 33, "intensity": 2.020408163265306, "date": "2021-05-10" }, { "rank": 34, "intensity": 1.989795918367347, "date": "2021-09-20" }, { "rank": 35, "intensity": 1.9591836734693877, "date": "2021-03-17" }, { "rank": 36, "intensity": 1.9285714285714286, "date": "2021-06-09" }, { "rank": 37, "intensity": 1.8979591836734695, "date": "2021-07-29" }, { "rank": 38, "intensity": 1.8673469387755102, "date": "2021-01-02" }, { "rank": 39, "intensity": 1.836734693877551, "date": "2021-10-07" }, { "rank": 40, "intensity": 1.806122448979592, "date": "2021-11-03" }, { "rank": 41, "intensity": 1.7755102040816326, "date": "2021-07-16" }, { "rank": 42, "intensity": 1.7448979591836735, "date": "2021-03-12" }, { "rank": 43, "intensity": 1.7142857142857144, "date": "2021-11-22" }, { "rank": 44, "intensity": 1.683673469387755, "date": "2021-05-23" }, { "rank": 45, "intensity": 1.653061224489796, "date": "2021-07-22" }, { "rank": 46, "intensity": 1.6224489795918369, "date": "2021-06-30" }, { "rank": 47, "intensity": 1.5918367346938775, "date": "2021-11-10" }, { "rank": 48, "intensity": 1.5612244897959184, "date": "2021-12-29" }, { "rank": 49, "intensity": 1.5306122448979593, "date": "2021-12-26" }, { "rank": 50, "intensity": 1.5, "date": "2021-07-26" } ], "source": "causal_rivers" } }, { "id": "L3_T1_Composite_Trend_00187", "level": 3, "level_name": "Semantic Reasoning", "category": "Composite Trend", "subtask": "Composite Trend", "question": "Among days in channel 495 during 2019 that exhibit the trend pattern 'slow fall, then rapid rise, then steady stable', identify the top-5 days where the slow fall segment is the slowest. (Output format: a ranked list of dates, e.g., ['YYYY-MM-DD', ...])", "answer": "['2019-01-12', '2019-10-07', '2019-03-05', '2019-04-21', '2019-11-10']", "ground_truth": [ "2019-01-12", "2019-10-07", "2019-03-05", "2019-04-21", "2019-11-10" ], "eval_metric": "set_f1", "channel": "495", "ts_data_path": "ts_data/L3_T1_Composite_Trend_00187.csv", "meta": { "segments": [ { "kind": "fall", "adj": "slow" }, { "kind": "rise", "adj": "rapid" }, { "kind": "stable", "adj": "steady" } ], "pattern_phrase": "slow fall, then rapid rise, then steady stable", "rank_target_idx": 0, "rank_kind": "fall", "rank_adj": "slow", "rank_axis": "slope", "rank_direction": "ASC", "rank_extreme": "slowest", "top_segments_meta": [ { "adj": "slow", "kind": "fall", "label": "slow fall", "start_idx": 0, "end_idx": 40 }, { "adj": "rapid", "kind": "rise", "label": "rapid rise", "start_idx": 40, "end_idx": 52 }, { "adj": "steady", "kind": "stable", "label": "steady stable", "start_idx": 52, "end_idx": 98 } ], "points_per_day": 96, "pattern": "slow fall, then rapid rise, then steady stable", "year": 2019, "top_k": [ "2019-01-12", "2019-10-07", "2019-03-05", "2019-04-21", "2019-11-10" ], "all_injections": [ { "rank": 1, "intensity": 1.5, "date": "2019-01-12" }, { "rank": 2, "intensity": 1.530612244897959, "date": "2019-10-07" }, { "rank": 3, "intensity": 1.5612244897959184, "date": "2019-03-05" }, { "rank": 4, "intensity": 1.5918367346938775, "date": "2019-04-21" }, { "rank": 5, "intensity": 1.6224489795918366, "date": "2019-11-10" }, { "rank": 6, "intensity": 1.653061224489796, "date": "2019-07-16" }, { "rank": 7, "intensity": 1.683673469387755, "date": "2019-09-04" }, { "rank": 8, "intensity": 1.7142857142857142, "date": "2019-01-26" }, { "rank": 9, "intensity": 1.7448979591836735, "date": "2019-08-17" }, { "rank": 10, "intensity": 1.7755102040816326, "date": "2019-08-15" }, { "rank": 11, "intensity": 1.806122448979592, "date": "2019-06-05" }, { "rank": 12, "intensity": 1.836734693877551, "date": "2019-08-13" }, { "rank": 13, "intensity": 1.8673469387755102, "date": "2019-01-18" }, { "rank": 14, "intensity": 1.8979591836734695, "date": "2019-01-05" }, { "rank": 15, "intensity": 1.9285714285714286, "date": "2019-08-28" }, { "rank": 16, "intensity": 1.9591836734693877, "date": "2019-03-03" }, { "rank": 17, "intensity": 1.989795918367347, "date": "2019-06-01" }, { "rank": 18, "intensity": 2.020408163265306, "date": "2019-08-04" }, { "rank": 19, "intensity": 2.0510204081632653, "date": "2019-11-06" }, { "rank": 20, "intensity": 2.0816326530612246, "date": "2019-05-08" }, { "rank": 21, "intensity": 2.112244897959184, "date": "2019-04-28" }, { "rank": 22, "intensity": 2.142857142857143, "date": "2019-12-30" }, { "rank": 23, "intensity": 2.173469387755102, "date": "2019-04-08" }, { "rank": 24, "intensity": 2.204081632653061, "date": "2019-06-15" }, { "rank": 25, "intensity": 2.2346938775510203, "date": "2019-02-19" }, { "rank": 26, "intensity": 2.2653061224489797, "date": "2019-12-24" }, { "rank": 27, "intensity": 2.295918367346939, "date": "2019-09-20" }, { "rank": 28, "intensity": 2.326530612244898, "date": "2019-12-03" }, { "rank": 29, "intensity": 2.357142857142857, "date": "2019-01-22" }, { "rank": 30, "intensity": 2.387755102040816, "date": "2019-08-06" }, { "rank": 31, "intensity": 2.4183673469387754, "date": "2019-11-15" }, { "rank": 32, "intensity": 2.4489795918367347, "date": "2019-07-08" }, { "rank": 33, "intensity": 2.479591836734694, "date": "2019-07-31" }, { "rank": 34, "intensity": 2.510204081632653, "date": "2019-05-05" }, { "rank": 35, "intensity": 2.5408163265306123, "date": "2019-02-03" }, { "rank": 36, "intensity": 2.571428571428571, "date": "2019-03-11" }, { "rank": 37, "intensity": 2.6020408163265305, "date": "2019-03-18" }, { "rank": 38, "intensity": 2.63265306122449, "date": "2019-06-11" }, { "rank": 39, "intensity": 2.663265306122449, "date": "2019-08-21" }, { "rank": 40, "intensity": 2.693877551020408, "date": "2019-09-18" }, { "rank": 41, "intensity": 2.7244897959183674, "date": "2019-05-16" }, { "rank": 42, "intensity": 2.7551020408163263, "date": "2019-05-21" }, { "rank": 43, "intensity": 2.7857142857142856, "date": "2019-04-13" }, { "rank": 44, "intensity": 2.816326530612245, "date": "2019-09-12" }, { "rank": 45, "intensity": 2.8469387755102042, "date": "2019-09-07" }, { "rank": 46, "intensity": 2.877551020408163, "date": "2019-02-07" }, { "rank": 47, "intensity": 2.9081632653061225, "date": "2019-07-04" }, { "rank": 48, "intensity": 2.9387755102040813, "date": "2019-12-20" }, { "rank": 49, "intensity": 2.9693877551020407, "date": "2019-03-20" }, { "rank": 50, "intensity": 3.0, "date": "2019-04-05" } ], "source": "causal_rivers" } }, { "id": "L3_T1_Composite_Trend_00188", "level": 3, "level_name": "Semantic Reasoning", "category": "Composite Trend", "subtask": "Composite Trend", "question": "Among days in channel 496 during 2021 that exhibit the trend pattern 'slow rise, then steady stable, then rapid rise', identify the top-5 days where the slow rise segment is the slowest. (Output format: a ranked list of dates, e.g., ['YYYY-MM-DD', ...])", "answer": "['2021-10-23', '2021-09-25', '2021-12-29', '2021-06-24', '2021-08-14']", "ground_truth": [ "2021-10-23", "2021-09-25", "2021-12-29", "2021-06-24", "2021-08-14" ], "eval_metric": "set_f1", "channel": "496", "ts_data_path": "ts_data/L3_T1_Composite_Trend_00188.csv", "meta": { "segments": [ { "kind": "rise", "adj": "slow" }, { "kind": "stable", "adj": "steady" }, { "kind": "rise", "adj": "rapid" } ], "pattern_phrase": "slow rise, then steady stable, then rapid rise", "rank_target_idx": 0, "rank_kind": "rise", "rank_adj": "slow", "rank_axis": "slope", "rank_direction": "ASC", "rank_extreme": "slowest", "top_segments_meta": [ { "adj": "slow", "kind": "rise", "label": "slow rise", "start_idx": 0, "end_idx": 39 }, { "adj": "steady", "kind": "stable", "label": "steady stable", "start_idx": 39, "end_idx": 87 }, { "adj": "rapid", "kind": "rise", "label": "rapid rise", "start_idx": 87, "end_idx": 98 } ], "points_per_day": 96, "pattern": "slow rise, then steady stable, then rapid rise", "year": 2021, "top_k": [ "2021-10-23", "2021-09-25", "2021-12-29", "2021-06-24", "2021-08-14" ], "all_injections": [ { "rank": 1, "intensity": 1.5, "date": "2021-10-23" }, { "rank": 2, "intensity": 1.530612244897959, "date": "2021-09-25" }, { "rank": 3, "intensity": 1.5612244897959184, "date": "2021-12-29" }, { "rank": 4, "intensity": 1.5918367346938775, "date": "2021-06-24" }, { "rank": 5, "intensity": 1.6224489795918366, "date": "2021-08-14" }, { "rank": 6, "intensity": 1.653061224489796, "date": "2021-08-31" }, { "rank": 7, "intensity": 1.683673469387755, "date": "2021-01-30" }, { "rank": 8, "intensity": 1.7142857142857142, "date": "2021-05-14" }, { "rank": 9, "intensity": 1.7448979591836735, "date": "2021-01-01" }, { "rank": 10, "intensity": 1.7755102040816326, "date": "2021-12-20" }, { "rank": 11, "intensity": 1.806122448979592, "date": "2021-02-02" }, { "rank": 12, "intensity": 1.836734693877551, "date": "2021-01-26" }, { "rank": 13, "intensity": 1.8673469387755102, "date": "2021-12-04" }, { "rank": 14, "intensity": 1.8979591836734695, "date": "2021-03-04" }, { "rank": 15, "intensity": 1.9285714285714286, "date": "2021-09-30" }, { "rank": 16, "intensity": 1.9591836734693877, "date": "2021-08-07" }, { "rank": 17, "intensity": 1.989795918367347, "date": "2021-08-29" }, { "rank": 18, "intensity": 2.020408163265306, "date": "2021-03-31" }, { "rank": 19, "intensity": 2.0510204081632653, "date": "2021-07-27" }, { "rank": 20, "intensity": 2.0816326530612246, "date": "2021-04-11" }, { "rank": 21, "intensity": 2.112244897959184, "date": "2021-02-12" }, { "rank": 22, "intensity": 2.142857142857143, "date": "2021-08-02" }, { "rank": 23, "intensity": 2.173469387755102, "date": "2021-02-20" }, { "rank": 24, "intensity": 2.204081632653061, "date": "2021-07-05" }, { "rank": 25, "intensity": 2.2346938775510203, "date": "2021-10-07" }, { "rank": 26, "intensity": 2.2653061224489797, "date": "2021-03-16" }, { "rank": 27, "intensity": 2.295918367346939, "date": "2021-11-14" }, { "rank": 28, "intensity": 2.326530612244898, "date": "2021-02-09" }, { "rank": 29, "intensity": 2.357142857142857, "date": "2021-08-11" }, { "rank": 30, "intensity": 2.387755102040816, "date": "2021-07-10" }, { "rank": 31, "intensity": 2.4183673469387754, "date": "2021-02-17" }, { "rank": 32, "intensity": 2.4489795918367347, "date": "2021-11-10" }, { "rank": 33, "intensity": 2.479591836734694, "date": "2021-04-28" }, { "rank": 34, "intensity": 2.510204081632653, "date": "2021-02-22" }, { "rank": 35, "intensity": 2.5408163265306123, "date": "2021-12-18" }, { "rank": 36, "intensity": 2.571428571428571, "date": "2021-05-20" }, { "rank": 37, "intensity": 2.6020408163265305, "date": "2021-04-20" }, { "rank": 38, "intensity": 2.63265306122449, "date": "2021-11-27" }, { "rank": 39, "intensity": 2.663265306122449, "date": "2021-09-21" }, { "rank": 40, "intensity": 2.693877551020408, "date": "2021-08-24" }, { "rank": 41, "intensity": 2.7244897959183674, "date": "2021-01-17" }, { "rank": 42, "intensity": 2.7551020408163263, "date": "2021-06-17" }, { "rank": 43, "intensity": 2.7857142857142856, "date": "2021-07-24" }, { "rank": 44, "intensity": 2.816326530612245, "date": "2021-09-28" }, { "rank": 45, "intensity": 2.8469387755102042, "date": "2021-10-26" }, { "rank": 46, "intensity": 2.877551020408163, "date": "2021-12-09" }, { "rank": 47, "intensity": 2.9081632653061225, "date": "2021-05-31" }, { "rank": 48, "intensity": 2.9387755102040813, "date": "2021-03-21" }, { "rank": 49, "intensity": 2.9693877551020407, "date": "2021-10-30" }, { "rank": 50, "intensity": 3.0, "date": "2021-09-07" } ], "source": "causal_rivers" } }, { "id": "L3_T1_Composite_Trend_00189", "level": 3, "level_name": "Semantic Reasoning", "category": "Composite Trend", "subtask": "Composite Trend", "question": "Among days in channel 501 during 2022 that exhibit the trend pattern 'slow fall, then fall', identify the top-5 days where the slow fall segment is the slowest. (Output format: a ranked list of dates, e.g., ['YYYY-MM-DD', ...])", "answer": "['2022-09-29', '2022-12-27', '2022-06-07', '2022-08-09', '2022-11-26']", "ground_truth": [ "2022-09-29", "2022-12-27", "2022-06-07", "2022-08-09", "2022-11-26" ], "eval_metric": "set_f1", "channel": "501", "ts_data_path": "ts_data/L3_T1_Composite_Trend_00189.csv", "meta": { "segments": [ { "kind": "fall", "adj": "slow" }, { "kind": "fall", "adj": "none" } ], "pattern_phrase": "slow fall, then fall", "rank_target_idx": 0, "rank_kind": "fall", "rank_adj": "slow", "rank_axis": "slope", "rank_direction": "ASC", "rank_extreme": "slowest", "top_segments_meta": [ { "adj": "slow", "kind": "fall", "label": "slow fall", "start_idx": 0, "end_idx": 64 }, { "adj": "none", "kind": "fall", "label": "none fall", "start_idx": 64, "end_idx": 98 } ], "points_per_day": 96, "pattern": "slow fall, then fall", "year": 2022, "top_k": [ "2022-09-29", "2022-12-27", "2022-06-07", "2022-08-09", "2022-11-26" ], "all_injections": [ { "rank": 1, "intensity": 1.5, "date": "2022-09-29" }, { "rank": 2, "intensity": 1.530612244897959, "date": "2022-12-27" }, { "rank": 3, "intensity": 1.5612244897959184, "date": "2022-06-07" }, { "rank": 4, "intensity": 1.5918367346938775, "date": "2022-08-09" }, { "rank": 5, "intensity": 1.6224489795918366, "date": "2022-11-26" }, { "rank": 6, "intensity": 1.653061224489796, "date": "2022-01-29" }, { "rank": 7, "intensity": 1.683673469387755, "date": "2022-01-16" }, { "rank": 8, "intensity": 1.7142857142857142, "date": "2022-08-02" }, { "rank": 9, "intensity": 1.7448979591836735, "date": "2022-03-20" }, { "rank": 10, "intensity": 1.7755102040816326, "date": "2022-12-01" }, { "rank": 11, "intensity": 1.806122448979592, "date": "2022-10-21" }, { "rank": 12, "intensity": 1.836734693877551, "date": "2022-03-27" }, { "rank": 13, "intensity": 1.8673469387755102, "date": "2022-08-19" }, { "rank": 14, "intensity": 1.8979591836734695, "date": "2022-06-02" }, { "rank": 15, "intensity": 1.9285714285714286, "date": "2022-01-06" }, { "rank": 16, "intensity": 1.9591836734693877, "date": "2022-05-30" }, { "rank": 17, "intensity": 1.989795918367347, "date": "2022-04-20" }, { "rank": 18, "intensity": 2.020408163265306, "date": "2022-02-21" }, { "rank": 19, "intensity": 2.0510204081632653, "date": "2022-04-09" }, { "rank": 20, "intensity": 2.0816326530612246, "date": "2022-01-02" }, { "rank": 21, "intensity": 2.112244897959184, "date": "2022-08-11" }, { "rank": 22, "intensity": 2.142857142857143, "date": "2022-07-11" }, { "rank": 23, "intensity": 2.173469387755102, "date": "2022-03-18" }, { "rank": 24, "intensity": 2.204081632653061, "date": "2022-02-18" }, { "rank": 25, "intensity": 2.2346938775510203, "date": "2022-08-17" }, { "rank": 26, "intensity": 2.2653061224489797, "date": "2022-11-15" }, { "rank": 27, "intensity": 2.295918367346939, "date": "2022-10-18" }, { "rank": 28, "intensity": 2.326530612244898, "date": "2022-05-26" }, { "rank": 29, "intensity": 2.357142857142857, "date": "2022-06-21" }, { "rank": 30, "intensity": 2.387755102040816, "date": "2022-05-13" }, { "rank": 31, "intensity": 2.4183673469387754, "date": "2022-02-24" }, { "rank": 32, "intensity": 2.4489795918367347, "date": "2022-10-11" }, { "rank": 33, "intensity": 2.479591836734694, "date": "2022-04-23" }, { "rank": 34, "intensity": 2.510204081632653, "date": "2022-03-24" }, { "rank": 35, "intensity": 2.5408163265306123, "date": "2022-02-14" }, { "rank": 36, "intensity": 2.571428571428571, "date": "2022-07-31" }, { "rank": 37, "intensity": 2.6020408163265305, "date": "2022-05-05" }, { "rank": 38, "intensity": 2.63265306122449, "date": "2022-11-06" }, { "rank": 39, "intensity": 2.663265306122449, "date": "2022-09-12" }, { "rank": 40, "intensity": 2.693877551020408, "date": "2022-09-06" }, { "rank": 41, "intensity": 2.7244897959183674, "date": "2022-03-14" }, { "rank": 42, "intensity": 2.7551020408163263, "date": "2022-02-27" }, { "rank": 43, "intensity": 2.7857142857142856, "date": "2022-05-17" }, { "rank": 44, "intensity": 2.816326530612245, "date": "2022-10-02" }, { "rank": 45, "intensity": 2.8469387755102042, "date": "2022-08-04" }, { "rank": 46, "intensity": 2.877551020408163, "date": "2022-08-07" }, { "rank": 47, "intensity": 2.9081632653061225, "date": "2022-11-12" }, { "rank": 48, "intensity": 2.9387755102040813, "date": "2022-09-01" }, { "rank": 49, "intensity": 2.9693877551020407, "date": "2022-01-24" }, { "rank": 50, "intensity": 3.0, "date": "2022-04-14" } ], "source": "causal_rivers" } }, { "id": "L3_T1_Composite_Trend_00190", "level": 3, "level_name": "Semantic Reasoning", "category": "Composite Trend", "subtask": "Composite Trend", "question": "Among days in channel 578 during 2023 that exhibit the trend pattern 'rise, then steady stable, then rapid fall', identify the top-5 days where the rapid fall segment is the fastest. (Output format: a ranked list of dates, e.g., ['YYYY-MM-DD', ...])", "answer": "['2023-02-08', '2023-12-21', '2023-01-08', '2023-06-25', '2023-06-10']", "ground_truth": [ "2023-02-08", "2023-12-21", "2023-01-08", "2023-06-25", "2023-06-10" ], "eval_metric": "set_f1", "channel": "578", "ts_data_path": "ts_data/L3_T1_Composite_Trend_00190.csv", "meta": { "segments": [ { "kind": "rise", "adj": "none" }, { "kind": "stable", "adj": "steady" }, { "kind": "fall", "adj": "rapid" } ], "pattern_phrase": "rise, then steady stable, then rapid fall", "rank_target_idx": 2, "rank_kind": "fall", "rank_adj": "rapid", "rank_axis": "slope", "rank_direction": "DESC", "rank_extreme": "fastest", "top_segments_meta": [ { "adj": "none", "kind": "rise", "label": "none rise", "start_idx": 0, "end_idx": 29 }, { "adj": "steady", "kind": "stable", "label": "steady stable", "start_idx": 29, "end_idx": 84 }, { "adj": "rapid", "kind": "fall", "label": "rapid fall", "start_idx": 84, "end_idx": 98 } ], "points_per_day": 96, "pattern": "rise, then steady stable, then rapid fall", "year": 2023, "top_k": [ "2023-02-08", "2023-12-21", "2023-01-08", "2023-06-25", "2023-06-10" ], "all_injections": [ { "rank": 1, "intensity": 3.0, "date": "2023-02-08" }, { "rank": 2, "intensity": 2.9693877551020407, "date": "2023-12-21" }, { "rank": 3, "intensity": 2.938775510204082, "date": "2023-01-08" }, { "rank": 4, "intensity": 2.9081632653061225, "date": "2023-06-25" }, { "rank": 5, "intensity": 2.877551020408163, "date": "2023-06-10" }, { "rank": 6, "intensity": 2.8469387755102042, "date": "2023-03-15" }, { "rank": 7, "intensity": 2.816326530612245, "date": "2023-03-05" }, { "rank": 8, "intensity": 2.7857142857142856, "date": "2023-07-25" }, { "rank": 9, "intensity": 2.7551020408163267, "date": "2023-10-13" }, { "rank": 10, "intensity": 2.7244897959183674, "date": "2023-09-17" }, { "rank": 11, "intensity": 2.693877551020408, "date": "2023-06-13" }, { "rank": 12, "intensity": 2.663265306122449, "date": "2023-08-24" }, { "rank": 13, "intensity": 2.63265306122449, "date": "2023-12-05" }, { "rank": 14, "intensity": 2.6020408163265305, "date": "2023-03-26" }, { "rank": 15, "intensity": 2.5714285714285716, "date": "2023-02-16" }, { "rank": 16, "intensity": 2.5408163265306123, "date": "2023-01-27" }, { "rank": 17, "intensity": 2.510204081632653, "date": "2023-09-11" }, { "rank": 18, "intensity": 2.479591836734694, "date": "2023-06-03" }, { "rank": 19, "intensity": 2.4489795918367347, "date": "2023-12-26" }, { "rank": 20, "intensity": 2.4183673469387754, "date": "2023-12-16" }, { "rank": 21, "intensity": 2.387755102040816, "date": "2023-01-18" }, { "rank": 22, "intensity": 2.357142857142857, "date": "2023-01-01" }, { "rank": 23, "intensity": 2.326530612244898, "date": "2023-06-17" }, { "rank": 24, "intensity": 2.295918367346939, "date": "2023-03-28" }, { "rank": 25, "intensity": 2.2653061224489797, "date": "2023-07-05" }, { "rank": 26, "intensity": 2.2346938775510203, "date": "2023-02-24" }, { "rank": 27, "intensity": 2.204081632653061, "date": "2023-01-16" }, { "rank": 28, "intensity": 2.173469387755102, "date": "2023-11-23" }, { "rank": 29, "intensity": 2.142857142857143, "date": "2023-12-01" }, { "rank": 30, "intensity": 2.112244897959184, "date": "2023-01-14" }, { "rank": 31, "intensity": 2.0816326530612246, "date": "2023-08-08" }, { "rank": 32, "intensity": 2.0510204081632653, "date": "2023-11-12" }, { "rank": 33, "intensity": 2.020408163265306, "date": "2023-10-03" }, { "rank": 34, "intensity": 1.989795918367347, "date": "2023-01-31" }, { "rank": 35, "intensity": 1.9591836734693877, "date": "2023-11-20" }, { "rank": 36, "intensity": 1.9285714285714286, "date": "2023-07-28" }, { "rank": 37, "intensity": 1.8979591836734695, "date": "2023-07-12" }, { "rank": 38, "intensity": 1.8673469387755102, "date": "2023-04-05" }, { "rank": 39, "intensity": 1.836734693877551, "date": "2023-11-05" }, { "rank": 40, "intensity": 1.806122448979592, "date": "2023-06-22" }, { "rank": 41, "intensity": 1.7755102040816326, "date": "2023-08-10" }, { "rank": 42, "intensity": 1.7448979591836735, "date": "2023-04-11" }, { "rank": 43, "intensity": 1.7142857142857144, "date": "2023-06-28" }, { "rank": 44, "intensity": 1.683673469387755, "date": "2023-11-27" }, { "rank": 45, "intensity": 1.653061224489796, "date": "2023-08-04" }, { "rank": 46, "intensity": 1.6224489795918369, "date": "2023-08-29" }, { "rank": 47, "intensity": 1.5918367346938775, "date": "2023-08-31" }, { "rank": 48, "intensity": 1.5612244897959184, "date": "2023-07-10" }, { "rank": 49, "intensity": 1.5306122448979593, "date": "2023-12-09" }, { "rank": 50, "intensity": 1.5, "date": "2023-09-28" } ], "source": "causal_rivers" } }, { "id": "L3_T1_Composite_Trend_00191", "level": 3, "level_name": "Semantic Reasoning", "category": "Composite Trend", "subtask": "Composite Trend", "question": "Among days in channel 580 during 2019 that exhibit the trend pattern 'fluctuating stable, then rapid rise', identify the top-5 days where the rapid rise segment is the fastest. (Output format: a ranked list of dates, e.g., ['YYYY-MM-DD', ...])", "answer": "['2019-05-10', '2019-08-30', '2019-08-15', '2019-02-27', '2019-09-02']", "ground_truth": [ "2019-05-10", "2019-08-30", "2019-08-15", "2019-02-27", "2019-09-02" ], "eval_metric": "set_f1", "channel": "580", "ts_data_path": "ts_data/L3_T1_Composite_Trend_00191.csv", "meta": { "segments": [ { "kind": "stable", "adj": "fluctuating" }, { "kind": "rise", "adj": "rapid" } ], "pattern_phrase": "fluctuating stable, then rapid rise", "rank_target_idx": 1, "rank_kind": "rise", "rank_adj": "rapid", "rank_axis": "slope", "rank_direction": "DESC", "rank_extreme": "fastest", "top_segments_meta": [ { "adj": "fluctuating", "kind": "stable", "label": "fluctuating stable", "start_idx": 0, "end_idx": 71 }, { "adj": "rapid", "kind": "rise", "label": "rapid rise", "start_idx": 71, "end_idx": 98 } ], "points_per_day": 96, "pattern": "fluctuating stable, then rapid rise", "year": 2019, "top_k": [ "2019-05-10", "2019-08-30", "2019-08-15", "2019-02-27", "2019-09-02" ], "all_injections": [ { "rank": 1, "intensity": 3.0, "date": "2019-05-10" }, { "rank": 2, "intensity": 2.9693877551020407, "date": "2019-08-30" }, { "rank": 3, "intensity": 2.938775510204082, "date": "2019-08-15" }, { "rank": 4, "intensity": 2.9081632653061225, "date": "2019-02-27" }, { "rank": 5, "intensity": 2.877551020408163, "date": "2019-09-02" }, { "rank": 6, "intensity": 2.8469387755102042, "date": "2019-03-26" }, { "rank": 7, "intensity": 2.816326530612245, "date": "2019-04-22" }, { "rank": 8, "intensity": 2.7857142857142856, "date": "2019-06-18" }, { "rank": 9, "intensity": 2.7551020408163267, "date": "2019-05-29" }, { "rank": 10, "intensity": 2.7244897959183674, "date": "2019-10-29" }, { "rank": 11, "intensity": 2.693877551020408, "date": "2019-02-02" }, { "rank": 12, "intensity": 2.663265306122449, "date": "2019-02-18" }, { "rank": 13, "intensity": 2.63265306122449, "date": "2019-10-07" }, { "rank": 14, "intensity": 2.6020408163265305, "date": "2019-02-20" }, { "rank": 15, "intensity": 2.5714285714285716, "date": "2019-10-03" }, { "rank": 16, "intensity": 2.5408163265306123, "date": "2019-12-01" }, { "rank": 17, "intensity": 2.510204081632653, "date": "2019-06-02" }, { "rank": 18, "intensity": 2.479591836734694, "date": "2019-09-15" }, { "rank": 19, "intensity": 2.4489795918367347, "date": "2019-12-09" }, { "rank": 20, "intensity": 2.4183673469387754, "date": "2019-09-13" }, { "rank": 21, "intensity": 2.387755102040816, "date": "2019-05-08" }, { "rank": 22, "intensity": 2.357142857142857, "date": "2019-06-08" }, { "rank": 23, "intensity": 2.326530612244898, "date": "2019-11-26" }, { "rank": 24, "intensity": 2.295918367346939, "date": "2019-06-04" }, { "rank": 25, "intensity": 2.2653061224489797, "date": "2019-08-24" }, { "rank": 26, "intensity": 2.2346938775510203, "date": "2019-02-15" }, { "rank": 27, "intensity": 2.204081632653061, "date": "2019-01-18" }, { "rank": 28, "intensity": 2.173469387755102, "date": "2019-10-18" }, { "rank": 29, "intensity": 2.142857142857143, "date": "2019-12-17" }, { "rank": 30, "intensity": 2.112244897959184, "date": "2019-01-08" }, { "rank": 31, "intensity": 2.0816326530612246, "date": "2019-02-11" }, { "rank": 32, "intensity": 2.0510204081632653, "date": "2019-10-21" }, { "rank": 33, "intensity": 2.020408163265306, "date": "2019-12-03" }, { "rank": 34, "intensity": 1.989795918367347, "date": "2019-01-11" }, { "rank": 35, "intensity": 1.9591836734693877, "date": "2019-11-02" }, { "rank": 36, "intensity": 1.9285714285714286, "date": "2019-09-22" }, { "rank": 37, "intensity": 1.8979591836734695, "date": "2019-08-05" }, { "rank": 38, "intensity": 1.8673469387755102, "date": "2019-04-28" }, { "rank": 39, "intensity": 1.836734693877551, "date": "2019-01-04" }, { "rank": 40, "intensity": 1.806122448979592, "date": "2019-12-24" }, { "rank": 41, "intensity": 1.7755102040816326, "date": "2019-05-06" }, { "rank": 42, "intensity": 1.7448979591836735, "date": "2019-04-20" }, { "rank": 43, "intensity": 1.7142857142857144, "date": "2019-04-10" }, { "rank": 44, "intensity": 1.683673469387755, "date": "2019-05-04" }, { "rank": 45, "intensity": 1.653061224489796, "date": "2019-11-20" }, { "rank": 46, "intensity": 1.6224489795918369, "date": "2019-03-17" }, { "rank": 47, "intensity": 1.5918367346938775, "date": "2019-12-07" }, { "rank": 48, "intensity": 1.5612244897959184, "date": "2019-09-10" }, { "rank": 49, "intensity": 1.5306122448979593, "date": "2019-01-14" }, { "rank": 50, "intensity": 1.5, "date": "2019-07-11" } ], "source": "causal_rivers" } }, { "id": "L3_T1_Composite_Trend_00192", "level": 3, "level_name": "Semantic Reasoning", "category": "Composite Trend", "subtask": "Composite Trend", "question": "Among days in channel 589 during 2020 that exhibit the trend pattern 'fluctuating stable, then fall', identify the top-5 days where the fluctuating stable segment is the most fluctuating. (Output format: a ranked list of dates, e.g., ['YYYY-MM-DD', ...])", "answer": "['2020-06-29', '2020-09-25', '2020-05-08', '2020-07-27', '2020-02-13']", "ground_truth": [ "2020-06-29", "2020-09-25", "2020-05-08", "2020-07-27", "2020-02-13" ], "eval_metric": "set_f1", "channel": "589", "ts_data_path": "ts_data/L3_T1_Composite_Trend_00192.csv", "meta": { "segments": [ { "kind": "stable", "adj": "fluctuating" }, { "kind": "fall", "adj": "none" } ], "pattern_phrase": "fluctuating stable, then fall", "rank_target_idx": 0, "rank_kind": "stable", "rank_adj": "fluctuating", "rank_axis": "std", "rank_direction": "DESC", "rank_extreme": "most fluctuating", "top_segments_meta": [ { "adj": "fluctuating", "kind": "stable", "label": "fluctuating stable", "start_idx": 0, "end_idx": 54 }, { "adj": "none", "kind": "fall", "label": "none fall", "start_idx": 54, "end_idx": 98 } ], "points_per_day": 96, "pattern": "fluctuating stable, then fall", "year": 2020, "top_k": [ "2020-06-29", "2020-09-25", "2020-05-08", "2020-07-27", "2020-02-13" ], "all_injections": [ { "rank": 1, "intensity": 3.0, "date": "2020-06-29" }, { "rank": 2, "intensity": 2.9693877551020407, "date": "2020-09-25" }, { "rank": 3, "intensity": 2.938775510204082, "date": "2020-05-08" }, { "rank": 4, "intensity": 2.9081632653061225, "date": "2020-07-27" }, { "rank": 5, "intensity": 2.877551020408163, "date": "2020-02-13" }, { "rank": 6, "intensity": 2.8469387755102042, "date": "2020-04-30" }, { "rank": 7, "intensity": 2.816326530612245, "date": "2020-10-03" }, { "rank": 8, "intensity": 2.7857142857142856, "date": "2020-09-20" }, { "rank": 9, "intensity": 2.7551020408163267, "date": "2020-09-18" }, { "rank": 10, "intensity": 2.7244897959183674, "date": "2020-10-07" }, { "rank": 11, "intensity": 2.693877551020408, "date": "2020-12-24" }, { "rank": 12, "intensity": 2.663265306122449, "date": "2020-09-08" }, { "rank": 13, "intensity": 2.63265306122449, "date": "2020-08-19" }, { "rank": 14, "intensity": 2.6020408163265305, "date": "2020-01-07" }, { "rank": 15, "intensity": 2.5714285714285716, "date": "2020-12-29" }, { "rank": 16, "intensity": 2.5408163265306123, "date": "2020-12-10" }, { "rank": 17, "intensity": 2.510204081632653, "date": "2020-11-13" }, { "rank": 18, "intensity": 2.479591836734694, "date": "2020-08-14" }, { "rank": 19, "intensity": 2.4489795918367347, "date": "2020-11-16" }, { "rank": 20, "intensity": 2.4183673469387754, "date": "2020-05-30" }, { "rank": 21, "intensity": 2.387755102040816, "date": "2020-02-27" }, { "rank": 22, "intensity": 2.357142857142857, "date": "2020-08-10" }, { "rank": 23, "intensity": 2.326530612244898, "date": "2020-11-01" }, { "rank": 24, "intensity": 2.295918367346939, "date": "2020-03-31" }, { "rank": 25, "intensity": 2.2653061224489797, "date": "2020-07-08" }, { "rank": 26, "intensity": 2.2346938775510203, "date": "2020-03-05" }, { "rank": 27, "intensity": 2.204081632653061, "date": "2020-06-26" }, { "rank": 28, "intensity": 2.173469387755102, "date": "2020-06-10" }, { "rank": 29, "intensity": 2.142857142857143, "date": "2020-08-29" }, { "rank": 30, "intensity": 2.112244897959184, "date": "2020-09-16" }, { "rank": 31, "intensity": 2.0816326530612246, "date": "2020-11-27" }, { "rank": 32, "intensity": 2.0510204081632653, "date": "2020-05-24" }, { "rank": 33, "intensity": 2.020408163265306, "date": "2020-01-01" }, { "rank": 34, "intensity": 1.989795918367347, "date": "2020-08-07" }, { "rank": 35, "intensity": 1.9591836734693877, "date": "2020-11-25" }, { "rank": 36, "intensity": 1.9285714285714286, "date": "2020-08-24" }, { "rank": 37, "intensity": 1.8979591836734695, "date": "2020-04-16" }, { "rank": 38, "intensity": 1.8673469387755102, "date": "2020-03-14" }, { "rank": 39, "intensity": 1.836734693877551, "date": "2020-03-23" }, { "rank": 40, "intensity": 1.806122448979592, "date": "2020-05-03" }, { "rank": 41, "intensity": 1.7755102040816326, "date": "2020-02-23" }, { "rank": 42, "intensity": 1.7448979591836735, "date": "2020-02-19" }, { "rank": 43, "intensity": 1.7142857142857144, "date": "2020-04-12" }, { "rank": 44, "intensity": 1.683673469387755, "date": "2020-09-12" }, { "rank": 45, "intensity": 1.653061224489796, "date": "2020-06-19" }, { "rank": 46, "intensity": 1.6224489795918369, "date": "2020-08-26" }, { "rank": 47, "intensity": 1.5918367346938775, "date": "2020-06-05" }, { "rank": 48, "intensity": 1.5612244897959184, "date": "2020-12-14" }, { "rank": 49, "intensity": 1.5306122448979593, "date": "2020-01-31" }, { "rank": 50, "intensity": 1.5, "date": "2020-10-26" } ], "source": "causal_rivers" } }, { "id": "L3_T1_Composite_Trend_00193", "level": 3, "level_name": "Semantic Reasoning", "category": "Composite Trend", "subtask": "Composite Trend", "question": "Among days in channel 591 during 2023 that exhibit the trend pattern 'fluctuating stable, then rise, then steady stable, then rapid rise', identify the top-5 days where the rapid rise segment is the fastest. (Output format: a ranked list of dates, e.g., ['YYYY-MM-DD', ...])", "answer": "['2023-04-09', '2023-01-19', '2023-10-16', '2023-10-01', '2023-05-03']", "ground_truth": [ "2023-04-09", "2023-01-19", "2023-10-16", "2023-10-01", "2023-05-03" ], "eval_metric": "set_f1", "channel": "591", "ts_data_path": "ts_data/L3_T1_Composite_Trend_00193.csv", "meta": { "segments": [ { "kind": "stable", "adj": "fluctuating" }, { "kind": "rise", "adj": "none" }, { "kind": "stable", "adj": "steady" }, { "kind": "rise", "adj": "rapid" } ], "pattern_phrase": "fluctuating stable, then rise, then steady stable, then rapid rise", "rank_target_idx": 3, "rank_kind": "rise", "rank_adj": "rapid", "rank_axis": "slope", "rank_direction": "DESC", "rank_extreme": "fastest", "top_segments_meta": [ { "adj": "fluctuating", "kind": "stable", "label": "fluctuating stable", "start_idx": 0, "end_idx": 24 }, { "adj": "none", "kind": "rise", "label": "none rise", "start_idx": 24, "end_idx": 46 }, { "adj": "steady", "kind": "stable", "label": "steady stable", "start_idx": 46, "end_idx": 87 }, { "adj": "rapid", "kind": "rise", "label": "rapid rise", "start_idx": 87, "end_idx": 98 } ], "points_per_day": 96, "pattern": "fluctuating stable, then rise, then steady stable, then rapid rise", "year": 2023, "top_k": [ "2023-04-09", "2023-01-19", "2023-10-16", "2023-10-01", "2023-05-03" ], "all_injections": [ { "rank": 1, "intensity": 3.0, "date": "2023-04-09" }, { "rank": 2, "intensity": 2.9693877551020407, "date": "2023-01-19" }, { "rank": 3, "intensity": 2.938775510204082, "date": "2023-10-16" }, { "rank": 4, "intensity": 2.9081632653061225, "date": "2023-10-01" }, { "rank": 5, "intensity": 2.877551020408163, "date": "2023-05-03" }, { "rank": 6, "intensity": 2.8469387755102042, "date": "2023-01-15" }, { "rank": 7, "intensity": 2.816326530612245, "date": "2023-07-01" }, { "rank": 8, "intensity": 2.7857142857142856, "date": "2023-12-23" }, { "rank": 9, "intensity": 2.7551020408163267, "date": "2023-12-11" }, { "rank": 10, "intensity": 2.7244897959183674, "date": "2023-06-19" }, { "rank": 11, "intensity": 2.693877551020408, "date": "2023-05-12" }, { "rank": 12, "intensity": 2.663265306122449, "date": "2023-09-18" }, { "rank": 13, "intensity": 2.63265306122449, "date": "2023-10-27" }, { "rank": 14, "intensity": 2.6020408163265305, "date": "2023-06-07" }, { "rank": 15, "intensity": 2.5714285714285716, "date": "2023-03-24" }, { "rank": 16, "intensity": 2.5408163265306123, "date": "2023-12-20" }, { "rank": 17, "intensity": 2.510204081632653, "date": "2023-05-09" }, { "rank": 18, "intensity": 2.479591836734694, "date": "2023-07-14" }, { "rank": 19, "intensity": 2.4489795918367347, "date": "2023-01-06" }, { "rank": 20, "intensity": 2.4183673469387754, "date": "2023-01-09" }, { "rank": 21, "intensity": 2.387755102040816, "date": "2023-02-08" }, { "rank": 22, "intensity": 2.357142857142857, "date": "2023-08-04" }, { "rank": 23, "intensity": 2.326530612244898, "date": "2023-11-06" }, { "rank": 24, "intensity": 2.295918367346939, "date": "2023-11-09" }, { "rank": 25, "intensity": 2.2653061224489797, "date": "2023-01-30" }, { "rank": 26, "intensity": 2.2346938775510203, "date": "2023-02-20" }, { "rank": 27, "intensity": 2.204081632653061, "date": "2023-07-08" }, { "rank": 28, "intensity": 2.173469387755102, "date": "2023-04-23" }, { "rank": 29, "intensity": 2.142857142857143, "date": "2023-11-24" }, { "rank": 30, "intensity": 2.112244897959184, "date": "2023-05-25" }, { "rank": 31, "intensity": 2.0816326530612246, "date": "2023-09-25" }, { "rank": 32, "intensity": 2.0510204081632653, "date": "2023-11-15" }, { "rank": 33, "intensity": 2.020408163265306, "date": "2023-06-22" }, { "rank": 34, "intensity": 1.989795918367347, "date": "2023-03-20" }, { "rank": 35, "intensity": 1.9591836734693877, "date": "2023-04-25" }, { "rank": 36, "intensity": 1.9285714285714286, "date": "2023-11-18" }, { "rank": 37, "intensity": 1.8979591836734695, "date": "2023-11-03" }, { "rank": 38, "intensity": 1.8673469387755102, "date": "2023-09-08" }, { "rank": 39, "intensity": 1.836734693877551, "date": "2023-09-20" }, { "rank": 40, "intensity": 1.806122448979592, "date": "2023-12-30" }, { "rank": 41, "intensity": 1.7755102040816326, "date": "2023-09-02" }, { "rank": 42, "intensity": 1.7448979591836735, "date": "2023-02-12" }, { "rank": 43, "intensity": 1.7142857142857144, "date": "2023-10-20" }, { "rank": 44, "intensity": 1.683673469387755, "date": "2023-06-10" }, { "rank": 45, "intensity": 1.653061224489796, "date": "2023-06-15" }, { "rank": 46, "intensity": 1.6224489795918369, "date": "2023-04-14" }, { "rank": 47, "intensity": 1.5918367346938775, "date": "2023-02-16" }, { "rank": 48, "intensity": 1.5612244897959184, "date": "2023-03-09" }, { "rank": 49, "intensity": 1.5306122448979593, "date": "2023-03-06" }, { "rank": 50, "intensity": 1.5, "date": "2023-01-27" } ], "source": "causal_rivers" } }, { "id": "L3_T1_Composite_Trend_00194", "level": 3, "level_name": "Semantic Reasoning", "category": "Composite Trend", "subtask": "Composite Trend", "question": "Among days in channel 595 during 2020 that exhibit the trend pattern 'slow fall, then fluctuating stable, then slow rise, then fluctuating stable', identify the top-5 days where the slow fall segment is the slowest. (Output format: a ranked list of dates, e.g., ['YYYY-MM-DD', ...])", "answer": "['2020-08-31', '2020-03-06', '2020-06-15', '2020-10-23', '2020-04-27']", "ground_truth": [ "2020-08-31", "2020-03-06", "2020-06-15", "2020-10-23", "2020-04-27" ], "eval_metric": "set_f1", "channel": "595", "ts_data_path": "ts_data/L3_T1_Composite_Trend_00194.csv", "meta": { "segments": [ { "kind": "fall", "adj": "slow" }, { "kind": "stable", "adj": "fluctuating" }, { "kind": "rise", "adj": "slow" }, { "kind": "stable", "adj": "fluctuating" } ], "pattern_phrase": "slow fall, then fluctuating stable, then slow rise, then fluctuating stable", "rank_target_idx": 0, "rank_kind": "fall", "rank_adj": "slow", "rank_axis": "slope", "rank_direction": "ASC", "rank_extreme": "slowest", "top_segments_meta": [ { "adj": "slow", "kind": "fall", "label": "slow fall", "start_idx": 0, "end_idx": 30 }, { "adj": "fluctuating", "kind": "stable", "label": "fluctuating stable", "start_idx": 30, "end_idx": 49 }, { "adj": "slow", "kind": "rise", "label": "slow rise", "start_idx": 49, "end_idx": 76 }, { "adj": "fluctuating", "kind": "stable", "label": "fluctuating stable", "start_idx": 76, "end_idx": 98 } ], "points_per_day": 96, "pattern": "slow fall, then fluctuating stable, then slow rise, then fluctuating stable", "year": 2020, "top_k": [ "2020-08-31", "2020-03-06", "2020-06-15", "2020-10-23", "2020-04-27" ], "all_injections": [ { "rank": 1, "intensity": 1.5, "date": "2020-08-31" }, { "rank": 2, "intensity": 1.530612244897959, "date": "2020-03-06" }, { "rank": 3, "intensity": 1.5612244897959184, "date": "2020-06-15" }, { "rank": 4, "intensity": 1.5918367346938775, "date": "2020-10-23" }, { "rank": 5, "intensity": 1.6224489795918366, "date": "2020-04-27" }, { "rank": 6, "intensity": 1.653061224489796, "date": "2020-07-11" }, { "rank": 7, "intensity": 1.683673469387755, "date": "2020-10-13" }, { "rank": 8, "intensity": 1.7142857142857142, "date": "2020-08-29" }, { "rank": 9, "intensity": 1.7448979591836735, "date": "2020-03-31" }, { "rank": 10, "intensity": 1.7755102040816326, "date": "2020-04-08" }, { "rank": 11, "intensity": 1.806122448979592, "date": "2020-04-12" }, { "rank": 12, "intensity": 1.836734693877551, "date": "2020-07-03" }, { "rank": 13, "intensity": 1.8673469387755102, "date": "2020-08-09" }, { "rank": 14, "intensity": 1.8979591836734695, "date": "2020-12-08" }, { "rank": 15, "intensity": 1.9285714285714286, "date": "2020-05-21" }, { "rank": 16, "intensity": 1.9591836734693877, "date": "2020-04-10" }, { "rank": 17, "intensity": 1.989795918367347, "date": "2020-10-27" }, { "rank": 18, "intensity": 2.020408163265306, "date": "2020-06-09" }, { "rank": 19, "intensity": 2.0510204081632653, "date": "2020-10-18" }, { "rank": 20, "intensity": 2.0816326530612246, "date": "2020-11-09" }, { "rank": 21, "intensity": 2.112244897959184, "date": "2020-07-28" }, { "rank": 22, "intensity": 2.142857142857143, "date": "2020-01-14" }, { "rank": 23, "intensity": 2.173469387755102, "date": "2020-06-27" }, { "rank": 24, "intensity": 2.204081632653061, "date": "2020-04-17" }, { "rank": 25, "intensity": 2.2346938775510203, "date": "2020-06-18" }, { "rank": 26, "intensity": 2.2653061224489797, "date": "2020-01-19" }, { "rank": 27, "intensity": 2.295918367346939, "date": "2020-05-19" }, { "rank": 28, "intensity": 2.326530612244898, "date": "2020-07-16" }, { "rank": 29, "intensity": 2.357142857142857, "date": "2020-01-28" }, { "rank": 30, "intensity": 2.387755102040816, "date": "2020-09-22" }, { "rank": 31, "intensity": 2.4183673469387754, "date": "2020-11-30" }, { "rank": 32, "intensity": 2.4489795918367347, "date": "2020-02-11" }, { "rank": 33, "intensity": 2.479591836734694, "date": "2020-07-22" }, { "rank": 34, "intensity": 2.510204081632653, "date": "2020-08-15" }, { "rank": 35, "intensity": 2.5408163265306123, "date": "2020-05-24" }, { "rank": 36, "intensity": 2.571428571428571, "date": "2020-05-13" }, { "rank": 37, "intensity": 2.6020408163265305, "date": "2020-12-04" }, { "rank": 38, "intensity": 2.63265306122449, "date": "2020-09-02" }, { "rank": 39, "intensity": 2.663265306122449, "date": "2020-06-25" }, { "rank": 40, "intensity": 2.693877551020408, "date": "2020-01-07" }, { "rank": 41, "intensity": 2.7244897959183674, "date": "2020-12-10" }, { "rank": 42, "intensity": 2.7551020408163263, "date": "2020-05-09" }, { "rank": 43, "intensity": 2.7857142857142856, "date": "2020-11-22" }, { "rank": 44, "intensity": 2.816326530612245, "date": "2020-12-19" }, { "rank": 45, "intensity": 2.8469387755102042, "date": "2020-10-31" }, { "rank": 46, "intensity": 2.877551020408163, "date": "2020-07-09" }, { "rank": 47, "intensity": 2.9081632653061225, "date": "2020-03-15" }, { "rank": 48, "intensity": 2.9387755102040813, "date": "2020-04-14" }, { "rank": 49, "intensity": 2.9693877551020407, "date": "2020-03-25" }, { "rank": 50, "intensity": 3.0, "date": "2020-10-15" } ], "source": "causal_rivers" } }, { "id": "L3_T1_Composite_Trend_00195", "level": 3, "level_name": "Semantic Reasoning", "category": "Composite Trend", "subtask": "Composite Trend", "question": "Among days in channel 625 during 2022 that exhibit the trend pattern 'rapid rise, then slow fall', identify the top-5 days where the rapid rise segment is the fastest. (Output format: a ranked list of dates, e.g., ['YYYY-MM-DD', ...])", "answer": "['2022-04-23', '2022-05-26', '2022-09-30', '2022-10-05', '2022-02-22']", "ground_truth": [ "2022-04-23", "2022-05-26", "2022-09-30", "2022-10-05", "2022-02-22" ], "eval_metric": "set_f1", "channel": "625", "ts_data_path": "ts_data/L3_T1_Composite_Trend_00195.csv", "meta": { "segments": [ { "kind": "rise", "adj": "rapid" }, { "kind": "fall", "adj": "slow" } ], "pattern_phrase": "rapid rise, then slow fall", "rank_target_idx": 0, "rank_kind": "rise", "rank_adj": "rapid", "rank_axis": "slope", "rank_direction": "DESC", "rank_extreme": "fastest", "top_segments_meta": [ { "adj": "rapid", "kind": "rise", "label": "rapid rise", "start_idx": 0, "end_idx": 22 }, { "adj": "slow", "kind": "fall", "label": "slow fall", "start_idx": 22, "end_idx": 98 } ], "points_per_day": 96, "pattern": "rapid rise, then slow fall", "year": 2022, "top_k": [ "2022-04-23", "2022-05-26", "2022-09-30", "2022-10-05", "2022-02-22" ], "all_injections": [ { "rank": 1, "intensity": 3.0, "date": "2022-04-23" }, { "rank": 2, "intensity": 2.9693877551020407, "date": "2022-05-26" }, { "rank": 3, "intensity": 2.938775510204082, "date": "2022-09-30" }, { "rank": 4, "intensity": 2.9081632653061225, "date": "2022-10-05" }, { "rank": 5, "intensity": 2.877551020408163, "date": "2022-02-22" }, { "rank": 6, "intensity": 2.8469387755102042, "date": "2022-07-25" }, { "rank": 7, "intensity": 2.816326530612245, "date": "2022-04-07" }, { "rank": 8, "intensity": 2.7857142857142856, "date": "2022-12-01" }, { "rank": 9, "intensity": 2.7551020408163267, "date": "2022-07-11" }, { "rank": 10, "intensity": 2.7244897959183674, "date": "2022-11-03" }, { "rank": 11, "intensity": 2.693877551020408, "date": "2022-02-11" }, { "rank": 12, "intensity": 2.663265306122449, "date": "2022-04-03" }, { "rank": 13, "intensity": 2.63265306122449, "date": "2022-08-19" }, { "rank": 14, "intensity": 2.6020408163265305, "date": "2022-03-11" }, { "rank": 15, "intensity": 2.5714285714285716, "date": "2022-06-12" }, { "rank": 16, "intensity": 2.5408163265306123, "date": "2022-11-07" }, { "rank": 17, "intensity": 2.510204081632653, "date": "2022-05-02" }, { "rank": 18, "intensity": 2.479591836734694, "date": "2022-02-14" }, { "rank": 19, "intensity": 2.4489795918367347, "date": "2022-01-11" }, { "rank": 20, "intensity": 2.4183673469387754, "date": "2022-07-09" }, { "rank": 21, "intensity": 2.387755102040816, "date": "2022-05-09" }, { "rank": 22, "intensity": 2.357142857142857, "date": "2022-12-08" }, { "rank": 23, "intensity": 2.326530612244898, "date": "2022-06-02" }, { "rank": 24, "intensity": 2.295918367346939, "date": "2022-08-15" }, { "rank": 25, "intensity": 2.2653061224489797, "date": "2022-05-31" }, { "rank": 26, "intensity": 2.2346938775510203, "date": "2022-12-10" }, { "rank": 27, "intensity": 2.204081632653061, "date": "2022-03-01" }, { "rank": 28, "intensity": 2.173469387755102, "date": "2022-11-21" }, { "rank": 29, "intensity": 2.142857142857143, "date": "2022-09-22" }, { "rank": 30, "intensity": 2.112244897959184, "date": "2022-08-21" }, { "rank": 31, "intensity": 2.0816326530612246, "date": "2022-09-13" }, { "rank": 32, "intensity": 2.0510204081632653, "date": "2022-02-02" }, { "rank": 33, "intensity": 2.020408163265306, "date": "2022-09-17" }, { "rank": 34, "intensity": 1.989795918367347, "date": "2022-12-30" }, { "rank": 35, "intensity": 1.9591836734693877, "date": "2022-06-10" }, { "rank": 36, "intensity": 1.9285714285714286, "date": "2022-09-15" }, { "rank": 37, "intensity": 1.8979591836734695, "date": "2022-12-18" }, { "rank": 38, "intensity": 1.8673469387755102, "date": "2022-04-20" }, { "rank": 39, "intensity": 1.836734693877551, "date": "2022-11-16" }, { "rank": 40, "intensity": 1.806122448979592, "date": "2022-08-09" }, { "rank": 41, "intensity": 1.7755102040816326, "date": "2022-12-26" }, { "rank": 42, "intensity": 1.7448979591836735, "date": "2022-04-05" }, { "rank": 43, "intensity": 1.7142857142857144, "date": "2022-03-05" }, { "rank": 44, "intensity": 1.683673469387755, "date": "2022-07-07" }, { "rank": 45, "intensity": 1.653061224489796, "date": "2022-03-16" }, { "rank": 46, "intensity": 1.6224489795918369, "date": "2022-06-24" }, { "rank": 47, "intensity": 1.5918367346938775, "date": "2022-11-29" }, { "rank": 48, "intensity": 1.5612244897959184, "date": "2022-10-25" }, { "rank": 49, "intensity": 1.5306122448979593, "date": "2022-04-13" }, { "rank": 50, "intensity": 1.5, "date": "2022-01-26" } ], "source": "causal_rivers" } }, { "id": "L3_T1_Composite_Trend_00196", "level": 3, "level_name": "Semantic Reasoning", "category": "Composite Trend", "subtask": "Composite Trend", "question": "Among days in channel 626 during 2019 that exhibit the trend pattern 'steady stable, then rapid rise', identify the top-5 days where the rapid rise segment is the fastest. (Output format: a ranked list of dates, e.g., ['YYYY-MM-DD', ...])", "answer": "['2019-11-25', '2019-12-17', '2019-03-07', '2019-08-17', '2019-06-30']", "ground_truth": [ "2019-11-25", "2019-12-17", "2019-03-07", "2019-08-17", "2019-06-30" ], "eval_metric": "set_f1", "channel": "626", "ts_data_path": "ts_data/L3_T1_Composite_Trend_00196.csv", "meta": { "segments": [ { "kind": "stable", "adj": "steady" }, { "kind": "rise", "adj": "rapid" } ], "pattern_phrase": "steady stable, then rapid rise", "rank_target_idx": 1, "rank_kind": "rise", "rank_adj": "rapid", "rank_axis": "slope", "rank_direction": "DESC", "rank_extreme": "fastest", "top_segments_meta": [ { "adj": "steady", "kind": "stable", "label": "steady stable", "start_idx": 0, "end_idx": 78 }, { "adj": "rapid", "kind": "rise", "label": "rapid rise", "start_idx": 78, "end_idx": 98 } ], "points_per_day": 96, "pattern": "steady stable, then rapid rise", "year": 2019, "top_k": [ "2019-11-25", "2019-12-17", "2019-03-07", "2019-08-17", "2019-06-30" ], "all_injections": [ { "rank": 1, "intensity": 3.0, "date": "2019-11-25" }, { "rank": 2, "intensity": 2.9693877551020407, "date": "2019-12-17" }, { "rank": 3, "intensity": 2.938775510204082, "date": "2019-03-07" }, { "rank": 4, "intensity": 2.9081632653061225, "date": "2019-08-17" }, { "rank": 5, "intensity": 2.877551020408163, "date": "2019-06-30" }, { "rank": 6, "intensity": 2.8469387755102042, "date": "2019-10-15" }, { "rank": 7, "intensity": 2.816326530612245, "date": "2019-03-26" }, { "rank": 8, "intensity": 2.7857142857142856, "date": "2019-04-27" }, { "rank": 9, "intensity": 2.7551020408163267, "date": "2019-04-20" }, { "rank": 10, "intensity": 2.7244897959183674, "date": "2019-07-17" }, { "rank": 11, "intensity": 2.693877551020408, "date": "2019-10-05" }, { "rank": 12, "intensity": 2.663265306122449, "date": "2019-02-07" }, { "rank": 13, "intensity": 2.63265306122449, "date": "2019-09-27" }, { "rank": 14, "intensity": 2.6020408163265305, "date": "2019-05-05" }, { "rank": 15, "intensity": 2.5714285714285716, "date": "2019-06-06" }, { "rank": 16, "intensity": 2.5408163265306123, "date": "2019-10-29" }, { "rank": 17, "intensity": 2.510204081632653, "date": "2019-07-02" }, { "rank": 18, "intensity": 2.479591836734694, "date": "2019-05-23" }, { "rank": 19, "intensity": 2.4489795918367347, "date": "2019-02-05" }, { "rank": 20, "intensity": 2.4183673469387754, "date": "2019-10-10" }, { "rank": 21, "intensity": 2.387755102040816, "date": "2019-01-16" }, { "rank": 22, "intensity": 2.357142857142857, "date": "2019-01-08" }, { "rank": 23, "intensity": 2.326530612244898, "date": "2019-06-16" }, { "rank": 24, "intensity": 2.295918367346939, "date": "2019-11-20" }, { "rank": 25, "intensity": 2.2653061224489797, "date": "2019-09-17" }, { "rank": 26, "intensity": 2.2346938775510203, "date": "2019-06-26" }, { "rank": 27, "intensity": 2.204081632653061, "date": "2019-01-02" }, { "rank": 28, "intensity": 2.173469387755102, "date": "2019-09-06" }, { "rank": 29, "intensity": 2.142857142857143, "date": "2019-08-01" }, { "rank": 30, "intensity": 2.112244897959184, "date": "2019-01-13" }, { "rank": 31, "intensity": 2.0816326530612246, "date": "2019-02-02" }, { "rank": 32, "intensity": 2.0510204081632653, "date": "2019-05-27" }, { "rank": 33, "intensity": 2.020408163265306, "date": "2019-10-24" }, { "rank": 34, "intensity": 1.989795918367347, "date": "2019-04-23" }, { "rank": 35, "intensity": 1.9591836734693877, "date": "2019-07-11" }, { "rank": 36, "intensity": 1.9285714285714286, "date": "2019-12-10" }, { "rank": 37, "intensity": 1.8979591836734695, "date": "2019-01-28" }, { "rank": 38, "intensity": 1.8673469387755102, "date": "2019-10-02" }, { "rank": 39, "intensity": 1.836734693877551, "date": "2019-03-02" }, { "rank": 40, "intensity": 1.806122448979592, "date": "2019-03-17" }, { "rank": 41, "intensity": 1.7755102040816326, "date": "2019-09-25" }, { "rank": 42, "intensity": 1.7448979591836735, "date": "2019-03-23" }, { "rank": 43, "intensity": 1.7142857142857144, "date": "2019-03-13" }, { "rank": 44, "intensity": 1.683673469387755, "date": "2019-05-01" }, { "rank": 45, "intensity": 1.653061224489796, "date": "2019-02-11" }, { "rank": 46, "intensity": 1.6224489795918369, "date": "2019-01-31" }, { "rank": 47, "intensity": 1.5918367346938775, "date": "2019-08-19" }, { "rank": 48, "intensity": 1.5612244897959184, "date": "2019-01-25" }, { "rank": 49, "intensity": 1.5306122448979593, "date": "2019-07-08" }, { "rank": 50, "intensity": 1.5, "date": "2019-06-22" } ], "source": "causal_rivers" } }, { "id": "L3_T1_Composite_Trend_00197", "level": 3, "level_name": "Semantic Reasoning", "category": "Composite Trend", "subtask": "Composite Trend", "question": "Among days in channel 627 during 2019 that exhibit the trend pattern 'slow rise, then rapid fall', identify the top-5 days where the slow rise segment is the slowest. (Output format: a ranked list of dates, e.g., ['YYYY-MM-DD', ...])", "answer": "['2019-05-17', '2019-12-13', '2019-05-06', '2019-07-25', '2019-10-20']", "ground_truth": [ "2019-05-17", "2019-12-13", "2019-05-06", "2019-07-25", "2019-10-20" ], "eval_metric": "set_f1", "channel": "627", "ts_data_path": "ts_data/L3_T1_Composite_Trend_00197.csv", "meta": { "segments": [ { "kind": "rise", "adj": "slow" }, { "kind": "fall", "adj": "rapid" } ], "pattern_phrase": "slow rise, then rapid fall", "rank_target_idx": 0, "rank_kind": "rise", "rank_adj": "slow", "rank_axis": "slope", "rank_direction": "ASC", "rank_extreme": "slowest", "top_segments_meta": [ { "adj": "slow", "kind": "rise", "label": "slow rise", "start_idx": 0, "end_idx": 75 }, { "adj": "rapid", "kind": "fall", "label": "rapid fall", "start_idx": 75, "end_idx": 98 } ], "points_per_day": 96, "pattern": "slow rise, then rapid fall", "year": 2019, "top_k": [ "2019-05-17", "2019-12-13", "2019-05-06", "2019-07-25", "2019-10-20" ], "all_injections": [ { "rank": 1, "intensity": 1.5, "date": "2019-05-17" }, { "rank": 2, "intensity": 1.530612244897959, "date": "2019-12-13" }, { "rank": 3, "intensity": 1.5612244897959184, "date": "2019-05-06" }, { "rank": 4, "intensity": 1.5918367346938775, "date": "2019-07-25" }, { "rank": 5, "intensity": 1.6224489795918366, "date": "2019-10-20" }, { "rank": 6, "intensity": 1.653061224489796, "date": "2019-09-29" }, { "rank": 7, "intensity": 1.683673469387755, "date": "2019-07-06" }, { "rank": 8, "intensity": 1.7142857142857142, "date": "2019-08-13" }, { "rank": 9, "intensity": 1.7448979591836735, "date": "2019-03-05" }, { "rank": 10, "intensity": 1.7755102040816326, "date": "2019-02-13" }, { "rank": 11, "intensity": 1.806122448979592, "date": "2019-01-09" }, { "rank": 12, "intensity": 1.836734693877551, "date": "2019-04-09" }, { "rank": 13, "intensity": 1.8673469387755102, "date": "2019-08-24" }, { "rank": 14, "intensity": 1.8979591836734695, "date": "2019-06-25" }, { "rank": 15, "intensity": 1.9285714285714286, "date": "2019-09-15" }, { "rank": 16, "intensity": 1.9591836734693877, "date": "2019-08-06" }, { "rank": 17, "intensity": 1.989795918367347, "date": "2019-11-18" }, { "rank": 18, "intensity": 2.020408163265306, "date": "2019-06-28" }, { "rank": 19, "intensity": 2.0510204081632653, "date": "2019-05-28" }, { "rank": 20, "intensity": 2.0816326530612246, "date": "2019-02-17" }, { "rank": 21, "intensity": 2.112244897959184, "date": "2019-06-11" }, { "rank": 22, "intensity": 2.142857142857143, "date": "2019-03-07" }, { "rank": 23, "intensity": 2.173469387755102, "date": "2019-06-16" }, { "rank": 24, "intensity": 2.204081632653061, "date": "2019-10-31" }, { "rank": 25, "intensity": 2.2346938775510203, "date": "2019-04-17" }, { "rank": 26, "intensity": 2.2653061224489797, "date": "2019-07-10" }, { "rank": 27, "intensity": 2.295918367346939, "date": "2019-11-30" }, { "rank": 28, "intensity": 2.326530612244898, "date": "2019-09-20" }, { "rank": 29, "intensity": 2.357142857142857, "date": "2019-01-06" }, { "rank": 30, "intensity": 2.387755102040816, "date": "2019-11-20" }, { "rank": 31, "intensity": 2.4183673469387754, "date": "2019-01-28" }, { "rank": 32, "intensity": 2.4489795918367347, "date": "2019-11-04" }, { "rank": 33, "intensity": 2.479591836734694, "date": "2019-11-06" }, { "rank": 34, "intensity": 2.510204081632653, "date": "2019-09-26" }, { "rank": 35, "intensity": 2.5408163265306123, "date": "2019-02-27" }, { "rank": 36, "intensity": 2.571428571428571, "date": "2019-10-09" }, { "rank": 37, "intensity": 2.6020408163265305, "date": "2019-07-15" }, { "rank": 38, "intensity": 2.63265306122449, "date": "2019-11-09" }, { "rank": 39, "intensity": 2.663265306122449, "date": "2019-08-15" }, { "rank": 40, "intensity": 2.693877551020408, "date": "2019-09-18" }, { "rank": 41, "intensity": 2.7244897959183674, "date": "2019-07-17" }, { "rank": 42, "intensity": 2.7551020408163263, "date": "2019-03-20" }, { "rank": 43, "intensity": 2.7857142857142856, "date": "2019-01-31" }, { "rank": 44, "intensity": 2.816326530612245, "date": "2019-01-16" }, { "rank": 45, "intensity": 2.8469387755102042, "date": "2019-02-04" }, { "rank": 46, "intensity": 2.877551020408163, "date": "2019-06-23" }, { "rank": 47, "intensity": 2.9081632653061225, "date": "2019-10-17" }, { "rank": 48, "intensity": 2.9387755102040813, "date": "2019-04-23" }, { "rank": 49, "intensity": 2.9693877551020407, "date": "2019-03-26" }, { "rank": 50, "intensity": 3.0, "date": "2019-10-24" } ], "source": "causal_rivers" } }, { "id": "L3_T1_Composite_Trend_00198", "level": 3, "level_name": "Semantic Reasoning", "category": "Composite Trend", "subtask": "Composite Trend", "question": "Among days in channel 647 during 2023 that exhibit the trend pattern 'slow fall, then rapid rise, then rise, then steady stable', identify the top-5 days where the slow fall segment is the slowest. (Output format: a ranked list of dates, e.g., ['YYYY-MM-DD', ...])", "answer": "['2023-05-28', '2023-09-10', '2023-04-23', '2023-03-23', '2023-11-30']", "ground_truth": [ "2023-05-28", "2023-09-10", "2023-04-23", "2023-03-23", "2023-11-30" ], "eval_metric": "set_f1", "channel": "647", "ts_data_path": "ts_data/L3_T1_Composite_Trend_00198.csv", "meta": { "segments": [ { "kind": "fall", "adj": "slow" }, { "kind": "rise", "adj": "rapid" }, { "kind": "rise", "adj": "none" }, { "kind": "stable", "adj": "steady" } ], "pattern_phrase": "slow fall, then rapid rise, then rise, then steady stable", "rank_target_idx": 0, "rank_kind": "fall", "rank_adj": "slow", "rank_axis": "slope", "rank_direction": "ASC", "rank_extreme": "slowest", "top_segments_meta": [ { "adj": "slow", "kind": "fall", "label": "slow fall", "start_idx": 0, "end_idx": 33 }, { "adj": "rapid", "kind": "rise", "label": "rapid rise", "start_idx": 33, "end_idx": 42 }, { "adj": "none", "kind": "rise", "label": "none rise", "start_idx": 42, "end_idx": 60 }, { "adj": "steady", "kind": "stable", "label": "steady stable", "start_idx": 60, "end_idx": 98 } ], "points_per_day": 96, "pattern": "slow fall, then rapid rise, then rise, then steady stable", "year": 2023, "top_k": [ "2023-05-28", "2023-09-10", "2023-04-23", "2023-03-23", "2023-11-30" ], "all_injections": [ { "rank": 1, "intensity": 1.5, "date": "2023-05-28" }, { "rank": 2, "intensity": 1.530612244897959, "date": "2023-09-10" }, { "rank": 3, "intensity": 1.5612244897959184, "date": "2023-04-23" }, { "rank": 4, "intensity": 1.5918367346938775, "date": "2023-03-23" }, { "rank": 5, "intensity": 1.6224489795918366, "date": "2023-11-30" }, { "rank": 6, "intensity": 1.653061224489796, "date": "2023-08-11" }, { "rank": 7, "intensity": 1.683673469387755, "date": "2023-07-20" }, { "rank": 8, "intensity": 1.7142857142857142, "date": "2023-03-04" }, { "rank": 9, "intensity": 1.7448979591836735, "date": "2023-12-17" }, { "rank": 10, "intensity": 1.7755102040816326, "date": "2023-07-06" }, { "rank": 11, "intensity": 1.806122448979592, "date": "2023-06-14" }, { "rank": 12, "intensity": 1.836734693877551, "date": "2023-01-20" }, { "rank": 13, "intensity": 1.8673469387755102, "date": "2023-05-26" }, { "rank": 14, "intensity": 1.8979591836734695, "date": "2023-01-31" }, { "rank": 15, "intensity": 1.9285714285714286, "date": "2023-11-26" }, { "rank": 16, "intensity": 1.9591836734693877, "date": "2023-04-09" }, { "rank": 17, "intensity": 1.989795918367347, "date": "2023-03-10" }, { "rank": 18, "intensity": 2.020408163265306, "date": "2023-02-10" }, { "rank": 19, "intensity": 2.0510204081632653, "date": "2023-09-04" }, { "rank": 20, "intensity": 2.0816326530612246, "date": "2023-05-03" }, { "rank": 21, "intensity": 2.112244897959184, "date": "2023-01-23" }, { "rank": 22, "intensity": 2.142857142857143, "date": "2023-07-03" }, { "rank": 23, "intensity": 2.173469387755102, "date": "2023-04-12" }, { "rank": 24, "intensity": 2.204081632653061, "date": "2023-10-15" }, { "rank": 25, "intensity": 2.2346938775510203, "date": "2023-01-15" }, { "rank": 26, "intensity": 2.2653061224489797, "date": "2023-06-04" }, { "rank": 27, "intensity": 2.295918367346939, "date": "2023-08-26" }, { "rank": 28, "intensity": 2.326530612244898, "date": "2023-01-07" }, { "rank": 29, "intensity": 2.357142857142857, "date": "2023-10-31" }, { "rank": 30, "intensity": 2.387755102040816, "date": "2023-02-18" }, { "rank": 31, "intensity": 2.4183673469387754, "date": "2023-06-12" }, { "rank": 32, "intensity": 2.4489795918367347, "date": "2023-06-23" }, { "rank": 33, "intensity": 2.479591836734694, "date": "2023-01-11" }, { "rank": 34, "intensity": 2.510204081632653, "date": "2023-05-31" }, { "rank": 35, "intensity": 2.5408163265306123, "date": "2023-12-29" }, { "rank": 36, "intensity": 2.571428571428571, "date": "2023-11-21" }, { "rank": 37, "intensity": 2.6020408163265305, "date": "2023-08-21" }, { "rank": 38, "intensity": 2.63265306122449, "date": "2023-01-27" }, { "rank": 39, "intensity": 2.663265306122449, "date": "2023-04-04" }, { "rank": 40, "intensity": 2.693877551020408, "date": "2023-06-18" }, { "rank": 41, "intensity": 2.7244897959183674, "date": "2023-07-18" }, { "rank": 42, "intensity": 2.7551020408163263, "date": "2023-08-05" }, { "rank": 43, "intensity": 2.7857142857142856, "date": "2023-08-15" }, { "rank": 44, "intensity": 2.816326530612245, "date": "2023-04-17" }, { "rank": 45, "intensity": 2.8469387755102042, "date": "2023-06-28" }, { "rank": 46, "intensity": 2.877551020408163, "date": "2023-03-06" }, { "rank": 47, "intensity": 2.9081632653061225, "date": "2023-10-24" }, { "rank": 48, "intensity": 2.9387755102040813, "date": "2023-12-20" }, { "rank": 49, "intensity": 2.9693877551020407, "date": "2023-12-25" }, { "rank": 50, "intensity": 3.0, "date": "2023-07-08" } ], "source": "causal_rivers" } }, { "id": "L3_T1_Composite_Trend_00199", "level": 3, "level_name": "Semantic Reasoning", "category": "Composite Trend", "subtask": "Composite Trend", "question": "Among days in channel 680 during 2019 that exhibit the trend pattern 'steady stable, then slow rise, then rapid rise', identify the top-5 days where the slow rise segment is the slowest. (Output format: a ranked list of dates, e.g., ['YYYY-MM-DD', ...])", "answer": "['2019-01-10', '2019-09-20', '2019-04-05', '2019-08-28', '2019-12-03']", "ground_truth": [ "2019-01-10", "2019-09-20", "2019-04-05", "2019-08-28", "2019-12-03" ], "eval_metric": "set_f1", "channel": "680", "ts_data_path": "ts_data/L3_T1_Composite_Trend_00199.csv", "meta": { "segments": [ { "kind": "stable", "adj": "steady" }, { "kind": "rise", "adj": "slow" }, { "kind": "rise", "adj": "rapid" } ], "pattern_phrase": "steady stable, then slow rise, then rapid rise", "rank_target_idx": 1, "rank_kind": "rise", "rank_adj": "slow", "rank_axis": "slope", "rank_direction": "ASC", "rank_extreme": "slowest", "top_segments_meta": [ { "adj": "steady", "kind": "stable", "label": "steady stable", "start_idx": 0, "end_idx": 47 }, { "adj": "slow", "kind": "rise", "label": "slow rise", "start_idx": 47, "end_idx": 88 }, { "adj": "rapid", "kind": "rise", "label": "rapid rise", "start_idx": 88, "end_idx": 98 } ], "points_per_day": 96, "pattern": "steady stable, then slow rise, then rapid rise", "year": 2019, "top_k": [ "2019-01-10", "2019-09-20", "2019-04-05", "2019-08-28", "2019-12-03" ], "all_injections": [ { "rank": 1, "intensity": 1.5, "date": "2019-01-10" }, { "rank": 2, "intensity": 1.530612244897959, "date": "2019-09-20" }, { "rank": 3, "intensity": 1.5612244897959184, "date": "2019-04-05" }, { "rank": 4, "intensity": 1.5918367346938775, "date": "2019-08-28" }, { "rank": 5, "intensity": 1.6224489795918366, "date": "2019-12-03" }, { "rank": 6, "intensity": 1.653061224489796, "date": "2019-06-21" }, { "rank": 7, "intensity": 1.683673469387755, "date": "2019-09-13" }, { "rank": 8, "intensity": 1.7142857142857142, "date": "2019-05-14" }, { "rank": 9, "intensity": 1.7448979591836735, "date": "2019-04-22" }, { "rank": 10, "intensity": 1.7755102040816326, "date": "2019-07-29" }, { "rank": 11, "intensity": 1.806122448979592, "date": "2019-03-11" }, { "rank": 12, "intensity": 1.836734693877551, "date": "2019-05-19" }, { "rank": 13, "intensity": 1.8673469387755102, "date": "2019-05-08" }, { "rank": 14, "intensity": 1.8979591836734695, "date": "2019-02-11" }, { "rank": 15, "intensity": 1.9285714285714286, "date": "2019-03-20" }, { "rank": 16, "intensity": 1.9591836734693877, "date": "2019-12-27" }, { "rank": 17, "intensity": 1.989795918367347, "date": "2019-01-12" }, { "rank": 18, "intensity": 2.020408163265306, "date": "2019-03-13" }, { "rank": 19, "intensity": 2.0510204081632653, "date": "2019-02-18" }, { "rank": 20, "intensity": 2.0816326530612246, "date": "2019-12-29" }, { "rank": 21, "intensity": 2.112244897959184, "date": "2019-05-10" }, { "rank": 22, "intensity": 2.142857142857143, "date": "2019-04-13" }, { "rank": 23, "intensity": 2.173469387755102, "date": "2019-03-30" }, { "rank": 24, "intensity": 2.204081632653061, "date": "2019-09-15" }, { "rank": 25, "intensity": 2.2346938775510203, "date": "2019-01-08" }, { "rank": 26, "intensity": 2.2653061224489797, "date": "2019-02-09" }, { "rank": 27, "intensity": 2.295918367346939, "date": "2019-12-15" }, { "rank": 28, "intensity": 2.326530612244898, "date": "2019-03-28" }, { "rank": 29, "intensity": 2.357142857142857, "date": "2019-11-02" }, { "rank": 30, "intensity": 2.387755102040816, "date": "2019-04-07" }, { "rank": 31, "intensity": 2.4183673469387754, "date": "2019-10-29" }, { "rank": 32, "intensity": 2.4489795918367347, "date": "2019-09-27" }, { "rank": 33, "intensity": 2.479591836734694, "date": "2019-11-28" }, { "rank": 34, "intensity": 2.510204081632653, "date": "2019-09-04" }, { "rank": 35, "intensity": 2.5408163265306123, "date": "2019-05-06" }, { "rank": 36, "intensity": 2.571428571428571, "date": "2019-02-24" }, { "rank": 37, "intensity": 2.6020408163265305, "date": "2019-12-08" }, { "rank": 38, "intensity": 2.63265306122449, "date": "2019-02-27" }, { "rank": 39, "intensity": 2.663265306122449, "date": "2019-02-15" }, { "rank": 40, "intensity": 2.693877551020408, "date": "2019-09-18" }, { "rank": 41, "intensity": 2.7244897959183674, "date": "2019-08-30" }, { "rank": 42, "intensity": 2.7551020408163263, "date": "2019-04-15" }, { "rank": 43, "intensity": 2.7857142857142856, "date": "2019-12-20" }, { "rank": 44, "intensity": 2.816326530612245, "date": "2019-04-29" }, { "rank": 45, "intensity": 2.8469387755102042, "date": "2019-01-03" }, { "rank": 46, "intensity": 2.877551020408163, "date": "2019-05-27" }, { "rank": 47, "intensity": 2.9081632653061225, "date": "2019-11-19" }, { "rank": 48, "intensity": 2.9387755102040813, "date": "2019-06-19" }, { "rank": 49, "intensity": 2.9693877551020407, "date": "2019-06-10" }, { "rank": 50, "intensity": 3.0, "date": "2019-08-14" } ], "source": "causal_rivers" } }, { "id": "L3_T1_Composite_Trend_00200", "level": 3, "level_name": "Semantic Reasoning", "category": "Composite Trend", "subtask": "Composite Trend", "question": "Among days in channel 683 during 2022 that exhibit the trend pattern 'rapid fall, then slow fall, then rapid fall', identify the top-5 days where the slow fall segment is the slowest. (Output format: a ranked list of dates, e.g., ['YYYY-MM-DD', ...])", "answer": "['2022-01-20', '2022-08-08', '2022-09-19', '2022-01-23', '2022-11-18']", "ground_truth": [ "2022-01-20", "2022-08-08", "2022-09-19", "2022-01-23", "2022-11-18" ], "eval_metric": "set_f1", "channel": "683", "ts_data_path": "ts_data/L3_T1_Composite_Trend_00200.csv", "meta": { "segments": [ { "kind": "fall", "adj": "rapid" }, { "kind": "fall", "adj": "slow" }, { "kind": "fall", "adj": "rapid" } ], "pattern_phrase": "rapid fall, then slow fall, then rapid fall", "rank_target_idx": 1, "rank_kind": "fall", "rank_adj": "slow", "rank_axis": "slope", "rank_direction": "ASC", "rank_extreme": "slowest", "top_segments_meta": [ { "adj": "rapid", "kind": "fall", "label": "rapid fall", "start_idx": 0, "end_idx": 18 }, { "adj": "slow", "kind": "fall", "label": "slow fall", "start_idx": 18, "end_idx": 81 }, { "adj": "rapid", "kind": "fall", "label": "rapid fall", "start_idx": 81, "end_idx": 98 } ], "points_per_day": 96, "pattern": "rapid fall, then slow fall, then rapid fall", "year": 2022, "top_k": [ "2022-01-20", "2022-08-08", "2022-09-19", "2022-01-23", "2022-11-18" ], "all_injections": [ { "rank": 1, "intensity": 1.5, "date": "2022-01-20" }, { "rank": 2, "intensity": 1.530612244897959, "date": "2022-08-08" }, { "rank": 3, "intensity": 1.5612244897959184, "date": "2022-09-19" }, { "rank": 4, "intensity": 1.5918367346938775, "date": "2022-01-23" }, { "rank": 5, "intensity": 1.6224489795918366, "date": "2022-11-18" }, { "rank": 6, "intensity": 1.653061224489796, "date": "2022-12-20" }, { "rank": 7, "intensity": 1.683673469387755, "date": "2022-07-25" }, { "rank": 8, "intensity": 1.7142857142857142, "date": "2022-10-21" }, { "rank": 9, "intensity": 1.7448979591836735, "date": "2022-03-22" }, { "rank": 10, "intensity": 1.7755102040816326, "date": "2022-02-23" }, { "rank": 11, "intensity": 1.806122448979592, "date": "2022-07-22" }, { "rank": 12, "intensity": 1.836734693877551, "date": "2022-06-05" }, { "rank": 13, "intensity": 1.8673469387755102, "date": "2022-02-25" }, { "rank": 14, "intensity": 1.8979591836734695, "date": "2022-07-31" }, { "rank": 15, "intensity": 1.9285714285714286, "date": "2022-10-12" }, { "rank": 16, "intensity": 1.9591836734693877, "date": "2022-12-18" }, { "rank": 17, "intensity": 1.989795918367347, "date": "2022-09-22" }, { "rank": 18, "intensity": 2.020408163265306, "date": "2022-01-11" }, { "rank": 19, "intensity": 2.0510204081632653, "date": "2022-06-24" }, { "rank": 20, "intensity": 2.0816326530612246, "date": "2022-03-19" }, { "rank": 21, "intensity": 2.112244897959184, "date": "2022-06-18" }, { "rank": 22, "intensity": 2.142857142857143, "date": "2022-09-02" }, { "rank": 23, "intensity": 2.173469387755102, "date": "2022-03-02" }, { "rank": 24, "intensity": 2.204081632653061, "date": "2022-01-06" }, { "rank": 25, "intensity": 2.2346938775510203, "date": "2022-01-02" }, { "rank": 26, "intensity": 2.2653061224489797, "date": "2022-04-10" }, { "rank": 27, "intensity": 2.295918367346939, "date": "2022-06-12" }, { "rank": 28, "intensity": 2.326530612244898, "date": "2022-03-08" }, { "rank": 29, "intensity": 2.357142857142857, "date": "2022-09-26" }, { "rank": 30, "intensity": 2.387755102040816, "date": "2022-12-26" }, { "rank": 31, "intensity": 2.4183673469387754, "date": "2022-09-28" }, { "rank": 32, "intensity": 2.4489795918367347, "date": "2022-02-05" }, { "rank": 33, "intensity": 2.479591836734694, "date": "2022-11-24" }, { "rank": 34, "intensity": 2.510204081632653, "date": "2022-04-02" }, { "rank": 35, "intensity": 2.5408163265306123, "date": "2022-11-10" }, { "rank": 36, "intensity": 2.571428571428571, "date": "2022-04-19" }, { "rank": 37, "intensity": 2.6020408163265305, "date": "2022-08-28" }, { "rank": 38, "intensity": 2.63265306122449, "date": "2022-02-19" }, { "rank": 39, "intensity": 2.663265306122449, "date": "2022-06-28" }, { "rank": 40, "intensity": 2.693877551020408, "date": "2022-08-11" }, { "rank": 41, "intensity": 2.7244897959183674, "date": "2022-12-09" }, { "rank": 42, "intensity": 2.7551020408163263, "date": "2022-11-07" }, { "rank": 43, "intensity": 2.7857142857142856, "date": "2022-11-20" }, { "rank": 44, "intensity": 2.816326530612245, "date": "2022-03-17" }, { "rank": 45, "intensity": 2.8469387755102042, "date": "2022-12-22" }, { "rank": 46, "intensity": 2.877551020408163, "date": "2022-06-01" }, { "rank": 47, "intensity": 2.9081632653061225, "date": "2022-11-01" }, { "rank": 48, "intensity": 2.9387755102040813, "date": "2022-04-14" }, { "rank": 49, "intensity": 2.9693877551020407, "date": "2022-07-03" }, { "rank": 50, "intensity": 3.0, "date": "2022-07-06" } ], "source": "causal_rivers" } }, { "id": "L3_T1_Composite_Trend_00201", "level": 3, "level_name": "Semantic Reasoning", "category": "Composite Trend", "subtask": "Composite Trend", "question": "Among days in channel 727 during 2023 that exhibit the trend pattern 'fluctuating stable, then steady stable', identify the top-5 days where the fluctuating stable segment is the most fluctuating. (Output format: a ranked list of dates, e.g., ['YYYY-MM-DD', ...])", "answer": "['2023-08-01', '2023-04-02', '2023-03-24', '2023-08-12', '2023-10-30']", "ground_truth": [ "2023-08-01", "2023-04-02", "2023-03-24", "2023-08-12", "2023-10-30" ], "eval_metric": "set_f1", "channel": "727", "ts_data_path": "ts_data/L3_T1_Composite_Trend_00201.csv", "meta": { "segments": [ { "kind": "stable", "adj": "fluctuating" }, { "kind": "stable", "adj": "steady" } ], "pattern_phrase": "fluctuating stable, then steady stable", "rank_target_idx": 0, "rank_kind": "stable", "rank_adj": "fluctuating", "rank_axis": "std", "rank_direction": "DESC", "rank_extreme": "most fluctuating", "top_segments_meta": [ { "adj": "fluctuating", "kind": "stable", "label": "fluctuating stable", "start_idx": 0, "end_idx": 38 }, { "adj": "steady", "kind": "stable", "label": "steady stable", "start_idx": 38, "end_idx": 98 } ], "points_per_day": 96, "pattern": "fluctuating stable, then steady stable", "year": 2023, "top_k": [ "2023-08-01", "2023-04-02", "2023-03-24", "2023-08-12", "2023-10-30" ], "all_injections": [ { "rank": 1, "intensity": 3.0, "date": "2023-08-01" }, { "rank": 2, "intensity": 2.9693877551020407, "date": "2023-04-02" }, { "rank": 3, "intensity": 2.938775510204082, "date": "2023-03-24" }, { "rank": 4, "intensity": 2.9081632653061225, "date": "2023-08-12" }, { "rank": 5, "intensity": 2.877551020408163, "date": "2023-10-30" }, { "rank": 6, "intensity": 2.8469387755102042, "date": "2023-02-16" }, { "rank": 7, "intensity": 2.816326530612245, "date": "2023-11-19" }, { "rank": 8, "intensity": 2.7857142857142856, "date": "2023-02-26" }, { "rank": 9, "intensity": 2.7551020408163267, "date": "2023-09-04" }, { "rank": 10, "intensity": 2.7244897959183674, "date": "2023-12-11" }, { "rank": 11, "intensity": 2.693877551020408, "date": "2023-01-24" }, { "rank": 12, "intensity": 2.663265306122449, "date": "2023-12-08" }, { "rank": 13, "intensity": 2.63265306122449, "date": "2023-07-17" }, { "rank": 14, "intensity": 2.6020408163265305, "date": "2023-01-07" }, { "rank": 15, "intensity": 2.5714285714285716, "date": "2023-08-06" }, { "rank": 16, "intensity": 2.5408163265306123, "date": "2023-10-13" }, { "rank": 17, "intensity": 2.510204081632653, "date": "2023-09-12" }, { "rank": 18, "intensity": 2.479591836734694, "date": "2023-01-02" }, { "rank": 19, "intensity": 2.4489795918367347, "date": "2023-07-12" }, { "rank": 20, "intensity": 2.4183673469387754, "date": "2023-05-25" }, { "rank": 21, "intensity": 2.387755102040816, "date": "2023-05-27" }, { "rank": 22, "intensity": 2.357142857142857, "date": "2023-12-21" }, { "rank": 23, "intensity": 2.326530612244898, "date": "2023-12-27" }, { "rank": 24, "intensity": 2.295918367346939, "date": "2023-11-13" }, { "rank": 25, "intensity": 2.2653061224489797, "date": "2023-06-23" }, { "rank": 26, "intensity": 2.2346938775510203, "date": "2023-06-20" }, { "rank": 27, "intensity": 2.204081632653061, "date": "2023-11-06" }, { "rank": 28, "intensity": 2.173469387755102, "date": "2023-05-07" }, { "rank": 29, "intensity": 2.142857142857143, "date": "2023-06-03" }, { "rank": 30, "intensity": 2.112244897959184, "date": "2023-06-27" }, { "rank": 31, "intensity": 2.0816326530612246, "date": "2023-06-07" }, { "rank": 32, "intensity": 2.0510204081632653, "date": "2023-08-29" }, { "rank": 33, "intensity": 2.020408163265306, "date": "2023-02-28" }, { "rank": 34, "intensity": 1.989795918367347, "date": "2023-03-03" }, { "rank": 35, "intensity": 1.9591836734693877, "date": "2023-05-12" }, { "rank": 36, "intensity": 1.9285714285714286, "date": "2023-02-14" }, { "rank": 37, "intensity": 1.8979591836734695, "date": "2023-05-05" }, { "rank": 38, "intensity": 1.8673469387755102, "date": "2023-03-29" }, { "rank": 39, "intensity": 1.836734693877551, "date": "2023-06-12" }, { "rank": 40, "intensity": 1.806122448979592, "date": "2023-11-21" }, { "rank": 41, "intensity": 1.7755102040816326, "date": "2023-03-27" }, { "rank": 42, "intensity": 1.7448979591836735, "date": "2023-08-09" }, { "rank": 43, "intensity": 1.7142857142857144, "date": "2023-02-02" }, { "rank": 44, "intensity": 1.683673469387755, "date": "2023-04-06" }, { "rank": 45, "intensity": 1.653061224489796, "date": "2023-01-27" }, { "rank": 46, "intensity": 1.6224489795918369, "date": "2023-10-05" }, { "rank": 47, "intensity": 1.5918367346938775, "date": "2023-04-10" }, { "rank": 48, "intensity": 1.5612244897959184, "date": "2023-04-08" }, { "rank": 49, "intensity": 1.5306122448979593, "date": "2023-11-27" }, { "rank": 50, "intensity": 1.5, "date": "2023-08-04" } ], "source": "causal_rivers" } }, { "id": "L3_T1_Composite_Trend_00202", "level": 3, "level_name": "Semantic Reasoning", "category": "Composite Trend", "subtask": "Composite Trend", "question": "Among days in channel 728 during 2020 that exhibit the trend pattern 'rise, then fluctuating stable', identify the top-5 days where the fluctuating stable segment is the most fluctuating. (Output format: a ranked list of dates, e.g., ['YYYY-MM-DD', ...])", "answer": "['2020-07-17', '2020-09-06', '2020-09-23', '2020-07-03', '2020-04-09']", "ground_truth": [ "2020-07-17", "2020-09-06", "2020-09-23", "2020-07-03", "2020-04-09" ], "eval_metric": "set_f1", "channel": "728", "ts_data_path": "ts_data/L3_T1_Composite_Trend_00202.csv", "meta": { "segments": [ { "kind": "rise", "adj": "none" }, { "kind": "stable", "adj": "fluctuating" } ], "pattern_phrase": "rise, then fluctuating stable", "rank_target_idx": 1, "rank_kind": "stable", "rank_adj": "fluctuating", "rank_axis": "std", "rank_direction": "DESC", "rank_extreme": "most fluctuating", "top_segments_meta": [ { "adj": "none", "kind": "rise", "label": "none rise", "start_idx": 0, "end_idx": 43 }, { "adj": "fluctuating", "kind": "stable", "label": "fluctuating stable", "start_idx": 43, "end_idx": 98 } ], "points_per_day": 96, "pattern": "rise, then fluctuating stable", "year": 2020, "top_k": [ "2020-07-17", "2020-09-06", "2020-09-23", "2020-07-03", "2020-04-09" ], "all_injections": [ { "rank": 1, "intensity": 3.0, "date": "2020-07-17" }, { "rank": 2, "intensity": 2.9693877551020407, "date": "2020-09-06" }, { "rank": 3, "intensity": 2.938775510204082, "date": "2020-09-23" }, { "rank": 4, "intensity": 2.9081632653061225, "date": "2020-07-03" }, { "rank": 5, "intensity": 2.877551020408163, "date": "2020-04-09" }, { "rank": 6, "intensity": 2.8469387755102042, "date": "2020-08-06" }, { "rank": 7, "intensity": 2.816326530612245, "date": "2020-01-18" }, { "rank": 8, "intensity": 2.7857142857142856, "date": "2020-11-02" }, { "rank": 9, "intensity": 2.7551020408163267, "date": "2020-08-27" }, { "rank": 10, "intensity": 2.7244897959183674, "date": "2020-12-23" }, { "rank": 11, "intensity": 2.693877551020408, "date": "2020-04-26" }, { "rank": 12, "intensity": 2.663265306122449, "date": "2020-08-30" }, { "rank": 13, "intensity": 2.63265306122449, "date": "2020-10-17" }, { "rank": 14, "intensity": 2.6020408163265305, "date": "2020-11-14" }, { "rank": 15, "intensity": 2.5714285714285716, "date": "2020-02-22" }, { "rank": 16, "intensity": 2.5408163265306123, "date": "2020-08-04" }, { "rank": 17, "intensity": 2.510204081632653, "date": "2020-02-28" }, { "rank": 18, "intensity": 2.479591836734694, "date": "2020-09-21" }, { "rank": 19, "intensity": 2.4489795918367347, "date": "2020-09-18" }, { "rank": 20, "intensity": 2.4183673469387754, "date": "2020-06-02" }, { "rank": 21, "intensity": 2.387755102040816, "date": "2020-05-04" }, { "rank": 22, "intensity": 2.357142857142857, "date": "2020-03-19" }, { "rank": 23, "intensity": 2.326530612244898, "date": "2020-08-09" }, { "rank": 24, "intensity": 2.295918367346939, "date": "2020-10-04" }, { "rank": 25, "intensity": 2.2653061224489797, "date": "2020-06-04" }, { "rank": 26, "intensity": 2.2346938775510203, "date": "2020-04-11" }, { "rank": 27, "intensity": 2.204081632653061, "date": "2020-05-02" }, { "rank": 28, "intensity": 2.173469387755102, "date": "2020-01-20" }, { "rank": 29, "intensity": 2.142857142857143, "date": "2020-02-20" }, { "rank": 30, "intensity": 2.112244897959184, "date": "2020-11-04" }, { "rank": 31, "intensity": 2.0816326530612246, "date": "2020-07-01" }, { "rank": 32, "intensity": 2.0510204081632653, "date": "2020-07-29" }, { "rank": 33, "intensity": 2.020408163265306, "date": "2020-04-22" }, { "rank": 34, "intensity": 1.989795918367347, "date": "2020-04-15" }, { "rank": 35, "intensity": 1.9591836734693877, "date": "2020-05-09" }, { "rank": 36, "intensity": 1.9285714285714286, "date": "2020-11-30" }, { "rank": 37, "intensity": 1.8979591836734695, "date": "2020-06-23" }, { "rank": 38, "intensity": 1.8673469387755102, "date": "2020-08-16" }, { "rank": 39, "intensity": 1.836734693877551, "date": "2020-03-05" }, { "rank": 40, "intensity": 1.806122448979592, "date": "2020-01-03" }, { "rank": 41, "intensity": 1.7755102040816326, "date": "2020-08-12" }, { "rank": 42, "intensity": 1.7448979591836735, "date": "2020-05-07" }, { "rank": 43, "intensity": 1.7142857142857144, "date": "2020-11-11" }, { "rank": 44, "intensity": 1.683673469387755, "date": "2020-05-25" }, { "rank": 45, "intensity": 1.653061224489796, "date": "2020-09-09" }, { "rank": 46, "intensity": 1.6224489795918369, "date": "2020-11-20" }, { "rank": 47, "intensity": 1.5918367346938775, "date": "2020-08-18" }, { "rank": 48, "intensity": 1.5612244897959184, "date": "2020-03-31" }, { "rank": 49, "intensity": 1.5306122448979593, "date": "2020-02-16" }, { "rank": 50, "intensity": 1.5, "date": "2020-08-14" } ], "source": "causal_rivers" } }, { "id": "L3_T1_Composite_Trend_00203", "level": 3, "level_name": "Semantic Reasoning", "category": "Composite Trend", "subtask": "Composite Trend", "question": "Among days in channel 729 during 2022 that exhibit the trend pattern 'rapid rise, then slow rise', identify the top-5 days where the rapid rise segment is the fastest. (Output format: a ranked list of dates, e.g., ['YYYY-MM-DD', ...])", "answer": "['2022-12-11', '2022-06-26', '2022-07-25', '2022-06-23', '2022-06-07']", "ground_truth": [ "2022-12-11", "2022-06-26", "2022-07-25", "2022-06-23", "2022-06-07" ], "eval_metric": "set_f1", "channel": "729", "ts_data_path": "ts_data/L3_T1_Composite_Trend_00203.csv", "meta": { "segments": [ { "kind": "rise", "adj": "rapid" }, { "kind": "rise", "adj": "slow" } ], "pattern_phrase": "rapid rise, then slow rise", "rank_target_idx": 0, "rank_kind": "rise", "rank_adj": "rapid", "rank_axis": "slope", "rank_direction": "DESC", "rank_extreme": "fastest", "top_segments_meta": [ { "adj": "rapid", "kind": "rise", "label": "rapid rise", "start_idx": 0, "end_idx": 23 }, { "adj": "slow", "kind": "rise", "label": "slow rise", "start_idx": 23, "end_idx": 98 } ], "points_per_day": 96, "pattern": "rapid rise, then slow rise", "year": 2022, "top_k": [ "2022-12-11", "2022-06-26", "2022-07-25", "2022-06-23", "2022-06-07" ], "all_injections": [ { "rank": 1, "intensity": 3.0, "date": "2022-12-11" }, { "rank": 2, "intensity": 2.9693877551020407, "date": "2022-06-26" }, { "rank": 3, "intensity": 2.938775510204082, "date": "2022-07-25" }, { "rank": 4, "intensity": 2.9081632653061225, "date": "2022-06-23" }, { "rank": 5, "intensity": 2.877551020408163, "date": "2022-06-07" }, { "rank": 6, "intensity": 2.8469387755102042, "date": "2022-06-21" }, { "rank": 7, "intensity": 2.816326530612245, "date": "2022-01-07" }, { "rank": 8, "intensity": 2.7857142857142856, "date": "2022-07-16" }, { "rank": 9, "intensity": 2.7551020408163267, "date": "2022-02-15" }, { "rank": 10, "intensity": 2.7244897959183674, "date": "2022-09-24" }, { "rank": 11, "intensity": 2.693877551020408, "date": "2022-01-17" }, { "rank": 12, "intensity": 2.663265306122449, "date": "2022-01-23" }, { "rank": 13, "intensity": 2.63265306122449, "date": "2022-08-20" }, { "rank": 14, "intensity": 2.6020408163265305, "date": "2022-02-21" }, { "rank": 15, "intensity": 2.5714285714285716, "date": "2022-07-12" }, { "rank": 16, "intensity": 2.5408163265306123, "date": "2022-05-15" }, { "rank": 17, "intensity": 2.510204081632653, "date": "2022-08-02" }, { "rank": 18, "intensity": 2.479591836734694, "date": "2022-11-26" }, { "rank": 19, "intensity": 2.4489795918367347, "date": "2022-03-23" }, { "rank": 20, "intensity": 2.4183673469387754, "date": "2022-01-10" }, { "rank": 21, "intensity": 2.387755102040816, "date": "2022-11-21" }, { "rank": 22, "intensity": 2.357142857142857, "date": "2022-11-08" }, { "rank": 23, "intensity": 2.326530612244898, "date": "2022-04-04" }, { "rank": 24, "intensity": 2.295918367346939, "date": "2022-08-04" }, { "rank": 25, "intensity": 2.2653061224489797, "date": "2022-03-19" }, { "rank": 26, "intensity": 2.2346938775510203, "date": "2022-07-18" }, { "rank": 27, "intensity": 2.204081632653061, "date": "2022-11-16" }, { "rank": 28, "intensity": 2.173469387755102, "date": "2022-05-10" }, { "rank": 29, "intensity": 2.142857142857143, "date": "2022-08-27" }, { "rank": 30, "intensity": 2.112244897959184, "date": "2022-12-16" }, { "rank": 31, "intensity": 2.0816326530612246, "date": "2022-05-08" }, { "rank": 32, "intensity": 2.0510204081632653, "date": "2022-09-26" }, { "rank": 33, "intensity": 2.020408163265306, "date": "2022-02-25" }, { "rank": 34, "intensity": 1.989795918367347, "date": "2022-10-31" }, { "rank": 35, "intensity": 1.9591836734693877, "date": "2022-02-05" }, { "rank": 36, "intensity": 1.9285714285714286, "date": "2022-09-08" }, { "rank": 37, "intensity": 1.8979591836734695, "date": "2022-12-13" }, { "rank": 38, "intensity": 1.8673469387755102, "date": "2022-05-25" }, { "rank": 39, "intensity": 1.836734693877551, "date": "2022-11-30" }, { "rank": 40, "intensity": 1.806122448979592, "date": "2022-10-12" }, { "rank": 41, "intensity": 1.7755102040816326, "date": "2022-03-25" }, { "rank": 42, "intensity": 1.7448979591836735, "date": "2022-05-31" }, { "rank": 43, "intensity": 1.7142857142857144, "date": "2022-09-11" }, { "rank": 44, "intensity": 1.683673469387755, "date": "2022-02-17" }, { "rank": 45, "intensity": 1.653061224489796, "date": "2022-09-02" }, { "rank": 46, "intensity": 1.6224489795918369, "date": "2022-04-12" }, { "rank": 47, "intensity": 1.5918367346938775, "date": "2022-11-10" }, { "rank": 48, "intensity": 1.5612244897959184, "date": "2022-07-30" }, { "rank": 49, "intensity": 1.5306122448979593, "date": "2022-02-11" }, { "rank": 50, "intensity": 1.5, "date": "2022-01-14" } ], "source": "causal_rivers" } }, { "id": "L3_T1_Composite_Trend_00204", "level": 3, "level_name": "Semantic Reasoning", "category": "Composite Trend", "subtask": "Composite Trend", "question": "Among days in channel 754 during 2020 that exhibit the trend pattern 'rapid rise, then fall, then rise', identify the top-5 days where the rapid rise segment is the fastest. (Output format: a ranked list of dates, e.g., ['YYYY-MM-DD', ...])", "answer": "['2020-04-08', '2020-03-28', '2020-05-07', '2020-08-14', '2020-12-05']", "ground_truth": [ "2020-04-08", "2020-03-28", "2020-05-07", "2020-08-14", "2020-12-05" ], "eval_metric": "set_f1", "channel": "754", "ts_data_path": "ts_data/L3_T1_Composite_Trend_00204.csv", "meta": { "segments": [ { "kind": "rise", "adj": "rapid" }, { "kind": "fall", "adj": "none" }, { "kind": "rise", "adj": "none" } ], "pattern_phrase": "rapid rise, then fall, then rise", "rank_target_idx": 0, "rank_kind": "rise", "rank_adj": "rapid", "rank_axis": "slope", "rank_direction": "DESC", "rank_extreme": "fastest", "top_segments_meta": [ { "adj": "rapid", "kind": "rise", "label": "rapid rise", "start_idx": 0, "end_idx": 21 }, { "adj": "none", "kind": "fall", "label": "none fall", "start_idx": 21, "end_idx": 58 }, { "adj": "none", "kind": "rise", "label": "none rise", "start_idx": 58, "end_idx": 98 } ], "points_per_day": 96, "pattern": "rapid rise, then fall, then rise", "year": 2020, "top_k": [ "2020-04-08", "2020-03-28", "2020-05-07", "2020-08-14", "2020-12-05" ], "all_injections": [ { "rank": 1, "intensity": 3.0, "date": "2020-04-08" }, { "rank": 2, "intensity": 2.9693877551020407, "date": "2020-03-28" }, { "rank": 3, "intensity": 2.938775510204082, "date": "2020-05-07" }, { "rank": 4, "intensity": 2.9081632653061225, "date": "2020-08-14" }, { "rank": 5, "intensity": 2.877551020408163, "date": "2020-12-05" }, { "rank": 6, "intensity": 2.8469387755102042, "date": "2020-03-18" }, { "rank": 7, "intensity": 2.816326530612245, "date": "2020-05-04" }, { "rank": 8, "intensity": 2.7857142857142856, "date": "2020-01-14" }, { "rank": 9, "intensity": 2.7551020408163267, "date": "2020-05-21" }, { "rank": 10, "intensity": 2.7244897959183674, "date": "2020-08-31" }, { "rank": 11, "intensity": 2.693877551020408, "date": "2020-12-13" }, { "rank": 12, "intensity": 2.663265306122449, "date": "2020-12-02" }, { "rank": 13, "intensity": 2.63265306122449, "date": "2020-11-15" }, { "rank": 14, "intensity": 2.6020408163265305, "date": "2020-08-22" }, { "rank": 15, "intensity": 2.5714285714285716, "date": "2020-10-31" }, { "rank": 16, "intensity": 2.5408163265306123, "date": "2020-05-19" }, { "rank": 17, "intensity": 2.510204081632653, "date": "2020-01-06" }, { "rank": 18, "intensity": 2.479591836734694, "date": "2020-04-23" }, { "rank": 19, "intensity": 2.4489795918367347, "date": "2020-02-16" }, { "rank": 20, "intensity": 2.4183673469387754, "date": "2020-12-22" }, { "rank": 21, "intensity": 2.387755102040816, "date": "2020-07-16" }, { "rank": 22, "intensity": 2.357142857142857, "date": "2020-02-24" }, { "rank": 23, "intensity": 2.326530612244898, "date": "2020-10-19" }, { "rank": 24, "intensity": 2.295918367346939, "date": "2020-04-27" }, { "rank": 25, "intensity": 2.2653061224489797, "date": "2020-09-24" }, { "rank": 26, "intensity": 2.2346938775510203, "date": "2020-10-10" }, { "rank": 27, "intensity": 2.204081632653061, "date": "2020-08-08" }, { "rank": 28, "intensity": 2.173469387755102, "date": "2020-06-21" }, { "rank": 29, "intensity": 2.142857142857143, "date": "2020-09-13" }, { "rank": 30, "intensity": 2.112244897959184, "date": "2020-09-27" }, { "rank": 31, "intensity": 2.0816326530612246, "date": "2020-03-23" }, { "rank": 32, "intensity": 2.0510204081632653, "date": "2020-05-17" }, { "rank": 33, "intensity": 2.020408163265306, "date": "2020-01-24" }, { "rank": 34, "intensity": 1.989795918367347, "date": "2020-11-21" }, { "rank": 35, "intensity": 1.9591836734693877, "date": "2020-05-13" }, { "rank": 36, "intensity": 1.9285714285714286, "date": "2020-05-29" }, { "rank": 37, "intensity": 1.8979591836734695, "date": "2020-08-11" }, { "rank": 38, "intensity": 1.8673469387755102, "date": "2020-08-18" }, { "rank": 39, "intensity": 1.836734693877551, "date": "2020-08-02" }, { "rank": 40, "intensity": 1.806122448979592, "date": "2020-02-14" }, { "rank": 41, "intensity": 1.7755102040816326, "date": "2020-06-25" }, { "rank": 42, "intensity": 1.7448979591836735, "date": "2020-11-25" }, { "rank": 43, "intensity": 1.7142857142857144, "date": "2020-04-02" }, { "rank": 44, "intensity": 1.683673469387755, "date": "2020-02-10" }, { "rank": 45, "intensity": 1.653061224489796, "date": "2020-05-23" }, { "rank": 46, "intensity": 1.6224489795918369, "date": "2020-02-28" }, { "rank": 47, "intensity": 1.5918367346938775, "date": "2020-03-30" }, { "rank": 48, "intensity": 1.5612244897959184, "date": "2020-09-19" }, { "rank": 49, "intensity": 1.5306122448979593, "date": "2020-02-03" }, { "rank": 50, "intensity": 1.5, "date": "2020-04-25" } ], "source": "causal_rivers" } }, { "id": "L3_T1_Composite_Trend_00205", "level": 3, "level_name": "Semantic Reasoning", "category": "Composite Trend", "subtask": "Composite Trend", "question": "Among days in channel 762 during 2021 that exhibit the trend pattern 'rise, then slow rise', identify the top-5 days where the slow rise segment is the slowest. (Output format: a ranked list of dates, e.g., ['YYYY-MM-DD', ...])", "answer": "['2021-03-29', '2021-09-21', '2021-01-10', '2021-01-27', '2021-08-28']", "ground_truth": [ "2021-03-29", "2021-09-21", "2021-01-10", "2021-01-27", "2021-08-28" ], "eval_metric": "set_f1", "channel": "762", "ts_data_path": "ts_data/L3_T1_Composite_Trend_00205.csv", "meta": { "segments": [ { "kind": "rise", "adj": "none" }, { "kind": "rise", "adj": "slow" } ], "pattern_phrase": "rise, then slow rise", "rank_target_idx": 1, "rank_kind": "rise", "rank_adj": "slow", "rank_axis": "slope", "rank_direction": "ASC", "rank_extreme": "slowest", "top_segments_meta": [ { "adj": "none", "kind": "rise", "label": "none rise", "start_idx": 0, "end_idx": 32 }, { "adj": "slow", "kind": "rise", "label": "slow rise", "start_idx": 32, "end_idx": 98 } ], "points_per_day": 96, "pattern": "rise, then slow rise", "year": 2021, "top_k": [ "2021-03-29", "2021-09-21", "2021-01-10", "2021-01-27", "2021-08-28" ], "all_injections": [ { "rank": 1, "intensity": 1.5, "date": "2021-03-29" }, { "rank": 2, "intensity": 1.530612244897959, "date": "2021-09-21" }, { "rank": 3, "intensity": 1.5612244897959184, "date": "2021-01-10" }, { "rank": 4, "intensity": 1.5918367346938775, "date": "2021-01-27" }, { "rank": 5, "intensity": 1.6224489795918366, "date": "2021-08-28" }, { "rank": 6, "intensity": 1.653061224489796, "date": "2021-08-05" }, { "rank": 7, "intensity": 1.683673469387755, "date": "2021-05-07" }, { "rank": 8, "intensity": 1.7142857142857142, "date": "2021-06-14" }, { "rank": 9, "intensity": 1.7448979591836735, "date": "2021-07-23" }, { "rank": 10, "intensity": 1.7755102040816326, "date": "2021-01-14" }, { "rank": 11, "intensity": 1.806122448979592, "date": "2021-07-09" }, { "rank": 12, "intensity": 1.836734693877551, "date": "2021-04-12" }, { "rank": 13, "intensity": 1.8673469387755102, "date": "2021-12-11" }, { "rank": 14, "intensity": 1.8979591836734695, "date": "2021-11-02" }, { "rank": 15, "intensity": 1.9285714285714286, "date": "2021-03-24" }, { "rank": 16, "intensity": 1.9591836734693877, "date": "2021-02-19" }, { "rank": 17, "intensity": 1.989795918367347, "date": "2021-08-30" }, { "rank": 18, "intensity": 2.020408163265306, "date": "2021-07-25" }, { "rank": 19, "intensity": 2.0510204081632653, "date": "2021-02-16" }, { "rank": 20, "intensity": 2.0816326530612246, "date": "2021-08-11" }, { "rank": 21, "intensity": 2.112244897959184, "date": "2021-06-02" }, { "rank": 22, "intensity": 2.142857142857143, "date": "2021-06-27" }, { "rank": 23, "intensity": 2.173469387755102, "date": "2021-05-21" }, { "rank": 24, "intensity": 2.204081632653061, "date": "2021-01-08" }, { "rank": 25, "intensity": 2.2346938775510203, "date": "2021-11-24" }, { "rank": 26, "intensity": 2.2653061224489797, "date": "2021-09-24" }, { "rank": 27, "intensity": 2.295918367346939, "date": "2021-09-12" }, { "rank": 28, "intensity": 2.326530612244898, "date": "2021-08-19" }, { "rank": 29, "intensity": 2.357142857142857, "date": "2021-03-05" }, { "rank": 30, "intensity": 2.387755102040816, "date": "2021-11-19" }, { "rank": 31, "intensity": 2.4183673469387754, "date": "2021-03-16" }, { "rank": 32, "intensity": 2.4489795918367347, "date": "2021-04-04" }, { "rank": 33, "intensity": 2.479591836734694, "date": "2021-12-26" }, { "rank": 34, "intensity": 2.510204081632653, "date": "2021-01-04" }, { "rank": 35, "intensity": 2.5408163265306123, "date": "2021-09-01" }, { "rank": 36, "intensity": 2.571428571428571, "date": "2021-09-14" }, { "rank": 37, "intensity": 2.6020408163265305, "date": "2021-12-13" }, { "rank": 38, "intensity": 2.63265306122449, "date": "2021-12-07" }, { "rank": 39, "intensity": 2.663265306122449, "date": "2021-02-14" }, { "rank": 40, "intensity": 2.693877551020408, "date": "2021-05-16" }, { "rank": 41, "intensity": 2.7244897959183674, "date": "2021-06-04" }, { "rank": 42, "intensity": 2.7551020408163263, "date": "2021-03-20" }, { "rank": 43, "intensity": 2.7857142857142856, "date": "2021-11-10" }, { "rank": 44, "intensity": 2.816326530612245, "date": "2021-01-29" }, { "rank": 45, "intensity": 2.8469387755102042, "date": "2021-02-24" }, { "rank": 46, "intensity": 2.877551020408163, "date": "2021-01-23" }, { "rank": 47, "intensity": 2.9081632653061225, "date": "2021-09-16" }, { "rank": 48, "intensity": 2.9387755102040813, "date": "2021-02-09" }, { "rank": 49, "intensity": 2.9693877551020407, "date": "2021-08-17" }, { "rank": 50, "intensity": 3.0, "date": "2021-06-06" } ], "source": "causal_rivers" } }, { "id": "L3_T1_Composite_Trend_00206", "level": 3, "level_name": "Semantic Reasoning", "category": "Composite Trend", "subtask": "Composite Trend", "question": "Among days in channel 811 during 2019 that exhibit the trend pattern 'fluctuating stable, then rapid fall, then fall', identify the top-5 days where the rapid fall segment is the fastest. (Output format: a ranked list of dates, e.g., ['YYYY-MM-DD', ...])", "answer": "['2019-03-30', '2019-07-30', '2019-10-12', '2019-01-23', '2019-11-29']", "ground_truth": [ "2019-03-30", "2019-07-30", "2019-10-12", "2019-01-23", "2019-11-29" ], "eval_metric": "set_f1", "channel": "811", "ts_data_path": "ts_data/L3_T1_Composite_Trend_00206.csv", "meta": { "segments": [ { "kind": "stable", "adj": "fluctuating" }, { "kind": "fall", "adj": "rapid" }, { "kind": "fall", "adj": "none" } ], "pattern_phrase": "fluctuating stable, then rapid fall, then fall", "rank_target_idx": 1, "rank_kind": "fall", "rank_adj": "rapid", "rank_axis": "slope", "rank_direction": "DESC", "rank_extreme": "fastest", "top_segments_meta": [ { "adj": "fluctuating", "kind": "stable", "label": "fluctuating stable", "start_idx": 0, "end_idx": 42 }, { "adj": "rapid", "kind": "fall", "label": "rapid fall", "start_idx": 42, "end_idx": 60 }, { "adj": "none", "kind": "fall", "label": "none fall", "start_idx": 60, "end_idx": 98 } ], "points_per_day": 96, "pattern": "fluctuating stable, then rapid fall, then fall", "year": 2019, "top_k": [ "2019-03-30", "2019-07-30", "2019-10-12", "2019-01-23", "2019-11-29" ], "all_injections": [ { "rank": 1, "intensity": 3.0, "date": "2019-03-30" }, { "rank": 2, "intensity": 2.9693877551020407, "date": "2019-07-30" }, { "rank": 3, "intensity": 2.938775510204082, "date": "2019-10-12" }, { "rank": 4, "intensity": 2.9081632653061225, "date": "2019-01-23" }, { "rank": 5, "intensity": 2.877551020408163, "date": "2019-11-29" }, { "rank": 6, "intensity": 2.8469387755102042, "date": "2019-05-02" }, { "rank": 7, "intensity": 2.816326530612245, "date": "2019-03-06" }, { "rank": 8, "intensity": 2.7857142857142856, "date": "2019-10-20" }, { "rank": 9, "intensity": 2.7551020408163267, "date": "2019-10-16" }, { "rank": 10, "intensity": 2.7244897959183674, "date": "2019-09-22" }, { "rank": 11, "intensity": 2.693877551020408, "date": "2019-09-11" }, { "rank": 12, "intensity": 2.663265306122449, "date": "2019-02-10" }, { "rank": 13, "intensity": 2.63265306122449, "date": "2019-03-14" }, { "rank": 14, "intensity": 2.6020408163265305, "date": "2019-06-26" }, { "rank": 15, "intensity": 2.5714285714285716, "date": "2019-05-04" }, { "rank": 16, "intensity": 2.5408163265306123, "date": "2019-04-14" }, { "rank": 17, "intensity": 2.510204081632653, "date": "2019-05-21" }, { "rank": 18, "intensity": 2.479591836734694, "date": "2019-09-16" }, { "rank": 19, "intensity": 2.4489795918367347, "date": "2019-07-04" }, { "rank": 20, "intensity": 2.4183673469387754, "date": "2019-07-06" }, { "rank": 21, "intensity": 2.387755102040816, "date": "2019-01-08" }, { "rank": 22, "intensity": 2.357142857142857, "date": "2019-02-28" }, { "rank": 23, "intensity": 2.326530612244898, "date": "2019-01-13" }, { "rank": 24, "intensity": 2.295918367346939, "date": "2019-07-13" }, { "rank": 25, "intensity": 2.2653061224489797, "date": "2019-05-23" }, { "rank": 26, "intensity": 2.2346938775510203, "date": "2019-05-18" }, { "rank": 27, "intensity": 2.204081632653061, "date": "2019-09-02" }, { "rank": 28, "intensity": 2.173469387755102, "date": "2019-09-04" }, { "rank": 29, "intensity": 2.142857142857143, "date": "2019-06-16" }, { "rank": 30, "intensity": 2.112244897959184, "date": "2019-07-10" }, { "rank": 31, "intensity": 2.0816326530612246, "date": "2019-08-24" }, { "rank": 32, "intensity": 2.0510204081632653, "date": "2019-07-15" }, { "rank": 33, "intensity": 2.020408163265306, "date": "2019-04-28" }, { "rank": 34, "intensity": 1.989795918367347, "date": "2019-07-27" }, { "rank": 35, "intensity": 1.9591836734693877, "date": "2019-07-08" }, { "rank": 36, "intensity": 1.9285714285714286, "date": "2019-08-15" }, { "rank": 37, "intensity": 1.8979591836734695, "date": "2019-12-23" }, { "rank": 38, "intensity": 1.8673469387755102, "date": "2019-06-30" }, { "rank": 39, "intensity": 1.836734693877551, "date": "2019-05-14" }, { "rank": 40, "intensity": 1.806122448979592, "date": "2019-08-30" }, { "rank": 41, "intensity": 1.7755102040816326, "date": "2019-05-06" }, { "rank": 42, "intensity": 1.7448979591836735, "date": "2019-06-08" }, { "rank": 43, "intensity": 1.7142857142857144, "date": "2019-02-04" }, { "rank": 44, "intensity": 1.683673469387755, "date": "2019-10-14" }, { "rank": 45, "intensity": 1.653061224489796, "date": "2019-12-11" }, { "rank": 46, "intensity": 1.6224489795918369, "date": "2019-02-22" }, { "rank": 47, "intensity": 1.5918367346938775, "date": "2019-12-07" }, { "rank": 48, "intensity": 1.5612244897959184, "date": "2019-05-25" }, { "rank": 49, "intensity": 1.5306122448979593, "date": "2019-09-06" }, { "rank": 50, "intensity": 1.5, "date": "2019-04-03" } ], "source": "causal_rivers" } }, { "id": "L3_T1_Composite_Trend_00207", "level": 3, "level_name": "Semantic Reasoning", "category": "Composite Trend", "subtask": "Composite Trend", "question": "Among days in channel 813 during 2023 that exhibit the trend pattern 'slow fall, then steady stable, then slow rise', identify the top-5 days where the slow fall segment is the slowest. (Output format: a ranked list of dates, e.g., ['YYYY-MM-DD', ...])", "answer": "['2023-08-01', '2023-11-16', '2023-12-05', '2023-07-30', '2023-08-18']", "ground_truth": [ "2023-08-01", "2023-11-16", "2023-12-05", "2023-07-30", "2023-08-18" ], "eval_metric": "set_f1", "channel": "813", "ts_data_path": "ts_data/L3_T1_Composite_Trend_00207.csv", "meta": { "segments": [ { "kind": "fall", "adj": "slow" }, { "kind": "stable", "adj": "steady" }, { "kind": "rise", "adj": "slow" } ], "pattern_phrase": "slow fall, then steady stable, then slow rise", "rank_target_idx": 0, "rank_kind": "fall", "rank_adj": "slow", "rank_axis": "slope", "rank_direction": "ASC", "rank_extreme": "slowest", "top_segments_meta": [ { "adj": "slow", "kind": "fall", "label": "slow fall", "start_idx": 0, "end_idx": 31 }, { "adj": "steady", "kind": "stable", "label": "steady stable", "start_idx": 31, "end_idx": 67 }, { "adj": "slow", "kind": "rise", "label": "slow rise", "start_idx": 67, "end_idx": 98 } ], "points_per_day": 96, "pattern": "slow fall, then steady stable, then slow rise", "year": 2023, "top_k": [ "2023-08-01", "2023-11-16", "2023-12-05", "2023-07-30", "2023-08-18" ], "all_injections": [ { "rank": 1, "intensity": 1.5, "date": "2023-08-01" }, { "rank": 2, "intensity": 1.530612244897959, "date": "2023-11-16" }, { "rank": 3, "intensity": 1.5612244897959184, "date": "2023-12-05" }, { "rank": 4, "intensity": 1.5918367346938775, "date": "2023-07-30" }, { "rank": 5, "intensity": 1.6224489795918366, "date": "2023-08-18" }, { "rank": 6, "intensity": 1.653061224489796, "date": "2023-10-11" }, { "rank": 7, "intensity": 1.683673469387755, "date": "2023-10-30" }, { "rank": 8, "intensity": 1.7142857142857142, "date": "2023-06-07" }, { "rank": 9, "intensity": 1.7448979591836735, "date": "2023-04-30" }, { "rank": 10, "intensity": 1.7755102040816326, "date": "2023-06-04" }, { "rank": 11, "intensity": 1.806122448979592, "date": "2023-07-08" }, { "rank": 12, "intensity": 1.836734693877551, "date": "2023-01-09" }, { "rank": 13, "intensity": 1.8673469387755102, "date": "2023-10-17" }, { "rank": 14, "intensity": 1.8979591836734695, "date": "2023-11-28" }, { "rank": 15, "intensity": 1.9285714285714286, "date": "2023-02-12" }, { "rank": 16, "intensity": 1.9591836734693877, "date": "2023-05-08" }, { "rank": 17, "intensity": 1.989795918367347, "date": "2023-05-05" }, { "rank": 18, "intensity": 2.020408163265306, "date": "2023-07-17" }, { "rank": 19, "intensity": 2.0510204081632653, "date": "2023-04-01" }, { "rank": 20, "intensity": 2.0816326530612246, "date": "2023-07-21" }, { "rank": 21, "intensity": 2.112244897959184, "date": "2023-09-09" }, { "rank": 22, "intensity": 2.142857142857143, "date": "2023-08-07" }, { "rank": 23, "intensity": 2.173469387755102, "date": "2023-12-17" }, { "rank": 24, "intensity": 2.204081632653061, "date": "2023-12-08" }, { "rank": 25, "intensity": 2.2346938775510203, "date": "2023-04-18" }, { "rank": 26, "intensity": 2.2653061224489797, "date": "2023-04-26" }, { "rank": 27, "intensity": 2.295918367346939, "date": "2023-06-25" }, { "rank": 28, "intensity": 2.326530612244898, "date": "2023-12-28" }, { "rank": 29, "intensity": 2.357142857142857, "date": "2023-06-14" }, { "rank": 30, "intensity": 2.387755102040816, "date": "2023-09-16" }, { "rank": 31, "intensity": 2.4183673469387754, "date": "2023-02-26" }, { "rank": 32, "intensity": 2.4489795918367347, "date": "2023-09-02" }, { "rank": 33, "intensity": 2.479591836734694, "date": "2023-03-30" }, { "rank": 34, "intensity": 2.510204081632653, "date": "2023-08-31" }, { "rank": 35, "intensity": 2.5408163265306123, "date": "2023-11-10" }, { "rank": 36, "intensity": 2.571428571428571, "date": "2023-10-15" }, { "rank": 37, "intensity": 2.6020408163265305, "date": "2023-10-05" }, { "rank": 38, "intensity": 2.63265306122449, "date": "2023-05-24" }, { "rank": 39, "intensity": 2.663265306122449, "date": "2023-01-15" }, { "rank": 40, "intensity": 2.693877551020408, "date": "2023-07-15" }, { "rank": 41, "intensity": 2.7244897959183674, "date": "2023-03-21" }, { "rank": 42, "intensity": 2.7551020408163263, "date": "2023-05-02" }, { "rank": 43, "intensity": 2.7857142857142856, "date": "2023-02-08" }, { "rank": 44, "intensity": 2.816326530612245, "date": "2023-03-02" }, { "rank": 45, "intensity": 2.8469387755102042, "date": "2023-09-14" }, { "rank": 46, "intensity": 2.877551020408163, "date": "2023-10-19" }, { "rank": 47, "intensity": 2.9081632653061225, "date": "2023-08-13" }, { "rank": 48, "intensity": 2.9387755102040813, "date": "2023-02-03" }, { "rank": 49, "intensity": 2.9693877551020407, "date": "2023-10-24" }, { "rank": 50, "intensity": 3.0, "date": "2023-08-21" } ], "source": "causal_rivers" } }, { "id": "L3_T1_Composite_Trend_00208", "level": 3, "level_name": "Semantic Reasoning", "category": "Composite Trend", "subtask": "Composite Trend", "question": "Among days in channel 865 during 2020 that exhibit the trend pattern 'fluctuating stable, then slow fall', identify the top-5 days where the fluctuating stable segment is the most fluctuating. (Output format: a ranked list of dates, e.g., ['YYYY-MM-DD', ...])", "answer": "['2020-02-06', '2020-07-31', '2020-03-06', '2020-07-20', '2020-03-02']", "ground_truth": [ "2020-02-06", "2020-07-31", "2020-03-06", "2020-07-20", "2020-03-02" ], "eval_metric": "set_f1", "channel": "865", "ts_data_path": "ts_data/L3_T1_Composite_Trend_00208.csv", "meta": { "segments": [ { "kind": "stable", "adj": "fluctuating" }, { "kind": "fall", "adj": "slow" } ], "pattern_phrase": "fluctuating stable, then slow fall", "rank_target_idx": 0, "rank_kind": "stable", "rank_adj": "fluctuating", "rank_axis": "std", "rank_direction": "DESC", "rank_extreme": "most fluctuating", "top_segments_meta": [ { "adj": "fluctuating", "kind": "stable", "label": "fluctuating stable", "start_idx": 0, "end_idx": 39 }, { "adj": "slow", "kind": "fall", "label": "slow fall", "start_idx": 39, "end_idx": 98 } ], "points_per_day": 96, "pattern": "fluctuating stable, then slow fall", "year": 2020, "top_k": [ "2020-02-06", "2020-07-31", "2020-03-06", "2020-07-20", "2020-03-02" ], "all_injections": [ { "rank": 1, "intensity": 3.0, "date": "2020-02-06" }, { "rank": 2, "intensity": 2.9693877551020407, "date": "2020-07-31" }, { "rank": 3, "intensity": 2.938775510204082, "date": "2020-03-06" }, { "rank": 4, "intensity": 2.9081632653061225, "date": "2020-07-20" }, { "rank": 5, "intensity": 2.877551020408163, "date": "2020-03-02" }, { "rank": 6, "intensity": 2.8469387755102042, "date": "2020-05-20" }, { "rank": 7, "intensity": 2.816326530612245, "date": "2020-06-29" }, { "rank": 8, "intensity": 2.7857142857142856, "date": "2020-07-08" }, { "rank": 9, "intensity": 2.7551020408163267, "date": "2020-12-19" }, { "rank": 10, "intensity": 2.7244897959183674, "date": "2020-03-14" }, { "rank": 11, "intensity": 2.693877551020408, "date": "2020-08-20" }, { "rank": 12, "intensity": 2.663265306122449, "date": "2020-07-06" }, { "rank": 13, "intensity": 2.63265306122449, "date": "2020-01-13" }, { "rank": 14, "intensity": 2.6020408163265305, "date": "2020-04-26" }, { "rank": 15, "intensity": 2.5714285714285716, "date": "2020-01-15" }, { "rank": 16, "intensity": 2.5408163265306123, "date": "2020-06-07" }, { "rank": 17, "intensity": 2.510204081632653, "date": "2020-10-04" }, { "rank": 18, "intensity": 2.479591836734694, "date": "2020-06-11" }, { "rank": 19, "intensity": 2.4489795918367347, "date": "2020-03-22" }, { "rank": 20, "intensity": 2.4183673469387754, "date": "2020-07-28" }, { "rank": 21, "intensity": 2.387755102040816, "date": "2020-01-25" }, { "rank": 22, "intensity": 2.357142857142857, "date": "2020-09-12" }, { "rank": 23, "intensity": 2.326530612244898, "date": "2020-08-24" }, { "rank": 24, "intensity": 2.295918367346939, "date": "2020-04-01" }, { "rank": 25, "intensity": 2.2653061224489797, "date": "2020-12-21" }, { "rank": 26, "intensity": 2.2346938775510203, "date": "2020-02-25" }, { "rank": 27, "intensity": 2.204081632653061, "date": "2020-10-18" }, { "rank": 28, "intensity": 2.173469387755102, "date": "2020-11-24" }, { "rank": 29, "intensity": 2.142857142857143, "date": "2020-09-21" }, { "rank": 30, "intensity": 2.112244897959184, "date": "2020-07-11" }, { "rank": 31, "intensity": 2.0816326530612246, "date": "2020-04-09" }, { "rank": 32, "intensity": 2.0510204081632653, "date": "2020-12-05" }, { "rank": 33, "intensity": 2.020408163265306, "date": "2020-10-09" }, { "rank": 34, "intensity": 1.989795918367347, "date": "2020-05-12" }, { "rank": 35, "intensity": 1.9591836734693877, "date": "2020-11-16" }, { "rank": 36, "intensity": 1.9285714285714286, "date": "2020-05-10" }, { "rank": 37, "intensity": 1.8979591836734695, "date": "2020-01-30" }, { "rank": 38, "intensity": 1.8673469387755102, "date": "2020-04-17" }, { "rank": 39, "intensity": 1.836734693877551, "date": "2020-06-25" }, { "rank": 40, "intensity": 1.806122448979592, "date": "2020-05-03" }, { "rank": 41, "intensity": 1.7755102040816326, "date": "2020-09-25" }, { "rank": 42, "intensity": 1.7448979591836735, "date": "2020-01-23" }, { "rank": 43, "intensity": 1.7142857142857144, "date": "2020-05-01" }, { "rank": 44, "intensity": 1.683673469387755, "date": "2020-09-09" }, { "rank": 45, "intensity": 1.653061224489796, "date": "2020-11-20" }, { "rank": 46, "intensity": 1.6224489795918369, "date": "2020-05-31" }, { "rank": 47, "intensity": 1.5918367346938775, "date": "2020-03-27" }, { "rank": 48, "intensity": 1.5612244897959184, "date": "2020-06-15" }, { "rank": 49, "intensity": 1.5306122448979593, "date": "2020-12-02" }, { "rank": 50, "intensity": 1.5, "date": "2020-05-26" } ], "source": "causal_rivers" } }, { "id": "L3_T1_Composite_Trend_00209", "level": 3, "level_name": "Semantic Reasoning", "category": "Composite Trend", "subtask": "Composite Trend", "question": "Among days in channel 891 during 2019 that exhibit the trend pattern 'steady stable, then slow fall', identify the top-5 days where the slow fall segment is the slowest. (Output format: a ranked list of dates, e.g., ['YYYY-MM-DD', ...])", "answer": "['2019-02-14', '2019-01-09', '2019-12-03', '2019-06-04', '2019-02-05']", "ground_truth": [ "2019-02-14", "2019-01-09", "2019-12-03", "2019-06-04", "2019-02-05" ], "eval_metric": "set_f1", "channel": "891", "ts_data_path": "ts_data/L3_T1_Composite_Trend_00209.csv", "meta": { "segments": [ { "kind": "stable", "adj": "steady" }, { "kind": "fall", "adj": "slow" } ], "pattern_phrase": "steady stable, then slow fall", "rank_target_idx": 1, "rank_kind": "fall", "rank_adj": "slow", "rank_axis": "slope", "rank_direction": "ASC", "rank_extreme": "slowest", "top_segments_meta": [ { "adj": "steady", "kind": "stable", "label": "steady stable", "start_idx": 0, "end_idx": 57 }, { "adj": "slow", "kind": "fall", "label": "slow fall", "start_idx": 57, "end_idx": 98 } ], "points_per_day": 96, "pattern": "steady stable, then slow fall", "year": 2019, "top_k": [ "2019-02-14", "2019-01-09", "2019-12-03", "2019-06-04", "2019-02-05" ], "all_injections": [ { "rank": 1, "intensity": 1.5, "date": "2019-02-14" }, { "rank": 2, "intensity": 1.530612244897959, "date": "2019-01-09" }, { "rank": 3, "intensity": 1.5612244897959184, "date": "2019-12-03" }, { "rank": 4, "intensity": 1.5918367346938775, "date": "2019-06-04" }, { "rank": 5, "intensity": 1.6224489795918366, "date": "2019-02-05" }, { "rank": 6, "intensity": 1.653061224489796, "date": "2019-04-16" }, { "rank": 7, "intensity": 1.683673469387755, "date": "2019-03-27" }, { "rank": 8, "intensity": 1.7142857142857142, "date": "2019-07-03" }, { "rank": 9, "intensity": 1.7448979591836735, "date": "2019-11-08" }, { "rank": 10, "intensity": 1.7755102040816326, "date": "2019-07-27" }, { "rank": 11, "intensity": 1.806122448979592, "date": "2019-03-21" }, { "rank": 12, "intensity": 1.836734693877551, "date": "2019-10-12" }, { "rank": 13, "intensity": 1.8673469387755102, "date": "2019-06-01" }, { "rank": 14, "intensity": 1.8979591836734695, "date": "2019-09-10" }, { "rank": 15, "intensity": 1.9285714285714286, "date": "2019-10-06" }, { "rank": 16, "intensity": 1.9591836734693877, "date": "2019-07-17" }, { "rank": 17, "intensity": 1.989795918367347, "date": "2019-07-25" }, { "rank": 18, "intensity": 2.020408163265306, "date": "2019-09-06" }, { "rank": 19, "intensity": 2.0510204081632653, "date": "2019-09-22" }, { "rank": 20, "intensity": 2.0816326530612246, "date": "2019-03-29" }, { "rank": 21, "intensity": 2.112244897959184, "date": "2019-12-13" }, { "rank": 22, "intensity": 2.142857142857143, "date": "2019-02-09" }, { "rank": 23, "intensity": 2.173469387755102, "date": "2019-01-04" }, { "rank": 24, "intensity": 2.204081632653061, "date": "2019-08-23" }, { "rank": 25, "intensity": 2.2346938775510203, "date": "2019-01-13" }, { "rank": 26, "intensity": 2.2653061224489797, "date": "2019-09-26" }, { "rank": 27, "intensity": 2.295918367346939, "date": "2019-10-08" }, { "rank": 28, "intensity": 2.326530612244898, "date": "2019-07-09" }, { "rank": 29, "intensity": 2.357142857142857, "date": "2019-12-30" }, { "rank": 30, "intensity": 2.387755102040816, "date": "2019-01-22" }, { "rank": 31, "intensity": 2.4183673469387754, "date": "2019-05-07" }, { "rank": 32, "intensity": 2.4489795918367347, "date": "2019-06-19" }, { "rank": 33, "intensity": 2.479591836734694, "date": "2019-12-05" }, { "rank": 34, "intensity": 2.510204081632653, "date": "2019-12-25" }, { "rank": 35, "intensity": 2.5408163265306123, "date": "2019-10-31" }, { "rank": 36, "intensity": 2.571428571428571, "date": "2019-03-09" }, { "rank": 37, "intensity": 2.6020408163265305, "date": "2019-04-11" }, { "rank": 38, "intensity": 2.63265306122449, "date": "2019-02-28" }, { "rank": 39, "intensity": 2.663265306122449, "date": "2019-01-27" }, { "rank": 40, "intensity": 2.693877551020408, "date": "2019-05-04" }, { "rank": 41, "intensity": 2.7244897959183674, "date": "2019-04-04" }, { "rank": 42, "intensity": 2.7551020408163263, "date": "2019-08-18" }, { "rank": 43, "intensity": 2.7857142857142856, "date": "2019-03-24" }, { "rank": 44, "intensity": 2.816326530612245, "date": "2019-06-28" }, { "rank": 45, "intensity": 2.8469387755102042, "date": "2019-10-23" }, { "rank": 46, "intensity": 2.877551020408163, "date": "2019-10-18" }, { "rank": 47, "intensity": 2.9081632653061225, "date": "2019-06-22" }, { "rank": 48, "intensity": 2.9387755102040813, "date": "2019-01-19" }, { "rank": 49, "intensity": 2.9693877551020407, "date": "2019-01-17" }, { "rank": 50, "intensity": 3.0, "date": "2019-06-07" } ], "source": "causal_rivers" } }, { "id": "L3_T1_Composite_Trend_00210", "level": 3, "level_name": "Semantic Reasoning", "category": "Composite Trend", "subtask": "Composite Trend", "question": "Among days in channel 894 during 2020 that exhibit the trend pattern 'slow fall, then fall', identify the top-5 days where the slow fall segment is the slowest. (Output format: a ranked list of dates, e.g., ['YYYY-MM-DD', ...])", "answer": "['2020-03-21', '2020-06-24', '2020-08-07', '2020-09-25', '2020-12-05']", "ground_truth": [ "2020-03-21", "2020-06-24", "2020-08-07", "2020-09-25", "2020-12-05" ], "eval_metric": "set_f1", "channel": "894", "ts_data_path": "ts_data/L3_T1_Composite_Trend_00210.csv", "meta": { "segments": [ { "kind": "fall", "adj": "slow" }, { "kind": "fall", "adj": "none" } ], "pattern_phrase": "slow fall, then fall", "rank_target_idx": 0, "rank_kind": "fall", "rank_adj": "slow", "rank_axis": "slope", "rank_direction": "ASC", "rank_extreme": "slowest", "top_segments_meta": [ { "adj": "slow", "kind": "fall", "label": "slow fall", "start_idx": 0, "end_idx": 62 }, { "adj": "none", "kind": "fall", "label": "none fall", "start_idx": 62, "end_idx": 98 } ], "points_per_day": 96, "pattern": "slow fall, then fall", "year": 2020, "top_k": [ "2020-03-21", "2020-06-24", "2020-08-07", "2020-09-25", "2020-12-05" ], "all_injections": [ { "rank": 1, "intensity": 1.5, "date": "2020-03-21" }, { "rank": 2, "intensity": 1.530612244897959, "date": "2020-06-24" }, { "rank": 3, "intensity": 1.5612244897959184, "date": "2020-08-07" }, { "rank": 4, "intensity": 1.5918367346938775, "date": "2020-09-25" }, { "rank": 5, "intensity": 1.6224489795918366, "date": "2020-12-05" }, { "rank": 6, "intensity": 1.653061224489796, "date": "2020-11-20" }, { "rank": 7, "intensity": 1.683673469387755, "date": "2020-03-25" }, { "rank": 8, "intensity": 1.7142857142857142, "date": "2020-12-11" }, { "rank": 9, "intensity": 1.7448979591836735, "date": "2020-10-12" }, { "rank": 10, "intensity": 1.7755102040816326, "date": "2020-04-03" }, { "rank": 11, "intensity": 1.806122448979592, "date": "2020-04-24" }, { "rank": 12, "intensity": 1.836734693877551, "date": "2020-01-20" }, { "rank": 13, "intensity": 1.8673469387755102, "date": "2020-08-04" }, { "rank": 14, "intensity": 1.8979591836734695, "date": "2020-02-14" }, { "rank": 15, "intensity": 1.9285714285714286, "date": "2020-01-29" }, { "rank": 16, "intensity": 1.9591836734693877, "date": "2020-08-20" }, { "rank": 17, "intensity": 1.989795918367347, "date": "2020-03-09" }, { "rank": 18, "intensity": 2.020408163265306, "date": "2020-08-27" }, { "rank": 19, "intensity": 2.0510204081632653, "date": "2020-06-02" }, { "rank": 20, "intensity": 2.0816326530612246, "date": "2020-03-13" }, { "rank": 21, "intensity": 2.112244897959184, "date": "2020-04-19" }, { "rank": 22, "intensity": 2.142857142857143, "date": "2020-12-13" }, { "rank": 23, "intensity": 2.173469387755102, "date": "2020-04-30" }, { "rank": 24, "intensity": 2.204081632653061, "date": "2020-08-17" }, { "rank": 25, "intensity": 2.2346938775510203, "date": "2020-03-01" }, { "rank": 26, "intensity": 2.2653061224489797, "date": "2020-01-06" }, { "rank": 27, "intensity": 2.295918367346939, "date": "2020-03-27" }, { "rank": 28, "intensity": 2.326530612244898, "date": "2020-09-20" }, { "rank": 29, "intensity": 2.357142857142857, "date": "2020-09-27" }, { "rank": 30, "intensity": 2.387755102040816, "date": "2020-07-25" }, { "rank": 31, "intensity": 2.4183673469387754, "date": "2020-09-02" }, { "rank": 32, "intensity": 2.4489795918367347, "date": "2020-12-18" }, { "rank": 33, "intensity": 2.479591836734694, "date": "2020-02-16" }, { "rank": 34, "intensity": 2.510204081632653, "date": "2020-03-23" }, { "rank": 35, "intensity": 2.5408163265306123, "date": "2020-06-27" }, { "rank": 36, "intensity": 2.571428571428571, "date": "2020-10-06" }, { "rank": 37, "intensity": 2.6020408163265305, "date": "2020-12-07" }, { "rank": 38, "intensity": 2.63265306122449, "date": "2020-09-16" }, { "rank": 39, "intensity": 2.663265306122449, "date": "2020-12-22" }, { "rank": 40, "intensity": 2.693877551020408, "date": "2020-03-31" }, { "rank": 41, "intensity": 2.7244897959183674, "date": "2020-10-24" }, { "rank": 42, "intensity": 2.7551020408163263, "date": "2020-04-10" }, { "rank": 43, "intensity": 2.7857142857142856, "date": "2020-11-27" }, { "rank": 44, "intensity": 2.816326530612245, "date": "2020-04-05" }, { "rank": 45, "intensity": 2.8469387755102042, "date": "2020-11-09" }, { "rank": 46, "intensity": 2.877551020408163, "date": "2020-05-15" }, { "rank": 47, "intensity": 2.9081632653061225, "date": "2020-07-20" }, { "rank": 48, "intensity": 2.9387755102040813, "date": "2020-02-04" }, { "rank": 49, "intensity": 2.9693877551020407, "date": "2020-03-06" }, { "rank": 50, "intensity": 3.0, "date": "2020-02-12" } ], "source": "causal_rivers" } }, { "id": "L3_T1_Composite_Trend_00211", "level": 3, "level_name": "Semantic Reasoning", "category": "Composite Trend", "subtask": "Composite Trend", "question": "Among days in channel 895 during 2021 that exhibit the trend pattern 'steady stable, then rapid rise', identify the top-5 days where the rapid rise segment is the fastest. (Output format: a ranked list of dates, e.g., ['YYYY-MM-DD', ...])", "answer": "['2021-03-28', '2021-03-16', '2021-11-15', '2021-07-03', '2021-04-15']", "ground_truth": [ "2021-03-28", "2021-03-16", "2021-11-15", "2021-07-03", "2021-04-15" ], "eval_metric": "set_f1", "channel": "895", "ts_data_path": "ts_data/L3_T1_Composite_Trend_00211.csv", "meta": { "segments": [ { "kind": "stable", "adj": "steady" }, { "kind": "rise", "adj": "rapid" } ], "pattern_phrase": "steady stable, then rapid rise", "rank_target_idx": 1, "rank_kind": "rise", "rank_adj": "rapid", "rank_axis": "slope", "rank_direction": "DESC", "rank_extreme": "fastest", "top_segments_meta": [ { "adj": "steady", "kind": "stable", "label": "steady stable", "start_idx": 0, "end_idx": 78 }, { "adj": "rapid", "kind": "rise", "label": "rapid rise", "start_idx": 78, "end_idx": 98 } ], "points_per_day": 96, "pattern": "steady stable, then rapid rise", "year": 2021, "top_k": [ "2021-03-28", "2021-03-16", "2021-11-15", "2021-07-03", "2021-04-15" ], "all_injections": [ { "rank": 1, "intensity": 3.0, "date": "2021-03-28" }, { "rank": 2, "intensity": 2.9693877551020407, "date": "2021-03-16" }, { "rank": 3, "intensity": 2.938775510204082, "date": "2021-11-15" }, { "rank": 4, "intensity": 2.9081632653061225, "date": "2021-07-03" }, { "rank": 5, "intensity": 2.877551020408163, "date": "2021-04-15" }, { "rank": 6, "intensity": 2.8469387755102042, "date": "2021-06-10" }, { "rank": 7, "intensity": 2.816326530612245, "date": "2021-04-17" }, { "rank": 8, "intensity": 2.7857142857142856, "date": "2021-01-17" }, { "rank": 9, "intensity": 2.7551020408163267, "date": "2021-01-04" }, { "rank": 10, "intensity": 2.7244897959183674, "date": "2021-02-20" }, { "rank": 11, "intensity": 2.693877551020408, "date": "2021-09-01" }, { "rank": 12, "intensity": 2.663265306122449, "date": "2021-11-21" }, { "rank": 13, "intensity": 2.63265306122449, "date": "2021-03-01" }, { "rank": 14, "intensity": 2.6020408163265305, "date": "2021-10-23" }, { "rank": 15, "intensity": 2.5714285714285716, "date": "2021-12-15" }, { "rank": 16, "intensity": 2.5408163265306123, "date": "2021-03-04" }, { "rank": 17, "intensity": 2.510204081632653, "date": "2021-10-14" }, { "rank": 18, "intensity": 2.479591836734694, "date": "2021-08-02" }, { "rank": 19, "intensity": 2.4489795918367347, "date": "2021-09-10" }, { "rank": 20, "intensity": 2.4183673469387754, "date": "2021-10-10" }, { "rank": 21, "intensity": 2.387755102040816, "date": "2021-07-10" }, { "rank": 22, "intensity": 2.357142857142857, "date": "2021-06-17" }, { "rank": 23, "intensity": 2.326530612244898, "date": "2021-01-31" }, { "rank": 24, "intensity": 2.295918367346939, "date": "2021-12-28" }, { "rank": 25, "intensity": 2.2653061224489797, "date": "2021-10-30" }, { "rank": 26, "intensity": 2.2346938775510203, "date": "2021-05-15" }, { "rank": 27, "intensity": 2.204081632653061, "date": "2021-06-22" }, { "rank": 28, "intensity": 2.173469387755102, "date": "2021-07-22" }, { "rank": 29, "intensity": 2.142857142857143, "date": "2021-10-08" }, { "rank": 30, "intensity": 2.112244897959184, "date": "2021-06-01" }, { "rank": 31, "intensity": 2.0816326530612246, "date": "2021-02-16" }, { "rank": 32, "intensity": 2.0510204081632653, "date": "2021-12-07" }, { "rank": 33, "intensity": 2.020408163265306, "date": "2021-05-18" }, { "rank": 34, "intensity": 1.989795918367347, "date": "2021-05-05" }, { "rank": 35, "intensity": 1.9591836734693877, "date": "2021-10-28" }, { "rank": 36, "intensity": 1.9285714285714286, "date": "2021-02-05" }, { "rank": 37, "intensity": 1.8979591836734695, "date": "2021-01-21" }, { "rank": 38, "intensity": 1.8673469387755102, "date": "2021-08-15" }, { "rank": 39, "intensity": 1.836734693877551, "date": "2021-12-26" }, { "rank": 40, "intensity": 1.806122448979592, "date": "2021-05-07" }, { "rank": 41, "intensity": 1.7755102040816326, "date": "2021-12-09" }, { "rank": 42, "intensity": 1.7448979591836735, "date": "2021-09-15" }, { "rank": 43, "intensity": 1.7142857142857144, "date": "2021-05-22" }, { "rank": 44, "intensity": 1.683673469387755, "date": "2021-08-04" }, { "rank": 45, "intensity": 1.653061224489796, "date": "2021-09-27" }, { "rank": 46, "intensity": 1.6224489795918369, "date": "2021-12-13" }, { "rank": 47, "intensity": 1.5918367346938775, "date": "2021-07-29" }, { "rank": 48, "intensity": 1.5612244897959184, "date": "2021-02-18" }, { "rank": 49, "intensity": 1.5306122448979593, "date": "2021-01-15" }, { "rank": 50, "intensity": 1.5, "date": "2021-06-27" } ], "source": "causal_rivers" } }, { "id": "L3_T1_Composite_Trend_00212", "level": 3, "level_name": "Semantic Reasoning", "category": "Composite Trend", "subtask": "Composite Trend", "question": "Among days in channel 897 during 2019 that exhibit the trend pattern 'slow rise, then rise, then rapid fall', identify the top-5 days where the rapid fall segment is the fastest. (Output format: a ranked list of dates, e.g., ['YYYY-MM-DD', ...])", "answer": "['2019-12-06', '2019-02-25', '2019-12-18', '2019-09-09', '2019-11-26']", "ground_truth": [ "2019-12-06", "2019-02-25", "2019-12-18", "2019-09-09", "2019-11-26" ], "eval_metric": "set_f1", "channel": "897", "ts_data_path": "ts_data/L3_T1_Composite_Trend_00212.csv", "meta": { "segments": [ { "kind": "rise", "adj": "slow" }, { "kind": "rise", "adj": "none" }, { "kind": "fall", "adj": "rapid" } ], "pattern_phrase": "slow rise, then rise, then rapid fall", "rank_target_idx": 2, "rank_kind": "fall", "rank_adj": "rapid", "rank_axis": "slope", "rank_direction": "DESC", "rank_extreme": "fastest", "top_segments_meta": [ { "adj": "slow", "kind": "rise", "label": "slow rise", "start_idx": 0, "end_idx": 53 }, { "adj": "none", "kind": "rise", "label": "none rise", "start_idx": 53, "end_idx": 83 }, { "adj": "rapid", "kind": "fall", "label": "rapid fall", "start_idx": 83, "end_idx": 98 } ], "points_per_day": 96, "pattern": "slow rise, then rise, then rapid fall", "year": 2019, "top_k": [ "2019-12-06", "2019-02-25", "2019-12-18", "2019-09-09", "2019-11-26" ], "all_injections": [ { "rank": 1, "intensity": 3.0, "date": "2019-12-06" }, { "rank": 2, "intensity": 2.9693877551020407, "date": "2019-02-25" }, { "rank": 3, "intensity": 2.938775510204082, "date": "2019-12-18" }, { "rank": 4, "intensity": 2.9081632653061225, "date": "2019-09-09" }, { "rank": 5, "intensity": 2.877551020408163, "date": "2019-11-26" }, { "rank": 6, "intensity": 2.8469387755102042, "date": "2019-07-14" }, { "rank": 7, "intensity": 2.816326530612245, "date": "2019-04-12" }, { "rank": 8, "intensity": 2.7857142857142856, "date": "2019-10-21" }, { "rank": 9, "intensity": 2.7551020408163267, "date": "2019-02-22" }, { "rank": 10, "intensity": 2.7244897959183674, "date": "2019-05-13" }, { "rank": 11, "intensity": 2.693877551020408, "date": "2019-03-22" }, { "rank": 12, "intensity": 2.663265306122449, "date": "2019-11-09" }, { "rank": 13, "intensity": 2.63265306122449, "date": "2019-07-23" }, { "rank": 14, "intensity": 2.6020408163265305, "date": "2019-08-17" }, { "rank": 15, "intensity": 2.5714285714285716, "date": "2019-02-03" }, { "rank": 16, "intensity": 2.5408163265306123, "date": "2019-04-08" }, { "rank": 17, "intensity": 2.510204081632653, "date": "2019-02-17" }, { "rank": 18, "intensity": 2.479591836734694, "date": "2019-11-03" }, { "rank": 19, "intensity": 2.4489795918367347, "date": "2019-10-15" }, { "rank": 20, "intensity": 2.4183673469387754, "date": "2019-01-14" }, { "rank": 21, "intensity": 2.387755102040816, "date": "2019-10-18" }, { "rank": 22, "intensity": 2.357142857142857, "date": "2019-08-22" }, { "rank": 23, "intensity": 2.326530612244898, "date": "2019-05-20" }, { "rank": 24, "intensity": 2.295918367346939, "date": "2019-03-12" }, { "rank": 25, "intensity": 2.2653061224489797, "date": "2019-05-28" }, { "rank": 26, "intensity": 2.2346938775510203, "date": "2019-08-07" }, { "rank": 27, "intensity": 2.204081632653061, "date": "2019-11-23" }, { "rank": 28, "intensity": 2.173469387755102, "date": "2019-07-09" }, { "rank": 29, "intensity": 2.142857142857143, "date": "2019-06-09" }, { "rank": 30, "intensity": 2.112244897959184, "date": "2019-04-04" }, { "rank": 31, "intensity": 2.0816326530612246, "date": "2019-05-10" }, { "rank": 32, "intensity": 2.0510204081632653, "date": "2019-12-27" }, { "rank": 33, "intensity": 2.020408163265306, "date": "2019-10-07" }, { "rank": 34, "intensity": 1.989795918367347, "date": "2019-08-28" }, { "rank": 35, "intensity": 1.9591836734693877, "date": "2019-02-28" }, { "rank": 36, "intensity": 1.9285714285714286, "date": "2019-05-07" }, { "rank": 37, "intensity": 1.8979591836734695, "date": "2019-01-16" }, { "rank": 38, "intensity": 1.8673469387755102, "date": "2019-09-06" }, { "rank": 39, "intensity": 1.836734693877551, "date": "2019-07-02" }, { "rank": 40, "intensity": 1.806122448979592, "date": "2019-12-22" }, { "rank": 41, "intensity": 1.7755102040816326, "date": "2019-03-02" }, { "rank": 42, "intensity": 1.7448979591836735, "date": "2019-06-13" }, { "rank": 43, "intensity": 1.7142857142857144, "date": "2019-06-21" }, { "rank": 44, "intensity": 1.683673469387755, "date": "2019-10-11" }, { "rank": 45, "intensity": 1.653061224489796, "date": "2019-02-07" }, { "rank": 46, "intensity": 1.6224489795918369, "date": "2019-06-11" }, { "rank": 47, "intensity": 1.5918367346938775, "date": "2019-12-10" }, { "rank": 48, "intensity": 1.5612244897959184, "date": "2019-01-27" }, { "rank": 49, "intensity": 1.5306122448979593, "date": "2019-12-25" }, { "rank": 50, "intensity": 1.5, "date": "2019-10-27" } ], "source": "causal_rivers" } }, { "id": "L3_T1_Composite_Trend_00213", "level": 3, "level_name": "Semantic Reasoning", "category": "Composite Trend", "subtask": "Composite Trend", "question": "Among days in channel 933 during 2022 that exhibit the trend pattern 'slow rise, then rapid fall, then steady stable', identify the top-5 days where the rapid fall segment is the fastest. (Output format: a ranked list of dates, e.g., ['YYYY-MM-DD', ...])", "answer": "['2022-11-13', '2022-06-21', '2022-05-12', '2022-11-16', '2022-10-21']", "ground_truth": [ "2022-11-13", "2022-06-21", "2022-05-12", "2022-11-16", "2022-10-21" ], "eval_metric": "set_f1", "channel": "933", "ts_data_path": "ts_data/L3_T1_Composite_Trend_00213.csv", "meta": { "segments": [ { "kind": "rise", "adj": "slow" }, { "kind": "fall", "adj": "rapid" }, { "kind": "stable", "adj": "steady" } ], "pattern_phrase": "slow rise, then rapid fall, then steady stable", "rank_target_idx": 1, "rank_kind": "fall", "rank_adj": "rapid", "rank_axis": "slope", "rank_direction": "DESC", "rank_extreme": "fastest", "top_segments_meta": [ { "adj": "slow", "kind": "rise", "label": "slow rise", "start_idx": 0, "end_idx": 40 }, { "adj": "rapid", "kind": "fall", "label": "rapid fall", "start_idx": 40, "end_idx": 52 }, { "adj": "steady", "kind": "stable", "label": "steady stable", "start_idx": 52, "end_idx": 98 } ], "points_per_day": 96, "pattern": "slow rise, then rapid fall, then steady stable", "year": 2022, "top_k": [ "2022-11-13", "2022-06-21", "2022-05-12", "2022-11-16", "2022-10-21" ], "all_injections": [ { "rank": 1, "intensity": 3.0, "date": "2022-11-13" }, { "rank": 2, "intensity": 2.9693877551020407, "date": "2022-06-21" }, { "rank": 3, "intensity": 2.938775510204082, "date": "2022-05-12" }, { "rank": 4, "intensity": 2.9081632653061225, "date": "2022-11-16" }, { "rank": 5, "intensity": 2.877551020408163, "date": "2022-10-21" }, { "rank": 6, "intensity": 2.8469387755102042, "date": "2022-10-28" }, { "rank": 7, "intensity": 2.816326530612245, "date": "2022-11-24" }, { "rank": 8, "intensity": 2.7857142857142856, "date": "2022-12-16" }, { "rank": 9, "intensity": 2.7551020408163267, "date": "2022-09-16" }, { "rank": 10, "intensity": 2.7244897959183674, "date": "2022-12-14" }, { "rank": 11, "intensity": 2.693877551020408, "date": "2022-04-15" }, { "rank": 12, "intensity": 2.663265306122449, "date": "2022-01-29" }, { "rank": 13, "intensity": 2.63265306122449, "date": "2022-12-23" }, { "rank": 14, "intensity": 2.6020408163265305, "date": "2022-12-28" }, { "rank": 15, "intensity": 2.5714285714285716, "date": "2022-02-23" }, { "rank": 16, "intensity": 2.5408163265306123, "date": "2022-01-11" }, { "rank": 17, "intensity": 2.510204081632653, "date": "2022-08-25" }, { "rank": 18, "intensity": 2.479591836734694, "date": "2022-10-23" }, { "rank": 19, "intensity": 2.4489795918367347, "date": "2022-12-10" }, { "rank": 20, "intensity": 2.4183673469387754, "date": "2022-05-16" }, { "rank": 21, "intensity": 2.387755102040816, "date": "2022-03-23" }, { "rank": 22, "intensity": 2.357142857142857, "date": "2022-05-20" }, { "rank": 23, "intensity": 2.326530612244898, "date": "2022-07-14" }, { "rank": 24, "intensity": 2.295918367346939, "date": "2022-12-01" }, { "rank": 25, "intensity": 2.2653061224489797, "date": "2022-06-12" }, { "rank": 26, "intensity": 2.2346938775510203, "date": "2022-06-10" }, { "rank": 27, "intensity": 2.204081632653061, "date": "2022-08-28" }, { "rank": 28, "intensity": 2.173469387755102, "date": "2022-02-26" }, { "rank": 29, "intensity": 2.142857142857143, "date": "2022-09-05" }, { "rank": 30, "intensity": 2.112244897959184, "date": "2022-01-24" }, { "rank": 31, "intensity": 2.0816326530612246, "date": "2022-12-04" }, { "rank": 32, "intensity": 2.0510204081632653, "date": "2022-03-09" }, { "rank": 33, "intensity": 2.020408163265306, "date": "2022-01-18" }, { "rank": 34, "intensity": 1.989795918367347, "date": "2022-04-07" }, { "rank": 35, "intensity": 1.9591836734693877, "date": "2022-10-10" }, { "rank": 36, "intensity": 1.9285714285714286, "date": "2022-03-28" }, { "rank": 37, "intensity": 1.8979591836734695, "date": "2022-01-15" }, { "rank": 38, "intensity": 1.8673469387755102, "date": "2022-10-16" }, { "rank": 39, "intensity": 1.836734693877551, "date": "2022-01-21" }, { "rank": 40, "intensity": 1.806122448979592, "date": "2022-03-14" }, { "rank": 41, "intensity": 1.7755102040816326, "date": "2022-07-19" }, { "rank": 42, "intensity": 1.7448979591836735, "date": "2022-06-05" }, { "rank": 43, "intensity": 1.7142857142857144, "date": "2022-09-18" }, { "rank": 44, "intensity": 1.683673469387755, "date": "2022-10-12" }, { "rank": 45, "intensity": 1.653061224489796, "date": "2022-03-26" }, { "rank": 46, "intensity": 1.6224489795918369, "date": "2022-02-19" }, { "rank": 47, "intensity": 1.5918367346938775, "date": "2022-09-01" }, { "rank": 48, "intensity": 1.5612244897959184, "date": "2022-07-23" }, { "rank": 49, "intensity": 1.5306122448979593, "date": "2022-06-25" }, { "rank": 50, "intensity": 1.5, "date": "2022-06-08" } ], "source": "causal_rivers" } }, { "id": "L3_T1_Composite_Trend_00214", "level": 3, "level_name": "Semantic Reasoning", "category": "Composite Trend", "subtask": "Composite Trend", "question": "Among days in channel HUFL during 2017 that exhibit the trend pattern 'steady stable, then rapid rise, then steady stable', identify the top-5 days where the rapid rise segment is the fastest. (Output format: a ranked list of dates, e.g., ['YYYY-MM-DD', ...])", "answer": "['2017-10-14', '2017-07-31', '2017-08-13', '2017-10-18', '2017-01-18']", "ground_truth": [ "2017-10-14", "2017-07-31", "2017-08-13", "2017-10-18", "2017-01-18" ], "eval_metric": "set_f1", "channel": "HUFL", "ts_data_path": "ts_data/L3_T1_Composite_Trend_00214.csv", "meta": { "segments": [ { "kind": "stable", "adj": "steady" }, { "kind": "rise", "adj": "rapid" }, { "kind": "stable", "adj": "steady" } ], "pattern_phrase": "steady stable, then rapid rise, then steady stable", "rank_target_idx": 1, "rank_kind": "rise", "rank_adj": "rapid", "rank_axis": "slope", "rank_direction": "DESC", "rank_extreme": "fastest", "top_segments_meta": [ { "adj": "steady", "kind": "stable", "label": "steady stable", "start_idx": 0, "end_idx": 40 }, { "adj": "rapid", "kind": "rise", "label": "rapid rise", "start_idx": 40, "end_idx": 51 }, { "adj": "steady", "kind": "stable", "label": "steady stable", "start_idx": 51, "end_idx": 98 } ], "points_per_day": 96, "pattern": "steady stable, then rapid rise, then steady stable", "year": 2017, "top_k": [ "2017-10-14", "2017-07-31", "2017-08-13", "2017-10-18", "2017-01-18" ], "all_injections": [ { "rank": 1, "intensity": 3.0, "date": "2017-10-14" }, { "rank": 2, "intensity": 2.9693877551020407, "date": "2017-07-31" }, { "rank": 3, "intensity": 2.938775510204082, "date": "2017-08-13" }, { "rank": 4, "intensity": 2.9081632653061225, "date": "2017-10-18" }, { "rank": 5, "intensity": 2.877551020408163, "date": "2017-01-18" }, { "rank": 6, "intensity": 2.8469387755102042, "date": "2017-03-17" }, { "rank": 7, "intensity": 2.816326530612245, "date": "2017-06-29" }, { "rank": 8, "intensity": 2.7857142857142856, "date": "2017-06-26" }, { "rank": 9, "intensity": 2.7551020408163267, "date": "2017-06-24" }, { "rank": 10, "intensity": 2.7244897959183674, "date": "2017-12-07" }, { "rank": 11, "intensity": 2.693877551020408, "date": "2017-03-14" }, { "rank": 12, "intensity": 2.663265306122449, "date": "2017-09-22" }, { "rank": 13, "intensity": 2.63265306122449, "date": "2017-04-09" }, { "rank": 14, "intensity": 2.6020408163265305, "date": "2017-05-30" }, { "rank": 15, "intensity": 2.5714285714285716, "date": "2017-07-17" }, { "rank": 16, "intensity": 2.5408163265306123, "date": "2017-03-12" }, { "rank": 17, "intensity": 2.510204081632653, "date": "2017-02-25" }, { "rank": 18, "intensity": 2.479591836734694, "date": "2017-04-24" }, { "rank": 19, "intensity": 2.4489795918367347, "date": "2017-06-15" }, { "rank": 20, "intensity": 2.4183673469387754, "date": "2017-04-02" }, { "rank": 21, "intensity": 2.387755102040816, "date": "2017-12-20" }, { "rank": 22, "intensity": 2.357142857142857, "date": "2017-09-03" }, { "rank": 23, "intensity": 2.326530612244898, "date": "2017-06-02" }, { "rank": 24, "intensity": 2.295918367346939, "date": "2017-12-24" }, { "rank": 25, "intensity": 2.2653061224489797, "date": "2017-08-20" }, { "rank": 26, "intensity": 2.2346938775510203, "date": "2017-08-27" }, { "rank": 27, "intensity": 2.204081632653061, "date": "2017-05-07" }, { "rank": 28, "intensity": 2.173469387755102, "date": "2017-12-02" }, { "rank": 29, "intensity": 2.142857142857143, "date": "2017-02-17" }, { "rank": 30, "intensity": 2.112244897959184, "date": "2017-02-15" }, { "rank": 31, "intensity": 2.0816326530612246, "date": "2017-07-09" }, { "rank": 32, "intensity": 2.0510204081632653, "date": "2017-11-07" }, { "rank": 33, "intensity": 2.020408163265306, "date": "2017-01-05" }, { "rank": 34, "intensity": 1.989795918367347, "date": "2017-04-15" }, { "rank": 35, "intensity": 1.9591836734693877, "date": "2017-08-11" }, { "rank": 36, "intensity": 1.9285714285714286, "date": "2017-11-17" }, { "rank": 37, "intensity": 1.8979591836734695, "date": "2017-02-10" }, { "rank": 38, "intensity": 1.8673469387755102, "date": "2017-10-25" }, { "rank": 39, "intensity": 1.836734693877551, "date": "2017-09-19" }, { "rank": 40, "intensity": 1.806122448979592, "date": "2017-06-13" }, { "rank": 41, "intensity": 1.7755102040816326, "date": "2017-08-18" }, { "rank": 42, "intensity": 1.7448979591836735, "date": "2017-07-23" }, { "rank": 43, "intensity": 1.7142857142857144, "date": "2017-09-26" }, { "rank": 44, "intensity": 1.683673469387755, "date": "2017-02-01" }, { "rank": 45, "intensity": 1.653061224489796, "date": "2017-10-07" }, { "rank": 46, "intensity": 1.6224489795918369, "date": "2017-01-15" }, { "rank": 47, "intensity": 1.5918367346938775, "date": "2017-06-11" }, { "rank": 48, "intensity": 1.5612244897959184, "date": "2017-05-26" }, { "rank": 49, "intensity": 1.5306122448979593, "date": "2017-05-11" }, { "rank": 50, "intensity": 1.5, "date": "2017-08-25" } ], "source": "ettm1" } }, { "id": "L3_T1_Composite_Trend_00215", "level": 3, "level_name": "Semantic Reasoning", "category": "Composite Trend", "subtask": "Composite Trend", "question": "Among days in channel HULL during 2017 that exhibit the trend pattern 'rapid rise, then fluctuating stable, then slow rise, then steady stable', identify the top-5 days where the fluctuating stable segment is the most fluctuating. (Output format: a ranked list of dates, e.g., ['YYYY-MM-DD', ...])", "answer": "['2017-01-26', '2017-02-23', '2017-06-10', '2017-07-27', '2017-06-04']", "ground_truth": [ "2017-01-26", "2017-02-23", "2017-06-10", "2017-07-27", "2017-06-04" ], "eval_metric": "set_f1", "channel": "HULL", "ts_data_path": "ts_data/L3_T1_Composite_Trend_00215.csv", "meta": { "segments": [ { "kind": "rise", "adj": "rapid" }, { "kind": "stable", "adj": "fluctuating" }, { "kind": "rise", "adj": "slow" }, { "kind": "stable", "adj": "steady" } ], "pattern_phrase": "rapid rise, then fluctuating stable, then slow rise, then steady stable", "rank_target_idx": 1, "rank_kind": "stable", "rank_adj": "fluctuating", "rank_axis": "std", "rank_direction": "DESC", "rank_extreme": "most fluctuating", "top_segments_meta": [ { "adj": "rapid", "kind": "rise", "label": "rapid rise", "start_idx": 0, "end_idx": 9 }, { "adj": "fluctuating", "kind": "stable", "label": "fluctuating stable", "start_idx": 9, "end_idx": 31 }, { "adj": "slow", "kind": "rise", "label": "slow rise", "start_idx": 31, "end_idx": 59 }, { "adj": "steady", "kind": "stable", "label": "steady stable", "start_idx": 59, "end_idx": 98 } ], "points_per_day": 96, "pattern": "rapid rise, then fluctuating stable, then slow rise, then steady stable", "year": 2017, "top_k": [ "2017-01-26", "2017-02-23", "2017-06-10", "2017-07-27", "2017-06-04" ], "all_injections": [ { "rank": 1, "intensity": 3.0, "date": "2017-01-26" }, { "rank": 2, "intensity": 2.9693877551020407, "date": "2017-02-23" }, { "rank": 3, "intensity": 2.938775510204082, "date": "2017-06-10" }, { "rank": 4, "intensity": 2.9081632653061225, "date": "2017-07-27" }, { "rank": 5, "intensity": 2.877551020408163, "date": "2017-06-04" }, { "rank": 6, "intensity": 2.8469387755102042, "date": "2017-03-07" }, { "rank": 7, "intensity": 2.816326530612245, "date": "2017-07-07" }, { "rank": 8, "intensity": 2.7857142857142856, "date": "2017-08-24" }, { "rank": 9, "intensity": 2.7551020408163267, "date": "2017-06-23" }, { "rank": 10, "intensity": 2.7244897959183674, "date": "2017-03-28" }, { "rank": 11, "intensity": 2.693877551020408, "date": "2017-04-11" }, { "rank": 12, "intensity": 2.663265306122449, "date": "2017-05-12" }, { "rank": 13, "intensity": 2.63265306122449, "date": "2017-07-09" }, { "rank": 14, "intensity": 2.6020408163265305, "date": "2017-05-01" }, { "rank": 15, "intensity": 2.5714285714285716, "date": "2017-11-15" }, { "rank": 16, "intensity": 2.5408163265306123, "date": "2017-04-13" }, { "rank": 17, "intensity": 2.510204081632653, "date": "2017-10-16" }, { "rank": 18, "intensity": 2.479591836734694, "date": "2017-10-26" }, { "rank": 19, "intensity": 2.4489795918367347, "date": "2017-06-15" }, { "rank": 20, "intensity": 2.4183673469387754, "date": "2017-03-22" }, { "rank": 21, "intensity": 2.387755102040816, "date": "2017-02-06" }, { "rank": 22, "intensity": 2.357142857142857, "date": "2017-12-03" }, { "rank": 23, "intensity": 2.326530612244898, "date": "2017-02-27" }, { "rank": 24, "intensity": 2.295918367346939, "date": "2017-02-18" }, { "rank": 25, "intensity": 2.2653061224489797, "date": "2017-01-28" }, { "rank": 26, "intensity": 2.2346938775510203, "date": "2017-10-22" }, { "rank": 27, "intensity": 2.204081632653061, "date": "2017-01-23" }, { "rank": 28, "intensity": 2.173469387755102, "date": "2017-08-18" }, { "rank": 29, "intensity": 2.142857142857143, "date": "2017-11-02" }, { "rank": 30, "intensity": 2.112244897959184, "date": "2017-03-04" }, { "rank": 31, "intensity": 2.0816326530612246, "date": "2017-10-14" }, { "rank": 32, "intensity": 2.0510204081632653, "date": "2017-08-09" }, { "rank": 33, "intensity": 2.020408163265306, "date": "2017-01-04" }, { "rank": 34, "intensity": 1.989795918367347, "date": "2017-02-03" }, { "rank": 35, "intensity": 1.9591836734693877, "date": "2017-12-15" }, { "rank": 36, "intensity": 1.9285714285714286, "date": "2017-12-24" }, { "rank": 37, "intensity": 1.8979591836734695, "date": "2017-11-09" }, { "rank": 38, "intensity": 1.8673469387755102, "date": "2017-12-13" }, { "rank": 39, "intensity": 1.836734693877551, "date": "2017-07-05" }, { "rank": 40, "intensity": 1.806122448979592, "date": "2017-04-07" }, { "rank": 41, "intensity": 1.7755102040816326, "date": "2017-11-24" }, { "rank": 42, "intensity": 1.7448979591836735, "date": "2017-01-15" }, { "rank": 43, "intensity": 1.7142857142857144, "date": "2017-02-11" }, { "rank": 44, "intensity": 1.683673469387755, "date": "2017-05-19" }, { "rank": 45, "intensity": 1.653061224489796, "date": "2017-03-19" }, { "rank": 46, "intensity": 1.6224489795918369, "date": "2017-09-05" }, { "rank": 47, "intensity": 1.5918367346938775, "date": "2017-09-19" }, { "rank": 48, "intensity": 1.5612244897959184, "date": "2017-03-02" }, { "rank": 49, "intensity": 1.5306122448979593, "date": "2017-09-27" }, { "rank": 50, "intensity": 1.5, "date": "2017-10-12" } ], "source": "ettm1" } }, { "id": "L3_T1_Composite_Trend_00216", "level": 3, "level_name": "Semantic Reasoning", "category": "Composite Trend", "subtask": "Composite Trend", "question": "Among days in channel MUFL during 2017 that exhibit the trend pattern 'steady stable, then rapid fall', identify the top-5 days where the rapid fall segment is the fastest. (Output format: a ranked list of dates, e.g., ['YYYY-MM-DD', ...])", "answer": "['2017-03-27', '2017-12-18', '2017-04-23', '2017-01-07', '2017-08-23']", "ground_truth": [ "2017-03-27", "2017-12-18", "2017-04-23", "2017-01-07", "2017-08-23" ], "eval_metric": "set_f1", "channel": "MUFL", "ts_data_path": "ts_data/L3_T1_Composite_Trend_00216.csv", "meta": { "segments": [ { "kind": "stable", "adj": "steady" }, { "kind": "fall", "adj": "rapid" } ], "pattern_phrase": "steady stable, then rapid fall", "rank_target_idx": 1, "rank_kind": "fall", "rank_adj": "rapid", "rank_axis": "slope", "rank_direction": "DESC", "rank_extreme": "fastest", "top_segments_meta": [ { "adj": "steady", "kind": "stable", "label": "steady stable", "start_idx": 0, "end_idx": 78 }, { "adj": "rapid", "kind": "fall", "label": "rapid fall", "start_idx": 78, "end_idx": 98 } ], "points_per_day": 96, "pattern": "steady stable, then rapid fall", "year": 2017, "top_k": [ "2017-03-27", "2017-12-18", "2017-04-23", "2017-01-07", "2017-08-23" ], "all_injections": [ { "rank": 1, "intensity": 3.0, "date": "2017-03-27" }, { "rank": 2, "intensity": 2.9693877551020407, "date": "2017-12-18" }, { "rank": 3, "intensity": 2.938775510204082, "date": "2017-04-23" }, { "rank": 4, "intensity": 2.9081632653061225, "date": "2017-01-07" }, { "rank": 5, "intensity": 2.877551020408163, "date": "2017-08-23" }, { "rank": 6, "intensity": 2.8469387755102042, "date": "2017-10-29" }, { "rank": 7, "intensity": 2.816326530612245, "date": "2017-04-15" }, { "rank": 8, "intensity": 2.7857142857142856, "date": "2017-03-13" }, { "rank": 9, "intensity": 2.7551020408163267, "date": "2017-07-28" }, { "rank": 10, "intensity": 2.7244897959183674, "date": "2017-11-30" }, { "rank": 11, "intensity": 2.693877551020408, "date": "2017-08-06" }, { "rank": 12, "intensity": 2.663265306122449, "date": "2017-01-17" }, { "rank": 13, "intensity": 2.63265306122449, "date": "2017-04-02" }, { "rank": 14, "intensity": 2.6020408163265305, "date": "2017-12-09" }, { "rank": 15, "intensity": 2.5714285714285716, "date": "2017-09-07" }, { "rank": 16, "intensity": 2.5408163265306123, "date": "2017-04-10" }, { "rank": 17, "intensity": 2.510204081632653, "date": "2017-10-09" }, { "rank": 18, "intensity": 2.479591836734694, "date": "2017-01-31" }, { "rank": 19, "intensity": 2.4489795918367347, "date": "2017-01-26" }, { "rank": 20, "intensity": 2.4183673469387754, "date": "2017-01-09" }, { "rank": 21, "intensity": 2.387755102040816, "date": "2017-03-18" }, { "rank": 22, "intensity": 2.357142857142857, "date": "2017-01-21" }, { "rank": 23, "intensity": 2.326530612244898, "date": "2017-03-23" }, { "rank": 24, "intensity": 2.295918367346939, "date": "2017-04-18" }, { "rank": 25, "intensity": 2.2653061224489797, "date": "2017-12-13" }, { "rank": 26, "intensity": 2.2346938775510203, "date": "2017-01-28" }, { "rank": 27, "intensity": 2.204081632653061, "date": "2017-09-10" }, { "rank": 28, "intensity": 2.173469387755102, "date": "2017-12-26" }, { "rank": 29, "intensity": 2.142857142857143, "date": "2017-05-28" }, { "rank": 30, "intensity": 2.112244897959184, "date": "2017-08-14" }, { "rank": 31, "intensity": 2.0816326530612246, "date": "2017-11-01" }, { "rank": 32, "intensity": 2.0510204081632653, "date": "2017-01-01" }, { "rank": 33, "intensity": 2.020408163265306, "date": "2017-04-26" }, { "rank": 34, "intensity": 1.989795918367347, "date": "2017-01-12" }, { "rank": 35, "intensity": 1.9591836734693877, "date": "2017-11-08" }, { "rank": 36, "intensity": 1.9285714285714286, "date": "2017-02-18" }, { "rank": 37, "intensity": 1.8979591836734695, "date": "2017-12-20" }, { "rank": 38, "intensity": 1.8673469387755102, "date": "2017-06-03" }, { "rank": 39, "intensity": 1.836734693877551, "date": "2017-07-23" }, { "rank": 40, "intensity": 1.806122448979592, "date": "2017-09-29" }, { "rank": 41, "intensity": 1.7755102040816326, "date": "2017-02-04" }, { "rank": 42, "intensity": 1.7448979591836735, "date": "2017-11-10" }, { "rank": 43, "intensity": 1.7142857142857144, "date": "2017-11-05" }, { "rank": 44, "intensity": 1.683673469387755, "date": "2017-04-30" }, { "rank": 45, "intensity": 1.653061224489796, "date": "2017-07-06" }, { "rank": 46, "intensity": 1.6224489795918369, "date": "2017-04-13" }, { "rank": 47, "intensity": 1.5918367346938775, "date": "2017-02-22" }, { "rank": 48, "intensity": 1.5612244897959184, "date": "2017-06-05" }, { "rank": 49, "intensity": 1.5306122448979593, "date": "2017-12-23" }, { "rank": 50, "intensity": 1.5, "date": "2017-12-03" } ], "source": "ettm1" } }, { "id": "L3_T1_Composite_Trend_00217", "level": 3, "level_name": "Semantic Reasoning", "category": "Composite Trend", "subtask": "Composite Trend", "question": "Among days in channel MULL during 2017 that exhibit the trend pattern 'rapid rise, then rapid fall, then rise, then slow fall', identify the top-5 days where the rapid fall segment is the fastest. (Output format: a ranked list of dates, e.g., ['YYYY-MM-DD', ...])", "answer": "['2017-01-14', '2017-01-18', '2017-08-30', '2017-06-11', '2017-11-04']", "ground_truth": [ "2017-01-14", "2017-01-18", "2017-08-30", "2017-06-11", "2017-11-04" ], "eval_metric": "set_f1", "channel": "MULL", "ts_data_path": "ts_data/L3_T1_Composite_Trend_00217.csv", "meta": { "segments": [ { "kind": "rise", "adj": "rapid" }, { "kind": "fall", "adj": "rapid" }, { "kind": "rise", "adj": "none" }, { "kind": "fall", "adj": "slow" } ], "pattern_phrase": "rapid rise, then rapid fall, then rise, then slow fall", "rank_target_idx": 1, "rank_kind": "fall", "rank_adj": "rapid", "rank_axis": "slope", "rank_direction": "DESC", "rank_extreme": "fastest", "top_segments_meta": [ { "adj": "rapid", "kind": "rise", "label": "rapid rise", "start_idx": 0, "end_idx": 13 }, { "adj": "rapid", "kind": "fall", "label": "rapid fall", "start_idx": 13, "end_idx": 25 }, { "adj": "none", "kind": "rise", "label": "none rise", "start_idx": 25, "end_idx": 53 }, { "adj": "slow", "kind": "fall", "label": "slow fall", "start_idx": 53, "end_idx": 98 } ], "points_per_day": 96, "pattern": "rapid rise, then rapid fall, then rise, then slow fall", "year": 2017, "top_k": [ "2017-01-14", "2017-01-18", "2017-08-30", "2017-06-11", "2017-11-04" ], "all_injections": [ { "rank": 1, "intensity": 3.0, "date": "2017-01-14" }, { "rank": 2, "intensity": 2.9693877551020407, "date": "2017-01-18" }, { "rank": 3, "intensity": 2.938775510204082, "date": "2017-08-30" }, { "rank": 4, "intensity": 2.9081632653061225, "date": "2017-06-11" }, { "rank": 5, "intensity": 2.877551020408163, "date": "2017-11-04" }, { "rank": 6, "intensity": 2.8469387755102042, "date": "2017-03-27" }, { "rank": 7, "intensity": 2.816326530612245, "date": "2017-07-01" }, { "rank": 8, "intensity": 2.7857142857142856, "date": "2017-07-03" }, { "rank": 9, "intensity": 2.7551020408163267, "date": "2017-09-30" }, { "rank": 10, "intensity": 2.7244897959183674, "date": "2017-09-25" }, { "rank": 11, "intensity": 2.693877551020408, "date": "2017-12-10" }, { "rank": 12, "intensity": 2.663265306122449, "date": "2017-10-05" }, { "rank": 13, "intensity": 2.63265306122449, "date": "2017-10-07" }, { "rank": 14, "intensity": 2.6020408163265305, "date": "2017-02-27" }, { "rank": 15, "intensity": 2.5714285714285716, "date": "2017-06-13" }, { "rank": 16, "intensity": 2.5408163265306123, "date": "2017-01-08" }, { "rank": 17, "intensity": 2.510204081632653, "date": "2017-12-02" }, { "rank": 18, "intensity": 2.479591836734694, "date": "2017-02-04" }, { "rank": 19, "intensity": 2.4489795918367347, "date": "2017-06-23" }, { "rank": 20, "intensity": 2.4183673469387754, "date": "2017-06-16" }, { "rank": 21, "intensity": 2.387755102040816, "date": "2017-02-25" }, { "rank": 22, "intensity": 2.357142857142857, "date": "2017-03-22" }, { "rank": 23, "intensity": 2.326530612244898, "date": "2017-03-03" }, { "rank": 24, "intensity": 2.295918367346939, "date": "2017-10-09" }, { "rank": 25, "intensity": 2.2653061224489797, "date": "2017-09-21" }, { "rank": 26, "intensity": 2.2346938775510203, "date": "2017-04-08" }, { "rank": 27, "intensity": 2.204081632653061, "date": "2017-11-12" }, { "rank": 28, "intensity": 2.173469387755102, "date": "2017-05-24" }, { "rank": 29, "intensity": 2.142857142857143, "date": "2017-01-10" }, { "rank": 30, "intensity": 2.112244897959184, "date": "2017-07-28" }, { "rank": 31, "intensity": 2.0816326530612246, "date": "2017-04-21" }, { "rank": 32, "intensity": 2.0510204081632653, "date": "2017-09-28" }, { "rank": 33, "intensity": 2.020408163265306, "date": "2017-11-02" }, { "rank": 34, "intensity": 1.989795918367347, "date": "2017-08-14" }, { "rank": 35, "intensity": 1.9591836734693877, "date": "2017-09-23" }, { "rank": 36, "intensity": 1.9285714285714286, "date": "2017-11-26" }, { "rank": 37, "intensity": 1.8979591836734695, "date": "2017-12-21" }, { "rank": 38, "intensity": 1.8673469387755102, "date": "2017-12-19" }, { "rank": 39, "intensity": 1.836734693877551, "date": "2017-08-22" }, { "rank": 40, "intensity": 1.806122448979592, "date": "2017-06-29" }, { "rank": 41, "intensity": 1.7755102040816326, "date": "2017-01-29" }, { "rank": 42, "intensity": 1.7448979591836735, "date": "2017-12-23" }, { "rank": 43, "intensity": 1.7142857142857144, "date": "2017-06-25" }, { "rank": 44, "intensity": 1.683673469387755, "date": "2017-10-13" }, { "rank": 45, "intensity": 1.653061224489796, "date": "2017-03-24" }, { "rank": 46, "intensity": 1.6224489795918369, "date": "2017-07-24" }, { "rank": 47, "intensity": 1.5918367346938775, "date": "2017-06-27" }, { "rank": 48, "intensity": 1.5612244897959184, "date": "2017-02-10" }, { "rank": 49, "intensity": 1.5306122448979593, "date": "2017-05-03" }, { "rank": 50, "intensity": 1.5, "date": "2017-12-26" } ], "source": "ettm1" } }, { "id": "L3_T1_Composite_Trend_00218", "level": 3, "level_name": "Semantic Reasoning", "category": "Composite Trend", "subtask": "Composite Trend", "question": "Among days in channel LUFL during 2017 that exhibit the trend pattern 'fall, then steady stable, then rise, then rapid rise', identify the top-5 days where the rapid rise segment is the fastest. (Output format: a ranked list of dates, e.g., ['YYYY-MM-DD', ...])", "answer": "['2017-08-29', '2017-12-15', '2017-06-20', '2017-03-06', '2017-02-08']", "ground_truth": [ "2017-08-29", "2017-12-15", "2017-06-20", "2017-03-06", "2017-02-08" ], "eval_metric": "set_f1", "channel": "LUFL", "ts_data_path": "ts_data/L3_T1_Composite_Trend_00218.csv", "meta": { "segments": [ { "kind": "fall", "adj": "none" }, { "kind": "stable", "adj": "steady" }, { "kind": "rise", "adj": "none" }, { "kind": "rise", "adj": "rapid" } ], "pattern_phrase": "fall, then steady stable, then rise, then rapid rise", "rank_target_idx": 3, "rank_kind": "rise", "rank_adj": "rapid", "rank_axis": "slope", "rank_direction": "DESC", "rank_extreme": "fastest", "top_segments_meta": [ { "adj": "none", "kind": "fall", "label": "none fall", "start_idx": 0, "end_idx": 23 }, { "adj": "steady", "kind": "stable", "label": "steady stable", "start_idx": 23, "end_idx": 66 }, { "adj": "none", "kind": "rise", "label": "none rise", "start_idx": 66, "end_idx": 88 }, { "adj": "rapid", "kind": "rise", "label": "rapid rise", "start_idx": 88, "end_idx": 98 } ], "points_per_day": 96, "pattern": "fall, then steady stable, then rise, then rapid rise", "year": 2017, "top_k": [ "2017-08-29", "2017-12-15", "2017-06-20", "2017-03-06", "2017-02-08" ], "all_injections": [ { "rank": 1, "intensity": 3.0, "date": "2017-08-29" }, { "rank": 2, "intensity": 2.9693877551020407, "date": "2017-12-15" }, { "rank": 3, "intensity": 2.938775510204082, "date": "2017-06-20" }, { "rank": 4, "intensity": 2.9081632653061225, "date": "2017-03-06" }, { "rank": 5, "intensity": 2.877551020408163, "date": "2017-02-08" }, { "rank": 6, "intensity": 2.8469387755102042, "date": "2017-01-09" }, { "rank": 7, "intensity": 2.816326530612245, "date": "2017-03-18" }, { "rank": 8, "intensity": 2.7857142857142856, "date": "2017-08-26" }, { "rank": 9, "intensity": 2.7551020408163267, "date": "2017-12-10" }, { "rank": 10, "intensity": 2.7244897959183674, "date": "2017-04-06" }, { "rank": 11, "intensity": 2.693877551020408, "date": "2017-07-22" }, { "rank": 12, "intensity": 2.663265306122449, "date": "2017-06-27" }, { "rank": 13, "intensity": 2.63265306122449, "date": "2017-04-10" }, { "rank": 14, "intensity": 2.6020408163265305, "date": "2017-10-01" }, { "rank": 15, "intensity": 2.5714285714285716, "date": "2017-06-16" }, { "rank": 16, "intensity": 2.5408163265306123, "date": "2017-08-04" }, { "rank": 17, "intensity": 2.510204081632653, "date": "2017-07-17" }, { "rank": 18, "intensity": 2.479591836734694, "date": "2017-10-21" }, { "rank": 19, "intensity": 2.4489795918367347, "date": "2017-10-27" }, { "rank": 20, "intensity": 2.4183673469387754, "date": "2017-08-13" }, { "rank": 21, "intensity": 2.387755102040816, "date": "2017-05-14" }, { "rank": 22, "intensity": 2.357142857142857, "date": "2017-11-29" }, { "rank": 23, "intensity": 2.326530612244898, "date": "2017-03-22" }, { "rank": 24, "intensity": 2.295918367346939, "date": "2017-10-29" }, { "rank": 25, "intensity": 2.2653061224489797, "date": "2017-05-09" }, { "rank": 26, "intensity": 2.2346938775510203, "date": "2017-09-15" }, { "rank": 27, "intensity": 2.204081632653061, "date": "2017-05-16" }, { "rank": 28, "intensity": 2.173469387755102, "date": "2017-04-19" }, { "rank": 29, "intensity": 2.142857142857143, "date": "2017-10-17" }, { "rank": 30, "intensity": 2.112244897959184, "date": "2017-06-02" }, { "rank": 31, "intensity": 2.0816326530612246, "date": "2017-07-02" }, { "rank": 32, "intensity": 2.0510204081632653, "date": "2017-01-01" }, { "rank": 33, "intensity": 2.020408163265306, "date": "2017-02-25" }, { "rank": 34, "intensity": 1.989795918367347, "date": "2017-03-14" }, { "rank": 35, "intensity": 1.9591836734693877, "date": "2017-07-10" }, { "rank": 36, "intensity": 1.9285714285714286, "date": "2017-11-20" }, { "rank": 37, "intensity": 1.8979591836734695, "date": "2017-06-29" }, { "rank": 38, "intensity": 1.8673469387755102, "date": "2017-04-26" }, { "rank": 39, "intensity": 1.836734693877551, "date": "2017-09-24" }, { "rank": 40, "intensity": 1.806122448979592, "date": "2017-07-04" }, { "rank": 41, "intensity": 1.7755102040816326, "date": "2017-04-21" }, { "rank": 42, "intensity": 1.7448979591836735, "date": "2017-12-02" }, { "rank": 43, "intensity": 1.7142857142857144, "date": "2017-08-21" }, { "rank": 44, "intensity": 1.683673469387755, "date": "2017-10-25" }, { "rank": 45, "intensity": 1.653061224489796, "date": "2017-03-02" }, { "rank": 46, "intensity": 1.6224489795918369, "date": "2017-04-08" }, { "rank": 47, "intensity": 1.5918367346938775, "date": "2017-09-22" }, { "rank": 48, "intensity": 1.5612244897959184, "date": "2017-09-29" }, { "rank": 49, "intensity": 1.5306122448979593, "date": "2017-10-09" }, { "rank": 50, "intensity": 1.5, "date": "2017-05-23" } ], "source": "ettm1" } }, { "id": "L3_T1_Composite_Trend_00219", "level": 3, "level_name": "Semantic Reasoning", "category": "Composite Trend", "subtask": "Composite Trend", "question": "Among days in channel LULL during 2017 that exhibit the trend pattern 'slow fall, then steady stable', identify the top-5 days where the slow fall segment is the slowest. (Output format: a ranked list of dates, e.g., ['YYYY-MM-DD', ...])", "answer": "['2017-06-29', '2017-06-25', '2017-02-13', '2017-11-21', '2017-02-23']", "ground_truth": [ "2017-06-29", "2017-06-25", "2017-02-13", "2017-11-21", "2017-02-23" ], "eval_metric": "set_f1", "channel": "LULL", "ts_data_path": "ts_data/L3_T1_Composite_Trend_00219.csv", "meta": { "segments": [ { "kind": "fall", "adj": "slow" }, { "kind": "stable", "adj": "steady" } ], "pattern_phrase": "slow fall, then steady stable", "rank_target_idx": 0, "rank_kind": "fall", "rank_adj": "slow", "rank_axis": "slope", "rank_direction": "ASC", "rank_extreme": "slowest", "top_segments_meta": [ { "adj": "slow", "kind": "fall", "label": "slow fall", "start_idx": 0, "end_idx": 44 }, { "adj": "steady", "kind": "stable", "label": "steady stable", "start_idx": 44, "end_idx": 98 } ], "points_per_day": 96, "pattern": "slow fall, then steady stable", "year": 2017, "top_k": [ "2017-06-29", "2017-06-25", "2017-02-13", "2017-11-21", "2017-02-23" ], "all_injections": [ { "rank": 1, "intensity": 1.5, "date": "2017-06-29" }, { "rank": 2, "intensity": 1.530612244897959, "date": "2017-06-25" }, { "rank": 3, "intensity": 1.5612244897959184, "date": "2017-02-13" }, { "rank": 4, "intensity": 1.5918367346938775, "date": "2017-11-21" }, { "rank": 5, "intensity": 1.6224489795918366, "date": "2017-02-23" }, { "rank": 6, "intensity": 1.653061224489796, "date": "2017-04-17" }, { "rank": 7, "intensity": 1.683673469387755, "date": "2017-02-28" }, { "rank": 8, "intensity": 1.7142857142857142, "date": "2017-03-28" }, { "rank": 9, "intensity": 1.7448979591836735, "date": "2017-12-11" }, { "rank": 10, "intensity": 1.7755102040816326, "date": "2017-07-06" }, { "rank": 11, "intensity": 1.806122448979592, "date": "2017-11-11" }, { "rank": 12, "intensity": 1.836734693877551, "date": "2017-11-03" }, { "rank": 13, "intensity": 1.8673469387755102, "date": "2017-08-29" }, { "rank": 14, "intensity": 1.8979591836734695, "date": "2017-01-13" }, { "rank": 15, "intensity": 1.9285714285714286, "date": "2017-11-29" }, { "rank": 16, "intensity": 1.9591836734693877, "date": "2017-12-15" }, { "rank": 17, "intensity": 1.989795918367347, "date": "2017-12-29" }, { "rank": 18, "intensity": 2.020408163265306, "date": "2017-01-16" }, { "rank": 19, "intensity": 2.0510204081632653, "date": "2017-07-28" }, { "rank": 20, "intensity": 2.0816326530612246, "date": "2017-08-10" }, { "rank": 21, "intensity": 2.112244897959184, "date": "2017-06-12" }, { "rank": 22, "intensity": 2.142857142857143, "date": "2017-01-26" }, { "rank": 23, "intensity": 2.173469387755102, "date": "2017-03-04" }, { "rank": 24, "intensity": 2.204081632653061, "date": "2017-08-07" }, { "rank": 25, "intensity": 2.2346938775510203, "date": "2017-07-23" }, { "rank": 26, "intensity": 2.2653061224489797, "date": "2017-10-15" }, { "rank": 27, "intensity": 2.295918367346939, "date": "2017-12-02" }, { "rank": 28, "intensity": 2.326530612244898, "date": "2017-05-19" }, { "rank": 29, "intensity": 2.357142857142857, "date": "2017-06-20" }, { "rank": 30, "intensity": 2.387755102040816, "date": "2017-08-04" }, { "rank": 31, "intensity": 2.4183673469387754, "date": "2017-10-23" }, { "rank": 32, "intensity": 2.4489795918367347, "date": "2017-05-26" }, { "rank": 33, "intensity": 2.479591836734694, "date": "2017-12-17" }, { "rank": 34, "intensity": 2.510204081632653, "date": "2017-08-01" }, { "rank": 35, "intensity": 2.5408163265306123, "date": "2017-11-05" }, { "rank": 36, "intensity": 2.571428571428571, "date": "2017-10-28" }, { "rank": 37, "intensity": 2.6020408163265305, "date": "2017-03-31" }, { "rank": 38, "intensity": 2.63265306122449, "date": "2017-05-12" }, { "rank": 39, "intensity": 2.663265306122449, "date": "2017-03-06" }, { "rank": 40, "intensity": 2.693877551020408, "date": "2017-04-10" }, { "rank": 41, "intensity": 2.7244897959183674, "date": "2017-06-18" }, { "rank": 42, "intensity": 2.7551020408163263, "date": "2017-05-22" }, { "rank": 43, "intensity": 2.7857142857142856, "date": "2017-04-15" }, { "rank": 44, "intensity": 2.816326530612245, "date": "2017-10-30" }, { "rank": 45, "intensity": 2.8469387755102042, "date": "2017-04-26" }, { "rank": 46, "intensity": 2.877551020408163, "date": "2017-08-15" }, { "rank": 47, "intensity": 2.9081632653061225, "date": "2017-09-03" }, { "rank": 48, "intensity": 2.9387755102040813, "date": "2017-04-07" }, { "rank": 49, "intensity": 2.9693877551020407, "date": "2017-08-26" }, { "rank": 50, "intensity": 3.0, "date": "2017-12-08" } ], "source": "ettm1" } }, { "id": "L3_T1_Composite_Trend_00220", "level": 3, "level_name": "Semantic Reasoning", "category": "Composite Trend", "subtask": "Composite Trend", "question": "Among days in channel OT during 2017 that exhibit the trend pattern 'slow fall, then steady stable, then rapid rise, then steady stable', identify the top-5 days where the rapid rise segment is the fastest. (Output format: a ranked list of dates, e.g., ['YYYY-MM-DD', ...])", "answer": "['2017-09-18', '2017-06-25', '2017-05-19', '2017-06-20', '2017-08-07']", "ground_truth": [ "2017-09-18", "2017-06-25", "2017-05-19", "2017-06-20", "2017-08-07" ], "eval_metric": "set_f1", "channel": "OT", "ts_data_path": "ts_data/L3_T1_Composite_Trend_00220.csv", "meta": { "segments": [ { "kind": "fall", "adj": "slow" }, { "kind": "stable", "adj": "steady" }, { "kind": "rise", "adj": "rapid" }, { "kind": "stable", "adj": "steady" } ], "pattern_phrase": "slow fall, then steady stable, then rapid rise, then steady stable", "rank_target_idx": 2, "rank_kind": "rise", "rank_adj": "rapid", "rank_axis": "slope", "rank_direction": "DESC", "rank_extreme": "fastest", "top_segments_meta": [ { "adj": "slow", "kind": "fall", "label": "slow fall", "start_idx": 0, "end_idx": 27 }, { "adj": "steady", "kind": "stable", "label": "steady stable", "start_idx": 27, "end_idx": 60 }, { "adj": "rapid", "kind": "rise", "label": "rapid rise", "start_idx": 60, "end_idx": 68 }, { "adj": "steady", "kind": "stable", "label": "steady stable", "start_idx": 68, "end_idx": 98 } ], "points_per_day": 96, "pattern": "slow fall, then steady stable, then rapid rise, then steady stable", "year": 2017, "top_k": [ "2017-09-18", "2017-06-25", "2017-05-19", "2017-06-20", "2017-08-07" ], "all_injections": [ { "rank": 1, "intensity": 3.0, "date": "2017-09-18" }, { "rank": 2, "intensity": 2.9693877551020407, "date": "2017-06-25" }, { "rank": 3, "intensity": 2.938775510204082, "date": "2017-05-19" }, { "rank": 4, "intensity": 2.9081632653061225, "date": "2017-06-20" }, { "rank": 5, "intensity": 2.877551020408163, "date": "2017-08-07" }, { "rank": 6, "intensity": 2.8469387755102042, "date": "2017-07-10" }, { "rank": 7, "intensity": 2.816326530612245, "date": "2017-06-22" }, { "rank": 8, "intensity": 2.7857142857142856, "date": "2017-12-13" }, { "rank": 9, "intensity": 2.7551020408163267, "date": "2017-01-14" }, { "rank": 10, "intensity": 2.7244897959183674, "date": "2017-02-26" }, { "rank": 11, "intensity": 2.693877551020408, "date": "2017-06-13" }, { "rank": 12, "intensity": 2.663265306122449, "date": "2017-01-31" }, { "rank": 13, "intensity": 2.63265306122449, "date": "2017-04-05" }, { "rank": 14, "intensity": 2.6020408163265305, "date": "2017-05-21" }, { "rank": 15, "intensity": 2.5714285714285716, "date": "2017-11-26" }, { "rank": 16, "intensity": 2.5408163265306123, "date": "2017-09-12" }, { "rank": 17, "intensity": 2.510204081632653, "date": "2017-02-09" }, { "rank": 18, "intensity": 2.479591836734694, "date": "2017-07-23" }, { "rank": 19, "intensity": 2.4489795918367347, "date": "2017-09-30" }, { "rank": 20, "intensity": 2.4183673469387754, "date": "2017-03-03" }, { "rank": 21, "intensity": 2.387755102040816, "date": "2017-06-30" }, { "rank": 22, "intensity": 2.357142857142857, "date": "2017-04-07" }, { "rank": 23, "intensity": 2.326530612244898, "date": "2017-01-29" }, { "rank": 24, "intensity": 2.295918367346939, "date": "2017-11-06" }, { "rank": 25, "intensity": 2.2653061224489797, "date": "2017-12-27" }, { "rank": 26, "intensity": 2.2346938775510203, "date": "2017-07-05" }, { "rank": 27, "intensity": 2.204081632653061, "date": "2017-10-02" }, { "rank": 28, "intensity": 2.173469387755102, "date": "2017-01-23" }, { "rank": 29, "intensity": 2.142857142857143, "date": "2017-01-25" }, { "rank": 30, "intensity": 2.112244897959184, "date": "2017-04-15" }, { "rank": 31, "intensity": 2.0816326530612246, "date": "2017-05-25" }, { "rank": 32, "intensity": 2.0510204081632653, "date": "2017-09-01" }, { "rank": 33, "intensity": 2.020408163265306, "date": "2017-05-14" }, { "rank": 34, "intensity": 1.989795918367347, "date": "2017-12-15" }, { "rank": 35, "intensity": 1.9591836734693877, "date": "2017-12-05" }, { "rank": 36, "intensity": 1.9285714285714286, "date": "2017-07-03" }, { "rank": 37, "intensity": 1.8979591836734695, "date": "2017-11-17" }, { "rank": 38, "intensity": 1.8673469387755102, "date": "2017-10-13" }, { "rank": 39, "intensity": 1.836734693877551, "date": "2017-07-21" }, { "rank": 40, "intensity": 1.806122448979592, "date": "2017-03-05" }, { "rank": 41, "intensity": 1.7755102040816326, "date": "2017-04-26" }, { "rank": 42, "intensity": 1.7448979591836735, "date": "2017-01-07" }, { "rank": 43, "intensity": 1.7142857142857144, "date": "2017-09-24" }, { "rank": 44, "intensity": 1.683673469387755, "date": "2017-03-24" }, { "rank": 45, "intensity": 1.653061224489796, "date": "2017-03-14" }, { "rank": 46, "intensity": 1.6224489795918369, "date": "2017-03-29" }, { "rank": 47, "intensity": 1.5918367346938775, "date": "2017-08-26" }, { "rank": 48, "intensity": 1.5612244897959184, "date": "2017-10-30" }, { "rank": 49, "intensity": 1.5306122448979593, "date": "2017-05-11" }, { "rank": 50, "intensity": 1.5, "date": "2017-03-31" } ], "source": "ettm1" } }, { "id": "L3_T2_Contextual_Anomaly_00221", "level": 3, "level_name": "Semantic Reasoning", "category": "Contextual Anomaly", "subtask": "Contextual Anomaly", "question": "Identify the period in channel 67 during 2023 that experienced the most significant severe flood. (Output format: [YYYY-MM-DD, YYYY-MM-DD])", "answer": "[2023-06-20 20:30:00, 2023-09-17 20:15:00]", "ground_truth": [ "2023-06-20 20:30:00", "2023-09-17 20:15:00" ], "eval_metric": "iou", "channel": "67", "ts_data_path": "ts_data/L3_T2_Contextual_Anomaly_00221.csv", "meta": { "event": "flood", "intensity": 4.081063457775192, "year": 2023, "duration_days": 89, "source": "causal_rivers" } }, { "id": "L3_T2_Contextual_Anomaly_00223", "level": 3, "level_name": "Semantic Reasoning", "category": "Contextual Anomaly", "subtask": "Contextual Anomaly", "question": "Identify the period in channel 99 during 2019 that experienced the most significant severe flood. (Output format: [YYYY-MM-DD, YYYY-MM-DD])", "answer": "[2019-07-29 18:30:00, 2019-10-04 18:15:00]", "ground_truth": [ "2019-07-29 18:30:00", "2019-10-04 18:15:00" ], "eval_metric": "iou", "channel": "99", "ts_data_path": "ts_data/L3_T2_Contextual_Anomaly_00223.csv", "meta": { "event": "flood", "intensity": 3.3899502909967874, "year": 2019, "duration_days": 67, "source": "causal_rivers" } }, { "id": "L3_T2_Contextual_Anomaly_00224", "level": 3, "level_name": "Semantic Reasoning", "category": "Contextual Anomaly", "subtask": "Contextual Anomaly", "question": "Identify the period in channel 123 during 2019 that experienced the most significant extreme surge in flow. (Output format: [YYYY-MM-DD, YYYY-MM-DD])", "answer": "[2019-07-24 00:00:00, 2019-09-21 23:45:00]", "ground_truth": [ "2019-07-24 00:00:00", "2019-09-21 23:45:00" ], "eval_metric": "iou", "channel": "123", "ts_data_path": "ts_data/L3_T2_Contextual_Anomaly_00224.csv", "meta": { "event": "flood", "intensity": 4.374302618527894, "year": 2019, "duration_days": 60, "source": "causal_rivers" } }, { "id": "L3_T2_Contextual_Anomaly_00225", "level": 3, "level_name": "Semantic Reasoning", "category": "Contextual Anomaly", "subtask": "Contextual Anomaly", "question": "Identify the period in channel 124 during 2023 that experienced the most significant historically high water level. (Output format: [YYYY-MM-DD, YYYY-MM-DD])", "answer": "[2023-04-24 16:15:00, 2023-07-22 16:00:00]", "ground_truth": [ "2023-04-24 16:15:00", "2023-07-22 16:00:00" ], "eval_metric": "iou", "channel": "124", "ts_data_path": "ts_data/L3_T2_Contextual_Anomaly_00225.csv", "meta": { "event": "flood", "intensity": 4.81263932529971, "year": 2023, "duration_days": 89, "source": "causal_rivers" } }, { "id": "L3_T2_Contextual_Anomaly_00228", "level": 3, "level_name": "Semantic Reasoning", "category": "Contextual Anomaly", "subtask": "Contextual Anomaly", "question": "Identify the period in channel 151 during 2019 that experienced the most significant severe flood. (Output format: [YYYY-MM-DD, YYYY-MM-DD])", "answer": "[2019-08-17 19:15:00, 2019-10-17 19:00:00]", "ground_truth": [ "2019-08-17 19:15:00", "2019-10-17 19:00:00" ], "eval_metric": "iou", "channel": "151", "ts_data_path": "ts_data/L3_T2_Contextual_Anomaly_00228.csv", "meta": { "event": "flood", "intensity": 3.6684017348182354, "year": 2019, "duration_days": 61, "source": "causal_rivers" } }, { "id": "L3_T2_Contextual_Anomaly_00230", "level": 3, "level_name": "Semantic Reasoning", "category": "Contextual Anomaly", "subtask": "Contextual Anomaly", "question": "Identify the period in channel 155 during 2023 that experienced the most significant historically high water level. (Output format: [YYYY-MM-DD, YYYY-MM-DD])", "answer": "[2023-01-25 13:30:00, 2023-03-31 13:15:00]", "ground_truth": [ "2023-01-25 13:30:00", "2023-03-31 13:15:00" ], "eval_metric": "iou", "channel": "155", "ts_data_path": "ts_data/L3_T2_Contextual_Anomaly_00230.csv", "meta": { "event": "flood", "intensity": 3.5527891134628353, "year": 2023, "duration_days": 65, "source": "causal_rivers" } }, { "id": "L3_T2_Contextual_Anomaly_00231", "level": 3, "level_name": "Semantic Reasoning", "category": "Contextual Anomaly", "subtask": "Contextual Anomaly", "question": "Identify the period in channel 166 during 2023 that experienced the most significant dry-out period. (Output format: [YYYY-MM-DD, YYYY-MM-DD])", "answer": "[2023-03-20 04:30:00, 2023-06-18 04:15:00]", "ground_truth": [ "2023-03-20 04:30:00", "2023-06-18 04:15:00" ], "eval_metric": "iou", "channel": "166", "ts_data_path": "ts_data/L3_T2_Contextual_Anomaly_00231.csv", "meta": { "event": "drought", "intensity": 0.0919517171952789, "year": 2023, "duration_days": 90, "source": "causal_rivers" } }, { "id": "L3_T2_Contextual_Anomaly_00234", "level": 3, "level_name": "Semantic Reasoning", "category": "Contextual Anomaly", "subtask": "Contextual Anomaly", "question": "Identify the period in channel 172 during 2021 that experienced the most significant historically high water level. (Output format: [YYYY-MM-DD, YYYY-MM-DD])", "answer": "[2021-08-14 05:00:00, 2021-11-12 04:45:00]", "ground_truth": [ "2021-08-14 05:00:00", "2021-11-12 04:45:00" ], "eval_metric": "iou", "channel": "172", "ts_data_path": "ts_data/L3_T2_Contextual_Anomaly_00234.csv", "meta": { "event": "flood", "intensity": 3.763950080016283, "year": 2021, "duration_days": 90, "source": "causal_rivers" } }, { "id": "L3_T2_Contextual_Anomaly_00236", "level": 3, "level_name": "Semantic Reasoning", "category": "Contextual Anomaly", "subtask": "Contextual Anomaly", "question": "Identify the period in channel 177 during 2023 that experienced the most significant historically high water level. (Output format: [YYYY-MM-DD, YYYY-MM-DD])", "answer": "[2023-09-07 05:45:00, 2023-11-24 05:30:00]", "ground_truth": [ "2023-09-07 05:45:00", "2023-11-24 05:30:00" ], "eval_metric": "iou", "channel": "177", "ts_data_path": "ts_data/L3_T2_Contextual_Anomaly_00236.csv", "meta": { "event": "flood", "intensity": 4.502340394131296, "year": 2023, "duration_days": 78, "source": "causal_rivers" } }, { "id": "L3_T2_Contextual_Anomaly_00237", "level": 3, "level_name": "Semantic Reasoning", "category": "Contextual Anomaly", "subtask": "Contextual Anomaly", "question": "Identify the period in channel 237 during 2022 that experienced the most significant extreme surge in flow. (Output format: [YYYY-MM-DD, YYYY-MM-DD])", "answer": "[2022-01-21 06:45:00, 2022-04-18 06:30:00]", "ground_truth": [ "2022-01-21 06:45:00", "2022-04-18 06:30:00" ], "eval_metric": "iou", "channel": "237", "ts_data_path": "ts_data/L3_T2_Contextual_Anomaly_00237.csv", "meta": { "event": "flood", "intensity": 3.5782877896234044, "year": 2022, "duration_days": 87, "source": "causal_rivers" } }, { "id": "L3_T2_Contextual_Anomaly_00238", "level": 3, "level_name": "Semantic Reasoning", "category": "Contextual Anomaly", "subtask": "Contextual Anomaly", "question": "Identify the period in channel 245 during 2023 that experienced the most significant extreme surge in flow. (Output format: [YYYY-MM-DD, YYYY-MM-DD])", "answer": "[2023-07-14 18:15:00, 2023-10-01 18:00:00]", "ground_truth": [ "2023-07-14 18:15:00", "2023-10-01 18:00:00" ], "eval_metric": "iou", "channel": "245", "ts_data_path": "ts_data/L3_T2_Contextual_Anomaly_00238.csv", "meta": { "event": "flood", "intensity": 4.616262112116132, "year": 2023, "duration_days": 79, "source": "causal_rivers" } }, { "id": "L3_T2_Contextual_Anomaly_00240", "level": 3, "level_name": "Semantic Reasoning", "category": "Contextual Anomaly", "subtask": "Contextual Anomaly", "question": "Identify the period in channel 430 during 2019 that experienced the most significant historically high water level. (Output format: [YYYY-MM-DD, YYYY-MM-DD])", "answer": "[2019-08-03 08:30:00, 2019-10-26 08:15:00]", "ground_truth": [ "2019-08-03 08:30:00", "2019-10-26 08:15:00" ], "eval_metric": "iou", "channel": "430", "ts_data_path": "ts_data/L3_T2_Contextual_Anomaly_00240.csv", "meta": { "event": "flood", "intensity": 4.413850697877486, "year": 2019, "duration_days": 84, "source": "causal_rivers" } }, { "id": "L3_T2_Contextual_Anomaly_00241", "level": 3, "level_name": "Semantic Reasoning", "category": "Contextual Anomaly", "subtask": "Contextual Anomaly", "question": "Identify the period in channel 441 during 2023 that experienced the most significant extreme surge in flow. (Output format: [YYYY-MM-DD, YYYY-MM-DD])", "answer": "[2023-07-29 11:00:00, 2023-10-04 10:45:00]", "ground_truth": [ "2023-07-29 11:00:00", "2023-10-04 10:45:00" ], "eval_metric": "iou", "channel": "441", "ts_data_path": "ts_data/L3_T2_Contextual_Anomaly_00241.csv", "meta": { "event": "flood", "intensity": 3.5946458045106375, "year": 2023, "duration_days": 67, "source": "causal_rivers" } }, { "id": "L3_T2_Contextual_Anomaly_00242", "level": 3, "level_name": "Semantic Reasoning", "category": "Contextual Anomaly", "subtask": "Contextual Anomaly", "question": "Identify the period in channel 495 during 2022 that experienced the most significant dry-out period. (Output format: [YYYY-MM-DD, YYYY-MM-DD])", "answer": "[2022-05-12 17:45:00, 2022-07-21 17:30:00]", "ground_truth": [ "2022-05-12 17:45:00", "2022-07-21 17:30:00" ], "eval_metric": "iou", "channel": "495", "ts_data_path": "ts_data/L3_T2_Contextual_Anomaly_00242.csv", "meta": { "event": "drought", "intensity": 0.03649970634715126, "year": 2022, "duration_days": 70, "source": "causal_rivers" } }, { "id": "L3_T2_Contextual_Anomaly_00244", "level": 3, "level_name": "Semantic Reasoning", "category": "Contextual Anomaly", "subtask": "Contextual Anomaly", "question": "Identify the period in channel 501 during 2023 that experienced the most significant severe drought. (Output format: [YYYY-MM-DD, YYYY-MM-DD])", "answer": "[2023-04-16 07:45:00, 2023-07-12 07:30:00]", "ground_truth": [ "2023-04-16 07:45:00", "2023-07-12 07:30:00" ], "eval_metric": "iou", "channel": "501", "ts_data_path": "ts_data/L3_T2_Contextual_Anomaly_00244.csv", "meta": { "event": "drought", "intensity": 0.028072766719408105, "year": 2023, "duration_days": 87, "source": "causal_rivers" } }, { "id": "L3_T2_Contextual_Anomaly_00246", "level": 3, "level_name": "Semantic Reasoning", "category": "Contextual Anomaly", "subtask": "Contextual Anomaly", "question": "Identify the period in channel 580 during 2021 that experienced the most significant historically low water level. (Output format: [YYYY-MM-DD, YYYY-MM-DD])", "answer": "[2021-03-06 11:30:00, 2021-05-07 11:15:00]", "ground_truth": [ "2021-03-06 11:30:00", "2021-05-07 11:15:00" ], "eval_metric": "iou", "channel": "580", "ts_data_path": "ts_data/L3_T2_Contextual_Anomaly_00246.csv", "meta": { "event": "drought", "intensity": 0.04503659734432231, "year": 2021, "duration_days": 62, "source": "causal_rivers" } }, { "id": "L3_T2_Contextual_Anomaly_00247", "level": 3, "level_name": "Semantic Reasoning", "category": "Contextual Anomaly", "subtask": "Contextual Anomaly", "question": "Identify the period in channel 589 during 2019 that experienced the most significant extreme surge in flow. (Output format: [YYYY-MM-DD, YYYY-MM-DD])", "answer": "[2019-09-10 09:00:00, 2019-11-15 08:45:00]", "ground_truth": [ "2019-09-10 09:00:00", "2019-11-15 08:45:00" ], "eval_metric": "iou", "channel": "589", "ts_data_path": "ts_data/L3_T2_Contextual_Anomaly_00247.csv", "meta": { "event": "flood", "intensity": 3.422715638515336, "year": 2019, "duration_days": 66, "source": "causal_rivers" } }, { "id": "L3_T2_Contextual_Anomaly_00248", "level": 3, "level_name": "Semantic Reasoning", "category": "Contextual Anomaly", "subtask": "Contextual Anomaly", "question": "Identify the period in channel 591 during 2019 that experienced the most significant severe drought. (Output format: [YYYY-MM-DD, YYYY-MM-DD])", "answer": "[2019-09-02 19:15:00, 2019-11-07 19:00:00]", "ground_truth": [ "2019-09-02 19:15:00", "2019-11-07 19:00:00" ], "eval_metric": "iou", "channel": "591", "ts_data_path": "ts_data/L3_T2_Contextual_Anomaly_00248.csv", "meta": { "event": "drought", "intensity": 0.04644148921312766, "year": 2019, "duration_days": 66, "source": "causal_rivers" } }, { "id": "L3_T2_Contextual_Anomaly_00249", "level": 3, "level_name": "Semantic Reasoning", "category": "Contextual Anomaly", "subtask": "Contextual Anomaly", "question": "Identify the period in channel 595 during 2023 that experienced the most significant severe drought. (Output format: [YYYY-MM-DD, YYYY-MM-DD])", "answer": "[2023-08-27 06:15:00, 2023-11-23 06:00:00]", "ground_truth": [ "2023-08-27 06:15:00", "2023-11-23 06:00:00" ], "eval_metric": "iou", "channel": "595", "ts_data_path": "ts_data/L3_T2_Contextual_Anomaly_00249.csv", "meta": { "event": "drought", "intensity": 0.015724084344651255, "year": 2023, "duration_days": 88, "source": "causal_rivers" } }, { "id": "L3_T2_Contextual_Anomaly_00253", "level": 3, "level_name": "Semantic Reasoning", "category": "Contextual Anomaly", "subtask": "Contextual Anomaly", "question": "Identify the period in channel 647 during 2019 that experienced the most significant dry-out period. (Output format: [YYYY-MM-DD, YYYY-MM-DD])", "answer": "[2019-04-24 03:30:00, 2019-07-08 03:15:00]", "ground_truth": [ "2019-04-24 03:30:00", "2019-07-08 03:15:00" ], "eval_metric": "iou", "channel": "647", "ts_data_path": "ts_data/L3_T2_Contextual_Anomaly_00253.csv", "meta": { "event": "drought", "intensity": 0.04778087870156721, "year": 2019, "duration_days": 75, "source": "causal_rivers" } }, { "id": "L3_T2_Contextual_Anomaly_00254", "level": 3, "level_name": "Semantic Reasoning", "category": "Contextual Anomaly", "subtask": "Contextual Anomaly", "question": "Identify the period in channel 680 during 2019 that experienced the most significant historically low water level. (Output format: [YYYY-MM-DD, YYYY-MM-DD])", "answer": "[2019-10-09 06:30:00, 2019-12-26 06:15:00]", "ground_truth": [ "2019-10-09 06:30:00", "2019-12-26 06:15:00" ], "eval_metric": "iou", "channel": "680", "ts_data_path": "ts_data/L3_T2_Contextual_Anomaly_00254.csv", "meta": { "event": "drought", "intensity": 0.03557286508657863, "year": 2019, "duration_days": 78, "source": "causal_rivers" } }, { "id": "L3_T2_Contextual_Anomaly_00255", "level": 3, "level_name": "Semantic Reasoning", "category": "Contextual Anomaly", "subtask": "Contextual Anomaly", "question": "Identify the period in channel 683 during 2023 that experienced the most significant dry-out period. (Output format: [YYYY-MM-DD, YYYY-MM-DD])", "answer": "[2023-07-27 09:45:00, 2023-10-19 09:30:00]", "ground_truth": [ "2023-07-27 09:45:00", "2023-10-19 09:30:00" ], "eval_metric": "iou", "channel": "683", "ts_data_path": "ts_data/L3_T2_Contextual_Anomaly_00255.csv", "meta": { "event": "drought", "intensity": 0.04569993680870662, "year": 2023, "duration_days": 84, "source": "causal_rivers" } }, { "id": "L3_T2_Contextual_Anomaly_00259", "level": 3, "level_name": "Semantic Reasoning", "category": "Contextual Anomaly", "subtask": "Contextual Anomaly", "question": "Identify the period in channel 754 during 2022 that experienced the most significant extreme surge in flow. (Output format: [YYYY-MM-DD, YYYY-MM-DD])", "answer": "[2022-09-26 10:45:00, 2022-12-16 10:30:00]", "ground_truth": [ "2022-09-26 10:45:00", "2022-12-16 10:30:00" ], "eval_metric": "iou", "channel": "754", "ts_data_path": "ts_data/L3_T2_Contextual_Anomaly_00259.csv", "meta": { "event": "flood", "intensity": 3.4534015600874826, "year": 2022, "duration_days": 81, "source": "causal_rivers" } }, { "id": "L3_T2_Contextual_Anomaly_00261", "level": 3, "level_name": "Semantic Reasoning", "category": "Contextual Anomaly", "subtask": "Contextual Anomaly", "question": "Identify the period in channel 811 during 2019 that experienced the most significant dry-out period. (Output format: [YYYY-MM-DD, YYYY-MM-DD])", "answer": "[2019-02-22 16:00:00, 2019-05-17 15:45:00]", "ground_truth": [ "2019-02-22 16:00:00", "2019-05-17 15:45:00" ], "eval_metric": "iou", "channel": "811", "ts_data_path": "ts_data/L3_T2_Contextual_Anomaly_00261.csv", "meta": { "event": "drought", "intensity": 0.036724607593473464, "year": 2019, "duration_days": 84, "source": "causal_rivers" } }, { "id": "L3_T2_Contextual_Anomaly_00262", "level": 3, "level_name": "Semantic Reasoning", "category": "Contextual Anomaly", "subtask": "Contextual Anomaly", "question": "Identify the period in channel 813 during 2021 that experienced the most significant dry-out period. (Output format: [YYYY-MM-DD, YYYY-MM-DD])", "answer": "[2021-07-06 16:00:00, 2021-10-03 15:45:00]", "ground_truth": [ "2021-07-06 16:00:00", "2021-10-03 15:45:00" ], "eval_metric": "iou", "channel": "813", "ts_data_path": "ts_data/L3_T2_Contextual_Anomaly_00262.csv", "meta": { "event": "drought", "intensity": 0.012641128676304557, "year": 2021, "duration_days": 89, "source": "causal_rivers" } }, { "id": "L3_T2_Contextual_Anomaly_00265", "level": 3, "level_name": "Semantic Reasoning", "category": "Contextual Anomaly", "subtask": "Contextual Anomaly", "question": "Identify the period in channel 894 during 2020 that experienced the most significant extreme surge in flow. (Output format: [YYYY-MM-DD, YYYY-MM-DD])", "answer": "[2020-02-28 17:00:00, 2020-05-22 16:45:00]", "ground_truth": [ "2020-02-28 17:00:00", "2020-05-22 16:45:00" ], "eval_metric": "iou", "channel": "894", "ts_data_path": "ts_data/L3_T2_Contextual_Anomaly_00265.csv", "meta": { "event": "flood", "intensity": 3.406911895561768, "year": 2020, "duration_days": 84, "source": "causal_rivers" } }, { "id": "L3_T2_Contextual_Anomaly_00266", "level": 3, "level_name": "Semantic Reasoning", "category": "Contextual Anomaly", "subtask": "Contextual Anomaly", "question": "Identify the period in channel 895 during 2019 that experienced the most significant dry-out period. (Output format: [YYYY-MM-DD, YYYY-MM-DD])", "answer": "[2019-10-15 20:00:00, 2019-12-14 19:45:00]", "ground_truth": [ "2019-10-15 20:00:00", "2019-12-14 19:45:00" ], "eval_metric": "iou", "channel": "895", "ts_data_path": "ts_data/L3_T2_Contextual_Anomaly_00266.csv", "meta": { "event": "drought", "intensity": 0.023898676094126346, "year": 2019, "duration_days": 60, "source": "causal_rivers" } }, { "id": "L3_T2_Contextual_Anomaly_00268", "level": 3, "level_name": "Semantic Reasoning", "category": "Contextual Anomaly", "subtask": "Contextual Anomaly", "question": "Identify the period in channel 933 during 2019 that experienced the most significant severe flood. (Output format: [YYYY-MM-DD, YYYY-MM-DD])", "answer": "[2019-01-14 13:00:00, 2019-04-12 12:45:00]", "ground_truth": [ "2019-01-14 13:00:00", "2019-04-12 12:45:00" ], "eval_metric": "iou", "channel": "933", "ts_data_path": "ts_data/L3_T2_Contextual_Anomaly_00268.csv", "meta": { "event": "flood", "intensity": 3.7743066973653914, "year": 2019, "duration_days": 88, "source": "causal_rivers" } }, { "id": "L3_T2_Contextual_Anomaly_00270", "level": 3, "level_name": "Semantic Reasoning", "category": "Contextual Anomaly", "subtask": "Contextual Anomaly", "question": "Identify the period in channel HULL during 2017 that experienced the most significant severe flood. (Output format: [YYYY-MM-DD, YYYY-MM-DD])", "answer": "[2017-03-10 11:00:00, 2017-05-20 10:45:00]", "ground_truth": [ "2017-03-10 11:00:00", "2017-05-20 10:45:00" ], "eval_metric": "iou", "channel": "HULL", "ts_data_path": "ts_data/L3_T2_Contextual_Anomaly_00270.csv", "meta": { "event": "flood", "intensity": 3.8728798679544134, "year": 2017, "duration_days": 71, "source": "ettm1" } }, { "id": "L3_T2_Contextual_Anomaly_00271", "level": 3, "level_name": "Semantic Reasoning", "category": "Contextual Anomaly", "subtask": "Contextual Anomaly", "question": "Identify the period in channel MUFL during 2017 that experienced the most significant severe drought. (Output format: [YYYY-MM-DD, YYYY-MM-DD])", "answer": "[2017-04-02 15:00:00, 2017-06-28 14:45:00]", "ground_truth": [ "2017-04-02 15:00:00", "2017-06-28 14:45:00" ], "eval_metric": "iou", "channel": "MUFL", "ts_data_path": "ts_data/L3_T2_Contextual_Anomaly_00271.csv", "meta": { "event": "drought", "intensity": 0.03755167396708405, "year": 2017, "duration_days": 87, "source": "ettm1" } }, { "id": "L3_T2_Contextual_Anomaly_00273", "level": 3, "level_name": "Semantic Reasoning", "category": "Contextual Anomaly", "subtask": "Contextual Anomaly", "question": "Identify the period in channel LUFL during 2017 that experienced the most significant severe drought. (Output format: [YYYY-MM-DD, YYYY-MM-DD])", "answer": "[2017-06-06 15:30:00, 2017-08-16 15:15:00]", "ground_truth": [ "2017-06-06 15:30:00", "2017-08-16 15:15:00" ], "eval_metric": "iou", "channel": "LUFL", "ts_data_path": "ts_data/L3_T2_Contextual_Anomaly_00273.csv", "meta": { "event": "drought", "intensity": 0.05976785999819566, "year": 2017, "duration_days": 71, "source": "ettm1" } }, { "id": "L3_T2_Contextual_Anomaly_00274", "level": 3, "level_name": "Semantic Reasoning", "category": "Contextual Anomaly", "subtask": "Contextual Anomaly", "question": "Identify the period in channel LULL during 2017 that experienced the most significant dry-out period. (Output format: [YYYY-MM-DD, YYYY-MM-DD])", "answer": "[2017-06-23 17:45:00, 2017-09-21 17:30:00]", "ground_truth": [ "2017-06-23 17:45:00", "2017-09-21 17:30:00" ], "eval_metric": "iou", "channel": "LULL", "ts_data_path": "ts_data/L3_T2_Contextual_Anomaly_00274.csv", "meta": { "event": "drought", "intensity": 0.058574296129443405, "year": 2017, "duration_days": 90, "source": "ettm1" } }, { "id": "L3_T2_Contextual_Anomaly_00275", "level": 3, "level_name": "Semantic Reasoning", "category": "Contextual Anomaly", "subtask": "Contextual Anomaly", "question": "Identify the period in channel OT during 2017 that experienced the most significant dry-out period. (Output format: [YYYY-MM-DD, YYYY-MM-DD])", "answer": "[2017-05-23 19:30:00, 2017-08-03 19:15:00]", "ground_truth": [ "2017-05-23 19:30:00", "2017-08-03 19:15:00" ], "eval_metric": "iou", "channel": "OT", "ts_data_path": "ts_data/L3_T2_Contextual_Anomaly_00275.csv", "meta": { "event": "drought", "intensity": 0.05257676117820849, "year": 2017, "duration_days": 72, "source": "ettm1" } }, { "id": "L3_T2_Contextual_Anomaly_00276", "level": 3, "level_name": "Semantic Reasoning", "category": "Contextual Anomaly", "subtask": "Contextual Anomaly", "question": "Identify the period in channel 67 during 2019 that experienced the most significant dry-out period. (Output format: [YYYY-MM-DD, YYYY-MM-DD])", "answer": "[2019-01-24 18:45:00, 2019-04-11 18:30:00]", "ground_truth": [ "2019-01-24 18:45:00", "2019-04-11 18:30:00" ], "eval_metric": "iou", "channel": "67", "ts_data_path": "ts_data/L3_T2_Contextual_Anomaly_00276.csv", "meta": { "event": "drought", "intensity": 0.019052990909715052, "year": 2019, "duration_days": 77, "source": "causal_rivers" } }, { "id": "L3_T2_Contextual_Anomaly_00277", "level": 3, "level_name": "Semantic Reasoning", "category": "Contextual Anomaly", "subtask": "Contextual Anomaly", "question": "Identify the period in channel 71 during 2023 that experienced the most significant historically low water level. (Output format: [YYYY-MM-DD, YYYY-MM-DD])", "answer": "[2023-05-03 03:15:00, 2023-07-07 03:00:00]", "ground_truth": [ "2023-05-03 03:15:00", "2023-07-07 03:00:00" ], "eval_metric": "iou", "channel": "71", "ts_data_path": "ts_data/L3_T2_Contextual_Anomaly_00277.csv", "meta": { "event": "drought", "intensity": 0.06322024175181945, "year": 2023, "duration_days": 65, "source": "causal_rivers" } }, { "id": "L3_T2_Contextual_Anomaly_00278", "level": 3, "level_name": "Semantic Reasoning", "category": "Contextual Anomaly", "subtask": "Contextual Anomaly", "question": "Identify the period in channel 99 during 2023 that experienced the most significant severe flood. (Output format: [YYYY-MM-DD, YYYY-MM-DD])", "answer": "[2023-04-17 01:30:00, 2023-06-23 01:15:00]", "ground_truth": [ "2023-04-17 01:30:00", "2023-06-23 01:15:00" ], "eval_metric": "iou", "channel": "99", "ts_data_path": "ts_data/L3_T2_Contextual_Anomaly_00278.csv", "meta": { "event": "flood", "intensity": 4.500787486861651, "year": 2023, "duration_days": 67, "source": "causal_rivers" } }, { "id": "L3_T2_Contextual_Anomaly_00279", "level": 3, "level_name": "Semantic Reasoning", "category": "Contextual Anomaly", "subtask": "Contextual Anomaly", "question": "Identify the period in channel 123 during 2022 that experienced the most significant extreme surge in flow. (Output format: [YYYY-MM-DD, YYYY-MM-DD])", "answer": "[2022-09-13 04:45:00, 2022-11-22 04:30:00]", "ground_truth": [ "2022-09-13 04:45:00", "2022-11-22 04:30:00" ], "eval_metric": "iou", "channel": "123", "ts_data_path": "ts_data/L3_T2_Contextual_Anomaly_00279.csv", "meta": { "event": "flood", "intensity": 4.603721277721151, "year": 2022, "duration_days": 70, "source": "causal_rivers" } }, { "id": "L3_T2_Contextual_Anomaly_00280", "level": 3, "level_name": "Semantic Reasoning", "category": "Contextual Anomaly", "subtask": "Contextual Anomaly", "question": "Identify the period in channel 124 during 2019 that experienced the most significant severe drought. (Output format: [YYYY-MM-DD, YYYY-MM-DD])", "answer": "[2019-04-26 16:30:00, 2019-07-18 16:15:00]", "ground_truth": [ "2019-04-26 16:30:00", "2019-07-18 16:15:00" ], "eval_metric": "iou", "channel": "124", "ts_data_path": "ts_data/L3_T2_Contextual_Anomaly_00280.csv", "meta": { "event": "drought", "intensity": 0.017705564701360102, "year": 2019, "duration_days": 83, "source": "causal_rivers" } }, { "id": "L3_T2_Contextual_Anomaly_00281", "level": 3, "level_name": "Semantic Reasoning", "category": "Contextual Anomaly", "subtask": "Contextual Anomaly", "question": "Identify the period in channel 146 during 2021 that experienced the most significant dry-out period. (Output format: [YYYY-MM-DD, YYYY-MM-DD])", "answer": "[2021-01-22 09:45:00, 2021-04-12 09:30:00]", "ground_truth": [ "2021-01-22 09:45:00", "2021-04-12 09:30:00" ], "eval_metric": "iou", "channel": "146", "ts_data_path": "ts_data/L3_T2_Contextual_Anomaly_00281.csv", "meta": { "event": "drought", "intensity": 0.06957060074426631, "year": 2021, "duration_days": 80, "source": "causal_rivers" } }, { "id": "L3_T2_Contextual_Anomaly_00285", "level": 3, "level_name": "Semantic Reasoning", "category": "Contextual Anomaly", "subtask": "Contextual Anomaly", "question": "Identify the period in channel 155 during 2019 that experienced the most significant extreme surge in flow. (Output format: [YYYY-MM-DD, YYYY-MM-DD])", "answer": "[2019-03-22 10:30:00, 2019-06-14 10:15:00]", "ground_truth": [ "2019-03-22 10:30:00", "2019-06-14 10:15:00" ], "eval_metric": "iou", "channel": "155", "ts_data_path": "ts_data/L3_T2_Contextual_Anomaly_00285.csv", "meta": { "event": "flood", "intensity": 4.30047224211848, "year": 2019, "duration_days": 84, "source": "causal_rivers" } }, { "id": "L3_T2_Contextual_Anomaly_00286", "level": 3, "level_name": "Semantic Reasoning", "category": "Contextual Anomaly", "subtask": "Contextual Anomaly", "question": "Identify the period in channel 166 during 2023 that experienced the most significant historically high water level. (Output format: [YYYY-MM-DD, YYYY-MM-DD])", "answer": "[2023-06-10 15:15:00, 2023-08-29 15:00:00]", "ground_truth": [ "2023-06-10 15:15:00", "2023-08-29 15:00:00" ], "eval_metric": "iou", "channel": "166", "ts_data_path": "ts_data/L3_T2_Contextual_Anomaly_00286.csv", "meta": { "event": "flood", "intensity": 3.729337411369747, "year": 2023, "duration_days": 80, "source": "causal_rivers" } }, { "id": "L3_T2_Contextual_Anomaly_00287", "level": 3, "level_name": "Semantic Reasoning", "category": "Contextual Anomaly", "subtask": "Contextual Anomaly", "question": "Identify the period in channel 169 during 2020 that experienced the most significant historically low water level. (Output format: [YYYY-MM-DD, YYYY-MM-DD])", "answer": "[2020-08-23 14:45:00, 2020-11-07 14:30:00]", "ground_truth": [ "2020-08-23 14:45:00", "2020-11-07 14:30:00" ], "eval_metric": "iou", "channel": "169", "ts_data_path": "ts_data/L3_T2_Contextual_Anomaly_00287.csv", "meta": { "event": "drought", "intensity": 0.08937518020829102, "year": 2020, "duration_days": 76, "source": "causal_rivers" } }, { "id": "L3_T2_Contextual_Anomaly_00288", "level": 3, "level_name": "Semantic Reasoning", "category": "Contextual Anomaly", "subtask": "Contextual Anomaly", "question": "Identify the period in channel 170 during 2022 that experienced the most significant historically low water level. (Output format: [YYYY-MM-DD, YYYY-MM-DD])", "answer": "[2022-03-08 18:30:00, 2022-06-06 18:15:00]", "ground_truth": [ "2022-03-08 18:30:00", "2022-06-06 18:15:00" ], "eval_metric": "iou", "channel": "170", "ts_data_path": "ts_data/L3_T2_Contextual_Anomaly_00288.csv", "meta": { "event": "drought", "intensity": 0.07729407583421784, "year": 2022, "duration_days": 90, "source": "causal_rivers" } }, { "id": "L3_T2_Contextual_Anomaly_00289", "level": 3, "level_name": "Semantic Reasoning", "category": "Contextual Anomaly", "subtask": "Contextual Anomaly", "question": "Identify the period in channel 172 during 2023 that experienced the most significant severe flood. (Output format: [YYYY-MM-DD, YYYY-MM-DD])", "answer": "[2023-03-29 18:30:00, 2023-06-13 18:15:00]", "ground_truth": [ "2023-03-29 18:30:00", "2023-06-13 18:15:00" ], "eval_metric": "iou", "channel": "172", "ts_data_path": "ts_data/L3_T2_Contextual_Anomaly_00289.csv", "meta": { "event": "flood", "intensity": 4.3020369435283055, "year": 2023, "duration_days": 76, "source": "causal_rivers" } }, { "id": "L3_T2_Contextual_Anomaly_00290", "level": 3, "level_name": "Semantic Reasoning", "category": "Contextual Anomaly", "subtask": "Contextual Anomaly", "question": "Identify the period in channel 173 during 2020 that experienced the most significant severe flood. (Output format: [YYYY-MM-DD, YYYY-MM-DD])", "answer": "[2020-10-14 03:00:00, 2020-12-23 02:45:00]", "ground_truth": [ "2020-10-14 03:00:00", "2020-12-23 02:45:00" ], "eval_metric": "iou", "channel": "173", "ts_data_path": "ts_data/L3_T2_Contextual_Anomaly_00290.csv", "meta": { "event": "flood", "intensity": 4.756731065299258, "year": 2020, "duration_days": 70, "source": "causal_rivers" } }, { "id": "L3_T2_Contextual_Anomaly_00292", "level": 3, "level_name": "Semantic Reasoning", "category": "Contextual Anomaly", "subtask": "Contextual Anomaly", "question": "Identify the period in channel 237 during 2023 that experienced the most significant historically high water level. (Output format: [YYYY-MM-DD, YYYY-MM-DD])", "answer": "[2023-04-28 22:45:00, 2023-07-07 22:30:00]", "ground_truth": [ "2023-04-28 22:45:00", "2023-07-07 22:30:00" ], "eval_metric": "iou", "channel": "237", "ts_data_path": "ts_data/L3_T2_Contextual_Anomaly_00292.csv", "meta": { "event": "flood", "intensity": 4.223573548017182, "year": 2023, "duration_days": 70, "source": "causal_rivers" } }, { "id": "L3_T2_Contextual_Anomaly_00293", "level": 3, "level_name": "Semantic Reasoning", "category": "Contextual Anomaly", "subtask": "Contextual Anomaly", "question": "Identify the period in channel 245 during 2021 that experienced the most significant severe drought. (Output format: [YYYY-MM-DD, YYYY-MM-DD])", "answer": "[2021-03-27 01:45:00, 2021-06-06 01:30:00]", "ground_truth": [ "2021-03-27 01:45:00", "2021-06-06 01:30:00" ], "eval_metric": "iou", "channel": "245", "ts_data_path": "ts_data/L3_T2_Contextual_Anomaly_00293.csv", "meta": { "event": "drought", "intensity": 0.0963035197562849, "year": 2021, "duration_days": 71, "source": "causal_rivers" } }, { "id": "L3_T2_Contextual_Anomaly_00295", "level": 3, "level_name": "Semantic Reasoning", "category": "Contextual Anomaly", "subtask": "Contextual Anomaly", "question": "Identify the period in channel 430 during 2022 that experienced the most significant severe flood. (Output format: [YYYY-MM-DD, YYYY-MM-DD])", "answer": "[2022-06-06 08:30:00, 2022-08-06 08:15:00]", "ground_truth": [ "2022-06-06 08:30:00", "2022-08-06 08:15:00" ], "eval_metric": "iou", "channel": "430", "ts_data_path": "ts_data/L3_T2_Contextual_Anomaly_00295.csv", "meta": { "event": "flood", "intensity": 3.5928694005298483, "year": 2022, "duration_days": 61, "source": "causal_rivers" } }, { "id": "L3_T2_Contextual_Anomaly_00296", "level": 3, "level_name": "Semantic Reasoning", "category": "Contextual Anomaly", "subtask": "Contextual Anomaly", "question": "Identify the period in channel 441 during 2019 that experienced the most significant dry-out period. (Output format: [YYYY-MM-DD, YYYY-MM-DD])", "answer": "[2019-05-01 03:30:00, 2019-07-25 03:15:00]", "ground_truth": [ "2019-05-01 03:30:00", "2019-07-25 03:15:00" ], "eval_metric": "iou", "channel": "441", "ts_data_path": "ts_data/L3_T2_Contextual_Anomaly_00296.csv", "meta": { "event": "drought", "intensity": 0.04406416366603351, "year": 2019, "duration_days": 85, "source": "causal_rivers" } }, { "id": "L3_T2_Contextual_Anomaly_00297", "level": 3, "level_name": "Semantic Reasoning", "category": "Contextual Anomaly", "subtask": "Contextual Anomaly", "question": "Identify the period in channel 495 during 2021 that experienced the most significant extreme surge in flow. (Output format: [YYYY-MM-DD, YYYY-MM-DD])", "answer": "[2021-05-09 01:00:00, 2021-08-05 00:45:00]", "ground_truth": [ "2021-05-09 01:00:00", "2021-08-05 00:45:00" ], "eval_metric": "iou", "channel": "495", "ts_data_path": "ts_data/L3_T2_Contextual_Anomaly_00297.csv", "meta": { "event": "flood", "intensity": 4.601322098710811, "year": 2021, "duration_days": 88, "source": "causal_rivers" } }, { "id": "L3_T2_Contextual_Anomaly_00298", "level": 3, "level_name": "Semantic Reasoning", "category": "Contextual Anomaly", "subtask": "Contextual Anomaly", "question": "Identify the period in channel 496 during 2020 that experienced the most significant historically low water level. (Output format: [YYYY-MM-DD, YYYY-MM-DD])", "answer": "[2020-05-31 23:30:00, 2020-08-16 23:15:00]", "ground_truth": [ "2020-05-31 23:30:00", "2020-08-16 23:15:00" ], "eval_metric": "iou", "channel": "496", "ts_data_path": "ts_data/L3_T2_Contextual_Anomaly_00298.csv", "meta": { "event": "drought", "intensity": 0.07871422570141838, "year": 2020, "duration_days": 77, "source": "causal_rivers" } }, { "id": "L3_T2_Contextual_Anomaly_00299", "level": 3, "level_name": "Semantic Reasoning", "category": "Contextual Anomaly", "subtask": "Contextual Anomaly", "question": "Identify the period in channel 501 during 2020 that experienced the most significant historically low water level. (Output format: [YYYY-MM-DD, YYYY-MM-DD])", "answer": "[2020-10-09 12:00:00, 2020-12-14 11:45:00]", "ground_truth": [ "2020-10-09 12:00:00", "2020-12-14 11:45:00" ], "eval_metric": "iou", "channel": "501", "ts_data_path": "ts_data/L3_T2_Contextual_Anomaly_00299.csv", "meta": { "event": "drought", "intensity": 0.045152465293250545, "year": 2020, "duration_days": 66, "source": "causal_rivers" } }, { "id": "L3_T2_Contextual_Anomaly_00300", "level": 3, "level_name": "Semantic Reasoning", "category": "Contextual Anomaly", "subtask": "Contextual Anomaly", "question": "Identify the period in channel 578 during 2021 that experienced the most significant severe drought. (Output format: [YYYY-MM-DD, YYYY-MM-DD])", "answer": "[2021-03-14 15:15:00, 2021-06-11 15:00:00]", "ground_truth": [ "2021-03-14 15:15:00", "2021-06-11 15:00:00" ], "eval_metric": "iou", "channel": "578", "ts_data_path": "ts_data/L3_T2_Contextual_Anomaly_00300.csv", "meta": { "event": "drought", "intensity": 0.042996595690355244, "year": 2021, "duration_days": 89, "source": "causal_rivers" } }, { "id": "L3_T2_Contextual_Anomaly_00302", "level": 3, "level_name": "Semantic Reasoning", "category": "Contextual Anomaly", "subtask": "Contextual Anomaly", "question": "Identify the period in channel 589 during 2022 that experienced the most significant historically low water level. (Output format: [YYYY-MM-DD, YYYY-MM-DD])", "answer": "[2022-06-19 17:00:00, 2022-08-28 16:45:00]", "ground_truth": [ "2022-06-19 17:00:00", "2022-08-28 16:45:00" ], "eval_metric": "iou", "channel": "589", "ts_data_path": "ts_data/L3_T2_Contextual_Anomaly_00302.csv", "meta": { "event": "drought", "intensity": 0.05169185464075285, "year": 2022, "duration_days": 70, "source": "causal_rivers" } }, { "id": "L3_T2_Contextual_Anomaly_00303", "level": 3, "level_name": "Semantic Reasoning", "category": "Contextual Anomaly", "subtask": "Contextual Anomaly", "question": "Identify the period in channel 591 during 2021 that experienced the most significant historically low water level. (Output format: [YYYY-MM-DD, YYYY-MM-DD])", "answer": "[2021-06-08 22:30:00, 2021-08-20 22:15:00]", "ground_truth": [ "2021-06-08 22:30:00", "2021-08-20 22:15:00" ], "eval_metric": "iou", "channel": "591", "ts_data_path": "ts_data/L3_T2_Contextual_Anomaly_00303.csv", "meta": { "event": "drought", "intensity": 0.07715826720570033, "year": 2021, "duration_days": 73, "source": "causal_rivers" } }, { "id": "L3_T2_Contextual_Anomaly_00304", "level": 3, "level_name": "Semantic Reasoning", "category": "Contextual Anomaly", "subtask": "Contextual Anomaly", "question": "Identify the period in channel 595 during 2020 that experienced the most significant severe flood. (Output format: [YYYY-MM-DD, YYYY-MM-DD])", "answer": "[2020-03-05 07:30:00, 2020-05-30 07:15:00]", "ground_truth": [ "2020-03-05 07:30:00", "2020-05-30 07:15:00" ], "eval_metric": "iou", "channel": "595", "ts_data_path": "ts_data/L3_T2_Contextual_Anomaly_00304.csv", "meta": { "event": "flood", "intensity": 3.9329358681126463, "year": 2020, "duration_days": 86, "source": "causal_rivers" } }, { "id": "L3_T2_Contextual_Anomaly_00305", "level": 3, "level_name": "Semantic Reasoning", "category": "Contextual Anomaly", "subtask": "Contextual Anomaly", "question": "Identify the period in channel 625 during 2020 that experienced the most significant extreme surge in flow. (Output format: [YYYY-MM-DD, YYYY-MM-DD])", "answer": "[2020-02-09 20:45:00, 2020-05-04 20:30:00]", "ground_truth": [ "2020-02-09 20:45:00", "2020-05-04 20:30:00" ], "eval_metric": "iou", "channel": "625", "ts_data_path": "ts_data/L3_T2_Contextual_Anomaly_00305.csv", "meta": { "event": "flood", "intensity": 3.2233139377160307, "year": 2020, "duration_days": 85, "source": "causal_rivers" } }, { "id": "L3_T2_Contextual_Anomaly_00307", "level": 3, "level_name": "Semantic Reasoning", "category": "Contextual Anomaly", "subtask": "Contextual Anomaly", "question": "Identify the period in channel 627 during 2023 that experienced the most significant historically high water level. (Output format: [YYYY-MM-DD, YYYY-MM-DD])", "answer": "[2023-04-08 13:45:00, 2023-06-23 13:30:00]", "ground_truth": [ "2023-04-08 13:45:00", "2023-06-23 13:30:00" ], "eval_metric": "iou", "channel": "627", "ts_data_path": "ts_data/L3_T2_Contextual_Anomaly_00307.csv", "meta": { "event": "flood", "intensity": 4.152673069514141, "year": 2023, "duration_days": 76, "source": "causal_rivers" } }, { "id": "L3_T2_Contextual_Anomaly_00308", "level": 3, "level_name": "Semantic Reasoning", "category": "Contextual Anomaly", "subtask": "Contextual Anomaly", "question": "Identify the period in channel 647 during 2020 that experienced the most significant severe drought. (Output format: [YYYY-MM-DD, YYYY-MM-DD])", "answer": "[2020-08-25 11:30:00, 2020-10-31 11:15:00]", "ground_truth": [ "2020-08-25 11:30:00", "2020-10-31 11:15:00" ], "eval_metric": "iou", "channel": "647", "ts_data_path": "ts_data/L3_T2_Contextual_Anomaly_00308.csv", "meta": { "event": "drought", "intensity": 0.0660230164529963, "year": 2020, "duration_days": 67, "source": "causal_rivers" } }, { "id": "L3_T2_Contextual_Anomaly_00309", "level": 3, "level_name": "Semantic Reasoning", "category": "Contextual Anomaly", "subtask": "Contextual Anomaly", "question": "Identify the period in channel 680 during 2019 that experienced the most significant dry-out period. (Output format: [YYYY-MM-DD, YYYY-MM-DD])", "answer": "[2019-02-19 21:15:00, 2019-04-30 21:00:00]", "ground_truth": [ "2019-02-19 21:15:00", "2019-04-30 21:00:00" ], "eval_metric": "iou", "channel": "680", "ts_data_path": "ts_data/L3_T2_Contextual_Anomaly_00309.csv", "meta": { "event": "drought", "intensity": 0.05573609317113721, "year": 2019, "duration_days": 70, "source": "causal_rivers" } }, { "id": "L3_T2_Contextual_Anomaly_00310", "level": 3, "level_name": "Semantic Reasoning", "category": "Contextual Anomaly", "subtask": "Contextual Anomaly", "question": "Identify the period in channel 683 during 2019 that experienced the most significant historically low water level. (Output format: [YYYY-MM-DD, YYYY-MM-DD])", "answer": "[2019-05-24 10:00:00, 2019-08-17 09:45:00]", "ground_truth": [ "2019-05-24 10:00:00", "2019-08-17 09:45:00" ], "eval_metric": "iou", "channel": "683", "ts_data_path": "ts_data/L3_T2_Contextual_Anomaly_00310.csv", "meta": { "event": "drought", "intensity": 0.06922294393669426, "year": 2019, "duration_days": 85, "source": "causal_rivers" } }, { "id": "L3_T2_Contextual_Anomaly_00311", "level": 3, "level_name": "Semantic Reasoning", "category": "Contextual Anomaly", "subtask": "Contextual Anomaly", "question": "Identify the period in channel 727 during 2019 that experienced the most significant historically low water level. (Output format: [YYYY-MM-DD, YYYY-MM-DD])", "answer": "[2019-05-30 04:15:00, 2019-08-12 04:00:00]", "ground_truth": [ "2019-05-30 04:15:00", "2019-08-12 04:00:00" ], "eval_metric": "iou", "channel": "727", "ts_data_path": "ts_data/L3_T2_Contextual_Anomaly_00311.csv", "meta": { "event": "drought", "intensity": 0.09454638755594737, "year": 2019, "duration_days": 74, "source": "causal_rivers" } }, { "id": "L3_T2_Contextual_Anomaly_00312", "level": 3, "level_name": "Semantic Reasoning", "category": "Contextual Anomaly", "subtask": "Contextual Anomaly", "question": "Identify the period in channel 728 during 2022 that experienced the most significant historically low water level. (Output format: [YYYY-MM-DD, YYYY-MM-DD])", "answer": "[2022-05-08 23:15:00, 2022-07-28 23:00:00]", "ground_truth": [ "2022-05-08 23:15:00", "2022-07-28 23:00:00" ], "eval_metric": "iou", "channel": "728", "ts_data_path": "ts_data/L3_T2_Contextual_Anomaly_00312.csv", "meta": { "event": "drought", "intensity": 0.04840372154924953, "year": 2022, "duration_days": 81, "source": "causal_rivers" } }, { "id": "L3_T2_Contextual_Anomaly_00314", "level": 3, "level_name": "Semantic Reasoning", "category": "Contextual Anomaly", "subtask": "Contextual Anomaly", "question": "Identify the period in channel 754 during 2020 that experienced the most significant extreme surge in flow. (Output format: [YYYY-MM-DD, YYYY-MM-DD])", "answer": "[2020-04-19 03:00:00, 2020-07-02 02:45:00]", "ground_truth": [ "2020-04-19 03:00:00", "2020-07-02 02:45:00" ], "eval_metric": "iou", "channel": "754", "ts_data_path": "ts_data/L3_T2_Contextual_Anomaly_00314.csv", "meta": { "event": "flood", "intensity": 3.889497501582899, "year": 2020, "duration_days": 74, "source": "causal_rivers" } }, { "id": "L3_T2_Contextual_Anomaly_00315", "level": 3, "level_name": "Semantic Reasoning", "category": "Contextual Anomaly", "subtask": "Contextual Anomaly", "question": "Identify the period in channel 762 during 2023 that experienced the most significant dry-out period. (Output format: [YYYY-MM-DD, YYYY-MM-DD])", "answer": "[2023-04-27 10:15:00, 2023-07-04 10:00:00]", "ground_truth": [ "2023-04-27 10:15:00", "2023-07-04 10:00:00" ], "eval_metric": "iou", "channel": "762", "ts_data_path": "ts_data/L3_T2_Contextual_Anomaly_00315.csv", "meta": { "event": "drought", "intensity": 0.06199628492871238, "year": 2023, "duration_days": 68, "source": "causal_rivers" } }, { "id": "L3_T2_Contextual_Anomaly_00316", "level": 3, "level_name": "Semantic Reasoning", "category": "Contextual Anomaly", "subtask": "Contextual Anomaly", "question": "Identify the period in channel 811 during 2023 that experienced the most significant severe drought. (Output format: [YYYY-MM-DD, YYYY-MM-DD])", "answer": "[2023-07-15 05:15:00, 2023-09-29 05:00:00]", "ground_truth": [ "2023-07-15 05:15:00", "2023-09-29 05:00:00" ], "eval_metric": "iou", "channel": "811", "ts_data_path": "ts_data/L3_T2_Contextual_Anomaly_00316.csv", "meta": { "event": "drought", "intensity": 0.06124722523173937, "year": 2023, "duration_days": 76, "source": "causal_rivers" } }, { "id": "L3_T2_Contextual_Anomaly_00317", "level": 3, "level_name": "Semantic Reasoning", "category": "Contextual Anomaly", "subtask": "Contextual Anomaly", "question": "Identify the period in channel 813 during 2020 that experienced the most significant severe drought. (Output format: [YYYY-MM-DD, YYYY-MM-DD])", "answer": "[2020-07-22 19:30:00, 2020-09-20 19:15:00]", "ground_truth": [ "2020-07-22 19:30:00", "2020-09-20 19:15:00" ], "eval_metric": "iou", "channel": "813", "ts_data_path": "ts_data/L3_T2_Contextual_Anomaly_00317.csv", "meta": { "event": "drought", "intensity": 0.05233425305791631, "year": 2020, "duration_days": 60, "source": "causal_rivers" } }, { "id": "L3_T2_Contextual_Anomaly_00318", "level": 3, "level_name": "Semantic Reasoning", "category": "Contextual Anomaly", "subtask": "Contextual Anomaly", "question": "Identify the period in channel 865 during 2021 that experienced the most significant extreme surge in flow. (Output format: [YYYY-MM-DD, YYYY-MM-DD])", "answer": "[2021-07-08 02:15:00, 2021-09-10 02:00:00]", "ground_truth": [ "2021-07-08 02:15:00", "2021-09-10 02:00:00" ], "eval_metric": "iou", "channel": "865", "ts_data_path": "ts_data/L3_T2_Contextual_Anomaly_00318.csv", "meta": { "event": "flood", "intensity": 3.1229281328356393, "year": 2021, "duration_days": 64, "source": "causal_rivers" } }, { "id": "L3_T2_Contextual_Anomaly_00319", "level": 3, "level_name": "Semantic Reasoning", "category": "Contextual Anomaly", "subtask": "Contextual Anomaly", "question": "Identify the period in channel 891 during 2019 that experienced the most significant historically high water level. (Output format: [YYYY-MM-DD, YYYY-MM-DD])", "answer": "[2019-03-18 02:45:00, 2019-06-14 02:30:00]", "ground_truth": [ "2019-03-18 02:45:00", "2019-06-14 02:30:00" ], "eval_metric": "iou", "channel": "891", "ts_data_path": "ts_data/L3_T2_Contextual_Anomaly_00319.csv", "meta": { "event": "flood", "intensity": 4.6651483789727735, "year": 2019, "duration_days": 88, "source": "causal_rivers" } }, { "id": "L3_T2_Contextual_Anomaly_00320", "level": 3, "level_name": "Semantic Reasoning", "category": "Contextual Anomaly", "subtask": "Contextual Anomaly", "question": "Identify the period in channel 894 during 2023 that experienced the most significant dry-out period. (Output format: [YYYY-MM-DD, YYYY-MM-DD])", "answer": "[2023-07-24 10:30:00, 2023-10-06 10:15:00]", "ground_truth": [ "2023-07-24 10:30:00", "2023-10-06 10:15:00" ], "eval_metric": "iou", "channel": "894", "ts_data_path": "ts_data/L3_T2_Contextual_Anomaly_00320.csv", "meta": { "event": "drought", "intensity": 0.05073025097172564, "year": 2023, "duration_days": 74, "source": "causal_rivers" } }, { "id": "L3_T2_Contextual_Anomaly_00324", "level": 3, "level_name": "Semantic Reasoning", "category": "Contextual Anomaly", "subtask": "Contextual Anomaly", "question": "Identify the period in channel HUFL during 2017 that experienced the most significant extreme surge in flow. (Output format: [YYYY-MM-DD, YYYY-MM-DD])", "answer": "[2017-09-20 05:00:00, 2017-12-11 04:45:00]", "ground_truth": [ "2017-09-20 05:00:00", "2017-12-11 04:45:00" ], "eval_metric": "iou", "channel": "HUFL", "ts_data_path": "ts_data/L3_T2_Contextual_Anomaly_00324.csv", "meta": { "event": "flood", "intensity": 3.4084352490742402, "year": 2017, "duration_days": 82, "source": "ettm1" } }, { "id": "L3_T2_Contextual_Anomaly_00328", "level": 3, "level_name": "Semantic Reasoning", "category": "Contextual Anomaly", "subtask": "Contextual Anomaly", "question": "Identify the period in channel LUFL during 2017 that experienced the most significant historically high water level. (Output format: [YYYY-MM-DD, YYYY-MM-DD])", "answer": "[2017-08-16 10:15:00, 2017-10-18 10:00:00]", "ground_truth": [ "2017-08-16 10:15:00", "2017-10-18 10:00:00" ], "eval_metric": "iou", "channel": "LUFL", "ts_data_path": "ts_data/L3_T2_Contextual_Anomaly_00328.csv", "meta": { "event": "flood", "intensity": 4.436257223656813, "year": 2017, "duration_days": 63, "source": "ettm1" } }, { "id": "L3_T2_Contextual_Anomaly_00329", "level": 3, "level_name": "Semantic Reasoning", "category": "Contextual Anomaly", "subtask": "Contextual Anomaly", "question": "Identify the period in channel LULL during 2017 that experienced the most significant historically high water level. (Output format: [YYYY-MM-DD, YYYY-MM-DD])", "answer": "[2017-04-10 23:15:00, 2017-07-02 23:00:00]", "ground_truth": [ "2017-04-10 23:15:00", "2017-07-02 23:00:00" ], "eval_metric": "iou", "channel": "LULL", "ts_data_path": "ts_data/L3_T2_Contextual_Anomaly_00329.csv", "meta": { "event": "flood", "intensity": 4.670639769397781, "year": 2017, "duration_days": 83, "source": "ettm1" } }, { "id": "L3_T2_Contextual_Anomaly_00330", "level": 3, "level_name": "Semantic Reasoning", "category": "Contextual Anomaly", "subtask": "Contextual Anomaly", "question": "Identify the period in channel OT during 2017 that experienced the most significant historically high water level. (Output format: [YYYY-MM-DD, YYYY-MM-DD])", "answer": "[2017-07-09 03:45:00, 2017-09-15 03:30:00]", "ground_truth": [ "2017-07-09 03:45:00", "2017-09-15 03:30:00" ], "eval_metric": "iou", "channel": "OT", "ts_data_path": "ts_data/L3_T2_Contextual_Anomaly_00330.csv", "meta": { "event": "flood", "intensity": 3.9712440798530344, "year": 2017, "duration_days": 68, "source": "ettm1" } }, { "id": "L3_T2_Contextual_Anomaly_00335", "level": 3, "level_name": "Semantic Reasoning", "category": "Contextual Anomaly", "subtask": "Contextual Anomaly", "question": "Identify the period in channel 124 during 2021 that experienced the most significant dry-out period. (Output format: [YYYY-MM-DD, YYYY-MM-DD])", "answer": "[2021-01-13 22:45:00, 2021-04-03 22:30:00]", "ground_truth": [ "2021-01-13 22:45:00", "2021-04-03 22:30:00" ], "eval_metric": "iou", "channel": "124", "ts_data_path": "ts_data/L3_T2_Contextual_Anomaly_00335.csv", "meta": { "event": "drought", "intensity": 0.03324862740940752, "year": 2021, "duration_days": 80, "source": "causal_rivers" } }, { "id": "L3_T2_Contextual_Anomaly_00338", "level": 3, "level_name": "Semantic Reasoning", "category": "Contextual Anomaly", "subtask": "Contextual Anomaly", "question": "Identify the period in channel 151 during 2023 that experienced the most significant historically high water level. (Output format: [YYYY-MM-DD, YYYY-MM-DD])", "answer": "[2023-04-24 00:00:00, 2023-07-21 23:45:00]", "ground_truth": [ "2023-04-24 00:00:00", "2023-07-21 23:45:00" ], "eval_metric": "iou", "channel": "151", "ts_data_path": "ts_data/L3_T2_Contextual_Anomaly_00338.csv", "meta": { "event": "flood", "intensity": 3.277367191198369, "year": 2023, "duration_days": 89, "source": "causal_rivers" } }, { "id": "L3_T2_Contextual_Anomaly_00343", "level": 3, "level_name": "Semantic Reasoning", "category": "Contextual Anomaly", "subtask": "Contextual Anomaly", "question": "Identify the period in channel 170 during 2019 that experienced the most significant severe drought. (Output format: [YYYY-MM-DD, YYYY-MM-DD])", "answer": "[2019-07-06 07:15:00, 2019-09-06 07:00:00]", "ground_truth": [ "2019-07-06 07:15:00", "2019-09-06 07:00:00" ], "eval_metric": "iou", "channel": "170", "ts_data_path": "ts_data/L3_T2_Contextual_Anomaly_00343.csv", "meta": { "event": "drought", "intensity": 0.07988537790581561, "year": 2019, "duration_days": 62, "source": "causal_rivers" } }, { "id": "L3_T2_Contextual_Anomaly_00344", "level": 3, "level_name": "Semantic Reasoning", "category": "Contextual Anomaly", "subtask": "Contextual Anomaly", "question": "Identify the period in channel 172 during 2023 that experienced the most significant dry-out period. (Output format: [YYYY-MM-DD, YYYY-MM-DD])", "answer": "[2023-10-02 17:15:00, 2023-12-14 17:00:00]", "ground_truth": [ "2023-10-02 17:15:00", "2023-12-14 17:00:00" ], "eval_metric": "iou", "channel": "172", "ts_data_path": "ts_data/L3_T2_Contextual_Anomaly_00344.csv", "meta": { "event": "drought", "intensity": 0.017575188514622878, "year": 2023, "duration_days": 73, "source": "causal_rivers" } }, { "id": "L3_T2_Contextual_Anomaly_00346", "level": 3, "level_name": "Semantic Reasoning", "category": "Contextual Anomaly", "subtask": "Contextual Anomaly", "question": "Identify the period in channel 177 during 2020 that experienced the most significant extreme surge in flow. (Output format: [YYYY-MM-DD, YYYY-MM-DD])", "answer": "[2020-08-03 21:15:00, 2020-10-20 21:00:00]", "ground_truth": [ "2020-08-03 21:15:00", "2020-10-20 21:00:00" ], "eval_metric": "iou", "channel": "177", "ts_data_path": "ts_data/L3_T2_Contextual_Anomaly_00346.csv", "meta": { "event": "flood", "intensity": 3.964761162484857, "year": 2020, "duration_days": 78, "source": "causal_rivers" } }, { "id": "L3_T2_Contextual_Anomaly_00351", "level": 3, "level_name": "Semantic Reasoning", "category": "Contextual Anomaly", "subtask": "Contextual Anomaly", "question": "Identify the period in channel 441 during 2023 that experienced the most significant extreme surge in flow. (Output format: [YYYY-MM-DD, YYYY-MM-DD])", "answer": "[2023-05-15 10:30:00, 2023-07-27 10:15:00]", "ground_truth": [ "2023-05-15 10:30:00", "2023-07-27 10:15:00" ], "eval_metric": "iou", "channel": "441", "ts_data_path": "ts_data/L3_T2_Contextual_Anomaly_00351.csv", "meta": { "event": "flood", "intensity": 4.3166452997403875, "year": 2023, "duration_days": 73, "source": "causal_rivers" } }, { "id": "L3_T2_Contextual_Anomaly_00354", "level": 3, "level_name": "Semantic Reasoning", "category": "Contextual Anomaly", "subtask": "Contextual Anomaly", "question": "Identify the period in channel 501 during 2020 that experienced the most significant dry-out period. (Output format: [YYYY-MM-DD, YYYY-MM-DD])", "answer": "[2020-03-18 09:00:00, 2020-05-18 08:45:00]", "ground_truth": [ "2020-03-18 09:00:00", "2020-05-18 08:45:00" ], "eval_metric": "iou", "channel": "501", "ts_data_path": "ts_data/L3_T2_Contextual_Anomaly_00354.csv", "meta": { "event": "drought", "intensity": 0.07799289569181898, "year": 2020, "duration_days": 61, "source": "causal_rivers" } }, { "id": "L3_T2_Contextual_Anomaly_00355", "level": 3, "level_name": "Semantic Reasoning", "category": "Contextual Anomaly", "subtask": "Contextual Anomaly", "question": "Identify the period in channel 578 during 2020 that experienced the most significant historically high water level. (Output format: [YYYY-MM-DD, YYYY-MM-DD])", "answer": "[2020-08-10 10:45:00, 2020-10-09 10:30:00]", "ground_truth": [ "2020-08-10 10:45:00", "2020-10-09 10:30:00" ], "eval_metric": "iou", "channel": "578", "ts_data_path": "ts_data/L3_T2_Contextual_Anomaly_00355.csv", "meta": { "event": "flood", "intensity": 3.3058039986107284, "year": 2020, "duration_days": 60, "source": "causal_rivers" } }, { "id": "L3_T2_Contextual_Anomaly_00356", "level": 3, "level_name": "Semantic Reasoning", "category": "Contextual Anomaly", "subtask": "Contextual Anomaly", "question": "Identify the period in channel 580 during 2020 that experienced the most significant extreme surge in flow. (Output format: [YYYY-MM-DD, YYYY-MM-DD])", "answer": "[2020-07-11 19:45:00, 2020-10-01 19:30:00]", "ground_truth": [ "2020-07-11 19:45:00", "2020-10-01 19:30:00" ], "eval_metric": "iou", "channel": "580", "ts_data_path": "ts_data/L3_T2_Contextual_Anomaly_00356.csv", "meta": { "event": "flood", "intensity": 3.5418086052544435, "year": 2020, "duration_days": 82, "source": "causal_rivers" } }, { "id": "L3_T2_Contextual_Anomaly_00357", "level": 3, "level_name": "Semantic Reasoning", "category": "Contextual Anomaly", "subtask": "Contextual Anomaly", "question": "Identify the period in channel 589 during 2023 that experienced the most significant historically high water level. (Output format: [YYYY-MM-DD, YYYY-MM-DD])", "answer": "[2023-09-01 03:00:00, 2023-11-30 02:45:00]", "ground_truth": [ "2023-09-01 03:00:00", "2023-11-30 02:45:00" ], "eval_metric": "iou", "channel": "589", "ts_data_path": "ts_data/L3_T2_Contextual_Anomaly_00357.csv", "meta": { "event": "flood", "intensity": 3.800281604345132, "year": 2023, "duration_days": 90, "source": "causal_rivers" } }, { "id": "L3_T2_Contextual_Anomaly_00358", "level": 3, "level_name": "Semantic Reasoning", "category": "Contextual Anomaly", "subtask": "Contextual Anomaly", "question": "Identify the period in channel 591 during 2019 that experienced the most significant extreme surge in flow. (Output format: [YYYY-MM-DD, YYYY-MM-DD])", "answer": "[2019-03-18 08:00:00, 2019-05-31 07:45:00]", "ground_truth": [ "2019-03-18 08:00:00", "2019-05-31 07:45:00" ], "eval_metric": "iou", "channel": "591", "ts_data_path": "ts_data/L3_T2_Contextual_Anomaly_00358.csv", "meta": { "event": "flood", "intensity": 4.397514968736639, "year": 2019, "duration_days": 74, "source": "causal_rivers" } }, { "id": "L3_T2_Contextual_Anomaly_00359", "level": 3, "level_name": "Semantic Reasoning", "category": "Contextual Anomaly", "subtask": "Contextual Anomaly", "question": "Identify the period in channel 595 during 2020 that experienced the most significant historically low water level. (Output format: [YYYY-MM-DD, YYYY-MM-DD])", "answer": "[2020-05-03 04:30:00, 2020-07-29 04:15:00]", "ground_truth": [ "2020-05-03 04:30:00", "2020-07-29 04:15:00" ], "eval_metric": "iou", "channel": "595", "ts_data_path": "ts_data/L3_T2_Contextual_Anomaly_00359.csv", "meta": { "event": "drought", "intensity": 0.02711779372172797, "year": 2020, "duration_days": 87, "source": "causal_rivers" } }, { "id": "L3_T2_Contextual_Anomaly_00360", "level": 3, "level_name": "Semantic Reasoning", "category": "Contextual Anomaly", "subtask": "Contextual Anomaly", "question": "Identify the period in channel 625 during 2021 that experienced the most significant historically low water level. (Output format: [YYYY-MM-DD, YYYY-MM-DD])", "answer": "[2021-04-30 06:15:00, 2021-07-21 06:00:00]", "ground_truth": [ "2021-04-30 06:15:00", "2021-07-21 06:00:00" ], "eval_metric": "iou", "channel": "625", "ts_data_path": "ts_data/L3_T2_Contextual_Anomaly_00360.csv", "meta": { "event": "drought", "intensity": 0.010150980969962954, "year": 2021, "duration_days": 82, "source": "causal_rivers" } }, { "id": "L3_T2_Contextual_Anomaly_00361", "level": 3, "level_name": "Semantic Reasoning", "category": "Contextual Anomaly", "subtask": "Contextual Anomaly", "question": "Identify the period in channel 626 during 2019 that experienced the most significant extreme surge in flow. (Output format: [YYYY-MM-DD, YYYY-MM-DD])", "answer": "[2019-06-24 09:00:00, 2019-09-14 08:45:00]", "ground_truth": [ "2019-06-24 09:00:00", "2019-09-14 08:45:00" ], "eval_metric": "iou", "channel": "626", "ts_data_path": "ts_data/L3_T2_Contextual_Anomaly_00361.csv", "meta": { "event": "flood", "intensity": 4.545046991831986, "year": 2019, "duration_days": 82, "source": "causal_rivers" } }, { "id": "L3_T2_Contextual_Anomaly_00365", "level": 3, "level_name": "Semantic Reasoning", "category": "Contextual Anomaly", "subtask": "Contextual Anomaly", "question": "Identify the period in channel 683 during 2019 that experienced the most significant historically low water level. (Output format: [YYYY-MM-DD, YYYY-MM-DD])", "answer": "[2019-05-08 12:15:00, 2019-07-20 12:00:00]", "ground_truth": [ "2019-05-08 12:15:00", "2019-07-20 12:00:00" ], "eval_metric": "iou", "channel": "683", "ts_data_path": "ts_data/L3_T2_Contextual_Anomaly_00365.csv", "meta": { "event": "drought", "intensity": 0.06107450709239265, "year": 2019, "duration_days": 73, "source": "causal_rivers" } }, { "id": "L3_T2_Contextual_Anomaly_00366", "level": 3, "level_name": "Semantic Reasoning", "category": "Contextual Anomaly", "subtask": "Contextual Anomaly", "question": "Identify the period in channel 727 during 2022 that experienced the most significant severe flood. (Output format: [YYYY-MM-DD, YYYY-MM-DD])", "answer": "[2022-01-23 04:45:00, 2022-04-18 04:30:00]", "ground_truth": [ "2022-01-23 04:45:00", "2022-04-18 04:30:00" ], "eval_metric": "iou", "channel": "727", "ts_data_path": "ts_data/L3_T2_Contextual_Anomaly_00366.csv", "meta": { "event": "flood", "intensity": 3.5186267626618126, "year": 2022, "duration_days": 85, "source": "causal_rivers" } }, { "id": "L3_T2_Contextual_Anomaly_00368", "level": 3, "level_name": "Semantic Reasoning", "category": "Contextual Anomaly", "subtask": "Contextual Anomaly", "question": "Identify the period in channel 729 during 2020 that experienced the most significant extreme surge in flow. (Output format: [YYYY-MM-DD, YYYY-MM-DD])", "answer": "[2020-01-12 11:30:00, 2020-03-13 11:15:00]", "ground_truth": [ "2020-01-12 11:30:00", "2020-03-13 11:15:00" ], "eval_metric": "iou", "channel": "729", "ts_data_path": "ts_data/L3_T2_Contextual_Anomaly_00368.csv", "meta": { "event": "flood", "intensity": 4.955116155865104, "year": 2020, "duration_days": 61, "source": "causal_rivers" } }, { "id": "L3_T2_Contextual_Anomaly_00369", "level": 3, "level_name": "Semantic Reasoning", "category": "Contextual Anomaly", "subtask": "Contextual Anomaly", "question": "Identify the period in channel 754 during 2019 that experienced the most significant extreme surge in flow. (Output format: [YYYY-MM-DD, YYYY-MM-DD])", "answer": "[2019-08-23 09:30:00, 2019-10-26 09:15:00]", "ground_truth": [ "2019-08-23 09:30:00", "2019-10-26 09:15:00" ], "eval_metric": "iou", "channel": "754", "ts_data_path": "ts_data/L3_T2_Contextual_Anomaly_00369.csv", "meta": { "event": "flood", "intensity": 4.725533560063594, "year": 2019, "duration_days": 64, "source": "causal_rivers" } }, { "id": "L3_T2_Contextual_Anomaly_00371", "level": 3, "level_name": "Semantic Reasoning", "category": "Contextual Anomaly", "subtask": "Contextual Anomaly", "question": "Identify the period in channel 811 during 2022 that experienced the most significant dry-out period. (Output format: [YYYY-MM-DD, YYYY-MM-DD])", "answer": "[2022-03-23 01:15:00, 2022-05-22 01:00:00]", "ground_truth": [ "2022-03-23 01:15:00", "2022-05-22 01:00:00" ], "eval_metric": "iou", "channel": "811", "ts_data_path": "ts_data/L3_T2_Contextual_Anomaly_00371.csv", "meta": { "event": "drought", "intensity": 0.056129267973265465, "year": 2022, "duration_days": 60, "source": "causal_rivers" } }, { "id": "L3_T2_Contextual_Anomaly_00372", "level": 3, "level_name": "Semantic Reasoning", "category": "Contextual Anomaly", "subtask": "Contextual Anomaly", "question": "Identify the period in channel 813 during 2022 that experienced the most significant severe flood. (Output format: [YYYY-MM-DD, YYYY-MM-DD])", "answer": "[2022-02-14 11:45:00, 2022-05-01 11:30:00]", "ground_truth": [ "2022-02-14 11:45:00", "2022-05-01 11:30:00" ], "eval_metric": "iou", "channel": "813", "ts_data_path": "ts_data/L3_T2_Contextual_Anomaly_00372.csv", "meta": { "event": "flood", "intensity": 4.624458205404114, "year": 2022, "duration_days": 76, "source": "causal_rivers" } }, { "id": "L3_T2_Contextual_Anomaly_00373", "level": 3, "level_name": "Semantic Reasoning", "category": "Contextual Anomaly", "subtask": "Contextual Anomaly", "question": "Identify the period in channel 865 during 2019 that experienced the most significant severe drought. (Output format: [YYYY-MM-DD, YYYY-MM-DD])", "answer": "[2019-02-19 13:15:00, 2019-04-20 13:00:00]", "ground_truth": [ "2019-02-19 13:15:00", "2019-04-20 13:00:00" ], "eval_metric": "iou", "channel": "865", "ts_data_path": "ts_data/L3_T2_Contextual_Anomaly_00373.csv", "meta": { "event": "drought", "intensity": 0.0355789849124589, "year": 2019, "duration_days": 60, "source": "causal_rivers" } }, { "id": "L3_T2_Contextual_Anomaly_00375", "level": 3, "level_name": "Semantic Reasoning", "category": "Contextual Anomaly", "subtask": "Contextual Anomaly", "question": "Identify the period in channel 894 during 2023 that experienced the most significant dry-out period. (Output format: [YYYY-MM-DD, YYYY-MM-DD])", "answer": "[2023-06-21 23:15:00, 2023-09-14 23:00:00]", "ground_truth": [ "2023-06-21 23:15:00", "2023-09-14 23:00:00" ], "eval_metric": "iou", "channel": "894", "ts_data_path": "ts_data/L3_T2_Contextual_Anomaly_00375.csv", "meta": { "event": "drought", "intensity": 0.06051966219844581, "year": 2023, "duration_days": 85, "source": "causal_rivers" } }, { "id": "L3_T2_Contextual_Anomaly_00377", "level": 3, "level_name": "Semantic Reasoning", "category": "Contextual Anomaly", "subtask": "Contextual Anomaly", "question": "Identify the period in channel 897 during 2022 that experienced the most significant historically low water level. (Output format: [YYYY-MM-DD, YYYY-MM-DD])", "answer": "[2022-06-21 00:00:00, 2022-08-20 23:45:00]", "ground_truth": [ "2022-06-21 00:00:00", "2022-08-20 23:45:00" ], "eval_metric": "iou", "channel": "897", "ts_data_path": "ts_data/L3_T2_Contextual_Anomaly_00377.csv", "meta": { "event": "drought", "intensity": 0.019288382307476475, "year": 2022, "duration_days": 61, "source": "causal_rivers" } }, { "id": "L3_T2_Contextual_Anomaly_00378", "level": 3, "level_name": "Semantic Reasoning", "category": "Contextual Anomaly", "subtask": "Contextual Anomaly", "question": "Identify the period in channel 933 during 2019 that experienced the most significant historically low water level. (Output format: [YYYY-MM-DD, YYYY-MM-DD])", "answer": "[2019-05-14 21:30:00, 2019-08-10 21:15:00]", "ground_truth": [ "2019-05-14 21:30:00", "2019-08-10 21:15:00" ], "eval_metric": "iou", "channel": "933", "ts_data_path": "ts_data/L3_T2_Contextual_Anomaly_00378.csv", "meta": { "event": "drought", "intensity": 0.02850058947439902, "year": 2019, "duration_days": 88, "source": "causal_rivers" } }, { "id": "L3_T2_Contextual_Anomaly_00380", "level": 3, "level_name": "Semantic Reasoning", "category": "Contextual Anomaly", "subtask": "Contextual Anomaly", "question": "Identify the period in channel HULL during 2017 that experienced the most significant dry-out period. (Output format: [YYYY-MM-DD, YYYY-MM-DD])", "answer": "[2017-04-23 00:30:00, 2017-07-19 00:15:00]", "ground_truth": [ "2017-04-23 00:30:00", "2017-07-19 00:15:00" ], "eval_metric": "iou", "channel": "HULL", "ts_data_path": "ts_data/L3_T2_Contextual_Anomaly_00380.csv", "meta": { "event": "drought", "intensity": 0.060881996763944325, "year": 2017, "duration_days": 87, "source": "ettm1" } }, { "id": "L3_T2_Contextual_Anomaly_00381", "level": 3, "level_name": "Semantic Reasoning", "category": "Contextual Anomaly", "subtask": "Contextual Anomaly", "question": "Identify the period in channel MUFL during 2017 that experienced the most significant historically high water level. (Output format: [YYYY-MM-DD, YYYY-MM-DD])", "answer": "[2017-02-04 21:15:00, 2017-05-03 21:00:00]", "ground_truth": [ "2017-02-04 21:15:00", "2017-05-03 21:00:00" ], "eval_metric": "iou", "channel": "MUFL", "ts_data_path": "ts_data/L3_T2_Contextual_Anomaly_00381.csv", "meta": { "event": "flood", "intensity": 4.849418266892554, "year": 2017, "duration_days": 88, "source": "ettm1" } }, { "id": "L3_T2_Contextual_Anomaly_00382", "level": 3, "level_name": "Semantic Reasoning", "category": "Contextual Anomaly", "subtask": "Contextual Anomaly", "question": "Identify the period in channel MULL during 2017 that experienced the most significant historically high water level. (Output format: [YYYY-MM-DD, YYYY-MM-DD])", "answer": "[2017-03-11 10:30:00, 2017-06-04 10:15:00]", "ground_truth": [ "2017-03-11 10:30:00", "2017-06-04 10:15:00" ], "eval_metric": "iou", "channel": "MULL", "ts_data_path": "ts_data/L3_T2_Contextual_Anomaly_00382.csv", "meta": { "event": "flood", "intensity": 3.8261402916444354, "year": 2017, "duration_days": 85, "source": "ettm1" } }, { "id": "L3_T2_Contextual_Anomaly_00383", "level": 3, "level_name": "Semantic Reasoning", "category": "Contextual Anomaly", "subtask": "Contextual Anomaly", "question": "Identify the period in channel LUFL during 2017 that experienced the most significant historically low water level. (Output format: [YYYY-MM-DD, YYYY-MM-DD])", "answer": "[2017-08-20 00:00:00, 2017-10-26 23:45:00]", "ground_truth": [ "2017-08-20 00:00:00", "2017-10-26 23:45:00" ], "eval_metric": "iou", "channel": "LUFL", "ts_data_path": "ts_data/L3_T2_Contextual_Anomaly_00383.csv", "meta": { "event": "drought", "intensity": 0.0332871090844075, "year": 2017, "duration_days": 68, "source": "ettm1" } }, { "id": "L3_T2_Contextual_Anomaly_00384", "level": 3, "level_name": "Semantic Reasoning", "category": "Contextual Anomaly", "subtask": "Contextual Anomaly", "question": "Identify the period in channel LULL during 2017 that experienced the most significant severe flood. (Output format: [YYYY-MM-DD, YYYY-MM-DD])", "answer": "[2017-07-30 19:15:00, 2017-10-23 19:00:00]", "ground_truth": [ "2017-07-30 19:15:00", "2017-10-23 19:00:00" ], "eval_metric": "iou", "channel": "LULL", "ts_data_path": "ts_data/L3_T2_Contextual_Anomaly_00384.csv", "meta": { "event": "flood", "intensity": 3.4845925257310326, "year": 2017, "duration_days": 85, "source": "ettm1" } }, { "id": "L3_T2_Contextual_Anomaly_00385", "level": 3, "level_name": "Semantic Reasoning", "category": "Contextual Anomaly", "subtask": "Contextual Anomaly", "question": "Identify the period in channel OT during 2017 that experienced the most significant historically low water level. (Output format: [YYYY-MM-DD, YYYY-MM-DD])", "answer": "[2017-10-10 18:15:00, 2017-12-17 18:00:00]", "ground_truth": [ "2017-10-10 18:15:00", "2017-12-17 18:00:00" ], "eval_metric": "iou", "channel": "OT", "ts_data_path": "ts_data/L3_T2_Contextual_Anomaly_00385.csv", "meta": { "event": "drought", "intensity": 0.07478446960115938, "year": 2017, "duration_days": 68, "source": "ettm1" } }, { "id": "L4_T1_Insight_Synthesis_00001", "level": 4, "level_name": "Insight Synthesis", "category": "Insight Synthesis", "subtask": "Insight Synthesis", "question": "Analyze the behavior of channel 67 for the period 2021-11.\nPlease use ONLY the following phrases for trend description: rapid rise, gradual rise, rapid fall, gradual fall, steady stable, fluctuating stable.\nProvide a structured report covering:\n1. Trend Segmentation: Describe each stage with precise start/end timestamps (HH:MM:SS) using the phrases above.\n2. Outlier Audit: Identify only significant outliers that deviate sharply from the local trend. Ignore minor background noise.\n(Output format: structured natural language report.)", "answer": "1. Trend Segmentation: from 2021-11-01 00:00:00 to 2021-11-22 10:15:00, the trend showed a gradual rise; from 2021-11-22 10:15:00 to 2021-11-30 23:45:00, the trend showed a rapid rise.\n2. Outlier Audit: A significant spike was detected at 2021-11-21 05:45:00 (value: 0.90).", "ground_truth": { "trend_segments": [ "from 2021-11-01 00:00:00 to 2021-11-22 10:15:00, the trend showed a gradual rise", "from 2021-11-22 10:15:00 to 2021-11-30 23:45:00, the trend showed a rapid rise" ], "segments_meta": [ { "adj": "gradual", "kind": "rise", "start_idx": 0, "end_idx": 2057 }, { "adj": "rapid", "kind": "rise", "start_idx": 2057, "end_idx": 2880 } ], "significant_anomaly": { "timestamp": "2021-11-21 05:45:00", "kind": "significant spike", "value": 0.8979430069162033 } }, "eval_metric": "report", "channel": "67", "ts_data_path": "ts_data/L4_T1_Insight_Synthesis_00001.csv", "meta": { "target_month": "2021-11", "target_year": 2021, "source": "causal_rivers" } }, { "id": "L4_T1_Insight_Synthesis_00002", "level": 4, "level_name": "Insight Synthesis", "category": "Insight Synthesis", "subtask": "Insight Synthesis", "question": "Analyze the behavior of channel 71 for the period 2021-02.\nPlease use ONLY the following phrases for trend description: rapid rise, gradual rise, rapid fall, gradual fall, steady stable, fluctuating stable.\nProvide a structured report covering:\n1. Trend Segmentation: Describe each stage with precise start/end timestamps (HH:MM:SS) using the phrases above.\n2. Outlier Audit: Identify only significant outliers that deviate sharply from the local trend. Ignore minor background noise.\n(Output format: structured natural language report.)", "answer": "1. Trend Segmentation: from 2021-02-01 00:00:00 to 2021-02-05 16:00:00, the trend showed a rapid fall; from 2021-02-05 16:00:00 to 2021-02-17 08:00:00, the trend showed a gradual rise; from 2021-02-17 08:00:00 to 2021-02-28 23:45:00, the trend showed a rapid fall.\n2. Outlier Audit: A significant drop was detected at 2021-02-21 22:15:00 (value: -8.13).", "ground_truth": { "trend_segments": [ "from 2021-02-01 00:00:00 to 2021-02-05 16:00:00, the trend showed a rapid fall", "from 2021-02-05 16:00:00 to 2021-02-17 08:00:00, the trend showed a gradual rise", "from 2021-02-17 08:00:00 to 2021-02-28 23:45:00, the trend showed a rapid fall" ], "segments_meta": [ { "adj": "rapid", "kind": "fall", "start_idx": 0, "end_idx": 448 }, { "adj": "gradual", "kind": "rise", "start_idx": 448, "end_idx": 1568 }, { "adj": "rapid", "kind": "fall", "start_idx": 1568, "end_idx": 2688 } ], "significant_anomaly": { "timestamp": "2021-02-21 22:15:00", "kind": "significant drop", "value": -8.126309872530218 } }, "eval_metric": "report", "channel": "71", "ts_data_path": "ts_data/L4_T1_Insight_Synthesis_00002.csv", "meta": { "target_month": "2021-02", "target_year": 2021, "source": "causal_rivers" } }, { "id": "L4_T1_Insight_Synthesis_00003", "level": 4, "level_name": "Insight Synthesis", "category": "Insight Synthesis", "subtask": "Insight Synthesis", "question": "Analyze the behavior of channel 99 for the period 2021-12.\nPlease use ONLY the following phrases for trend description: rapid rise, gradual rise, rapid fall, gradual fall, steady stable, fluctuating stable.\nProvide a structured report covering:\n1. Trend Segmentation: Describe each stage with precise start/end timestamps (HH:MM:SS) using the phrases above.\n2. Outlier Audit: Identify only significant outliers that deviate sharply from the local trend. Ignore minor background noise.\n(Output format: structured natural language report.)", "answer": "1. Trend Segmentation: from 2021-12-01 00:00:00 to 2021-12-24 06:00:00, the trend showed a steady stable; from 2021-12-24 06:00:00 to 2021-12-31 23:45:00, the trend showed a rapid rise.\n2. Outlier Audit: A significant drop was detected at 2021-12-19 14:00:00 (value: -0.65).", "ground_truth": { "trend_segments": [ "from 2021-12-01 00:00:00 to 2021-12-24 06:00:00, the trend showed a steady stable", "from 2021-12-24 06:00:00 to 2021-12-31 23:45:00, the trend showed a rapid rise" ], "segments_meta": [ { "adj": "steady", "kind": "stable", "start_idx": 0, "end_idx": 2232 }, { "adj": "rapid", "kind": "rise", "start_idx": 2232, "end_idx": 2976 } ], "significant_anomaly": { "timestamp": "2021-12-19 14:00:00", "kind": "significant drop", "value": -0.6455505690267764 } }, "eval_metric": "report", "channel": "99", "ts_data_path": "ts_data/L4_T1_Insight_Synthesis_00003.csv", "meta": { "target_month": "2021-12", "target_year": 2021, "source": "causal_rivers" } }, { "id": "L4_T1_Insight_Synthesis_00004", "level": 4, "level_name": "Insight Synthesis", "category": "Insight Synthesis", "subtask": "Insight Synthesis", "question": "Analyze the behavior of channel 123 for the period 2023-06.\nPlease use ONLY the following phrases for trend description: rapid rise, gradual rise, rapid fall, gradual fall, steady stable, fluctuating stable.\nProvide a structured report covering:\n1. Trend Segmentation: Describe each stage with precise start/end timestamps (HH:MM:SS) using the phrases above.\n2. Outlier Audit: Identify only significant outliers that deviate sharply from the local trend. Ignore minor background noise.\n(Output format: structured natural language report.)", "answer": "1. Trend Segmentation: from 2023-06-01 00:00:00 to 2023-06-11 14:00:00, the trend showed a gradual fall; from 2023-06-11 14:00:00 to 2023-06-18 15:30:00, the trend showed a fluctuating stable; from 2023-06-18 15:30:00 to 2023-06-25 17:00:00, the trend showed a gradual fall; from 2023-06-25 17:00:00 to 2023-06-30 23:45:00, the trend showed a rapid rise.\n2. Outlier Audit: A significant drop was detected at 2023-06-25 15:00:00 (value: -11.04).", "ground_truth": { "trend_segments": [ "from 2023-06-01 00:00:00 to 2023-06-11 14:00:00, the trend showed a gradual fall", "from 2023-06-11 14:00:00 to 2023-06-18 15:30:00, the trend showed a fluctuating stable", "from 2023-06-18 15:30:00 to 2023-06-25 17:00:00, the trend showed a gradual fall", "from 2023-06-25 17:00:00 to 2023-06-30 23:45:00, the trend showed a rapid rise" ], "segments_meta": [ { "adj": "gradual", "kind": "fall", "start_idx": 0, "end_idx": 1016 }, { "adj": "fluctuating", "kind": "stable", "start_idx": 1016, "end_idx": 1694 }, { "adj": "gradual", "kind": "fall", "start_idx": 1694, "end_idx": 2372 }, { "adj": "rapid", "kind": "rise", "start_idx": 2372, "end_idx": 2880 } ], "significant_anomaly": { "timestamp": "2023-06-25 15:00:00", "kind": "significant drop", "value": -11.039447081248394 } }, "eval_metric": "report", "channel": "123", "ts_data_path": "ts_data/L4_T1_Insight_Synthesis_00004.csv", "meta": { "target_month": "2023-06", "target_year": 2023, "source": "causal_rivers" } }, { "id": "L4_T1_Insight_Synthesis_00009", "level": 4, "level_name": "Insight Synthesis", "category": "Insight Synthesis", "subtask": "Insight Synthesis", "question": "Analyze the behavior of channel 154 for the period 2022-03.\nPlease use ONLY the following phrases for trend description: rapid rise, gradual rise, rapid fall, gradual fall, steady stable, fluctuating stable.\nProvide a structured report covering:\n1. Trend Segmentation: Describe each stage with precise start/end timestamps (HH:MM:SS) using the phrases above.\n2. Outlier Audit: Identify only significant outliers that deviate sharply from the local trend. Ignore minor background noise.\n(Output format: structured natural language report.)", "answer": "1. Trend Segmentation: from 2022-03-01 00:00:00 to 2022-03-15 07:30:00, the trend showed a gradual fall; from 2022-03-15 07:30:00 to 2022-03-27 05:45:00, the trend showed a fluctuating stable; from 2022-03-27 05:45:00 to 2022-03-31 23:45:00, the trend showed a steady stable.\n2. Outlier Audit: A significant spike was detected at 2022-03-15 18:00:00 (value: 4.88).", "ground_truth": { "trend_segments": [ "from 2022-03-01 00:00:00 to 2022-03-15 07:30:00, the trend showed a gradual fall", "from 2022-03-15 07:30:00 to 2022-03-27 05:45:00, the trend showed a fluctuating stable", "from 2022-03-27 05:45:00 to 2022-03-31 23:45:00, the trend showed a steady stable" ], "segments_meta": [ { "adj": "gradual", "kind": "fall", "start_idx": 0, "end_idx": 1374 }, { "adj": "fluctuating", "kind": "stable", "start_idx": 1374, "end_idx": 2519 }, { "adj": "steady", "kind": "stable", "start_idx": 2519, "end_idx": 2976 } ], "significant_anomaly": { "timestamp": "2022-03-15 18:00:00", "kind": "significant spike", "value": 4.881658173032763 } }, "eval_metric": "report", "channel": "154", "ts_data_path": "ts_data/L4_T1_Insight_Synthesis_00009.csv", "meta": { "target_month": "2022-03", "target_year": 2022, "source": "causal_rivers" } }, { "id": "L4_T1_Insight_Synthesis_00011", "level": 4, "level_name": "Insight Synthesis", "category": "Insight Synthesis", "subtask": "Insight Synthesis", "question": "Analyze the behavior of channel 166 for the period 2023-07.\nPlease use ONLY the following phrases for trend description: rapid rise, gradual rise, rapid fall, gradual fall, steady stable, fluctuating stable.\nProvide a structured report covering:\n1. Trend Segmentation: Describe each stage with precise start/end timestamps (HH:MM:SS) using the phrases above.\n2. Outlier Audit: Identify only significant outliers that deviate sharply from the local trend. Ignore minor background noise.\n(Output format: structured natural language report.)", "answer": "1. Trend Segmentation: from 2023-07-01 00:00:00 to 2023-07-15 02:15:00, the trend showed a gradual rise; from 2023-07-15 02:15:00 to 2023-07-31 23:45:00, the trend showed a fluctuating stable.\n2. Outlier Audit: A significant drop was detected at 2023-07-25 18:00:00 (value: -5794.19).", "ground_truth": { "trend_segments": [ "from 2023-07-01 00:00:00 to 2023-07-15 02:15:00, the trend showed a gradual rise", "from 2023-07-15 02:15:00 to 2023-07-31 23:45:00, the trend showed a fluctuating stable" ], "segments_meta": [ { "adj": "gradual", "kind": "rise", "start_idx": 0, "end_idx": 1353 }, { "adj": "fluctuating", "kind": "stable", "start_idx": 1353, "end_idx": 2976 } ], "significant_anomaly": { "timestamp": "2023-07-25 18:00:00", "kind": "significant drop", "value": -5794.190717857931 } }, "eval_metric": "report", "channel": "166", "ts_data_path": "ts_data/L4_T1_Insight_Synthesis_00011.csv", "meta": { "target_month": "2023-07", "target_year": 2023, "source": "causal_rivers" } }, { "id": "L4_T1_Insight_Synthesis_00012", "level": 4, "level_name": "Insight Synthesis", "category": "Insight Synthesis", "subtask": "Insight Synthesis", "question": "Analyze the behavior of channel 169 for the period 2023-07.\nPlease use ONLY the following phrases for trend description: rapid rise, gradual rise, rapid fall, gradual fall, steady stable, fluctuating stable.\nProvide a structured report covering:\n1. Trend Segmentation: Describe each stage with precise start/end timestamps (HH:MM:SS) using the phrases above.\n2. Outlier Audit: Identify only significant outliers that deviate sharply from the local trend. Ignore minor background noise.\n(Output format: structured natural language report.)", "answer": "1. Trend Segmentation: from 2023-07-01 00:00:00 to 2023-07-07 21:15:00, the trend showed a rapid fall; from 2023-07-07 21:15:00 to 2023-07-18 05:15:00, the trend showed a rapid rise; from 2023-07-18 05:15:00 to 2023-07-25 02:30:00, the trend showed a rapid fall; from 2023-07-25 02:30:00 to 2023-07-31 23:45:00, the trend showed a gradual rise.\n2. Outlier Audit: A significant drop was detected at 2023-07-24 03:30:00 (value: -5240.30).", "ground_truth": { "trend_segments": [ "from 2023-07-01 00:00:00 to 2023-07-07 21:15:00, the trend showed a rapid fall", "from 2023-07-07 21:15:00 to 2023-07-18 05:15:00, the trend showed a rapid rise", "from 2023-07-18 05:15:00 to 2023-07-25 02:30:00, the trend showed a rapid fall", "from 2023-07-25 02:30:00 to 2023-07-31 23:45:00, the trend showed a gradual rise" ], "segments_meta": [ { "adj": "rapid", "kind": "fall", "start_idx": 0, "end_idx": 661 }, { "adj": "rapid", "kind": "rise", "start_idx": 661, "end_idx": 1653 }, { "adj": "rapid", "kind": "fall", "start_idx": 1653, "end_idx": 2314 }, { "adj": "gradual", "kind": "rise", "start_idx": 2314, "end_idx": 2976 } ], "significant_anomaly": { "timestamp": "2023-07-24 03:30:00", "kind": "significant drop", "value": -5240.3008035457 } }, "eval_metric": "report", "channel": "169", "ts_data_path": "ts_data/L4_T1_Insight_Synthesis_00012.csv", "meta": { "target_month": "2023-07", "target_year": 2023, "source": "causal_rivers" } }, { "id": "L4_T1_Insight_Synthesis_00014", "level": 4, "level_name": "Insight Synthesis", "category": "Insight Synthesis", "subtask": "Insight Synthesis", "question": "Analyze the behavior of channel 172 for the period 2019-06.\nPlease use ONLY the following phrases for trend description: rapid rise, gradual rise, rapid fall, gradual fall, steady stable, fluctuating stable.\nProvide a structured report covering:\n1. Trend Segmentation: Describe each stage with precise start/end timestamps (HH:MM:SS) using the phrases above.\n2. Outlier Audit: Identify only significant outliers that deviate sharply from the local trend. Ignore minor background noise.\n(Output format: structured natural language report.)", "answer": "1. Trend Segmentation: from 2019-06-01 00:00:00 to 2019-06-09 13:45:00, the trend showed a fluctuating stable; from 2019-06-09 13:45:00 to 2019-06-30 23:45:00, the trend showed a gradual rise.\n2. Outlier Audit: A significant drop was detected at 2019-06-04 04:45:00 (value: -2678.03).", "ground_truth": { "trend_segments": [ "from 2019-06-01 00:00:00 to 2019-06-09 13:45:00, the trend showed a fluctuating stable", "from 2019-06-09 13:45:00 to 2019-06-30 23:45:00, the trend showed a gradual rise" ], "segments_meta": [ { "adj": "fluctuating", "kind": "stable", "start_idx": 0, "end_idx": 823 }, { "adj": "gradual", "kind": "rise", "start_idx": 823, "end_idx": 2880 } ], "significant_anomaly": { "timestamp": "2019-06-04 04:45:00", "kind": "significant drop", "value": -2678.0337606497033 } }, "eval_metric": "report", "channel": "172", "ts_data_path": "ts_data/L4_T1_Insight_Synthesis_00014.csv", "meta": { "target_month": "2019-06", "target_year": 2019, "source": "causal_rivers" } }, { "id": "L4_T1_Insight_Synthesis_00015", "level": 4, "level_name": "Insight Synthesis", "category": "Insight Synthesis", "subtask": "Insight Synthesis", "question": "Analyze the behavior of channel 173 for the period 2022-01.\nPlease use ONLY the following phrases for trend description: rapid rise, gradual rise, rapid fall, gradual fall, steady stable, fluctuating stable.\nProvide a structured report covering:\n1. Trend Segmentation: Describe each stage with precise start/end timestamps (HH:MM:SS) using the phrases above.\n2. Outlier Audit: Identify only significant outliers that deviate sharply from the local trend. Ignore minor background noise.\n(Output format: structured natural language report.)", "answer": "1. Trend Segmentation: from 2022-01-01 00:00:00 to 2022-01-10 07:15:00, the trend showed a fluctuating stable; from 2022-01-10 07:15:00 to 2022-01-16 12:00:00, the trend showed a gradual fall; from 2022-01-16 12:00:00 to 2022-01-22 16:45:00, the trend showed a fluctuating stable; from 2022-01-22 16:45:00 to 2022-01-31 23:45:00, the trend showed a steady stable.\n2. Outlier Audit: A significant spike was detected at 2022-01-09 05:30:00 (value: 1801.49).", "ground_truth": { "trend_segments": [ "from 2022-01-01 00:00:00 to 2022-01-10 07:15:00, the trend showed a fluctuating stable", "from 2022-01-10 07:15:00 to 2022-01-16 12:00:00, the trend showed a gradual fall", "from 2022-01-16 12:00:00 to 2022-01-22 16:45:00, the trend showed a fluctuating stable", "from 2022-01-22 16:45:00 to 2022-01-31 23:45:00, the trend showed a steady stable" ], "segments_meta": [ { "adj": "fluctuating", "kind": "stable", "start_idx": 0, "end_idx": 893 }, { "adj": "gradual", "kind": "fall", "start_idx": 893, "end_idx": 1488 }, { "adj": "fluctuating", "kind": "stable", "start_idx": 1488, "end_idx": 2083 }, { "adj": "steady", "kind": "stable", "start_idx": 2083, "end_idx": 2976 } ], "significant_anomaly": { "timestamp": "2022-01-09 05:30:00", "kind": "significant spike", "value": 1801.4864577943442 } }, "eval_metric": "report", "channel": "173", "ts_data_path": "ts_data/L4_T1_Insight_Synthesis_00015.csv", "meta": { "target_month": "2022-01", "target_year": 2022, "source": "causal_rivers" } }, { "id": "L4_T1_Insight_Synthesis_00016", "level": 4, "level_name": "Insight Synthesis", "category": "Insight Synthesis", "subtask": "Insight Synthesis", "question": "Analyze the behavior of channel 177 for the period 2022-11.\nPlease use ONLY the following phrases for trend description: rapid rise, gradual rise, rapid fall, gradual fall, steady stable, fluctuating stable.\nProvide a structured report covering:\n1. Trend Segmentation: Describe each stage with precise start/end timestamps (HH:MM:SS) using the phrases above.\n2. Outlier Audit: Identify only significant outliers that deviate sharply from the local trend. Ignore minor background noise.\n(Output format: structured natural language report.)", "answer": "1. Trend Segmentation: from 2022-11-01 00:00:00 to 2022-11-14 20:15:00, the trend showed a steady stable; from 2022-11-14 20:15:00 to 2022-11-26 09:15:00, the trend showed a gradual fall; from 2022-11-26 09:15:00 to 2022-11-30 23:45:00, the trend showed a fluctuating stable.\n2. Outlier Audit: A significant spike was detected at 2022-11-15 11:15:00 (value: 1298.53).", "ground_truth": { "trend_segments": [ "from 2022-11-01 00:00:00 to 2022-11-14 20:15:00, the trend showed a steady stable", "from 2022-11-14 20:15:00 to 2022-11-26 09:15:00, the trend showed a gradual fall", "from 2022-11-26 09:15:00 to 2022-11-30 23:45:00, the trend showed a fluctuating stable" ], "segments_meta": [ { "adj": "steady", "kind": "stable", "start_idx": 0, "end_idx": 1329 }, { "adj": "gradual", "kind": "fall", "start_idx": 1329, "end_idx": 2437 }, { "adj": "fluctuating", "kind": "stable", "start_idx": 2437, "end_idx": 2880 } ], "significant_anomaly": { "timestamp": "2022-11-15 11:15:00", "kind": "significant spike", "value": 1298.5272375000843 } }, "eval_metric": "report", "channel": "177", "ts_data_path": "ts_data/L4_T1_Insight_Synthesis_00016.csv", "meta": { "target_month": "2022-11", "target_year": 2022, "source": "causal_rivers" } }, { "id": "L4_T1_Insight_Synthesis_00019", "level": 4, "level_name": "Insight Synthesis", "category": "Insight Synthesis", "subtask": "Insight Synthesis", "question": "Analyze the behavior of channel 312 for the period 2019-03.\nPlease use ONLY the following phrases for trend description: rapid rise, gradual rise, rapid fall, gradual fall, steady stable, fluctuating stable.\nProvide a structured report covering:\n1. Trend Segmentation: Describe each stage with precise start/end timestamps (HH:MM:SS) using the phrases above.\n2. Outlier Audit: Identify only significant outliers that deviate sharply from the local trend. Ignore minor background noise.\n(Output format: structured natural language report.)", "answer": "1. Trend Segmentation: from 2019-03-01 00:00:00 to 2019-03-13 09:30:00, the trend showed a rapid rise; from 2019-03-13 09:30:00 to 2019-03-31 23:45:00, the trend showed a steady stable.\n2. Outlier Audit: A significant drop was detected at 2019-03-10 14:15:00 (value: -0.69).", "ground_truth": { "trend_segments": [ "from 2019-03-01 00:00:00 to 2019-03-13 09:30:00, the trend showed a rapid rise", "from 2019-03-13 09:30:00 to 2019-03-31 23:45:00, the trend showed a steady stable" ], "segments_meta": [ { "adj": "rapid", "kind": "rise", "start_idx": 0, "end_idx": 1190 }, { "adj": "steady", "kind": "stable", "start_idx": 1190, "end_idx": 2976 } ], "significant_anomaly": { "timestamp": "2019-03-10 14:15:00", "kind": "significant drop", "value": -0.6892109919836211 } }, "eval_metric": "report", "channel": "312", "ts_data_path": "ts_data/L4_T1_Insight_Synthesis_00019.csv", "meta": { "target_month": "2019-03", "target_year": 2019, "source": "causal_rivers" } }, { "id": "L4_T1_Insight_Synthesis_00020", "level": 4, "level_name": "Insight Synthesis", "category": "Insight Synthesis", "subtask": "Insight Synthesis", "question": "Analyze the behavior of channel 430 for the period 2019-07.\nPlease use ONLY the following phrases for trend description: rapid rise, gradual rise, rapid fall, gradual fall, steady stable, fluctuating stable.\nProvide a structured report covering:\n1. Trend Segmentation: Describe each stage with precise start/end timestamps (HH:MM:SS) using the phrases above.\n2. Outlier Audit: Identify only significant outliers that deviate sharply from the local trend. Ignore minor background noise.\n(Output format: structured natural language report.)", "answer": "1. Trend Segmentation: from 2019-07-01 00:00:00 to 2019-07-16 12:00:00, the trend showed a gradual fall; from 2019-07-16 12:00:00 to 2019-07-24 06:00:00, the trend showed a gradual rise; from 2019-07-24 06:00:00 to 2019-07-31 23:45:00, the trend showed a rapid rise.\n2. Outlier Audit: A significant spike was detected at 2019-07-06 22:15:00 (value: 2.18).", "ground_truth": { "trend_segments": [ "from 2019-07-01 00:00:00 to 2019-07-16 12:00:00, the trend showed a gradual fall", "from 2019-07-16 12:00:00 to 2019-07-24 06:00:00, the trend showed a gradual rise", "from 2019-07-24 06:00:00 to 2019-07-31 23:45:00, the trend showed a rapid rise" ], "segments_meta": [ { "adj": "gradual", "kind": "fall", "start_idx": 0, "end_idx": 1488 }, { "adj": "gradual", "kind": "rise", "start_idx": 1488, "end_idx": 2232 }, { "adj": "rapid", "kind": "rise", "start_idx": 2232, "end_idx": 2976 } ], "significant_anomaly": { "timestamp": "2019-07-06 22:15:00", "kind": "significant spike", "value": 2.1782935553114213 } }, "eval_metric": "report", "channel": "430", "ts_data_path": "ts_data/L4_T1_Insight_Synthesis_00020.csv", "meta": { "target_month": "2019-07", "target_year": 2019, "source": "causal_rivers" } }, { "id": "L4_T1_Insight_Synthesis_00021", "level": 4, "level_name": "Insight Synthesis", "category": "Insight Synthesis", "subtask": "Insight Synthesis", "question": "Analyze the behavior of channel 441 for the period 2023-11.\nPlease use ONLY the following phrases for trend description: rapid rise, gradual rise, rapid fall, gradual fall, steady stable, fluctuating stable.\nProvide a structured report covering:\n1. Trend Segmentation: Describe each stage with precise start/end timestamps (HH:MM:SS) using the phrases above.\n2. Outlier Audit: Identify only significant outliers that deviate sharply from the local trend. Ignore minor background noise.\n(Output format: structured natural language report.)", "answer": "1. Trend Segmentation: from 2023-11-01 00:00:00 to 2023-11-09 00:00:00, the trend showed a fluctuating stable; from 2023-11-09 00:00:00 to 2023-11-19 00:00:00, the trend showed a gradual fall; from 2023-11-19 00:00:00 to 2023-11-30 23:45:00, the trend showed a fluctuating stable.\n2. Outlier Audit: A significant drop was detected at 2023-11-06 19:15:00 (value: -188.84).", "ground_truth": { "trend_segments": [ "from 2023-11-01 00:00:00 to 2023-11-09 00:00:00, the trend showed a fluctuating stable", "from 2023-11-09 00:00:00 to 2023-11-19 00:00:00, the trend showed a gradual fall", "from 2023-11-19 00:00:00 to 2023-11-30 23:45:00, the trend showed a fluctuating stable" ], "segments_meta": [ { "adj": "fluctuating", "kind": "stable", "start_idx": 0, "end_idx": 768 }, { "adj": "gradual", "kind": "fall", "start_idx": 768, "end_idx": 1728 }, { "adj": "fluctuating", "kind": "stable", "start_idx": 1728, "end_idx": 2880 } ], "significant_anomaly": { "timestamp": "2023-11-06 19:15:00", "kind": "significant drop", "value": -188.83697145047643 } }, "eval_metric": "report", "channel": "441", "ts_data_path": "ts_data/L4_T1_Insight_Synthesis_00021.csv", "meta": { "target_month": "2023-11", "target_year": 2023, "source": "causal_rivers" } }, { "id": "L4_T1_Insight_Synthesis_00024", "level": 4, "level_name": "Insight Synthesis", "category": "Insight Synthesis", "subtask": "Insight Synthesis", "question": "Analyze the behavior of channel 501 for the period 2021-05.\nPlease use ONLY the following phrases for trend description: rapid rise, gradual rise, rapid fall, gradual fall, steady stable, fluctuating stable.\nProvide a structured report covering:\n1. Trend Segmentation: Describe each stage with precise start/end timestamps (HH:MM:SS) using the phrases above.\n2. Outlier Audit: Identify only significant outliers that deviate sharply from the local trend. Ignore minor background noise.\n(Output format: structured natural language report.)", "answer": "1. Trend Segmentation: from 2021-05-01 00:00:00 to 2021-05-13 22:00:00, the trend showed a gradual fall; from 2021-05-13 22:00:00 to 2021-05-24 06:00:00, the trend showed a fluctuating stable; from 2021-05-24 06:00:00 to 2021-05-31 23:45:00, the trend showed a steady stable.\n2. Outlier Audit: A significant drop was detected at 2021-05-14 02:15:00 (value: -11.28).", "ground_truth": { "trend_segments": [ "from 2021-05-01 00:00:00 to 2021-05-13 22:00:00, the trend showed a gradual fall", "from 2021-05-13 22:00:00 to 2021-05-24 06:00:00, the trend showed a fluctuating stable", "from 2021-05-24 06:00:00 to 2021-05-31 23:45:00, the trend showed a steady stable" ], "segments_meta": [ { "adj": "gradual", "kind": "fall", "start_idx": 0, "end_idx": 1240 }, { "adj": "fluctuating", "kind": "stable", "start_idx": 1240, "end_idx": 2232 }, { "adj": "steady", "kind": "stable", "start_idx": 2232, "end_idx": 2976 } ], "significant_anomaly": { "timestamp": "2021-05-14 02:15:00", "kind": "significant drop", "value": -11.281407294798479 } }, "eval_metric": "report", "channel": "501", "ts_data_path": "ts_data/L4_T1_Insight_Synthesis_00024.csv", "meta": { "target_month": "2021-05", "target_year": 2021, "source": "causal_rivers" } }, { "id": "L4_T1_Insight_Synthesis_00026", "level": 4, "level_name": "Insight Synthesis", "category": "Insight Synthesis", "subtask": "Insight Synthesis", "question": "Analyze the behavior of channel 580 for the period 2022-12.\nPlease use ONLY the following phrases for trend description: rapid rise, gradual rise, rapid fall, gradual fall, steady stable, fluctuating stable.\nProvide a structured report covering:\n1. Trend Segmentation: Describe each stage with precise start/end timestamps (HH:MM:SS) using the phrases above.\n2. Outlier Audit: Identify only significant outliers that deviate sharply from the local trend. Ignore minor background noise.\n(Output format: structured natural language report.)", "answer": "1. Trend Segmentation: from 2022-12-01 00:00:00 to 2022-12-16 12:00:00, the trend showed a rapid rise; from 2022-12-16 12:00:00 to 2022-12-31 23:45:00, the trend showed a fluctuating stable.\n2. Outlier Audit: A significant drop was detected at 2022-12-07 19:15:00 (value: -1.38).", "ground_truth": { "trend_segments": [ "from 2022-12-01 00:00:00 to 2022-12-16 12:00:00, the trend showed a rapid rise", "from 2022-12-16 12:00:00 to 2022-12-31 23:45:00, the trend showed a fluctuating stable" ], "segments_meta": [ { "adj": "rapid", "kind": "rise", "start_idx": 0, "end_idx": 1488 }, { "adj": "fluctuating", "kind": "stable", "start_idx": 1488, "end_idx": 2976 } ], "significant_anomaly": { "timestamp": "2022-12-07 19:15:00", "kind": "significant drop", "value": -1.375932825208361 } }, "eval_metric": "report", "channel": "580", "ts_data_path": "ts_data/L4_T1_Insight_Synthesis_00026.csv", "meta": { "target_month": "2022-12", "target_year": 2022, "source": "causal_rivers" } }, { "id": "L4_T1_Insight_Synthesis_00027", "level": 4, "level_name": "Insight Synthesis", "category": "Insight Synthesis", "subtask": "Insight Synthesis", "question": "Analyze the behavior of channel 589 for the period 2022-03.\nPlease use ONLY the following phrases for trend description: rapid rise, gradual rise, rapid fall, gradual fall, steady stable, fluctuating stable.\nProvide a structured report covering:\n1. Trend Segmentation: Describe each stage with precise start/end timestamps (HH:MM:SS) using the phrases above.\n2. Outlier Audit: Identify only significant outliers that deviate sharply from the local trend. Ignore minor background noise.\n(Output format: structured natural language report.)", "answer": "1. Trend Segmentation: from 2022-03-01 00:00:00 to 2022-03-16 12:00:00, the trend showed a fluctuating stable; from 2022-03-16 12:00:00 to 2022-03-31 23:45:00, the trend showed a gradual fall.\n2. Outlier Audit: A significant spike was detected at 2022-03-21 10:15:00 (value: 2655.12).", "ground_truth": { "trend_segments": [ "from 2022-03-01 00:00:00 to 2022-03-16 12:00:00, the trend showed a fluctuating stable", "from 2022-03-16 12:00:00 to 2022-03-31 23:45:00, the trend showed a gradual fall" ], "segments_meta": [ { "adj": "fluctuating", "kind": "stable", "start_idx": 0, "end_idx": 1488 }, { "adj": "gradual", "kind": "fall", "start_idx": 1488, "end_idx": 2976 } ], "significant_anomaly": { "timestamp": "2022-03-21 10:15:00", "kind": "significant spike", "value": 2655.1154316535417 } }, "eval_metric": "report", "channel": "589", "ts_data_path": "ts_data/L4_T1_Insight_Synthesis_00027.csv", "meta": { "target_month": "2022-03", "target_year": 2022, "source": "causal_rivers" } }, { "id": "L4_T1_Insight_Synthesis_00029", "level": 4, "level_name": "Insight Synthesis", "category": "Insight Synthesis", "subtask": "Insight Synthesis", "question": "Analyze the behavior of channel 595 for the period 2019-07.\nPlease use ONLY the following phrases for trend description: rapid rise, gradual rise, rapid fall, gradual fall, steady stable, fluctuating stable.\nProvide a structured report covering:\n1. Trend Segmentation: Describe each stage with precise start/end timestamps (HH:MM:SS) using the phrases above.\n2. Outlier Audit: Identify only significant outliers that deviate sharply from the local trend. Ignore minor background noise.\n(Output format: structured natural language report.)", "answer": "1. Trend Segmentation: from 2019-07-01 00:00:00 to 2019-07-08 09:15:00, the trend showed a steady stable; from 2019-07-08 09:15:00 to 2019-07-17 05:45:00, the trend showed a gradual fall; from 2019-07-17 05:45:00 to 2019-07-24 15:00:00, the trend showed a steady stable; from 2019-07-24 15:00:00 to 2019-07-31 23:45:00, the trend showed a gradual rise.\n2. Outlier Audit: A significant spike was detected at 2019-07-28 20:00:00 (value: 17.86).", "ground_truth": { "trend_segments": [ "from 2019-07-01 00:00:00 to 2019-07-08 09:15:00, the trend showed a steady stable", "from 2019-07-08 09:15:00 to 2019-07-17 05:45:00, the trend showed a gradual fall", "from 2019-07-17 05:45:00 to 2019-07-24 15:00:00, the trend showed a steady stable", "from 2019-07-24 15:00:00 to 2019-07-31 23:45:00, the trend showed a gradual rise" ], "segments_meta": [ { "adj": "steady", "kind": "stable", "start_idx": 0, "end_idx": 709 }, { "adj": "gradual", "kind": "fall", "start_idx": 709, "end_idx": 1559 }, { "adj": "steady", "kind": "stable", "start_idx": 1559, "end_idx": 2268 }, { "adj": "gradual", "kind": "rise", "start_idx": 2268, "end_idx": 2976 } ], "significant_anomaly": { "timestamp": "2019-07-28 20:00:00", "kind": "significant spike", "value": 17.858130819254107 } }, "eval_metric": "report", "channel": "595", "ts_data_path": "ts_data/L4_T1_Insight_Synthesis_00029.csv", "meta": { "target_month": "2019-07", "target_year": 2019, "source": "causal_rivers" } }, { "id": "L4_T1_Insight_Synthesis_00031", "level": 4, "level_name": "Insight Synthesis", "category": "Insight Synthesis", "subtask": "Insight Synthesis", "question": "Analyze the behavior of channel 626 for the period 2021-07.\nPlease use ONLY the following phrases for trend description: rapid rise, gradual rise, rapid fall, gradual fall, steady stable, fluctuating stable.\nProvide a structured report covering:\n1. Trend Segmentation: Describe each stage with precise start/end timestamps (HH:MM:SS) using the phrases above.\n2. Outlier Audit: Identify only significant outliers that deviate sharply from the local trend. Ignore minor background noise.\n(Output format: structured natural language report.)", "answer": "1. Trend Segmentation: from 2021-07-01 00:00:00 to 2021-07-13 22:00:00, the trend showed a gradual rise; from 2021-07-13 22:00:00 to 2021-07-19 02:00:00, the trend showed a gradual fall; from 2021-07-19 02:00:00 to 2021-07-26 20:00:00, the trend showed a steady stable; from 2021-07-26 20:00:00 to 2021-07-31 23:45:00, the trend showed a rapid fall.\n2. Outlier Audit: A significant spike was detected at 2021-07-15 06:00:00 (value: 8.55).", "ground_truth": { "trend_segments": [ "from 2021-07-01 00:00:00 to 2021-07-13 22:00:00, the trend showed a gradual rise", "from 2021-07-13 22:00:00 to 2021-07-19 02:00:00, the trend showed a gradual fall", "from 2021-07-19 02:00:00 to 2021-07-26 20:00:00, the trend showed a steady stable", "from 2021-07-26 20:00:00 to 2021-07-31 23:45:00, the trend showed a rapid fall" ], "segments_meta": [ { "adj": "gradual", "kind": "rise", "start_idx": 0, "end_idx": 1240 }, { "adj": "gradual", "kind": "fall", "start_idx": 1240, "end_idx": 1736 }, { "adj": "steady", "kind": "stable", "start_idx": 1736, "end_idx": 2480 }, { "adj": "rapid", "kind": "fall", "start_idx": 2480, "end_idx": 2976 } ], "significant_anomaly": { "timestamp": "2021-07-15 06:00:00", "kind": "significant spike", "value": 8.552560504571774 } }, "eval_metric": "report", "channel": "626", "ts_data_path": "ts_data/L4_T1_Insight_Synthesis_00031.csv", "meta": { "target_month": "2021-07", "target_year": 2021, "source": "causal_rivers" } }, { "id": "L4_T1_Insight_Synthesis_00033", "level": 4, "level_name": "Insight Synthesis", "category": "Insight Synthesis", "subtask": "Insight Synthesis", "question": "Analyze the behavior of channel 647 for the period 2021-05.\nPlease use ONLY the following phrases for trend description: rapid rise, gradual rise, rapid fall, gradual fall, steady stable, fluctuating stable.\nProvide a structured report covering:\n1. Trend Segmentation: Describe each stage with precise start/end timestamps (HH:MM:SS) using the phrases above.\n2. Outlier Audit: Identify only significant outliers that deviate sharply from the local trend. Ignore minor background noise.\n(Output format: structured natural language report.)", "answer": "1. Trend Segmentation: from 2021-05-01 00:00:00 to 2021-05-12 06:30:00, the trend showed a steady stable; from 2021-05-12 06:30:00 to 2021-05-26 08:45:00, the trend showed a gradual fall; from 2021-05-26 08:45:00 to 2021-05-31 23:45:00, the trend showed a rapid rise.\n2. Outlier Audit: A significant spike was detected at 2021-05-04 06:15:00 (value: 9.50).", "ground_truth": { "trend_segments": [ "from 2021-05-01 00:00:00 to 2021-05-12 06:30:00, the trend showed a steady stable", "from 2021-05-12 06:30:00 to 2021-05-26 08:45:00, the trend showed a gradual fall", "from 2021-05-26 08:45:00 to 2021-05-31 23:45:00, the trend showed a rapid rise" ], "segments_meta": [ { "adj": "steady", "kind": "stable", "start_idx": 0, "end_idx": 1082 }, { "adj": "gradual", "kind": "fall", "start_idx": 1082, "end_idx": 2435 }, { "adj": "rapid", "kind": "rise", "start_idx": 2435, "end_idx": 2976 } ], "significant_anomaly": { "timestamp": "2021-05-04 06:15:00", "kind": "significant spike", "value": 9.495295045824925 } }, "eval_metric": "report", "channel": "647", "ts_data_path": "ts_data/L4_T1_Insight_Synthesis_00033.csv", "meta": { "target_month": "2021-05", "target_year": 2021, "source": "causal_rivers" } }, { "id": "L4_T1_Insight_Synthesis_00034", "level": 4, "level_name": "Insight Synthesis", "category": "Insight Synthesis", "subtask": "Insight Synthesis", "question": "Analyze the behavior of channel 680 for the period 2019-12.\nPlease use ONLY the following phrases for trend description: rapid rise, gradual rise, rapid fall, gradual fall, steady stable, fluctuating stable.\nProvide a structured report covering:\n1. Trend Segmentation: Describe each stage with precise start/end timestamps (HH:MM:SS) using the phrases above.\n2. Outlier Audit: Identify only significant outliers that deviate sharply from the local trend. Ignore minor background noise.\n(Output format: structured natural language report.)", "answer": "1. Trend Segmentation: from 2019-12-01 00:00:00 to 2019-12-09 20:30:00, the trend showed a rapid rise; from 2019-12-09 20:30:00 to 2019-12-15 18:15:00, the trend showed a gradual fall; from 2019-12-15 18:15:00 to 2019-12-23 03:30:00, the trend showed a rapid rise; from 2019-12-23 03:30:00 to 2019-12-31 23:45:00, the trend showed a rapid fall.\n2. Outlier Audit: A significant spike was detected at 2019-12-25 07:45:00 (value: 60.94).", "ground_truth": { "trend_segments": [ "from 2019-12-01 00:00:00 to 2019-12-09 20:30:00, the trend showed a rapid rise", "from 2019-12-09 20:30:00 to 2019-12-15 18:15:00, the trend showed a gradual fall", "from 2019-12-15 18:15:00 to 2019-12-23 03:30:00, the trend showed a rapid rise", "from 2019-12-23 03:30:00 to 2019-12-31 23:45:00, the trend showed a rapid fall" ], "segments_meta": [ { "adj": "rapid", "kind": "rise", "start_idx": 0, "end_idx": 850 }, { "adj": "gradual", "kind": "fall", "start_idx": 850, "end_idx": 1417 }, { "adj": "rapid", "kind": "rise", "start_idx": 1417, "end_idx": 2126 }, { "adj": "rapid", "kind": "fall", "start_idx": 2126, "end_idx": 2976 } ], "significant_anomaly": { "timestamp": "2019-12-25 07:45:00", "kind": "significant spike", "value": 60.93553688767955 } }, "eval_metric": "report", "channel": "680", "ts_data_path": "ts_data/L4_T1_Insight_Synthesis_00034.csv", "meta": { "target_month": "2019-12", "target_year": 2019, "source": "causal_rivers" } }, { "id": "L4_T1_Insight_Synthesis_00035", "level": 4, "level_name": "Insight Synthesis", "category": "Insight Synthesis", "subtask": "Insight Synthesis", "question": "Analyze the behavior of channel 683 for the period 2023-01.\nPlease use ONLY the following phrases for trend description: rapid rise, gradual rise, rapid fall, gradual fall, steady stable, fluctuating stable.\nProvide a structured report covering:\n1. Trend Segmentation: Describe each stage with precise start/end timestamps (HH:MM:SS) using the phrases above.\n2. Outlier Audit: Identify only significant outliers that deviate sharply from the local trend. Ignore minor background noise.\n(Output format: structured natural language report.)", "answer": "1. Trend Segmentation: from 2023-01-01 00:00:00 to 2023-01-23 03:30:00, the trend showed a rapid fall; from 2023-01-23 03:30:00 to 2023-01-31 23:45:00, the trend showed a steady stable.\n2. Outlier Audit: A significant drop was detected at 2023-01-19 01:45:00 (value: -4.43).", "ground_truth": { "trend_segments": [ "from 2023-01-01 00:00:00 to 2023-01-23 03:30:00, the trend showed a rapid fall", "from 2023-01-23 03:30:00 to 2023-01-31 23:45:00, the trend showed a steady stable" ], "segments_meta": [ { "adj": "rapid", "kind": "fall", "start_idx": 0, "end_idx": 2126 }, { "adj": "steady", "kind": "stable", "start_idx": 2126, "end_idx": 2976 } ], "significant_anomaly": { "timestamp": "2023-01-19 01:45:00", "kind": "significant drop", "value": -4.429990782588247 } }, "eval_metric": "report", "channel": "683", "ts_data_path": "ts_data/L4_T1_Insight_Synthesis_00035.csv", "meta": { "target_month": "2023-01", "target_year": 2023, "source": "causal_rivers" } }, { "id": "L4_T1_Insight_Synthesis_00036", "level": 4, "level_name": "Insight Synthesis", "category": "Insight Synthesis", "subtask": "Insight Synthesis", "question": "Analyze the behavior of channel 727 for the period 2019-04.\nPlease use ONLY the following phrases for trend description: rapid rise, gradual rise, rapid fall, gradual fall, steady stable, fluctuating stable.\nProvide a structured report covering:\n1. Trend Segmentation: Describe each stage with precise start/end timestamps (HH:MM:SS) using the phrases above.\n2. Outlier Audit: Identify only significant outliers that deviate sharply from the local trend. Ignore minor background noise.\n(Output format: structured natural language report.)", "answer": "1. Trend Segmentation: from 2019-04-01 00:00:00 to 2019-04-19 18:00:00, the trend showed a rapid rise; from 2019-04-19 18:00:00 to 2019-04-30 23:45:00, the trend showed a rapid fall.\n2. Outlier Audit: A significant spike was detected at 2019-04-14 20:15:00 (value: 637.67).", "ground_truth": { "trend_segments": [ "from 2019-04-01 00:00:00 to 2019-04-19 18:00:00, the trend showed a rapid rise", "from 2019-04-19 18:00:00 to 2019-04-30 23:45:00, the trend showed a rapid fall" ], "segments_meta": [ { "adj": "rapid", "kind": "rise", "start_idx": 0, "end_idx": 1800 }, { "adj": "rapid", "kind": "fall", "start_idx": 1800, "end_idx": 2880 } ], "significant_anomaly": { "timestamp": "2019-04-14 20:15:00", "kind": "significant spike", "value": 637.6718949327435 } }, "eval_metric": "report", "channel": "727", "ts_data_path": "ts_data/L4_T1_Insight_Synthesis_00036.csv", "meta": { "target_month": "2019-04", "target_year": 2019, "source": "causal_rivers" } }, { "id": "L4_T1_Insight_Synthesis_00037", "level": 4, "level_name": "Insight Synthesis", "category": "Insight Synthesis", "subtask": "Insight Synthesis", "question": "Analyze the behavior of channel 728 for the period 2020-07.\nPlease use ONLY the following phrases for trend description: rapid rise, gradual rise, rapid fall, gradual fall, steady stable, fluctuating stable.\nProvide a structured report covering:\n1. Trend Segmentation: Describe each stage with precise start/end timestamps (HH:MM:SS) using the phrases above.\n2. Outlier Audit: Identify only significant outliers that deviate sharply from the local trend. Ignore minor background noise.\n(Output format: structured natural language report.)", "answer": "1. Trend Segmentation: from 2020-07-01 00:00:00 to 2020-07-07 04:45:00, the trend showed a rapid fall; from 2020-07-07 04:45:00 to 2020-07-17 12:45:00, the trend showed a rapid rise; from 2020-07-17 12:45:00 to 2020-07-21 16:00:00, the trend showed a steady stable; from 2020-07-21 16:00:00 to 2020-07-31 23:45:00, the trend showed a rapid rise.\n2. Outlier Audit: A significant spike was detected at 2020-07-26 04:15:00 (value: 418.23).", "ground_truth": { "trend_segments": [ "from 2020-07-01 00:00:00 to 2020-07-07 04:45:00, the trend showed a rapid fall", "from 2020-07-07 04:45:00 to 2020-07-17 12:45:00, the trend showed a rapid rise", "from 2020-07-17 12:45:00 to 2020-07-21 16:00:00, the trend showed a steady stable", "from 2020-07-21 16:00:00 to 2020-07-31 23:45:00, the trend showed a rapid rise" ], "segments_meta": [ { "adj": "rapid", "kind": "fall", "start_idx": 0, "end_idx": 595 }, { "adj": "rapid", "kind": "rise", "start_idx": 595, "end_idx": 1587 }, { "adj": "steady", "kind": "stable", "start_idx": 1587, "end_idx": 1984 }, { "adj": "rapid", "kind": "rise", "start_idx": 1984, "end_idx": 2976 } ], "significant_anomaly": { "timestamp": "2020-07-26 04:15:00", "kind": "significant spike", "value": 418.23247996295504 } }, "eval_metric": "report", "channel": "728", "ts_data_path": "ts_data/L4_T1_Insight_Synthesis_00037.csv", "meta": { "target_month": "2020-07", "target_year": 2020, "source": "causal_rivers" } }, { "id": "L4_T1_Insight_Synthesis_00038", "level": 4, "level_name": "Insight Synthesis", "category": "Insight Synthesis", "subtask": "Insight Synthesis", "question": "Analyze the behavior of channel 729 for the period 2023-07.\nPlease use ONLY the following phrases for trend description: rapid rise, gradual rise, rapid fall, gradual fall, steady stable, fluctuating stable.\nProvide a structured report covering:\n1. Trend Segmentation: Describe each stage with precise start/end timestamps (HH:MM:SS) using the phrases above.\n2. Outlier Audit: Identify only significant outliers that deviate sharply from the local trend. Ignore minor background noise.\n(Output format: structured natural language report.)", "answer": "1. Trend Segmentation: from 2023-07-01 00:00:00 to 2023-07-12 22:15:00, the trend showed a fluctuating stable; from 2023-07-12 22:15:00 to 2023-07-24 20:30:00, the trend showed a gradual rise; from 2023-07-24 20:30:00 to 2023-07-31 23:45:00, the trend showed a fluctuating stable.\n2. Outlier Audit: A significant drop was detected at 2023-07-16 01:00:00 (value: -683.72).", "ground_truth": { "trend_segments": [ "from 2023-07-01 00:00:00 to 2023-07-12 22:15:00, the trend showed a fluctuating stable", "from 2023-07-12 22:15:00 to 2023-07-24 20:30:00, the trend showed a gradual rise", "from 2023-07-24 20:30:00 to 2023-07-31 23:45:00, the trend showed a fluctuating stable" ], "segments_meta": [ { "adj": "fluctuating", "kind": "stable", "start_idx": 0, "end_idx": 1145 }, { "adj": "gradual", "kind": "rise", "start_idx": 1145, "end_idx": 2290 }, { "adj": "fluctuating", "kind": "stable", "start_idx": 2290, "end_idx": 2976 } ], "significant_anomaly": { "timestamp": "2023-07-16 01:00:00", "kind": "significant drop", "value": -683.7166829578673 } }, "eval_metric": "report", "channel": "729", "ts_data_path": "ts_data/L4_T1_Insight_Synthesis_00038.csv", "meta": { "target_month": "2023-07", "target_year": 2023, "source": "causal_rivers" } }, { "id": "L4_T1_Insight_Synthesis_00041", "level": 4, "level_name": "Insight Synthesis", "category": "Insight Synthesis", "subtask": "Insight Synthesis", "question": "Analyze the behavior of channel 811 for the period 2022-12.\nPlease use ONLY the following phrases for trend description: rapid rise, gradual rise, rapid fall, gradual fall, steady stable, fluctuating stable.\nProvide a structured report covering:\n1. Trend Segmentation: Describe each stage with precise start/end timestamps (HH:MM:SS) using the phrases above.\n2. Outlier Audit: Identify only significant outliers that deviate sharply from the local trend. Ignore minor background noise.\n(Output format: structured natural language report.)", "answer": "1. Trend Segmentation: from 2022-12-01 00:00:00 to 2022-12-18 05:15:00, the trend showed a gradual rise; from 2022-12-18 05:15:00 to 2022-12-31 23:45:00, the trend showed a steady stable.\n2. Outlier Audit: A significant spike was detected at 2022-12-20 18:15:00 (value: 90.03).", "ground_truth": { "trend_segments": [ "from 2022-12-01 00:00:00 to 2022-12-18 05:15:00, the trend showed a gradual rise", "from 2022-12-18 05:15:00 to 2022-12-31 23:45:00, the trend showed a steady stable" ], "segments_meta": [ { "adj": "gradual", "kind": "rise", "start_idx": 0, "end_idx": 1653 }, { "adj": "steady", "kind": "stable", "start_idx": 1653, "end_idx": 2976 } ], "significant_anomaly": { "timestamp": "2022-12-20 18:15:00", "kind": "significant spike", "value": 90.03151868291532 } }, "eval_metric": "report", "channel": "811", "ts_data_path": "ts_data/L4_T1_Insight_Synthesis_00041.csv", "meta": { "target_month": "2022-12", "target_year": 2022, "source": "causal_rivers" } }, { "id": "L4_T1_Insight_Synthesis_00045", "level": 4, "level_name": "Insight Synthesis", "category": "Insight Synthesis", "subtask": "Insight Synthesis", "question": "Analyze the behavior of channel 894 for the period 2023-12.\nPlease use ONLY the following phrases for trend description: rapid rise, gradual rise, rapid fall, gradual fall, steady stable, fluctuating stable.\nProvide a structured report covering:\n1. Trend Segmentation: Describe each stage with precise start/end timestamps (HH:MM:SS) using the phrases above.\n2. Outlier Audit: Identify only significant outliers that deviate sharply from the local trend. Ignore minor background noise.\n(Output format: structured natural language report.)", "answer": "1. Trend Segmentation: from 2023-12-01 00:00:00 to 2023-12-10 13:00:00, the trend showed a fluctuating stable; from 2023-12-10 13:00:00 to 2023-12-20 02:00:00, the trend showed a gradual fall; from 2023-12-20 02:00:00 to 2023-12-31 23:45:00, the trend showed a steady stable.\n2. Outlier Audit: A significant spike was detected at 2023-12-17 18:30:00 (value: 68.01).", "ground_truth": { "trend_segments": [ "from 2023-12-01 00:00:00 to 2023-12-10 13:00:00, the trend showed a fluctuating stable", "from 2023-12-10 13:00:00 to 2023-12-20 02:00:00, the trend showed a gradual fall", "from 2023-12-20 02:00:00 to 2023-12-31 23:45:00, the trend showed a steady stable" ], "segments_meta": [ { "adj": "fluctuating", "kind": "stable", "start_idx": 0, "end_idx": 916 }, { "adj": "gradual", "kind": "fall", "start_idx": 916, "end_idx": 1832 }, { "adj": "steady", "kind": "stable", "start_idx": 1832, "end_idx": 2976 } ], "significant_anomaly": { "timestamp": "2023-12-17 18:30:00", "kind": "significant spike", "value": 68.01299171860394 } }, "eval_metric": "report", "channel": "894", "ts_data_path": "ts_data/L4_T1_Insight_Synthesis_00045.csv", "meta": { "target_month": "2023-12", "target_year": 2023, "source": "causal_rivers" } }, { "id": "L4_T1_Insight_Synthesis_00046", "level": 4, "level_name": "Insight Synthesis", "category": "Insight Synthesis", "subtask": "Insight Synthesis", "question": "Analyze the behavior of channel 895 for the period 2022-03.\nPlease use ONLY the following phrases for trend description: rapid rise, gradual rise, rapid fall, gradual fall, steady stable, fluctuating stable.\nProvide a structured report covering:\n1. Trend Segmentation: Describe each stage with precise start/end timestamps (HH:MM:SS) using the phrases above.\n2. Outlier Audit: Identify only significant outliers that deviate sharply from the local trend. Ignore minor background noise.\n(Output format: structured natural language report.)", "answer": "1. Trend Segmentation: from 2022-03-01 00:00:00 to 2022-03-05 10:15:00, the trend showed a gradual fall; from 2022-03-05 10:15:00 to 2022-03-14 06:45:00, the trend showed a steady stable; from 2022-03-14 06:45:00 to 2022-03-18 17:00:00, the trend showed a rapid rise; from 2022-03-18 17:00:00 to 2022-03-31 23:45:00, the trend showed a fluctuating stable.\n2. Outlier Audit: A significant drop was detected at 2022-03-05 10:45:00 (value: -17.41).", "ground_truth": { "trend_segments": [ "from 2022-03-01 00:00:00 to 2022-03-05 10:15:00, the trend showed a gradual fall", "from 2022-03-05 10:15:00 to 2022-03-14 06:45:00, the trend showed a steady stable", "from 2022-03-14 06:45:00 to 2022-03-18 17:00:00, the trend showed a rapid rise", "from 2022-03-18 17:00:00 to 2022-03-31 23:45:00, the trend showed a fluctuating stable" ], "segments_meta": [ { "adj": "gradual", "kind": "fall", "start_idx": 0, "end_idx": 425 }, { "adj": "steady", "kind": "stable", "start_idx": 425, "end_idx": 1275 }, { "adj": "rapid", "kind": "rise", "start_idx": 1275, "end_idx": 1700 }, { "adj": "fluctuating", "kind": "stable", "start_idx": 1700, "end_idx": 2976 } ], "significant_anomaly": { "timestamp": "2022-03-05 10:45:00", "kind": "significant drop", "value": -17.405466229703563 } }, "eval_metric": "report", "channel": "895", "ts_data_path": "ts_data/L4_T1_Insight_Synthesis_00046.csv", "meta": { "target_month": "2022-03", "target_year": 2022, "source": "causal_rivers" } }, { "id": "L4_T1_Insight_Synthesis_00049", "level": 4, "level_name": "Insight Synthesis", "category": "Insight Synthesis", "subtask": "Insight Synthesis", "question": "Analyze the behavior of channel HUFL for the period 2017-04.\nPlease use ONLY the following phrases for trend description: rapid rise, gradual rise, rapid fall, gradual fall, steady stable, fluctuating stable.\nProvide a structured report covering:\n1. Trend Segmentation: Describe each stage with precise start/end timestamps (HH:MM:SS) using the phrases above.\n2. Outlier Audit: Identify only significant outliers that deviate sharply from the local trend. Ignore minor background noise.\n(Output format: structured natural language report.)", "answer": "1. Trend Segmentation: from 2017-04-01 00:00:00 to 2017-04-19 18:00:00, the trend showed a gradual fall; from 2017-04-19 18:00:00 to 2017-04-30 23:45:00, the trend showed a rapid rise.\n2. Outlier Audit: A significant spike was detected at 2017-04-25 18:00:00 (value: 100.87).", "ground_truth": { "trend_segments": [ "from 2017-04-01 00:00:00 to 2017-04-19 18:00:00, the trend showed a gradual fall", "from 2017-04-19 18:00:00 to 2017-04-30 23:45:00, the trend showed a rapid rise" ], "segments_meta": [ { "adj": "gradual", "kind": "fall", "start_idx": 0, "end_idx": 1800 }, { "adj": "rapid", "kind": "rise", "start_idx": 1800, "end_idx": 2880 } ], "significant_anomaly": { "timestamp": "2017-04-25 18:00:00", "kind": "significant spike", "value": 100.86727380355559 } }, "eval_metric": "report", "channel": "HUFL", "ts_data_path": "ts_data/L4_T1_Insight_Synthesis_00049.csv", "meta": { "target_month": "2017-04", "target_year": 2017, "source": "ettm1" } }, { "id": "L4_T1_Insight_Synthesis_00050", "level": 4, "level_name": "Insight Synthesis", "category": "Insight Synthesis", "subtask": "Insight Synthesis", "question": "Analyze the behavior of channel MUFL for the period 2018-03.\nPlease use ONLY the following phrases for trend description: rapid rise, gradual rise, rapid fall, gradual fall, steady stable, fluctuating stable.\nProvide a structured report covering:\n1. Trend Segmentation: Describe each stage with precise start/end timestamps (HH:MM:SS) using the phrases above.\n2. Outlier Audit: Identify only significant outliers that deviate sharply from the local trend. Ignore minor background noise.\n(Output format: structured natural language report.)", "answer": "1. Trend Segmentation: from 2018-03-01 00:00:00 to 2018-03-11 08:00:00, the trend showed a rapid fall; from 2018-03-11 08:00:00 to 2018-03-31 23:45:00, the trend showed a fluctuating stable.\n2. Outlier Audit: A significant drop was detected at 2018-03-21 15:30:00 (value: -105.03).", "ground_truth": { "trend_segments": [ "from 2018-03-01 00:00:00 to 2018-03-11 08:00:00, the trend showed a rapid fall", "from 2018-03-11 08:00:00 to 2018-03-31 23:45:00, the trend showed a fluctuating stable" ], "segments_meta": [ { "adj": "rapid", "kind": "fall", "start_idx": 0, "end_idx": 992 }, { "adj": "fluctuating", "kind": "stable", "start_idx": 992, "end_idx": 2976 } ], "significant_anomaly": { "timestamp": "2018-03-21 15:30:00", "kind": "significant drop", "value": -105.033407159312 } }, "eval_metric": "report", "channel": "MUFL", "ts_data_path": "ts_data/L4_T1_Insight_Synthesis_00050.csv", "meta": { "target_month": "2018-03", "target_year": 2018, "source": "ettm1" } }, { "id": "L4_T1_Insight_Synthesis_00051", "level": 4, "level_name": "Insight Synthesis", "category": "Insight Synthesis", "subtask": "Insight Synthesis", "question": "Analyze the behavior of channel LULL for the period 2017-10.\nPlease use ONLY the following phrases for trend description: rapid rise, gradual rise, rapid fall, gradual fall, steady stable, fluctuating stable.\nProvide a structured report covering:\n1. Trend Segmentation: Describe each stage with precise start/end timestamps (HH:MM:SS) using the phrases above.\n2. Outlier Audit: Identify only significant outliers that deviate sharply from the local trend. Ignore minor background noise.\n(Output format: structured natural language report.)", "answer": "1. Trend Segmentation: from 2017-10-01 00:00:00 to 2017-10-08 07:00:00, the trend showed a rapid rise; from 2017-10-08 07:00:00 to 2017-10-15 14:00:00, the trend showed a gradual rise; from 2017-10-15 14:00:00 to 2017-10-24 16:45:00, the trend showed a rapid rise; from 2017-10-24 16:45:00 to 2017-10-31 23:45:00, the trend showed a fluctuating stable.\n2. Outlier Audit: A significant drop was detected at 2017-10-07 05:15:00 (value: -6.49).", "ground_truth": { "trend_segments": [ "from 2017-10-01 00:00:00 to 2017-10-08 07:00:00, the trend showed a rapid rise", "from 2017-10-08 07:00:00 to 2017-10-15 14:00:00, the trend showed a gradual rise", "from 2017-10-15 14:00:00 to 2017-10-24 16:45:00, the trend showed a rapid rise", "from 2017-10-24 16:45:00 to 2017-10-31 23:45:00, the trend showed a fluctuating stable" ], "segments_meta": [ { "adj": "rapid", "kind": "rise", "start_idx": 0, "end_idx": 700 }, { "adj": "gradual", "kind": "rise", "start_idx": 700, "end_idx": 1400 }, { "adj": "rapid", "kind": "rise", "start_idx": 1400, "end_idx": 2275 }, { "adj": "fluctuating", "kind": "stable", "start_idx": 2275, "end_idx": 2976 } ], "significant_anomaly": { "timestamp": "2017-10-07 05:15:00", "kind": "significant drop", "value": -6.494872716214878 } }, "eval_metric": "report", "channel": "LULL", "ts_data_path": "ts_data/L4_T1_Insight_Synthesis_00051.csv", "meta": { "target_month": "2017-10", "target_year": 2017, "source": "ettm1" } }, { "id": "L4_T1_Insight_Synthesis_00053", "level": 4, "level_name": "Insight Synthesis", "category": "Insight Synthesis", "subtask": "Insight Synthesis", "question": "Analyze the behavior of channel 67 for the period 2019-01.\nPlease use ONLY the following phrases for trend description: rapid rise, gradual rise, rapid fall, gradual fall, steady stable, fluctuating stable.\nProvide a structured report covering:\n1. Trend Segmentation: Describe each stage with precise start/end timestamps (HH:MM:SS) using the phrases above.\n2. Outlier Audit: Identify only significant outliers that deviate sharply from the local trend. Ignore minor background noise.\n(Output format: structured natural language report.)", "answer": "1. Trend Segmentation: from 2019-01-01 00:00:00 to 2019-01-12 06:30:00, the trend showed a rapid rise; from 2019-01-12 06:30:00 to 2019-01-17 21:45:00, the trend showed a fluctuating stable; from 2019-01-17 21:45:00 to 2019-01-31 23:45:00, the trend showed a rapid rise.\n2. Outlier Audit: A significant drop was detected at 2019-01-06 19:30:00 (value: -0.52).", "ground_truth": { "trend_segments": [ "from 2019-01-01 00:00:00 to 2019-01-12 06:30:00, the trend showed a rapid rise", "from 2019-01-12 06:30:00 to 2019-01-17 21:45:00, the trend showed a fluctuating stable", "from 2019-01-17 21:45:00 to 2019-01-31 23:45:00, the trend showed a rapid rise" ], "segments_meta": [ { "adj": "rapid", "kind": "rise", "start_idx": 0, "end_idx": 1082 }, { "adj": "fluctuating", "kind": "stable", "start_idx": 1082, "end_idx": 1623 }, { "adj": "rapid", "kind": "rise", "start_idx": 1623, "end_idx": 2976 } ], "significant_anomaly": { "timestamp": "2019-01-06 19:30:00", "kind": "significant drop", "value": -0.523025437273763 } }, "eval_metric": "report", "channel": "67", "ts_data_path": "ts_data/L4_T1_Insight_Synthesis_00053.csv", "meta": { "target_month": "2019-01", "target_year": 2019, "source": "causal_rivers" } }, { "id": "L4_T1_Insight_Synthesis_00054", "level": 4, "level_name": "Insight Synthesis", "category": "Insight Synthesis", "subtask": "Insight Synthesis", "question": "Analyze the behavior of channel 71 for the period 2022-01.\nPlease use ONLY the following phrases for trend description: rapid rise, gradual rise, rapid fall, gradual fall, steady stable, fluctuating stable.\nProvide a structured report covering:\n1. Trend Segmentation: Describe each stage with precise start/end timestamps (HH:MM:SS) using the phrases above.\n2. Outlier Audit: Identify only significant outliers that deviate sharply from the local trend. Ignore minor background noise.\n(Output format: structured natural language report.)", "answer": "1. Trend Segmentation: from 2022-01-01 00:00:00 to 2022-01-05 18:30:00, the trend showed a gradual fall; from 2022-01-05 18:30:00 to 2022-01-10 13:00:00, the trend showed a steady stable; from 2022-01-10 13:00:00 to 2022-01-17 16:45:00, the trend showed a gradual fall; from 2022-01-17 16:45:00 to 2022-01-31 23:45:00, the trend showed a rapid rise.\n2. Outlier Audit: A significant drop was detected at 2022-01-05 16:00:00 (value: -10.93).", "ground_truth": { "trend_segments": [ "from 2022-01-01 00:00:00 to 2022-01-05 18:30:00, the trend showed a gradual fall", "from 2022-01-05 18:30:00 to 2022-01-10 13:00:00, the trend showed a steady stable", "from 2022-01-10 13:00:00 to 2022-01-17 16:45:00, the trend showed a gradual fall", "from 2022-01-17 16:45:00 to 2022-01-31 23:45:00, the trend showed a rapid rise" ], "segments_meta": [ { "adj": "gradual", "kind": "fall", "start_idx": 0, "end_idx": 458 }, { "adj": "steady", "kind": "stable", "start_idx": 458, "end_idx": 916 }, { "adj": "gradual", "kind": "fall", "start_idx": 916, "end_idx": 1603 }, { "adj": "rapid", "kind": "rise", "start_idx": 1603, "end_idx": 2976 } ], "significant_anomaly": { "timestamp": "2022-01-05 16:00:00", "kind": "significant drop", "value": -10.927217760905055 } }, "eval_metric": "report", "channel": "71", "ts_data_path": "ts_data/L4_T1_Insight_Synthesis_00054.csv", "meta": { "target_month": "2022-01", "target_year": 2022, "source": "causal_rivers" } }, { "id": "L4_T1_Insight_Synthesis_00055", "level": 4, "level_name": "Insight Synthesis", "category": "Insight Synthesis", "subtask": "Insight Synthesis", "question": "Analyze the behavior of channel 99 for the period 2019-03.\nPlease use ONLY the following phrases for trend description: rapid rise, gradual rise, rapid fall, gradual fall, steady stable, fluctuating stable.\nProvide a structured report covering:\n1. Trend Segmentation: Describe each stage with precise start/end timestamps (HH:MM:SS) using the phrases above.\n2. Outlier Audit: Identify only significant outliers that deviate sharply from the local trend. Ignore minor background noise.\n(Output format: structured natural language report.)", "answer": "1. Trend Segmentation: from 2019-03-01 00:00:00 to 2019-03-05 10:15:00, the trend showed a rapid rise; from 2019-03-05 10:15:00 to 2019-03-14 06:45:00, the trend showed a gradual fall; from 2019-03-14 06:45:00 to 2019-03-18 17:00:00, the trend showed a gradual rise; from 2019-03-18 17:00:00 to 2019-03-31 23:45:00, the trend showed a gradual fall.\n2. Outlier Audit: A significant spike was detected at 2019-03-20 14:30:00 (value: 0.80).", "ground_truth": { "trend_segments": [ "from 2019-03-01 00:00:00 to 2019-03-05 10:15:00, the trend showed a rapid rise", "from 2019-03-05 10:15:00 to 2019-03-14 06:45:00, the trend showed a gradual fall", "from 2019-03-14 06:45:00 to 2019-03-18 17:00:00, the trend showed a gradual rise", "from 2019-03-18 17:00:00 to 2019-03-31 23:45:00, the trend showed a gradual fall" ], "segments_meta": [ { "adj": "rapid", "kind": "rise", "start_idx": 0, "end_idx": 425 }, { "adj": "gradual", "kind": "fall", "start_idx": 425, "end_idx": 1275 }, { "adj": "gradual", "kind": "rise", "start_idx": 1275, "end_idx": 1700 }, { "adj": "gradual", "kind": "fall", "start_idx": 1700, "end_idx": 2976 } ], "significant_anomaly": { "timestamp": "2019-03-20 14:30:00", "kind": "significant spike", "value": 0.7984925367805817 } }, "eval_metric": "report", "channel": "99", "ts_data_path": "ts_data/L4_T1_Insight_Synthesis_00055.csv", "meta": { "target_month": "2019-03", "target_year": 2019, "source": "causal_rivers" } }, { "id": "L4_T1_Insight_Synthesis_00056", "level": 4, "level_name": "Insight Synthesis", "category": "Insight Synthesis", "subtask": "Insight Synthesis", "question": "Analyze the behavior of channel 123 for the period 2023-12.\nPlease use ONLY the following phrases for trend description: rapid rise, gradual rise, rapid fall, gradual fall, steady stable, fluctuating stable.\nProvide a structured report covering:\n1. Trend Segmentation: Describe each stage with precise start/end timestamps (HH:MM:SS) using the phrases above.\n2. Outlier Audit: Identify only significant outliers that deviate sharply from the local trend. Ignore minor background noise.\n(Output format: structured natural language report.)", "answer": "1. Trend Segmentation: from 2023-12-01 00:00:00 to 2023-12-10 02:45:00, the trend showed a rapid fall; from 2023-12-10 02:45:00 to 2023-12-17 09:45:00, the trend showed a gradual rise; from 2023-12-17 09:45:00 to 2023-12-21 01:15:00, the trend showed a gradual fall; from 2023-12-21 01:15:00 to 2023-12-31 23:45:00, the trend showed a gradual rise.\n2. Outlier Audit: A significant drop was detected at 2023-12-13 13:00:00 (value: -12.28).", "ground_truth": { "trend_segments": [ "from 2023-12-01 00:00:00 to 2023-12-10 02:45:00, the trend showed a rapid fall", "from 2023-12-10 02:45:00 to 2023-12-17 09:45:00, the trend showed a gradual rise", "from 2023-12-17 09:45:00 to 2023-12-21 01:15:00, the trend showed a gradual fall", "from 2023-12-21 01:15:00 to 2023-12-31 23:45:00, the trend showed a gradual rise" ], "segments_meta": [ { "adj": "rapid", "kind": "fall", "start_idx": 0, "end_idx": 875 }, { "adj": "gradual", "kind": "rise", "start_idx": 875, "end_idx": 1575 }, { "adj": "gradual", "kind": "fall", "start_idx": 1575, "end_idx": 1925 }, { "adj": "gradual", "kind": "rise", "start_idx": 1925, "end_idx": 2976 } ], "significant_anomaly": { "timestamp": "2023-12-13 13:00:00", "kind": "significant drop", "value": -12.282460753376764 } }, "eval_metric": "report", "channel": "123", "ts_data_path": "ts_data/L4_T1_Insight_Synthesis_00056.csv", "meta": { "target_month": "2023-12", "target_year": 2023, "source": "causal_rivers" } }, { "id": "L4_T1_Insight_Synthesis_00057", "level": 4, "level_name": "Insight Synthesis", "category": "Insight Synthesis", "subtask": "Insight Synthesis", "question": "Analyze the behavior of channel 124 for the period 2021-01.\nPlease use ONLY the following phrases for trend description: rapid rise, gradual rise, rapid fall, gradual fall, steady stable, fluctuating stable.\nProvide a structured report covering:\n1. Trend Segmentation: Describe each stage with precise start/end timestamps (HH:MM:SS) using the phrases above.\n2. Outlier Audit: Identify only significant outliers that deviate sharply from the local trend. Ignore minor background noise.\n(Output format: structured natural language report.)", "answer": "1. Trend Segmentation: from 2021-01-01 00:00:00 to 2021-01-15 02:15:00, the trend showed a gradual rise; from 2021-01-15 02:15:00 to 2021-01-31 23:45:00, the trend showed a rapid rise.\n2. Outlier Audit: A significant drop was detected at 2021-01-19 19:15:00 (value: -2.35).", "ground_truth": { "trend_segments": [ "from 2021-01-01 00:00:00 to 2021-01-15 02:15:00, the trend showed a gradual rise", "from 2021-01-15 02:15:00 to 2021-01-31 23:45:00, the trend showed a rapid rise" ], "segments_meta": [ { "adj": "gradual", "kind": "rise", "start_idx": 0, "end_idx": 1353 }, { "adj": "rapid", "kind": "rise", "start_idx": 1353, "end_idx": 2976 } ], "significant_anomaly": { "timestamp": "2021-01-19 19:15:00", "kind": "significant drop", "value": -2.3512053192803837 } }, "eval_metric": "report", "channel": "124", "ts_data_path": "ts_data/L4_T1_Insight_Synthesis_00057.csv", "meta": { "target_month": "2021-01", "target_year": 2021, "source": "causal_rivers" } }, { "id": "L4_T1_Insight_Synthesis_00061", "level": 4, "level_name": "Insight Synthesis", "category": "Insight Synthesis", "subtask": "Insight Synthesis", "question": "Analyze the behavior of channel 154 for the period 2019-04.\nPlease use ONLY the following phrases for trend description: rapid rise, gradual rise, rapid fall, gradual fall, steady stable, fluctuating stable.\nProvide a structured report covering:\n1. Trend Segmentation: Describe each stage with precise start/end timestamps (HH:MM:SS) using the phrases above.\n2. Outlier Audit: Identify only significant outliers that deviate sharply from the local trend. Ignore minor background noise.\n(Output format: structured natural language report.)", "answer": "1. Trend Segmentation: from 2019-04-01 00:00:00 to 2019-04-12 06:00:00, the trend showed a fluctuating stable; from 2019-04-12 06:00:00 to 2019-04-30 23:45:00, the trend showed a gradual rise.\n2. Outlier Audit: A significant spike was detected at 2019-04-15 21:15:00 (value: 2.79).", "ground_truth": { "trend_segments": [ "from 2019-04-01 00:00:00 to 2019-04-12 06:00:00, the trend showed a fluctuating stable", "from 2019-04-12 06:00:00 to 2019-04-30 23:45:00, the trend showed a gradual rise" ], "segments_meta": [ { "adj": "fluctuating", "kind": "stable", "start_idx": 0, "end_idx": 1080 }, { "adj": "gradual", "kind": "rise", "start_idx": 1080, "end_idx": 2880 } ], "significant_anomaly": { "timestamp": "2019-04-15 21:15:00", "kind": "significant spike", "value": 2.7862113314235235 } }, "eval_metric": "report", "channel": "154", "ts_data_path": "ts_data/L4_T1_Insight_Synthesis_00061.csv", "meta": { "target_month": "2019-04", "target_year": 2019, "source": "causal_rivers" } }, { "id": "L4_T1_Insight_Synthesis_00062", "level": 4, "level_name": "Insight Synthesis", "category": "Insight Synthesis", "subtask": "Insight Synthesis", "question": "Analyze the behavior of channel 155 for the period 2023-06.\nPlease use ONLY the following phrases for trend description: rapid rise, gradual rise, rapid fall, gradual fall, steady stable, fluctuating stable.\nProvide a structured report covering:\n1. Trend Segmentation: Describe each stage with precise start/end timestamps (HH:MM:SS) using the phrases above.\n2. Outlier Audit: Identify only significant outliers that deviate sharply from the local trend. Ignore minor background noise.\n(Output format: structured natural language report.)", "answer": "1. Trend Segmentation: from 2023-06-01 00:00:00 to 2023-06-12 06:00:00, the trend showed a fluctuating stable; from 2023-06-12 06:00:00 to 2023-06-16 00:00:00, the trend showed a rapid fall; from 2023-06-16 00:00:00 to 2023-06-19 18:00:00, the trend showed a steady stable; from 2023-06-19 18:00:00 to 2023-06-30 23:45:00, the trend showed a rapid fall.\n2. Outlier Audit: A significant spike was detected at 2023-06-20 10:45:00 (value: 2.02).", "ground_truth": { "trend_segments": [ "from 2023-06-01 00:00:00 to 2023-06-12 06:00:00, the trend showed a fluctuating stable", "from 2023-06-12 06:00:00 to 2023-06-16 00:00:00, the trend showed a rapid fall", "from 2023-06-16 00:00:00 to 2023-06-19 18:00:00, the trend showed a steady stable", "from 2023-06-19 18:00:00 to 2023-06-30 23:45:00, the trend showed a rapid fall" ], "segments_meta": [ { "adj": "fluctuating", "kind": "stable", "start_idx": 0, "end_idx": 1080 }, { "adj": "rapid", "kind": "fall", "start_idx": 1080, "end_idx": 1440 }, { "adj": "steady", "kind": "stable", "start_idx": 1440, "end_idx": 1800 }, { "adj": "rapid", "kind": "fall", "start_idx": 1800, "end_idx": 2880 } ], "significant_anomaly": { "timestamp": "2023-06-20 10:45:00", "kind": "significant spike", "value": 2.0206346213066273 } }, "eval_metric": "report", "channel": "155", "ts_data_path": "ts_data/L4_T1_Insight_Synthesis_00062.csv", "meta": { "target_month": "2023-06", "target_year": 2023, "source": "causal_rivers" } }, { "id": "L4_T1_Insight_Synthesis_00065", "level": 4, "level_name": "Insight Synthesis", "category": "Insight Synthesis", "subtask": "Insight Synthesis", "question": "Analyze the behavior of channel 170 for the period 2019-01.\nPlease use ONLY the following phrases for trend description: rapid rise, gradual rise, rapid fall, gradual fall, steady stable, fluctuating stable.\nProvide a structured report covering:\n1. Trend Segmentation: Describe each stage with precise start/end timestamps (HH:MM:SS) using the phrases above.\n2. Outlier Audit: Identify only significant outliers that deviate sharply from the local trend. Ignore minor background noise.\n(Output format: structured natural language report.)", "answer": "1. Trend Segmentation: from 2019-01-01 00:00:00 to 2019-01-11 08:00:00, the trend showed a steady stable; from 2019-01-11 08:00:00 to 2019-01-16 12:00:00, the trend showed a rapid fall; from 2019-01-16 12:00:00 to 2019-01-31 23:45:00, the trend showed a rapid rise.\n2. Outlier Audit: A significant drop was detected at 2019-01-24 10:30:00 (value: -3053.58).", "ground_truth": { "trend_segments": [ "from 2019-01-01 00:00:00 to 2019-01-11 08:00:00, the trend showed a steady stable", "from 2019-01-11 08:00:00 to 2019-01-16 12:00:00, the trend showed a rapid fall", "from 2019-01-16 12:00:00 to 2019-01-31 23:45:00, the trend showed a rapid rise" ], "segments_meta": [ { "adj": "steady", "kind": "stable", "start_idx": 0, "end_idx": 992 }, { "adj": "rapid", "kind": "fall", "start_idx": 992, "end_idx": 1488 }, { "adj": "rapid", "kind": "rise", "start_idx": 1488, "end_idx": 2976 } ], "significant_anomaly": { "timestamp": "2019-01-24 10:30:00", "kind": "significant drop", "value": -3053.5801234185283 } }, "eval_metric": "report", "channel": "170", "ts_data_path": "ts_data/L4_T1_Insight_Synthesis_00065.csv", "meta": { "target_month": "2019-01", "target_year": 2019, "source": "causal_rivers" } }, { "id": "L4_T1_Insight_Synthesis_00066", "level": 4, "level_name": "Insight Synthesis", "category": "Insight Synthesis", "subtask": "Insight Synthesis", "question": "Analyze the behavior of channel 172 for the period 2020-08.\nPlease use ONLY the following phrases for trend description: rapid rise, gradual rise, rapid fall, gradual fall, steady stable, fluctuating stable.\nProvide a structured report covering:\n1. Trend Segmentation: Describe each stage with precise start/end timestamps (HH:MM:SS) using the phrases above.\n2. Outlier Audit: Identify only significant outliers that deviate sharply from the local trend. Ignore minor background noise.\n(Output format: structured natural language report.)", "answer": "1. Trend Segmentation: from 2020-08-01 00:00:00 to 2020-08-15 07:30:00, the trend showed a fluctuating stable; from 2020-08-15 07:30:00 to 2020-08-20 02:00:00, the trend showed a steady stable; from 2020-08-20 02:00:00 to 2020-08-24 20:30:00, the trend showed a fluctuating stable; from 2020-08-24 20:30:00 to 2020-08-31 23:45:00, the trend showed a gradual fall.\n2. Outlier Audit: A significant spike was detected at 2020-08-15 04:45:00 (value: 2341.55).", "ground_truth": { "trend_segments": [ "from 2020-08-01 00:00:00 to 2020-08-15 07:30:00, the trend showed a fluctuating stable", "from 2020-08-15 07:30:00 to 2020-08-20 02:00:00, the trend showed a steady stable", "from 2020-08-20 02:00:00 to 2020-08-24 20:30:00, the trend showed a fluctuating stable", "from 2020-08-24 20:30:00 to 2020-08-31 23:45:00, the trend showed a gradual fall" ], "segments_meta": [ { "adj": "fluctuating", "kind": "stable", "start_idx": 0, "end_idx": 1374 }, { "adj": "steady", "kind": "stable", "start_idx": 1374, "end_idx": 1832 }, { "adj": "fluctuating", "kind": "stable", "start_idx": 1832, "end_idx": 2290 }, { "adj": "gradual", "kind": "fall", "start_idx": 2290, "end_idx": 2976 } ], "significant_anomaly": { "timestamp": "2020-08-15 04:45:00", "kind": "significant spike", "value": 2341.551288006624 } }, "eval_metric": "report", "channel": "172", "ts_data_path": "ts_data/L4_T1_Insight_Synthesis_00066.csv", "meta": { "target_month": "2020-08", "target_year": 2020, "source": "causal_rivers" } }, { "id": "L4_T1_Insight_Synthesis_00067", "level": 4, "level_name": "Insight Synthesis", "category": "Insight Synthesis", "subtask": "Insight Synthesis", "question": "Analyze the behavior of channel 173 for the period 2021-10.\nPlease use ONLY the following phrases for trend description: rapid rise, gradual rise, rapid fall, gradual fall, steady stable, fluctuating stable.\nProvide a structured report covering:\n1. Trend Segmentation: Describe each stage with precise start/end timestamps (HH:MM:SS) using the phrases above.\n2. Outlier Audit: Identify only significant outliers that deviate sharply from the local trend. Ignore minor background noise.\n(Output format: structured natural language report.)", "answer": "1. Trend Segmentation: from 2021-10-01 00:00:00 to 2021-10-18 17:15:00, the trend showed a gradual fall; from 2021-10-18 17:15:00 to 2021-10-31 23:45:00, the trend showed a rapid fall.\n2. Outlier Audit: A significant drop was detected at 2021-10-24 09:45:00 (value: -1756.28).", "ground_truth": { "trend_segments": [ "from 2021-10-01 00:00:00 to 2021-10-18 17:15:00, the trend showed a gradual fall", "from 2021-10-18 17:15:00 to 2021-10-31 23:45:00, the trend showed a rapid fall" ], "segments_meta": [ { "adj": "gradual", "kind": "fall", "start_idx": 0, "end_idx": 1701 }, { "adj": "rapid", "kind": "fall", "start_idx": 1701, "end_idx": 2976 } ], "significant_anomaly": { "timestamp": "2021-10-24 09:45:00", "kind": "significant drop", "value": -1756.278364869465 } }, "eval_metric": "report", "channel": "173", "ts_data_path": "ts_data/L4_T1_Insight_Synthesis_00067.csv", "meta": { "target_month": "2021-10", "target_year": 2021, "source": "causal_rivers" } }, { "id": "L4_T1_Insight_Synthesis_00069", "level": 4, "level_name": "Insight Synthesis", "category": "Insight Synthesis", "subtask": "Insight Synthesis", "question": "Analyze the behavior of channel 237 for the period 2020-02.\nPlease use ONLY the following phrases for trend description: rapid rise, gradual rise, rapid fall, gradual fall, steady stable, fluctuating stable.\nProvide a structured report covering:\n1. Trend Segmentation: Describe each stage with precise start/end timestamps (HH:MM:SS) using the phrases above.\n2. Outlier Audit: Identify only significant outliers that deviate sharply from the local trend. Ignore minor background noise.\n(Output format: structured natural language report.)", "answer": "1. Trend Segmentation: from 2020-02-01 00:00:00 to 2020-02-15 12:00:00, the trend showed a rapid fall; from 2020-02-15 12:00:00 to 2020-02-25 04:00:00, the trend showed a gradual rise; from 2020-02-25 04:00:00 to 2020-02-29 23:45:00, the trend showed a rapid rise.\n2. Outlier Audit: A significant drop was detected at 2020-02-25 18:30:00 (value: -7.81).", "ground_truth": { "trend_segments": [ "from 2020-02-01 00:00:00 to 2020-02-15 12:00:00, the trend showed a rapid fall", "from 2020-02-15 12:00:00 to 2020-02-25 04:00:00, the trend showed a gradual rise", "from 2020-02-25 04:00:00 to 2020-02-29 23:45:00, the trend showed a rapid rise" ], "segments_meta": [ { "adj": "rapid", "kind": "fall", "start_idx": 0, "end_idx": 1392 }, { "adj": "gradual", "kind": "rise", "start_idx": 1392, "end_idx": 2320 }, { "adj": "rapid", "kind": "rise", "start_idx": 2320, "end_idx": 2784 } ], "significant_anomaly": { "timestamp": "2020-02-25 18:30:00", "kind": "significant drop", "value": -7.811311195107711 } }, "eval_metric": "report", "channel": "237", "ts_data_path": "ts_data/L4_T1_Insight_Synthesis_00069.csv", "meta": { "target_month": "2020-02", "target_year": 2020, "source": "causal_rivers" } }, { "id": "L4_T1_Insight_Synthesis_00070", "level": 4, "level_name": "Insight Synthesis", "category": "Insight Synthesis", "subtask": "Insight Synthesis", "question": "Analyze the behavior of channel 245 for the period 2022-07.\nPlease use ONLY the following phrases for trend description: rapid rise, gradual rise, rapid fall, gradual fall, steady stable, fluctuating stable.\nProvide a structured report covering:\n1. Trend Segmentation: Describe each stage with precise start/end timestamps (HH:MM:SS) using the phrases above.\n2. Outlier Audit: Identify only significant outliers that deviate sharply from the local trend. Ignore minor background noise.\n(Output format: structured natural language report.)", "answer": "1. Trend Segmentation: from 2022-07-01 00:00:00 to 2022-07-05 18:30:00, the trend showed a rapid fall; from 2022-07-05 18:30:00 to 2022-07-20 02:00:00, the trend showed a rapid rise; from 2022-07-20 02:00:00 to 2022-07-31 23:45:00, the trend showed a gradual fall.\n2. Outlier Audit: A significant spike was detected at 2022-07-16 19:00:00 (value: 2.95).", "ground_truth": { "trend_segments": [ "from 2022-07-01 00:00:00 to 2022-07-05 18:30:00, the trend showed a rapid fall", "from 2022-07-05 18:30:00 to 2022-07-20 02:00:00, the trend showed a rapid rise", "from 2022-07-20 02:00:00 to 2022-07-31 23:45:00, the trend showed a gradual fall" ], "segments_meta": [ { "adj": "rapid", "kind": "fall", "start_idx": 0, "end_idx": 458 }, { "adj": "rapid", "kind": "rise", "start_idx": 458, "end_idx": 1832 }, { "adj": "gradual", "kind": "fall", "start_idx": 1832, "end_idx": 2976 } ], "significant_anomaly": { "timestamp": "2022-07-16 19:00:00", "kind": "significant spike", "value": 2.953339894846565 } }, "eval_metric": "report", "channel": "245", "ts_data_path": "ts_data/L4_T1_Insight_Synthesis_00070.csv", "meta": { "target_month": "2022-07", "target_year": 2022, "source": "causal_rivers" } }, { "id": "L4_T1_Insight_Synthesis_00073", "level": 4, "level_name": "Insight Synthesis", "category": "Insight Synthesis", "subtask": "Insight Synthesis", "question": "Analyze the behavior of channel 441 for the period 2022-05.\nPlease use ONLY the following phrases for trend description: rapid rise, gradual rise, rapid fall, gradual fall, steady stable, fluctuating stable.\nProvide a structured report covering:\n1. Trend Segmentation: Describe each stage with precise start/end timestamps (HH:MM:SS) using the phrases above.\n2. Outlier Audit: Identify only significant outliers that deviate sharply from the local trend. Ignore minor background noise.\n(Output format: structured natural language report.)", "answer": "1. Trend Segmentation: from 2022-05-01 00:00:00 to 2022-05-15 07:30:00, the trend showed a rapid fall; from 2022-05-15 07:30:00 to 2022-05-27 05:45:00, the trend showed a steady stable; from 2022-05-27 05:45:00 to 2022-05-31 23:45:00, the trend showed a fluctuating stable.\n2. Outlier Audit: A significant spike was detected at 2022-05-20 04:45:00 (value: 141.86).", "ground_truth": { "trend_segments": [ "from 2022-05-01 00:00:00 to 2022-05-15 07:30:00, the trend showed a rapid fall", "from 2022-05-15 07:30:00 to 2022-05-27 05:45:00, the trend showed a steady stable", "from 2022-05-27 05:45:00 to 2022-05-31 23:45:00, the trend showed a fluctuating stable" ], "segments_meta": [ { "adj": "rapid", "kind": "fall", "start_idx": 0, "end_idx": 1374 }, { "adj": "steady", "kind": "stable", "start_idx": 1374, "end_idx": 2519 }, { "adj": "fluctuating", "kind": "stable", "start_idx": 2519, "end_idx": 2976 } ], "significant_anomaly": { "timestamp": "2022-05-20 04:45:00", "kind": "significant spike", "value": 141.85869545826796 } }, "eval_metric": "report", "channel": "441", "ts_data_path": "ts_data/L4_T1_Insight_Synthesis_00073.csv", "meta": { "target_month": "2022-05", "target_year": 2022, "source": "causal_rivers" } }, { "id": "L4_T1_Insight_Synthesis_00074", "level": 4, "level_name": "Insight Synthesis", "category": "Insight Synthesis", "subtask": "Insight Synthesis", "question": "Analyze the behavior of channel 495 for the period 2023-09.\nPlease use ONLY the following phrases for trend description: rapid rise, gradual rise, rapid fall, gradual fall, steady stable, fluctuating stable.\nProvide a structured report covering:\n1. Trend Segmentation: Describe each stage with precise start/end timestamps (HH:MM:SS) using the phrases above.\n2. Outlier Audit: Identify only significant outliers that deviate sharply from the local trend. Ignore minor background noise.\n(Output format: structured natural language report.)", "answer": "1. Trend Segmentation: from 2023-09-01 00:00:00 to 2023-09-23 12:00:00, the trend showed a rapid rise; from 2023-09-23 12:00:00 to 2023-09-30 23:45:00, the trend showed a steady stable.\n2. Outlier Audit: A significant drop was detected at 2023-09-23 15:30:00 (value: -12.31).", "ground_truth": { "trend_segments": [ "from 2023-09-01 00:00:00 to 2023-09-23 12:00:00, the trend showed a rapid rise", "from 2023-09-23 12:00:00 to 2023-09-30 23:45:00, the trend showed a steady stable" ], "segments_meta": [ { "adj": "rapid", "kind": "rise", "start_idx": 0, "end_idx": 2160 }, { "adj": "steady", "kind": "stable", "start_idx": 2160, "end_idx": 2880 } ], "significant_anomaly": { "timestamp": "2023-09-23 15:30:00", "kind": "significant drop", "value": -12.307010073802541 } }, "eval_metric": "report", "channel": "495", "ts_data_path": "ts_data/L4_T1_Insight_Synthesis_00074.csv", "meta": { "target_month": "2023-09", "target_year": 2023, "source": "causal_rivers" } }, { "id": "L4_T1_Insight_Synthesis_00075", "level": 4, "level_name": "Insight Synthesis", "category": "Insight Synthesis", "subtask": "Insight Synthesis", "question": "Analyze the behavior of channel 496 for the period 2021-01.\nPlease use ONLY the following phrases for trend description: rapid rise, gradual rise, rapid fall, gradual fall, steady stable, fluctuating stable.\nProvide a structured report covering:\n1. Trend Segmentation: Describe each stage with precise start/end timestamps (HH:MM:SS) using the phrases above.\n2. Outlier Audit: Identify only significant outliers that deviate sharply from the local trend. Ignore minor background noise.\n(Output format: structured natural language report.)", "answer": "1. Trend Segmentation: from 2021-01-01 00:00:00 to 2021-01-08 18:00:00, the trend showed a gradual rise; from 2021-01-08 18:00:00 to 2021-01-31 23:45:00, the trend showed a steady stable.\n2. Outlier Audit: A significant drop was detected at 2021-01-10 14:30:00 (value: -2.74).", "ground_truth": { "trend_segments": [ "from 2021-01-01 00:00:00 to 2021-01-08 18:00:00, the trend showed a gradual rise", "from 2021-01-08 18:00:00 to 2021-01-31 23:45:00, the trend showed a steady stable" ], "segments_meta": [ { "adj": "gradual", "kind": "rise", "start_idx": 0, "end_idx": 744 }, { "adj": "steady", "kind": "stable", "start_idx": 744, "end_idx": 2976 } ], "significant_anomaly": { "timestamp": "2021-01-10 14:30:00", "kind": "significant drop", "value": -2.736663372668701 } }, "eval_metric": "report", "channel": "496", "ts_data_path": "ts_data/L4_T1_Insight_Synthesis_00075.csv", "meta": { "target_month": "2021-01", "target_year": 2021, "source": "causal_rivers" } }, { "id": "L4_T1_Insight_Synthesis_00076", "level": 4, "level_name": "Insight Synthesis", "category": "Insight Synthesis", "subtask": "Insight Synthesis", "question": "Analyze the behavior of channel 501 for the period 2022-03.\nPlease use ONLY the following phrases for trend description: rapid rise, gradual rise, rapid fall, gradual fall, steady stable, fluctuating stable.\nProvide a structured report covering:\n1. Trend Segmentation: Describe each stage with precise start/end timestamps (HH:MM:SS) using the phrases above.\n2. Outlier Audit: Identify only significant outliers that deviate sharply from the local trend. Ignore minor background noise.\n(Output format: structured natural language report.)", "answer": "1. Trend Segmentation: from 2022-03-01 00:00:00 to 2022-03-09 20:30:00, the trend showed a steady stable; from 2022-03-09 20:30:00 to 2022-03-31 23:45:00, the trend showed a rapid rise.\n2. Outlier Audit: A significant spike was detected at 2022-03-11 17:45:00 (value: 14.80).", "ground_truth": { "trend_segments": [ "from 2022-03-01 00:00:00 to 2022-03-09 20:30:00, the trend showed a steady stable", "from 2022-03-09 20:30:00 to 2022-03-31 23:45:00, the trend showed a rapid rise" ], "segments_meta": [ { "adj": "steady", "kind": "stable", "start_idx": 0, "end_idx": 850 }, { "adj": "rapid", "kind": "rise", "start_idx": 850, "end_idx": 2976 } ], "significant_anomaly": { "timestamp": "2022-03-11 17:45:00", "kind": "significant spike", "value": 14.796079637790589 } }, "eval_metric": "report", "channel": "501", "ts_data_path": "ts_data/L4_T1_Insight_Synthesis_00076.csv", "meta": { "target_month": "2022-03", "target_year": 2022, "source": "causal_rivers" } }, { "id": "L4_T1_Insight_Synthesis_00079", "level": 4, "level_name": "Insight Synthesis", "category": "Insight Synthesis", "subtask": "Insight Synthesis", "question": "Analyze the behavior of channel 589 for the period 2022-07.\nPlease use ONLY the following phrases for trend description: rapid rise, gradual rise, rapid fall, gradual fall, steady stable, fluctuating stable.\nProvide a structured report covering:\n1. Trend Segmentation: Describe each stage with precise start/end timestamps (HH:MM:SS) using the phrases above.\n2. Outlier Audit: Identify only significant outliers that deviate sharply from the local trend. Ignore minor background noise.\n(Output format: structured natural language report.)", "answer": "1. Trend Segmentation: from 2022-07-01 00:00:00 to 2022-07-19 14:30:00, the trend showed a gradual rise; from 2022-07-19 14:30:00 to 2022-07-31 23:45:00, the trend showed a fluctuating stable.\n2. Outlier Audit: A significant drop was detected at 2022-07-27 16:45:00 (value: -1880.62).", "ground_truth": { "trend_segments": [ "from 2022-07-01 00:00:00 to 2022-07-19 14:30:00, the trend showed a gradual rise", "from 2022-07-19 14:30:00 to 2022-07-31 23:45:00, the trend showed a fluctuating stable" ], "segments_meta": [ { "adj": "gradual", "kind": "rise", "start_idx": 0, "end_idx": 1786 }, { "adj": "fluctuating", "kind": "stable", "start_idx": 1786, "end_idx": 2976 } ], "significant_anomaly": { "timestamp": "2022-07-27 16:45:00", "kind": "significant drop", "value": -1880.6217521167482 } }, "eval_metric": "report", "channel": "589", "ts_data_path": "ts_data/L4_T1_Insight_Synthesis_00079.csv", "meta": { "target_month": "2022-07", "target_year": 2022, "source": "causal_rivers" } }, { "id": "L4_T1_Insight_Synthesis_00080", "level": 4, "level_name": "Insight Synthesis", "category": "Insight Synthesis", "subtask": "Insight Synthesis", "question": "Analyze the behavior of channel 591 for the period 2019-04.\nPlease use ONLY the following phrases for trend description: rapid rise, gradual rise, rapid fall, gradual fall, steady stable, fluctuating stable.\nProvide a structured report covering:\n1. Trend Segmentation: Describe each stage with precise start/end timestamps (HH:MM:SS) using the phrases above.\n2. Outlier Audit: Identify only significant outliers that deviate sharply from the local trend. Ignore minor background noise.\n(Output format: structured natural language report.)", "answer": "1. Trend Segmentation: from 2019-04-01 00:00:00 to 2019-04-17 16:00:00, the trend showed a steady stable; from 2019-04-17 16:00:00 to 2019-04-30 23:45:00, the trend showed a rapid rise.\n2. Outlier Audit: A significant drop was detected at 2019-04-20 02:15:00 (value: -1329.33).", "ground_truth": { "trend_segments": [ "from 2019-04-01 00:00:00 to 2019-04-17 16:00:00, the trend showed a steady stable", "from 2019-04-17 16:00:00 to 2019-04-30 23:45:00, the trend showed a rapid rise" ], "segments_meta": [ { "adj": "steady", "kind": "stable", "start_idx": 0, "end_idx": 1600 }, { "adj": "rapid", "kind": "rise", "start_idx": 1600, "end_idx": 2880 } ], "significant_anomaly": { "timestamp": "2019-04-20 02:15:00", "kind": "significant drop", "value": -1329.3349754161418 } }, "eval_metric": "report", "channel": "591", "ts_data_path": "ts_data/L4_T1_Insight_Synthesis_00080.csv", "meta": { "target_month": "2019-04", "target_year": 2019, "source": "causal_rivers" } }, { "id": "L4_T1_Insight_Synthesis_00081", "level": 4, "level_name": "Insight Synthesis", "category": "Insight Synthesis", "subtask": "Insight Synthesis", "question": "Analyze the behavior of channel 595 for the period 2023-04.\nPlease use ONLY the following phrases for trend description: rapid rise, gradual rise, rapid fall, gradual fall, steady stable, fluctuating stable.\nProvide a structured report covering:\n1. Trend Segmentation: Describe each stage with precise start/end timestamps (HH:MM:SS) using the phrases above.\n2. Outlier Audit: Identify only significant outliers that deviate sharply from the local trend. Ignore minor background noise.\n(Output format: structured natural language report.)", "answer": "1. Trend Segmentation: from 2023-04-01 00:00:00 to 2023-04-16 00:00:00, the trend showed a rapid rise; from 2023-04-16 00:00:00 to 2023-04-30 23:45:00, the trend showed a fluctuating stable.\n2. Outlier Audit: A significant spike was detected at 2023-04-20 20:30:00 (value: 49.34).", "ground_truth": { "trend_segments": [ "from 2023-04-01 00:00:00 to 2023-04-16 00:00:00, the trend showed a rapid rise", "from 2023-04-16 00:00:00 to 2023-04-30 23:45:00, the trend showed a fluctuating stable" ], "segments_meta": [ { "adj": "rapid", "kind": "rise", "start_idx": 0, "end_idx": 1440 }, { "adj": "fluctuating", "kind": "stable", "start_idx": 1440, "end_idx": 2880 } ], "significant_anomaly": { "timestamp": "2023-04-20 20:30:00", "kind": "significant spike", "value": 49.34086153823347 } }, "eval_metric": "report", "channel": "595", "ts_data_path": "ts_data/L4_T1_Insight_Synthesis_00081.csv", "meta": { "target_month": "2023-04", "target_year": 2023, "source": "causal_rivers" } }, { "id": "L4_T1_Insight_Synthesis_00083", "level": 4, "level_name": "Insight Synthesis", "category": "Insight Synthesis", "subtask": "Insight Synthesis", "question": "Analyze the behavior of channel 626 for the period 2021-01.\nPlease use ONLY the following phrases for trend description: rapid rise, gradual rise, rapid fall, gradual fall, steady stable, fluctuating stable.\nProvide a structured report covering:\n1. Trend Segmentation: Describe each stage with precise start/end timestamps (HH:MM:SS) using the phrases above.\n2. Outlier Audit: Identify only significant outliers that deviate sharply from the local trend. Ignore minor background noise.\n(Output format: structured natural language report.)", "answer": "1. Trend Segmentation: from 2021-01-01 00:00:00 to 2021-01-08 18:00:00, the trend showed a gradual rise; from 2021-01-08 18:00:00 to 2021-01-16 12:00:00, the trend showed a fluctuating stable; from 2021-01-16 12:00:00 to 2021-01-24 06:00:00, the trend showed a rapid rise; from 2021-01-24 06:00:00 to 2021-01-31 23:45:00, the trend showed a fluctuating stable.\n2. Outlier Audit: A significant spike was detected at 2021-01-28 14:30:00 (value: 8.92).", "ground_truth": { "trend_segments": [ "from 2021-01-01 00:00:00 to 2021-01-08 18:00:00, the trend showed a gradual rise", "from 2021-01-08 18:00:00 to 2021-01-16 12:00:00, the trend showed a fluctuating stable", "from 2021-01-16 12:00:00 to 2021-01-24 06:00:00, the trend showed a rapid rise", "from 2021-01-24 06:00:00 to 2021-01-31 23:45:00, the trend showed a fluctuating stable" ], "segments_meta": [ { "adj": "gradual", "kind": "rise", "start_idx": 0, "end_idx": 744 }, { "adj": "fluctuating", "kind": "stable", "start_idx": 744, "end_idx": 1488 }, { "adj": "rapid", "kind": "rise", "start_idx": 1488, "end_idx": 2232 }, { "adj": "fluctuating", "kind": "stable", "start_idx": 2232, "end_idx": 2976 } ], "significant_anomaly": { "timestamp": "2021-01-28 14:30:00", "kind": "significant spike", "value": 8.924178584800975 } }, "eval_metric": "report", "channel": "626", "ts_data_path": "ts_data/L4_T1_Insight_Synthesis_00083.csv", "meta": { "target_month": "2021-01", "target_year": 2021, "source": "causal_rivers" } }, { "id": "L4_T1_Insight_Synthesis_00086", "level": 4, "level_name": "Insight Synthesis", "category": "Insight Synthesis", "subtask": "Insight Synthesis", "question": "Analyze the behavior of channel 680 for the period 2019-04.\nPlease use ONLY the following phrases for trend description: rapid rise, gradual rise, rapid fall, gradual fall, steady stable, fluctuating stable.\nProvide a structured report covering:\n1. Trend Segmentation: Describe each stage with precise start/end timestamps (HH:MM:SS) using the phrases above.\n2. Outlier Audit: Identify only significant outliers that deviate sharply from the local trend. Ignore minor background noise.\n(Output format: structured natural language report.)", "answer": "1. Trend Segmentation: from 2019-04-01 00:00:00 to 2019-04-07 16:00:00, the trend showed a steady stable; from 2019-04-07 16:00:00 to 2019-04-14 08:00:00, the trend showed a gradual fall; from 2019-04-14 08:00:00 to 2019-04-21 00:00:00, the trend showed a rapid fall; from 2019-04-21 00:00:00 to 2019-04-30 23:45:00, the trend showed a steady stable.\n2. Outlier Audit: A significant spike was detected at 2019-04-21 17:45:00 (value: 39.93).", "ground_truth": { "trend_segments": [ "from 2019-04-01 00:00:00 to 2019-04-07 16:00:00, the trend showed a steady stable", "from 2019-04-07 16:00:00 to 2019-04-14 08:00:00, the trend showed a gradual fall", "from 2019-04-14 08:00:00 to 2019-04-21 00:00:00, the trend showed a rapid fall", "from 2019-04-21 00:00:00 to 2019-04-30 23:45:00, the trend showed a steady stable" ], "segments_meta": [ { "adj": "steady", "kind": "stable", "start_idx": 0, "end_idx": 640 }, { "adj": "gradual", "kind": "fall", "start_idx": 640, "end_idx": 1280 }, { "adj": "rapid", "kind": "fall", "start_idx": 1280, "end_idx": 1920 }, { "adj": "steady", "kind": "stable", "start_idx": 1920, "end_idx": 2880 } ], "significant_anomaly": { "timestamp": "2019-04-21 17:45:00", "kind": "significant spike", "value": 39.92635737654389 } }, "eval_metric": "report", "channel": "680", "ts_data_path": "ts_data/L4_T1_Insight_Synthesis_00086.csv", "meta": { "target_month": "2019-04", "target_year": 2019, "source": "causal_rivers" } }, { "id": "L4_T1_Insight_Synthesis_00090", "level": 4, "level_name": "Insight Synthesis", "category": "Insight Synthesis", "subtask": "Insight Synthesis", "question": "Analyze the behavior of channel 729 for the period 2019-09.\nPlease use ONLY the following phrases for trend description: rapid rise, gradual rise, rapid fall, gradual fall, steady stable, fluctuating stable.\nProvide a structured report covering:\n1. Trend Segmentation: Describe each stage with precise start/end timestamps (HH:MM:SS) using the phrases above.\n2. Outlier Audit: Identify only significant outliers that deviate sharply from the local trend. Ignore minor background noise.\n(Output format: structured natural language report.)", "answer": "1. Trend Segmentation: from 2019-09-01 00:00:00 to 2019-09-12 06:00:00, the trend showed a gradual rise; from 2019-09-12 06:00:00 to 2019-09-23 12:00:00, the trend showed a fluctuating stable; from 2019-09-23 12:00:00 to 2019-09-30 23:45:00, the trend showed a rapid fall.\n2. Outlier Audit: A significant spike was detected at 2019-09-27 15:45:00 (value: 386.57).", "ground_truth": { "trend_segments": [ "from 2019-09-01 00:00:00 to 2019-09-12 06:00:00, the trend showed a gradual rise", "from 2019-09-12 06:00:00 to 2019-09-23 12:00:00, the trend showed a fluctuating stable", "from 2019-09-23 12:00:00 to 2019-09-30 23:45:00, the trend showed a rapid fall" ], "segments_meta": [ { "adj": "gradual", "kind": "rise", "start_idx": 0, "end_idx": 1080 }, { "adj": "fluctuating", "kind": "stable", "start_idx": 1080, "end_idx": 2160 }, { "adj": "rapid", "kind": "fall", "start_idx": 2160, "end_idx": 2880 } ], "significant_anomaly": { "timestamp": "2019-09-27 15:45:00", "kind": "significant spike", "value": 386.5655229016371 } }, "eval_metric": "report", "channel": "729", "ts_data_path": "ts_data/L4_T1_Insight_Synthesis_00090.csv", "meta": { "target_month": "2019-09", "target_year": 2019, "source": "causal_rivers" } }, { "id": "L4_T1_Insight_Synthesis_00091", "level": 4, "level_name": "Insight Synthesis", "category": "Insight Synthesis", "subtask": "Insight Synthesis", "question": "Analyze the behavior of channel 754 for the period 2022-09.\nPlease use ONLY the following phrases for trend description: rapid rise, gradual rise, rapid fall, gradual fall, steady stable, fluctuating stable.\nProvide a structured report covering:\n1. Trend Segmentation: Describe each stage with precise start/end timestamps (HH:MM:SS) using the phrases above.\n2. Outlier Audit: Identify only significant outliers that deviate sharply from the local trend. Ignore minor background noise.\n(Output format: structured natural language report.)", "answer": "1. Trend Segmentation: from 2022-09-01 00:00:00 to 2022-09-09 13:45:00, the trend showed a steady stable; from 2022-09-09 13:45:00 to 2022-09-20 07:00:00, the trend showed a rapid fall; from 2022-09-20 07:00:00 to 2022-09-24 13:45:00, the trend showed a steady stable; from 2022-09-24 13:45:00 to 2022-09-30 23:45:00, the trend showed a rapid rise.\n2. Outlier Audit: A significant drop was detected at 2022-09-13 04:00:00 (value: -1.64).", "ground_truth": { "trend_segments": [ "from 2022-09-01 00:00:00 to 2022-09-09 13:45:00, the trend showed a steady stable", "from 2022-09-09 13:45:00 to 2022-09-20 07:00:00, the trend showed a rapid fall", "from 2022-09-20 07:00:00 to 2022-09-24 13:45:00, the trend showed a steady stable", "from 2022-09-24 13:45:00 to 2022-09-30 23:45:00, the trend showed a rapid rise" ], "segments_meta": [ { "adj": "steady", "kind": "stable", "start_idx": 0, "end_idx": 823 }, { "adj": "rapid", "kind": "fall", "start_idx": 823, "end_idx": 1852 }, { "adj": "steady", "kind": "stable", "start_idx": 1852, "end_idx": 2263 }, { "adj": "rapid", "kind": "rise", "start_idx": 2263, "end_idx": 2880 } ], "significant_anomaly": { "timestamp": "2022-09-13 04:00:00", "kind": "significant drop", "value": -1.6404932967090844 } }, "eval_metric": "report", "channel": "754", "ts_data_path": "ts_data/L4_T1_Insight_Synthesis_00091.csv", "meta": { "target_month": "2022-09", "target_year": 2022, "source": "causal_rivers" } }, { "id": "L4_T1_Insight_Synthesis_00092", "level": 4, "level_name": "Insight Synthesis", "category": "Insight Synthesis", "subtask": "Insight Synthesis", "question": "Analyze the behavior of channel 762 for the period 2019-11.\nPlease use ONLY the following phrases for trend description: rapid rise, gradual rise, rapid fall, gradual fall, steady stable, fluctuating stable.\nProvide a structured report covering:\n1. Trend Segmentation: Describe each stage with precise start/end timestamps (HH:MM:SS) using the phrases above.\n2. Outlier Audit: Identify only significant outliers that deviate sharply from the local trend. Ignore minor background noise.\n(Output format: structured natural language report.)", "answer": "1. Trend Segmentation: from 2019-11-01 00:00:00 to 2019-11-16 00:00:00, the trend showed a gradual fall; from 2019-11-16 00:00:00 to 2019-11-30 23:45:00, the trend showed a fluctuating stable.\n2. Outlier Audit: A significant spike was detected at 2019-11-20 10:00:00 (value: 1.37).", "ground_truth": { "trend_segments": [ "from 2019-11-01 00:00:00 to 2019-11-16 00:00:00, the trend showed a gradual fall", "from 2019-11-16 00:00:00 to 2019-11-30 23:45:00, the trend showed a fluctuating stable" ], "segments_meta": [ { "adj": "gradual", "kind": "fall", "start_idx": 0, "end_idx": 1440 }, { "adj": "fluctuating", "kind": "stable", "start_idx": 1440, "end_idx": 2880 } ], "significant_anomaly": { "timestamp": "2019-11-20 10:00:00", "kind": "significant spike", "value": 1.3712137334169825 } }, "eval_metric": "report", "channel": "762", "ts_data_path": "ts_data/L4_T1_Insight_Synthesis_00092.csv", "meta": { "target_month": "2019-11", "target_year": 2019, "source": "causal_rivers" } }, { "id": "L4_T1_Insight_Synthesis_00093", "level": 4, "level_name": "Insight Synthesis", "category": "Insight Synthesis", "subtask": "Insight Synthesis", "question": "Analyze the behavior of channel 811 for the period 2020-07.\nPlease use ONLY the following phrases for trend description: rapid rise, gradual rise, rapid fall, gradual fall, steady stable, fluctuating stable.\nProvide a structured report covering:\n1. Trend Segmentation: Describe each stage with precise start/end timestamps (HH:MM:SS) using the phrases above.\n2. Outlier Audit: Identify only significant outliers that deviate sharply from the local trend. Ignore minor background noise.\n(Output format: structured natural language report.)", "answer": "1. Trend Segmentation: from 2020-07-01 00:00:00 to 2020-07-13 09:30:00, the trend showed a gradual fall; from 2020-07-13 09:30:00 to 2020-07-19 14:15:00, the trend showed a steady stable; from 2020-07-19 14:15:00 to 2020-07-31 23:45:00, the trend showed a gradual rise.\n2. Outlier Audit: A significant spike was detected at 2020-07-06 00:45:00 (value: 51.58).", "ground_truth": { "trend_segments": [ "from 2020-07-01 00:00:00 to 2020-07-13 09:30:00, the trend showed a gradual fall", "from 2020-07-13 09:30:00 to 2020-07-19 14:15:00, the trend showed a steady stable", "from 2020-07-19 14:15:00 to 2020-07-31 23:45:00, the trend showed a gradual rise" ], "segments_meta": [ { "adj": "gradual", "kind": "fall", "start_idx": 0, "end_idx": 1190 }, { "adj": "steady", "kind": "stable", "start_idx": 1190, "end_idx": 1785 }, { "adj": "gradual", "kind": "rise", "start_idx": 1785, "end_idx": 2976 } ], "significant_anomaly": { "timestamp": "2020-07-06 00:45:00", "kind": "significant spike", "value": 51.581502941809774 } }, "eval_metric": "report", "channel": "811", "ts_data_path": "ts_data/L4_T1_Insight_Synthesis_00093.csv", "meta": { "target_month": "2020-07", "target_year": 2020, "source": "causal_rivers" } }, { "id": "L4_T1_Insight_Synthesis_00096", "level": 4, "level_name": "Insight Synthesis", "category": "Insight Synthesis", "subtask": "Insight Synthesis", "question": "Analyze the behavior of channel 891 for the period 2020-08.\nPlease use ONLY the following phrases for trend description: rapid rise, gradual rise, rapid fall, gradual fall, steady stable, fluctuating stable.\nProvide a structured report covering:\n1. Trend Segmentation: Describe each stage with precise start/end timestamps (HH:MM:SS) using the phrases above.\n2. Outlier Audit: Identify only significant outliers that deviate sharply from the local trend. Ignore minor background noise.\n(Output format: structured natural language report.)", "answer": "1. Trend Segmentation: from 2020-08-01 00:00:00 to 2020-08-11 08:00:00, the trend showed a gradual fall; from 2020-08-11 08:00:00 to 2020-08-21 16:00:00, the trend showed a rapid rise; from 2020-08-21 16:00:00 to 2020-08-31 23:45:00, the trend showed a gradual fall.\n2. Outlier Audit: A significant drop was detected at 2020-08-28 09:45:00 (value: -21.97).", "ground_truth": { "trend_segments": [ "from 2020-08-01 00:00:00 to 2020-08-11 08:00:00, the trend showed a gradual fall", "from 2020-08-11 08:00:00 to 2020-08-21 16:00:00, the trend showed a rapid rise", "from 2020-08-21 16:00:00 to 2020-08-31 23:45:00, the trend showed a gradual fall" ], "segments_meta": [ { "adj": "gradual", "kind": "fall", "start_idx": 0, "end_idx": 992 }, { "adj": "rapid", "kind": "rise", "start_idx": 992, "end_idx": 1984 }, { "adj": "gradual", "kind": "fall", "start_idx": 1984, "end_idx": 2976 } ], "significant_anomaly": { "timestamp": "2020-08-28 09:45:00", "kind": "significant drop", "value": -21.968347153375067 } }, "eval_metric": "report", "channel": "891", "ts_data_path": "ts_data/L4_T1_Insight_Synthesis_00096.csv", "meta": { "target_month": "2020-08", "target_year": 2020, "source": "causal_rivers" } }, { "id": "L4_T1_Insight_Synthesis_00097", "level": 4, "level_name": "Insight Synthesis", "category": "Insight Synthesis", "subtask": "Insight Synthesis", "question": "Analyze the behavior of channel 894 for the period 2022-06.\nPlease use ONLY the following phrases for trend description: rapid rise, gradual rise, rapid fall, gradual fall, steady stable, fluctuating stable.\nProvide a structured report covering:\n1. Trend Segmentation: Describe each stage with precise start/end timestamps (HH:MM:SS) using the phrases above.\n2. Outlier Audit: Identify only significant outliers that deviate sharply from the local trend. Ignore minor background noise.\n(Output format: structured natural language report.)", "answer": "1. Trend Segmentation: from 2022-06-01 00:00:00 to 2022-06-17 08:45:00, the trend showed a rapid fall; from 2022-06-17 08:45:00 to 2022-06-30 23:45:00, the trend showed a gradual rise.\n2. Outlier Audit: A significant spike was detected at 2022-06-27 22:15:00 (value: 54.47).", "ground_truth": { "trend_segments": [ "from 2022-06-01 00:00:00 to 2022-06-17 08:45:00, the trend showed a rapid fall", "from 2022-06-17 08:45:00 to 2022-06-30 23:45:00, the trend showed a gradual rise" ], "segments_meta": [ { "adj": "rapid", "kind": "fall", "start_idx": 0, "end_idx": 1571 }, { "adj": "gradual", "kind": "rise", "start_idx": 1571, "end_idx": 2880 } ], "significant_anomaly": { "timestamp": "2022-06-27 22:15:00", "kind": "significant spike", "value": 54.46762219132994 } }, "eval_metric": "report", "channel": "894", "ts_data_path": "ts_data/L4_T1_Insight_Synthesis_00097.csv", "meta": { "target_month": "2022-06", "target_year": 2022, "source": "causal_rivers" } }, { "id": "L4_T1_Insight_Synthesis_00098", "level": 4, "level_name": "Insight Synthesis", "category": "Insight Synthesis", "subtask": "Insight Synthesis", "question": "Analyze the behavior of channel 895 for the period 2022-05.\nPlease use ONLY the following phrases for trend description: rapid rise, gradual rise, rapid fall, gradual fall, steady stable, fluctuating stable.\nProvide a structured report covering:\n1. Trend Segmentation: Describe each stage with precise start/end timestamps (HH:MM:SS) using the phrases above.\n2. Outlier Audit: Identify only significant outliers that deviate sharply from the local trend. Ignore minor background noise.\n(Output format: structured natural language report.)", "answer": "1. Trend Segmentation: from 2022-05-01 00:00:00 to 2022-05-12 01:45:00, the trend showed a rapid fall; from 2022-05-12 01:45:00 to 2022-05-18 17:15:00, the trend showed a fluctuating stable; from 2022-05-18 17:15:00 to 2022-05-27 13:45:00, the trend showed a gradual fall; from 2022-05-27 13:45:00 to 2022-05-31 23:45:00, the trend showed a rapid fall.\n2. Outlier Audit: A significant spike was detected at 2022-05-18 16:00:00 (value: 20.44).", "ground_truth": { "trend_segments": [ "from 2022-05-01 00:00:00 to 2022-05-12 01:45:00, the trend showed a rapid fall", "from 2022-05-12 01:45:00 to 2022-05-18 17:15:00, the trend showed a fluctuating stable", "from 2022-05-18 17:15:00 to 2022-05-27 13:45:00, the trend showed a gradual fall", "from 2022-05-27 13:45:00 to 2022-05-31 23:45:00, the trend showed a rapid fall" ], "segments_meta": [ { "adj": "rapid", "kind": "fall", "start_idx": 0, "end_idx": 1063 }, { "adj": "fluctuating", "kind": "stable", "start_idx": 1063, "end_idx": 1701 }, { "adj": "gradual", "kind": "fall", "start_idx": 1701, "end_idx": 2551 }, { "adj": "rapid", "kind": "fall", "start_idx": 2551, "end_idx": 2976 } ], "significant_anomaly": { "timestamp": "2022-05-18 16:00:00", "kind": "significant spike", "value": 20.444538650068047 } }, "eval_metric": "report", "channel": "895", "ts_data_path": "ts_data/L4_T1_Insight_Synthesis_00098.csv", "meta": { "target_month": "2022-05", "target_year": 2022, "source": "causal_rivers" } }, { "id": "L4_T1_Insight_Synthesis_00099", "level": 4, "level_name": "Insight Synthesis", "category": "Insight Synthesis", "subtask": "Insight Synthesis", "question": "Analyze the behavior of channel 897 for the period 2023-03.\nPlease use ONLY the following phrases for trend description: rapid rise, gradual rise, rapid fall, gradual fall, steady stable, fluctuating stable.\nProvide a structured report covering:\n1. Trend Segmentation: Describe each stage with precise start/end timestamps (HH:MM:SS) using the phrases above.\n2. Outlier Audit: Identify only significant outliers that deviate sharply from the local trend. Ignore minor background noise.\n(Output format: structured natural language report.)", "answer": "1. Trend Segmentation: from 2023-03-01 00:00:00 to 2023-03-15 07:30:00, the trend showed a gradual rise; from 2023-03-15 07:30:00 to 2023-03-20 02:00:00, the trend showed a gradual fall; from 2023-03-20 02:00:00 to 2023-03-31 23:45:00, the trend showed a steady stable.\n2. Outlier Audit: A significant drop was detected at 2023-03-23 22:45:00 (value: -1.35).", "ground_truth": { "trend_segments": [ "from 2023-03-01 00:00:00 to 2023-03-15 07:30:00, the trend showed a gradual rise", "from 2023-03-15 07:30:00 to 2023-03-20 02:00:00, the trend showed a gradual fall", "from 2023-03-20 02:00:00 to 2023-03-31 23:45:00, the trend showed a steady stable" ], "segments_meta": [ { "adj": "gradual", "kind": "rise", "start_idx": 0, "end_idx": 1374 }, { "adj": "gradual", "kind": "fall", "start_idx": 1374, "end_idx": 1832 }, { "adj": "steady", "kind": "stable", "start_idx": 1832, "end_idx": 2976 } ], "significant_anomaly": { "timestamp": "2023-03-23 22:45:00", "kind": "significant drop", "value": -1.354395432271354 } }, "eval_metric": "report", "channel": "897", "ts_data_path": "ts_data/L4_T1_Insight_Synthesis_00099.csv", "meta": { "target_month": "2023-03", "target_year": 2023, "source": "causal_rivers" } }, { "id": "L4_T1_Insight_Synthesis_00102", "level": 4, "level_name": "Insight Synthesis", "category": "Insight Synthesis", "subtask": "Insight Synthesis", "question": "Analyze the behavior of channel MUFL for the period 2016-09.\nPlease use ONLY the following phrases for trend description: rapid rise, gradual rise, rapid fall, gradual fall, steady stable, fluctuating stable.\nProvide a structured report covering:\n1. Trend Segmentation: Describe each stage with precise start/end timestamps (HH:MM:SS) using the phrases above.\n2. Outlier Audit: Identify only significant outliers that deviate sharply from the local trend. Ignore minor background noise.\n(Output format: structured natural language report.)", "answer": "1. Trend Segmentation: from 2016-09-01 00:00:00 to 2016-09-08 12:00:00, the trend showed a fluctuating stable; from 2016-09-08 12:00:00 to 2016-09-30 23:45:00, the trend showed a steady stable.\n2. Outlier Audit: A significant spike was detected at 2016-09-15 05:45:00 (value: 58.57).", "ground_truth": { "trend_segments": [ "from 2016-09-01 00:00:00 to 2016-09-08 12:00:00, the trend showed a fluctuating stable", "from 2016-09-08 12:00:00 to 2016-09-30 23:45:00, the trend showed a steady stable" ], "segments_meta": [ { "adj": "fluctuating", "kind": "stable", "start_idx": 0, "end_idx": 720 }, { "adj": "steady", "kind": "stable", "start_idx": 720, "end_idx": 2880 } ], "significant_anomaly": { "timestamp": "2016-09-15 05:45:00", "kind": "significant spike", "value": 58.56662297085945 } }, "eval_metric": "report", "channel": "MUFL", "ts_data_path": "ts_data/L4_T1_Insight_Synthesis_00102.csv", "meta": { "target_month": "2016-09", "target_year": 2016, "source": "ettm1" } }, { "id": "L4_T1_Insight_Synthesis_00104", "level": 4, "level_name": "Insight Synthesis", "category": "Insight Synthesis", "subtask": "Insight Synthesis", "question": "Analyze the behavior of channel LULL for the period 2016-07.\nPlease use ONLY the following phrases for trend description: rapid rise, gradual rise, rapid fall, gradual fall, steady stable, fluctuating stable.\nProvide a structured report covering:\n1. Trend Segmentation: Describe each stage with precise start/end timestamps (HH:MM:SS) using the phrases above.\n2. Outlier Audit: Identify only significant outliers that deviate sharply from the local trend. Ignore minor background noise.\n(Output format: structured natural language report.)", "answer": "1. Trend Segmentation: from 2016-07-01 00:00:00 to 2016-07-17 21:45:00, the trend showed a gradual rise; from 2016-07-17 21:45:00 to 2016-07-31 23:45:00, the trend showed a rapid rise.\n2. Outlier Audit: A significant spike was detected at 2016-07-06 06:15:00 (value: 8.86).", "ground_truth": { "trend_segments": [ "from 2016-07-01 00:00:00 to 2016-07-17 21:45:00, the trend showed a gradual rise", "from 2016-07-17 21:45:00 to 2016-07-31 23:45:00, the trend showed a rapid rise" ], "segments_meta": [ { "adj": "gradual", "kind": "rise", "start_idx": 0, "end_idx": 1623 }, { "adj": "rapid", "kind": "rise", "start_idx": 1623, "end_idx": 2976 } ], "significant_anomaly": { "timestamp": "2016-07-06 06:15:00", "kind": "significant spike", "value": 8.864051476348676 } }, "eval_metric": "report", "channel": "LULL", "ts_data_path": "ts_data/L4_T1_Insight_Synthesis_00104.csv", "meta": { "target_month": "2016-07", "target_year": 2016, "source": "ettm1" } }, { "id": "L4_T1_Insight_Synthesis_00105", "level": 4, "level_name": "Insight Synthesis", "category": "Insight Synthesis", "subtask": "Insight Synthesis", "question": "Analyze the behavior of channel OT for the period 2017-09.\nPlease use ONLY the following phrases for trend description: rapid rise, gradual rise, rapid fall, gradual fall, steady stable, fluctuating stable.\nProvide a structured report covering:\n1. Trend Segmentation: Describe each stage with precise start/end timestamps (HH:MM:SS) using the phrases above.\n2. Outlier Audit: Identify only significant outliers that deviate sharply from the local trend. Ignore minor background noise.\n(Output format: structured natural language report.)", "answer": "1. Trend Segmentation: from 2017-09-01 00:00:00 to 2017-09-11 14:00:00, the trend showed a gradual fall; from 2017-09-11 14:00:00 to 2017-09-20 09:45:00, the trend showed a gradual rise; from 2017-09-20 09:45:00 to 2017-09-30 23:45:00, the trend showed a gradual fall.\n2. Outlier Audit: A significant spike was detected at 2017-09-17 20:45:00 (value: 144.96).", "ground_truth": { "trend_segments": [ "from 2017-09-01 00:00:00 to 2017-09-11 14:00:00, the trend showed a gradual fall", "from 2017-09-11 14:00:00 to 2017-09-20 09:45:00, the trend showed a gradual rise", "from 2017-09-20 09:45:00 to 2017-09-30 23:45:00, the trend showed a gradual fall" ], "segments_meta": [ { "adj": "gradual", "kind": "fall", "start_idx": 0, "end_idx": 1016 }, { "adj": "gradual", "kind": "rise", "start_idx": 1016, "end_idx": 1863 }, { "adj": "gradual", "kind": "fall", "start_idx": 1863, "end_idx": 2880 } ], "significant_anomaly": { "timestamp": "2017-09-17 20:45:00", "kind": "significant spike", "value": 144.95646472665828 } }, "eval_metric": "report", "channel": "OT", "ts_data_path": "ts_data/L4_T1_Insight_Synthesis_00105.csv", "meta": { "target_month": "2017-09", "target_year": 2017, "source": "ettm1" } }, { "id": "L4_T1_Insight_Synthesis_00107", "level": 4, "level_name": "Insight Synthesis", "category": "Insight Synthesis", "subtask": "Insight Synthesis", "question": "Analyze the behavior of channel 71 for the period 2023-09.\nPlease use ONLY the following phrases for trend description: rapid rise, gradual rise, rapid fall, gradual fall, steady stable, fluctuating stable.\nProvide a structured report covering:\n1. Trend Segmentation: Describe each stage with precise start/end timestamps (HH:MM:SS) using the phrases above.\n2. Outlier Audit: Identify only significant outliers that deviate sharply from the local trend. Ignore minor background noise.\n(Output format: structured natural language report.)", "answer": "1. Trend Segmentation: from 2023-09-01 00:00:00 to 2023-09-11 00:00:00, the trend showed a rapid fall; from 2023-09-11 00:00:00 to 2023-09-23 00:00:00, the trend showed a fluctuating stable; from 2023-09-23 00:00:00 to 2023-09-30 23:45:00, the trend showed a rapid rise.\n2. Outlier Audit: A significant drop was detected at 2023-09-12 01:30:00 (value: -18.60).", "ground_truth": { "trend_segments": [ "from 2023-09-01 00:00:00 to 2023-09-11 00:00:00, the trend showed a rapid fall", "from 2023-09-11 00:00:00 to 2023-09-23 00:00:00, the trend showed a fluctuating stable", "from 2023-09-23 00:00:00 to 2023-09-30 23:45:00, the trend showed a rapid rise" ], "segments_meta": [ { "adj": "rapid", "kind": "fall", "start_idx": 0, "end_idx": 960 }, { "adj": "fluctuating", "kind": "stable", "start_idx": 960, "end_idx": 2112 }, { "adj": "rapid", "kind": "rise", "start_idx": 2112, "end_idx": 2880 } ], "significant_anomaly": { "timestamp": "2023-09-12 01:30:00", "kind": "significant drop", "value": -18.598229775816957 } }, "eval_metric": "report", "channel": "71", "ts_data_path": "ts_data/L4_T1_Insight_Synthesis_00107.csv", "meta": { "target_month": "2023-09", "target_year": 2023, "source": "causal_rivers" } }, { "id": "L4_T1_Insight_Synthesis_00108", "level": 4, "level_name": "Insight Synthesis", "category": "Insight Synthesis", "subtask": "Insight Synthesis", "question": "Analyze the behavior of channel 99 for the period 2019-05.\nPlease use ONLY the following phrases for trend description: rapid rise, gradual rise, rapid fall, gradual fall, steady stable, fluctuating stable.\nProvide a structured report covering:\n1. Trend Segmentation: Describe each stage with precise start/end timestamps (HH:MM:SS) using the phrases above.\n2. Outlier Audit: Identify only significant outliers that deviate sharply from the local trend. Ignore minor background noise.\n(Output format: structured natural language report.)", "answer": "1. Trend Segmentation: from 2019-05-01 00:00:00 to 2019-05-14 18:45:00, the trend showed a rapid fall; from 2019-05-14 18:45:00 to 2019-05-21 16:00:00, the trend showed a rapid rise; from 2019-05-21 16:00:00 to 2019-05-31 23:45:00, the trend showed a rapid fall.\n2. Outlier Audit: A significant drop was detected at 2019-05-05 02:30:00 (value: -0.50).", "ground_truth": { "trend_segments": [ "from 2019-05-01 00:00:00 to 2019-05-14 18:45:00, the trend showed a rapid fall", "from 2019-05-14 18:45:00 to 2019-05-21 16:00:00, the trend showed a rapid rise", "from 2019-05-21 16:00:00 to 2019-05-31 23:45:00, the trend showed a rapid fall" ], "segments_meta": [ { "adj": "rapid", "kind": "fall", "start_idx": 0, "end_idx": 1323 }, { "adj": "rapid", "kind": "rise", "start_idx": 1323, "end_idx": 1984 }, { "adj": "rapid", "kind": "fall", "start_idx": 1984, "end_idx": 2976 } ], "significant_anomaly": { "timestamp": "2019-05-05 02:30:00", "kind": "significant drop", "value": -0.49812506891266217 } }, "eval_metric": "report", "channel": "99", "ts_data_path": "ts_data/L4_T1_Insight_Synthesis_00108.csv", "meta": { "target_month": "2019-05", "target_year": 2019, "source": "causal_rivers" } }, { "id": "L4_T1_Insight_Synthesis_00110", "level": 4, "level_name": "Insight Synthesis", "category": "Insight Synthesis", "subtask": "Insight Synthesis", "question": "Analyze the behavior of channel 124 for the period 2023-01.\nPlease use ONLY the following phrases for trend description: rapid rise, gradual rise, rapid fall, gradual fall, steady stable, fluctuating stable.\nProvide a structured report covering:\n1. Trend Segmentation: Describe each stage with precise start/end timestamps (HH:MM:SS) using the phrases above.\n2. Outlier Audit: Identify only significant outliers that deviate sharply from the local trend. Ignore minor background noise.\n(Output format: structured natural language report.)", "answer": "1. Trend Segmentation: from 2023-01-01 00:00:00 to 2023-01-09 11:00:00, the trend showed a gradual fall; from 2023-01-09 11:00:00 to 2023-01-15 02:15:00, the trend showed a steady stable; from 2023-01-15 02:15:00 to 2023-01-26 08:45:00, the trend showed a rapid rise; from 2023-01-26 08:45:00 to 2023-01-31 23:45:00, the trend showed a fluctuating stable.\n2. Outlier Audit: A significant drop was detected at 2023-01-12 22:15:00 (value: -4.36).", "ground_truth": { "trend_segments": [ "from 2023-01-01 00:00:00 to 2023-01-09 11:00:00, the trend showed a gradual fall", "from 2023-01-09 11:00:00 to 2023-01-15 02:15:00, the trend showed a steady stable", "from 2023-01-15 02:15:00 to 2023-01-26 08:45:00, the trend showed a rapid rise", "from 2023-01-26 08:45:00 to 2023-01-31 23:45:00, the trend showed a fluctuating stable" ], "segments_meta": [ { "adj": "gradual", "kind": "fall", "start_idx": 0, "end_idx": 812 }, { "adj": "steady", "kind": "stable", "start_idx": 812, "end_idx": 1353 }, { "adj": "rapid", "kind": "rise", "start_idx": 1353, "end_idx": 2435 }, { "adj": "fluctuating", "kind": "stable", "start_idx": 2435, "end_idx": 2976 } ], "significant_anomaly": { "timestamp": "2023-01-12 22:15:00", "kind": "significant drop", "value": -4.363617089660094 } }, "eval_metric": "report", "channel": "124", "ts_data_path": "ts_data/L4_T1_Insight_Synthesis_00110.csv", "meta": { "target_month": "2023-01", "target_year": 2023, "source": "causal_rivers" } }, { "id": "L4_T1_Insight_Synthesis_00112", "level": 4, "level_name": "Insight Synthesis", "category": "Insight Synthesis", "subtask": "Insight Synthesis", "question": "Analyze the behavior of channel 147 for the period 2023-10.\nPlease use ONLY the following phrases for trend description: rapid rise, gradual rise, rapid fall, gradual fall, steady stable, fluctuating stable.\nProvide a structured report covering:\n1. Trend Segmentation: Describe each stage with precise start/end timestamps (HH:MM:SS) using the phrases above.\n2. Outlier Audit: Identify only significant outliers that deviate sharply from the local trend. Ignore minor background noise.\n(Output format: structured natural language report.)", "answer": "1. Trend Segmentation: from 2023-10-01 00:00:00 to 2023-10-04 06:15:00, the trend showed a rapid fall; from 2023-10-04 06:15:00 to 2023-10-14 01:15:00, the trend showed a gradual rise; from 2023-10-14 01:15:00 to 2023-10-22 05:00:00, the trend showed a fluctuating stable; from 2023-10-22 05:00:00 to 2023-10-31 23:45:00, the trend showed a rapid fall.\n2. Outlier Audit: A significant spike was detected at 2023-10-22 00:30:00 (value: 1.66).", "ground_truth": { "trend_segments": [ "from 2023-10-01 00:00:00 to 2023-10-04 06:15:00, the trend showed a rapid fall", "from 2023-10-04 06:15:00 to 2023-10-14 01:15:00, the trend showed a gradual rise", "from 2023-10-14 01:15:00 to 2023-10-22 05:00:00, the trend showed a fluctuating stable", "from 2023-10-22 05:00:00 to 2023-10-31 23:45:00, the trend showed a rapid fall" ], "segments_meta": [ { "adj": "rapid", "kind": "fall", "start_idx": 0, "end_idx": 313 }, { "adj": "gradual", "kind": "rise", "start_idx": 313, "end_idx": 1253 }, { "adj": "fluctuating", "kind": "stable", "start_idx": 1253, "end_idx": 2036 }, { "adj": "rapid", "kind": "fall", "start_idx": 2036, "end_idx": 2976 } ], "significant_anomaly": { "timestamp": "2023-10-22 00:30:00", "kind": "significant spike", "value": 1.6612344750985584 } }, "eval_metric": "report", "channel": "147", "ts_data_path": "ts_data/L4_T1_Insight_Synthesis_00112.csv", "meta": { "target_month": "2023-10", "target_year": 2023, "source": "causal_rivers" } }, { "id": "L4_T1_Insight_Synthesis_00113", "level": 4, "level_name": "Insight Synthesis", "category": "Insight Synthesis", "subtask": "Insight Synthesis", "question": "Analyze the behavior of channel 151 for the period 2021-09.\nPlease use ONLY the following phrases for trend description: rapid rise, gradual rise, rapid fall, gradual fall, steady stable, fluctuating stable.\nProvide a structured report covering:\n1. Trend Segmentation: Describe each stage with precise start/end timestamps (HH:MM:SS) using the phrases above.\n2. Outlier Audit: Identify only significant outliers that deviate sharply from the local trend. Ignore minor background noise.\n(Output format: structured natural language report.)", "answer": "1. Trend Segmentation: from 2021-09-01 00:00:00 to 2021-09-06 00:00:00, the trend showed a rapid rise; from 2021-09-06 00:00:00 to 2021-09-16 00:00:00, the trend showed a rapid fall; from 2021-09-16 00:00:00 to 2021-09-30 23:45:00, the trend showed a rapid rise.\n2. Outlier Audit: A significant drop was detected at 2021-09-21 07:15:00 (value: -4.30).", "ground_truth": { "trend_segments": [ "from 2021-09-01 00:00:00 to 2021-09-06 00:00:00, the trend showed a rapid rise", "from 2021-09-06 00:00:00 to 2021-09-16 00:00:00, the trend showed a rapid fall", "from 2021-09-16 00:00:00 to 2021-09-30 23:45:00, the trend showed a rapid rise" ], "segments_meta": [ { "adj": "rapid", "kind": "rise", "start_idx": 0, "end_idx": 480 }, { "adj": "rapid", "kind": "fall", "start_idx": 480, "end_idx": 1440 }, { "adj": "rapid", "kind": "rise", "start_idx": 1440, "end_idx": 2880 } ], "significant_anomaly": { "timestamp": "2021-09-21 07:15:00", "kind": "significant drop", "value": -4.303656102463912 } }, "eval_metric": "report", "channel": "151", "ts_data_path": "ts_data/L4_T1_Insight_Synthesis_00113.csv", "meta": { "target_month": "2021-09", "target_year": 2021, "source": "causal_rivers" } }, { "id": "L4_T1_Insight_Synthesis_00114", "level": 4, "level_name": "Insight Synthesis", "category": "Insight Synthesis", "subtask": "Insight Synthesis", "question": "Analyze the behavior of channel 154 for the period 2022-12.\nPlease use ONLY the following phrases for trend description: rapid rise, gradual rise, rapid fall, gradual fall, steady stable, fluctuating stable.\nProvide a structured report covering:\n1. Trend Segmentation: Describe each stage with precise start/end timestamps (HH:MM:SS) using the phrases above.\n2. Outlier Audit: Identify only significant outliers that deviate sharply from the local trend. Ignore minor background noise.\n(Output format: structured natural language report.)", "answer": "1. Trend Segmentation: from 2022-12-01 00:00:00 to 2022-12-07 15:30:00, the trend showed a steady stable; from 2022-12-07 15:30:00 to 2022-12-20 22:15:00, the trend showed a gradual fall; from 2022-12-20 22:15:00 to 2022-12-31 23:45:00, the trend showed a fluctuating stable.\n2. Outlier Audit: A significant drop was detected at 2022-12-15 23:15:00 (value: -4.15).", "ground_truth": { "trend_segments": [ "from 2022-12-01 00:00:00 to 2022-12-07 15:30:00, the trend showed a steady stable", "from 2022-12-07 15:30:00 to 2022-12-20 22:15:00, the trend showed a gradual fall", "from 2022-12-20 22:15:00 to 2022-12-31 23:45:00, the trend showed a fluctuating stable" ], "segments_meta": [ { "adj": "steady", "kind": "stable", "start_idx": 0, "end_idx": 638 }, { "adj": "gradual", "kind": "fall", "start_idx": 638, "end_idx": 1913 }, { "adj": "fluctuating", "kind": "stable", "start_idx": 1913, "end_idx": 2976 } ], "significant_anomaly": { "timestamp": "2022-12-15 23:15:00", "kind": "significant drop", "value": -4.151774433000523 } }, "eval_metric": "report", "channel": "154", "ts_data_path": "ts_data/L4_T1_Insight_Synthesis_00114.csv", "meta": { "target_month": "2022-12", "target_year": 2022, "source": "causal_rivers" } }, { "id": "L4_T1_Insight_Synthesis_00115", "level": 4, "level_name": "Insight Synthesis", "category": "Insight Synthesis", "subtask": "Insight Synthesis", "question": "Analyze the behavior of channel 155 for the period 2021-02.\nPlease use ONLY the following phrases for trend description: rapid rise, gradual rise, rapid fall, gradual fall, steady stable, fluctuating stable.\nProvide a structured report covering:\n1. Trend Segmentation: Describe each stage with precise start/end timestamps (HH:MM:SS) using the phrases above.\n2. Outlier Audit: Identify only significant outliers that deviate sharply from the local trend. Ignore minor background noise.\n(Output format: structured natural language report.)", "answer": "1. Trend Segmentation: from 2021-02-01 00:00:00 to 2021-02-12 04:45:00, the trend showed a fluctuating stable; from 2021-02-12 04:45:00 to 2021-02-28 23:45:00, the trend showed a gradual rise.\n2. Outlier Audit: A significant spike was detected at 2021-02-17 01:00:00 (value: 2.12).", "ground_truth": { "trend_segments": [ "from 2021-02-01 00:00:00 to 2021-02-12 04:45:00, the trend showed a fluctuating stable", "from 2021-02-12 04:45:00 to 2021-02-28 23:45:00, the trend showed a gradual rise" ], "segments_meta": [ { "adj": "fluctuating", "kind": "stable", "start_idx": 0, "end_idx": 1075 }, { "adj": "gradual", "kind": "rise", "start_idx": 1075, "end_idx": 2688 } ], "significant_anomaly": { "timestamp": "2021-02-17 01:00:00", "kind": "significant spike", "value": 2.121315365527478 } }, "eval_metric": "report", "channel": "155", "ts_data_path": "ts_data/L4_T1_Insight_Synthesis_00115.csv", "meta": { "target_month": "2021-02", "target_year": 2021, "source": "causal_rivers" } }, { "id": "L4_T1_Insight_Synthesis_00116", "level": 4, "level_name": "Insight Synthesis", "category": "Insight Synthesis", "subtask": "Insight Synthesis", "question": "Analyze the behavior of channel 166 for the period 2020-03.\nPlease use ONLY the following phrases for trend description: rapid rise, gradual rise, rapid fall, gradual fall, steady stable, fluctuating stable.\nProvide a structured report covering:\n1. Trend Segmentation: Describe each stage with precise start/end timestamps (HH:MM:SS) using the phrases above.\n2. Outlier Audit: Identify only significant outliers that deviate sharply from the local trend. Ignore minor background noise.\n(Output format: structured natural language report.)", "answer": "1. Trend Segmentation: from 2020-03-01 00:00:00 to 2020-03-16 12:00:00, the trend showed a rapid rise; from 2020-03-16 12:00:00 to 2020-03-31 23:45:00, the trend showed a gradual fall.\n2. Outlier Audit: A significant drop was detected at 2020-03-06 15:15:00 (value: -1856.47).", "ground_truth": { "trend_segments": [ "from 2020-03-01 00:00:00 to 2020-03-16 12:00:00, the trend showed a rapid rise", "from 2020-03-16 12:00:00 to 2020-03-31 23:45:00, the trend showed a gradual fall" ], "segments_meta": [ { "adj": "rapid", "kind": "rise", "start_idx": 0, "end_idx": 1488 }, { "adj": "gradual", "kind": "fall", "start_idx": 1488, "end_idx": 2976 } ], "significant_anomaly": { "timestamp": "2020-03-06 15:15:00", "kind": "significant drop", "value": -1856.4742935884747 } }, "eval_metric": "report", "channel": "166", "ts_data_path": "ts_data/L4_T1_Insight_Synthesis_00116.csv", "meta": { "target_month": "2020-03", "target_year": 2020, "source": "causal_rivers" } }, { "id": "L4_T1_Insight_Synthesis_00117", "level": 4, "level_name": "Insight Synthesis", "category": "Insight Synthesis", "subtask": "Insight Synthesis", "question": "Analyze the behavior of channel 169 for the period 2023-03.\nPlease use ONLY the following phrases for trend description: rapid rise, gradual rise, rapid fall, gradual fall, steady stable, fluctuating stable.\nProvide a structured report covering:\n1. Trend Segmentation: Describe each stage with precise start/end timestamps (HH:MM:SS) using the phrases above.\n2. Outlier Audit: Identify only significant outliers that deviate sharply from the local trend. Ignore minor background noise.\n(Output format: structured natural language report.)", "answer": "1. Trend Segmentation: from 2023-03-01 00:00:00 to 2023-03-10 16:30:00, the trend showed a rapid fall; from 2023-03-10 16:30:00 to 2023-03-20 09:00:00, the trend showed a steady stable; from 2023-03-20 09:00:00 to 2023-03-24 06:00:00, the trend showed a rapid rise; from 2023-03-24 06:00:00 to 2023-03-31 23:45:00, the trend showed a rapid fall.\n2. Outlier Audit: A significant drop was detected at 2023-03-26 01:30:00 (value: -4092.37).", "ground_truth": { "trend_segments": [ "from 2023-03-01 00:00:00 to 2023-03-10 16:30:00, the trend showed a rapid fall", "from 2023-03-10 16:30:00 to 2023-03-20 09:00:00, the trend showed a steady stable", "from 2023-03-20 09:00:00 to 2023-03-24 06:00:00, the trend showed a rapid rise", "from 2023-03-24 06:00:00 to 2023-03-31 23:45:00, the trend showed a rapid fall" ], "segments_meta": [ { "adj": "rapid", "kind": "fall", "start_idx": 0, "end_idx": 930 }, { "adj": "steady", "kind": "stable", "start_idx": 930, "end_idx": 1860 }, { "adj": "rapid", "kind": "rise", "start_idx": 1860, "end_idx": 2232 }, { "adj": "rapid", "kind": "fall", "start_idx": 2232, "end_idx": 2976 } ], "significant_anomaly": { "timestamp": "2023-03-26 01:30:00", "kind": "significant drop", "value": -4092.3657753269663 } }, "eval_metric": "report", "channel": "169", "ts_data_path": "ts_data/L4_T1_Insight_Synthesis_00117.csv", "meta": { "target_month": "2023-03", "target_year": 2023, "source": "causal_rivers" } }, { "id": "L4_T1_Insight_Synthesis_00118", "level": 4, "level_name": "Insight Synthesis", "category": "Insight Synthesis", "subtask": "Insight Synthesis", "question": "Analyze the behavior of channel 170 for the period 2020-06.\nPlease use ONLY the following phrases for trend description: rapid rise, gradual rise, rapid fall, gradual fall, steady stable, fluctuating stable.\nProvide a structured report covering:\n1. Trend Segmentation: Describe each stage with precise start/end timestamps (HH:MM:SS) using the phrases above.\n2. Outlier Audit: Identify only significant outliers that deviate sharply from the local trend. Ignore minor background noise.\n(Output format: structured natural language report.)", "answer": "1. Trend Segmentation: from 2020-06-01 00:00:00 to 2020-06-14 20:15:00, the trend showed a gradual rise; from 2020-06-14 20:15:00 to 2020-06-24 01:45:00, the trend showed a rapid fall; from 2020-06-24 01:45:00 to 2020-06-30 23:45:00, the trend showed a fluctuating stable.\n2. Outlier Audit: A significant spike was detected at 2020-06-18 21:00:00 (value: 2617.99).", "ground_truth": { "trend_segments": [ "from 2020-06-01 00:00:00 to 2020-06-14 20:15:00, the trend showed a gradual rise", "from 2020-06-14 20:15:00 to 2020-06-24 01:45:00, the trend showed a rapid fall", "from 2020-06-24 01:45:00 to 2020-06-30 23:45:00, the trend showed a fluctuating stable" ], "segments_meta": [ { "adj": "gradual", "kind": "rise", "start_idx": 0, "end_idx": 1329 }, { "adj": "rapid", "kind": "fall", "start_idx": 1329, "end_idx": 2215 }, { "adj": "fluctuating", "kind": "stable", "start_idx": 2215, "end_idx": 2880 } ], "significant_anomaly": { "timestamp": "2020-06-18 21:00:00", "kind": "significant spike", "value": 2617.991430120615 } }, "eval_metric": "report", "channel": "170", "ts_data_path": "ts_data/L4_T1_Insight_Synthesis_00118.csv", "meta": { "target_month": "2020-06", "target_year": 2020, "source": "causal_rivers" } }, { "id": "L4_T1_Insight_Synthesis_00119", "level": 4, "level_name": "Insight Synthesis", "category": "Insight Synthesis", "subtask": "Insight Synthesis", "question": "Analyze the behavior of channel 172 for the period 2022-01.\nPlease use ONLY the following phrases for trend description: rapid rise, gradual rise, rapid fall, gradual fall, steady stable, fluctuating stable.\nProvide a structured report covering:\n1. Trend Segmentation: Describe each stage with precise start/end timestamps (HH:MM:SS) using the phrases above.\n2. Outlier Audit: Identify only significant outliers that deviate sharply from the local trend. Ignore minor background noise.\n(Output format: structured natural language report.)", "answer": "1. Trend Segmentation: from 2022-01-01 00:00:00 to 2022-01-10 19:00:00, the trend showed a fluctuating stable; from 2022-01-10 19:00:00 to 2022-01-18 22:45:00, the trend showed a gradual fall; from 2022-01-18 22:45:00 to 2022-01-27 02:30:00, the trend showed a rapid rise; from 2022-01-27 02:30:00 to 2022-01-31 23:45:00, the trend showed a rapid fall.\n2. Outlier Audit: A significant spike was detected at 2022-01-10 10:15:00 (value: 2205.95).", "ground_truth": { "trend_segments": [ "from 2022-01-01 00:00:00 to 2022-01-10 19:00:00, the trend showed a fluctuating stable", "from 2022-01-10 19:00:00 to 2022-01-18 22:45:00, the trend showed a gradual fall", "from 2022-01-18 22:45:00 to 2022-01-27 02:30:00, the trend showed a rapid rise", "from 2022-01-27 02:30:00 to 2022-01-31 23:45:00, the trend showed a rapid fall" ], "segments_meta": [ { "adj": "fluctuating", "kind": "stable", "start_idx": 0, "end_idx": 940 }, { "adj": "gradual", "kind": "fall", "start_idx": 940, "end_idx": 1723 }, { "adj": "rapid", "kind": "rise", "start_idx": 1723, "end_idx": 2506 }, { "adj": "rapid", "kind": "fall", "start_idx": 2506, "end_idx": 2976 } ], "significant_anomaly": { "timestamp": "2022-01-10 10:15:00", "kind": "significant spike", "value": 2205.9513064919934 } }, "eval_metric": "report", "channel": "172", "ts_data_path": "ts_data/L4_T1_Insight_Synthesis_00119.csv", "meta": { "target_month": "2022-01", "target_year": 2022, "source": "causal_rivers" } }, { "id": "L4_T1_Insight_Synthesis_00120", "level": 4, "level_name": "Insight Synthesis", "category": "Insight Synthesis", "subtask": "Insight Synthesis", "question": "Analyze the behavior of channel 173 for the period 2020-11.\nPlease use ONLY the following phrases for trend description: rapid rise, gradual rise, rapid fall, gradual fall, steady stable, fluctuating stable.\nProvide a structured report covering:\n1. Trend Segmentation: Describe each stage with precise start/end timestamps (HH:MM:SS) using the phrases above.\n2. Outlier Audit: Identify only significant outliers that deviate sharply from the local trend. Ignore minor background noise.\n(Output format: structured natural language report.)", "answer": "1. Trend Segmentation: from 2020-11-01 00:00:00 to 2020-11-06 15:00:00, the trend showed a rapid rise; from 2020-11-06 15:00:00 to 2020-11-16 00:00:00, the trend showed a rapid fall; from 2020-11-16 00:00:00 to 2020-11-23 12:00:00, the trend showed a fluctuating stable; from 2020-11-23 12:00:00 to 2020-11-30 23:45:00, the trend showed a steady stable.\n2. Outlier Audit: A significant spike was detected at 2020-11-09 07:15:00 (value: 2434.47).", "ground_truth": { "trend_segments": [ "from 2020-11-01 00:00:00 to 2020-11-06 15:00:00, the trend showed a rapid rise", "from 2020-11-06 15:00:00 to 2020-11-16 00:00:00, the trend showed a rapid fall", "from 2020-11-16 00:00:00 to 2020-11-23 12:00:00, the trend showed a fluctuating stable", "from 2020-11-23 12:00:00 to 2020-11-30 23:45:00, the trend showed a steady stable" ], "segments_meta": [ { "adj": "rapid", "kind": "rise", "start_idx": 0, "end_idx": 540 }, { "adj": "rapid", "kind": "fall", "start_idx": 540, "end_idx": 1440 }, { "adj": "fluctuating", "kind": "stable", "start_idx": 1440, "end_idx": 2160 }, { "adj": "steady", "kind": "stable", "start_idx": 2160, "end_idx": 2880 } ], "significant_anomaly": { "timestamp": "2020-11-09 07:15:00", "kind": "significant spike", "value": 2434.467017453142 } }, "eval_metric": "report", "channel": "173", "ts_data_path": "ts_data/L4_T1_Insight_Synthesis_00120.csv", "meta": { "target_month": "2020-11", "target_year": 2020, "source": "causal_rivers" } }, { "id": "L4_T1_Insight_Synthesis_00122", "level": 4, "level_name": "Insight Synthesis", "category": "Insight Synthesis", "subtask": "Insight Synthesis", "question": "Analyze the behavior of channel 237 for the period 2019-04.\nPlease use ONLY the following phrases for trend description: rapid rise, gradual rise, rapid fall, gradual fall, steady stable, fluctuating stable.\nProvide a structured report covering:\n1. Trend Segmentation: Describe each stage with precise start/end timestamps (HH:MM:SS) using the phrases above.\n2. Outlier Audit: Identify only significant outliers that deviate sharply from the local trend. Ignore minor background noise.\n(Output format: structured natural language report.)", "answer": "1. Trend Segmentation: from 2019-04-01 00:00:00 to 2019-04-11 14:00:00, the trend showed a gradual fall; from 2019-04-11 14:00:00 to 2019-04-20 09:45:00, the trend showed a rapid rise; from 2019-04-20 09:45:00 to 2019-04-30 23:45:00, the trend showed a gradual rise.\n2. Outlier Audit: A significant spike was detected at 2019-04-12 05:00:00 (value: 21.97).", "ground_truth": { "trend_segments": [ "from 2019-04-01 00:00:00 to 2019-04-11 14:00:00, the trend showed a gradual fall", "from 2019-04-11 14:00:00 to 2019-04-20 09:45:00, the trend showed a rapid rise", "from 2019-04-20 09:45:00 to 2019-04-30 23:45:00, the trend showed a gradual rise" ], "segments_meta": [ { "adj": "gradual", "kind": "fall", "start_idx": 0, "end_idx": 1016 }, { "adj": "rapid", "kind": "rise", "start_idx": 1016, "end_idx": 1863 }, { "adj": "gradual", "kind": "rise", "start_idx": 1863, "end_idx": 2880 } ], "significant_anomaly": { "timestamp": "2019-04-12 05:00:00", "kind": "significant spike", "value": 21.970065539413298 } }, "eval_metric": "report", "channel": "237", "ts_data_path": "ts_data/L4_T1_Insight_Synthesis_00122.csv", "meta": { "target_month": "2019-04", "target_year": 2019, "source": "causal_rivers" } }, { "id": "L4_T1_Insight_Synthesis_00123", "level": 4, "level_name": "Insight Synthesis", "category": "Insight Synthesis", "subtask": "Insight Synthesis", "question": "Analyze the behavior of channel 245 for the period 2020-05.\nPlease use ONLY the following phrases for trend description: rapid rise, gradual rise, rapid fall, gradual fall, steady stable, fluctuating stable.\nProvide a structured report covering:\n1. Trend Segmentation: Describe each stage with precise start/end timestamps (HH:MM:SS) using the phrases above.\n2. Outlier Audit: Identify only significant outliers that deviate sharply from the local trend. Ignore minor background noise.\n(Output format: structured natural language report.)", "answer": "1. Trend Segmentation: from 2020-05-01 00:00:00 to 2020-05-08 03:45:00, the trend showed a gradual fall; from 2020-05-08 03:45:00 to 2020-05-22 11:15:00, the trend showed a rapid fall; from 2020-05-22 11:15:00 to 2020-05-31 23:45:00, the trend showed a gradual fall.\n2. Outlier Audit: A significant drop was detected at 2020-05-16 15:30:00 (value: -3.19).", "ground_truth": { "trend_segments": [ "from 2020-05-01 00:00:00 to 2020-05-08 03:45:00, the trend showed a gradual fall", "from 2020-05-08 03:45:00 to 2020-05-22 11:15:00, the trend showed a rapid fall", "from 2020-05-22 11:15:00 to 2020-05-31 23:45:00, the trend showed a gradual fall" ], "segments_meta": [ { "adj": "gradual", "kind": "fall", "start_idx": 0, "end_idx": 687 }, { "adj": "rapid", "kind": "fall", "start_idx": 687, "end_idx": 2061 }, { "adj": "gradual", "kind": "fall", "start_idx": 2061, "end_idx": 2976 } ], "significant_anomaly": { "timestamp": "2020-05-16 15:30:00", "kind": "significant drop", "value": -3.1939873573788957 } }, "eval_metric": "report", "channel": "245", "ts_data_path": "ts_data/L4_T1_Insight_Synthesis_00123.csv", "meta": { "target_month": "2020-05", "target_year": 2020, "source": "causal_rivers" } }, { "id": "L4_T1_Insight_Synthesis_00124", "level": 4, "level_name": "Insight Synthesis", "category": "Insight Synthesis", "subtask": "Insight Synthesis", "question": "Analyze the behavior of channel 312 for the period 2022-05.\nPlease use ONLY the following phrases for trend description: rapid rise, gradual rise, rapid fall, gradual fall, steady stable, fluctuating stable.\nProvide a structured report covering:\n1. Trend Segmentation: Describe each stage with precise start/end timestamps (HH:MM:SS) using the phrases above.\n2. Outlier Audit: Identify only significant outliers that deviate sharply from the local trend. Ignore minor background noise.\n(Output format: structured natural language report.)", "answer": "1. Trend Segmentation: from 2022-05-01 00:00:00 to 2022-05-15 07:30:00, the trend showed a rapid fall; from 2022-05-15 07:30:00 to 2022-05-27 05:45:00, the trend showed a gradual rise; from 2022-05-27 05:45:00 to 2022-05-31 23:45:00, the trend showed a rapid rise.\n2. Outlier Audit: A significant drop was detected at 2022-05-20 00:30:00 (value: -5.25).", "ground_truth": { "trend_segments": [ "from 2022-05-01 00:00:00 to 2022-05-15 07:30:00, the trend showed a rapid fall", "from 2022-05-15 07:30:00 to 2022-05-27 05:45:00, the trend showed a gradual rise", "from 2022-05-27 05:45:00 to 2022-05-31 23:45:00, the trend showed a rapid rise" ], "segments_meta": [ { "adj": "rapid", "kind": "fall", "start_idx": 0, "end_idx": 1374 }, { "adj": "gradual", "kind": "rise", "start_idx": 1374, "end_idx": 2519 }, { "adj": "rapid", "kind": "rise", "start_idx": 2519, "end_idx": 2976 } ], "significant_anomaly": { "timestamp": "2022-05-20 00:30:00", "kind": "significant drop", "value": -5.248748304745268 } }, "eval_metric": "report", "channel": "312", "ts_data_path": "ts_data/L4_T1_Insight_Synthesis_00124.csv", "meta": { "target_month": "2022-05", "target_year": 2022, "source": "causal_rivers" } }, { "id": "L4_T1_Insight_Synthesis_00125", "level": 4, "level_name": "Insight Synthesis", "category": "Insight Synthesis", "subtask": "Insight Synthesis", "question": "Analyze the behavior of channel 430 for the period 2020-01.\nPlease use ONLY the following phrases for trend description: rapid rise, gradual rise, rapid fall, gradual fall, steady stable, fluctuating stable.\nProvide a structured report covering:\n1. Trend Segmentation: Describe each stage with precise start/end timestamps (HH:MM:SS) using the phrases above.\n2. Outlier Audit: Identify only significant outliers that deviate sharply from the local trend. Ignore minor background noise.\n(Output format: structured natural language report.)", "answer": "1. Trend Segmentation: from 2020-01-01 00:00:00 to 2020-01-16 12:00:00, the trend showed a rapid rise; from 2020-01-16 12:00:00 to 2020-01-31 23:45:00, the trend showed a rapid fall.\n2. Outlier Audit: A significant spike was detected at 2020-01-18 11:00:00 (value: 2.55).", "ground_truth": { "trend_segments": [ "from 2020-01-01 00:00:00 to 2020-01-16 12:00:00, the trend showed a rapid rise", "from 2020-01-16 12:00:00 to 2020-01-31 23:45:00, the trend showed a rapid fall" ], "segments_meta": [ { "adj": "rapid", "kind": "rise", "start_idx": 0, "end_idx": 1488 }, { "adj": "rapid", "kind": "fall", "start_idx": 1488, "end_idx": 2976 } ], "significant_anomaly": { "timestamp": "2020-01-18 11:00:00", "kind": "significant spike", "value": 2.5541248974474096 } }, "eval_metric": "report", "channel": "430", "ts_data_path": "ts_data/L4_T1_Insight_Synthesis_00125.csv", "meta": { "target_month": "2020-01", "target_year": 2020, "source": "causal_rivers" } }, { "id": "L4_T1_Insight_Synthesis_00127", "level": 4, "level_name": "Insight Synthesis", "category": "Insight Synthesis", "subtask": "Insight Synthesis", "question": "Analyze the behavior of channel 495 for the period 2022-09.\nPlease use ONLY the following phrases for trend description: rapid rise, gradual rise, rapid fall, gradual fall, steady stable, fluctuating stable.\nProvide a structured report covering:\n1. Trend Segmentation: Describe each stage with precise start/end timestamps (HH:MM:SS) using the phrases above.\n2. Outlier Audit: Identify only significant outliers that deviate sharply from the local trend. Ignore minor background noise.\n(Output format: structured natural language report.)", "answer": "1. Trend Segmentation: from 2022-09-01 00:00:00 to 2022-09-13 00:00:00, the trend showed a fluctuating stable; from 2022-09-13 00:00:00 to 2022-09-30 23:45:00, the trend showed a rapid fall.\n2. Outlier Audit: A significant drop was detected at 2022-09-22 19:45:00 (value: -12.42).", "ground_truth": { "trend_segments": [ "from 2022-09-01 00:00:00 to 2022-09-13 00:00:00, the trend showed a fluctuating stable", "from 2022-09-13 00:00:00 to 2022-09-30 23:45:00, the trend showed a rapid fall" ], "segments_meta": [ { "adj": "fluctuating", "kind": "stable", "start_idx": 0, "end_idx": 1152 }, { "adj": "rapid", "kind": "fall", "start_idx": 1152, "end_idx": 2880 } ], "significant_anomaly": { "timestamp": "2022-09-22 19:45:00", "kind": "significant drop", "value": -12.424707735987997 } }, "eval_metric": "report", "channel": "495", "ts_data_path": "ts_data/L4_T1_Insight_Synthesis_00127.csv", "meta": { "target_month": "2022-09", "target_year": 2022, "source": "causal_rivers" } }, { "id": "L4_T1_Insight_Synthesis_00130", "level": 4, "level_name": "Insight Synthesis", "category": "Insight Synthesis", "subtask": "Insight Synthesis", "question": "Analyze the behavior of channel 578 for the period 2023-01.\nPlease use ONLY the following phrases for trend description: rapid rise, gradual rise, rapid fall, gradual fall, steady stable, fluctuating stable.\nProvide a structured report covering:\n1. Trend Segmentation: Describe each stage with precise start/end timestamps (HH:MM:SS) using the phrases above.\n2. Outlier Audit: Identify only significant outliers that deviate sharply from the local trend. Ignore minor background noise.\n(Output format: structured natural language report.)", "answer": "1. Trend Segmentation: from 2023-01-01 00:00:00 to 2023-01-08 07:00:00, the trend showed a rapid fall; from 2023-01-08 07:00:00 to 2023-01-17 09:45:00, the trend showed a gradual rise; from 2023-01-17 09:45:00 to 2023-01-24 16:45:00, the trend showed a rapid rise; from 2023-01-24 16:45:00 to 2023-01-31 23:45:00, the trend showed a fluctuating stable.\n2. Outlier Audit: A significant drop was detected at 2023-01-05 08:15:00 (value: -9.82).", "ground_truth": { "trend_segments": [ "from 2023-01-01 00:00:00 to 2023-01-08 07:00:00, the trend showed a rapid fall", "from 2023-01-08 07:00:00 to 2023-01-17 09:45:00, the trend showed a gradual rise", "from 2023-01-17 09:45:00 to 2023-01-24 16:45:00, the trend showed a rapid rise", "from 2023-01-24 16:45:00 to 2023-01-31 23:45:00, the trend showed a fluctuating stable" ], "segments_meta": [ { "adj": "rapid", "kind": "fall", "start_idx": 0, "end_idx": 700 }, { "adj": "gradual", "kind": "rise", "start_idx": 700, "end_idx": 1575 }, { "adj": "rapid", "kind": "rise", "start_idx": 1575, "end_idx": 2275 }, { "adj": "fluctuating", "kind": "stable", "start_idx": 2275, "end_idx": 2976 } ], "significant_anomaly": { "timestamp": "2023-01-05 08:15:00", "kind": "significant drop", "value": -9.82036740852871 } }, "eval_metric": "report", "channel": "578", "ts_data_path": "ts_data/L4_T1_Insight_Synthesis_00130.csv", "meta": { "target_month": "2023-01", "target_year": 2023, "source": "causal_rivers" } }, { "id": "L4_T1_Insight_Synthesis_00132", "level": 4, "level_name": "Insight Synthesis", "category": "Insight Synthesis", "subtask": "Insight Synthesis", "question": "Analyze the behavior of channel 589 for the period 2019-06.\nPlease use ONLY the following phrases for trend description: rapid rise, gradual rise, rapid fall, gradual fall, steady stable, fluctuating stable.\nProvide a structured report covering:\n1. Trend Segmentation: Describe each stage with precise start/end timestamps (HH:MM:SS) using the phrases above.\n2. Outlier Audit: Identify only significant outliers that deviate sharply from the local trend. Ignore minor background noise.\n(Output format: structured natural language report.)", "answer": "1. Trend Segmentation: from 2019-06-01 00:00:00 to 2019-06-16 00:00:00, the trend showed a fluctuating stable; from 2019-06-16 00:00:00 to 2019-06-30 23:45:00, the trend showed a rapid rise.\n2. Outlier Audit: A significant drop was detected at 2019-06-10 05:00:00 (value: -2403.76).", "ground_truth": { "trend_segments": [ "from 2019-06-01 00:00:00 to 2019-06-16 00:00:00, the trend showed a fluctuating stable", "from 2019-06-16 00:00:00 to 2019-06-30 23:45:00, the trend showed a rapid rise" ], "segments_meta": [ { "adj": "fluctuating", "kind": "stable", "start_idx": 0, "end_idx": 1440 }, { "adj": "rapid", "kind": "rise", "start_idx": 1440, "end_idx": 2880 } ], "significant_anomaly": { "timestamp": "2019-06-10 05:00:00", "kind": "significant drop", "value": -2403.7602080082656 } }, "eval_metric": "report", "channel": "589", "ts_data_path": "ts_data/L4_T1_Insight_Synthesis_00132.csv", "meta": { "target_month": "2019-06", "target_year": 2019, "source": "causal_rivers" } }, { "id": "L4_T1_Insight_Synthesis_00133", "level": 4, "level_name": "Insight Synthesis", "category": "Insight Synthesis", "subtask": "Insight Synthesis", "question": "Analyze the behavior of channel 591 for the period 2020-11.\nPlease use ONLY the following phrases for trend description: rapid rise, gradual rise, rapid fall, gradual fall, steady stable, fluctuating stable.\nProvide a structured report covering:\n1. Trend Segmentation: Describe each stage with precise start/end timestamps (HH:MM:SS) using the phrases above.\n2. Outlier Audit: Identify only significant outliers that deviate sharply from the local trend. Ignore minor background noise.\n(Output format: structured natural language report.)", "answer": "1. Trend Segmentation: from 2020-11-01 00:00:00 to 2020-11-10 00:00:00, the trend showed a rapid rise; from 2020-11-10 00:00:00 to 2020-11-19 00:00:00, the trend showed a gradual rise; from 2020-11-19 00:00:00 to 2020-11-25 00:00:00, the trend showed a fluctuating stable; from 2020-11-25 00:00:00 to 2020-11-30 23:45:00, the trend showed a rapid rise.\n2. Outlier Audit: A significant drop was detected at 2020-11-22 18:30:00 (value: -973.92).", "ground_truth": { "trend_segments": [ "from 2020-11-01 00:00:00 to 2020-11-10 00:00:00, the trend showed a rapid rise", "from 2020-11-10 00:00:00 to 2020-11-19 00:00:00, the trend showed a gradual rise", "from 2020-11-19 00:00:00 to 2020-11-25 00:00:00, the trend showed a fluctuating stable", "from 2020-11-25 00:00:00 to 2020-11-30 23:45:00, the trend showed a rapid rise" ], "segments_meta": [ { "adj": "rapid", "kind": "rise", "start_idx": 0, "end_idx": 864 }, { "adj": "gradual", "kind": "rise", "start_idx": 864, "end_idx": 1728 }, { "adj": "fluctuating", "kind": "stable", "start_idx": 1728, "end_idx": 2304 }, { "adj": "rapid", "kind": "rise", "start_idx": 2304, "end_idx": 2880 } ], "significant_anomaly": { "timestamp": "2020-11-22 18:30:00", "kind": "significant drop", "value": -973.9224992299503 } }, "eval_metric": "report", "channel": "591", "ts_data_path": "ts_data/L4_T1_Insight_Synthesis_00133.csv", "meta": { "target_month": "2020-11", "target_year": 2020, "source": "causal_rivers" } }, { "id": "L4_T1_Insight_Synthesis_00134", "level": 4, "level_name": "Insight Synthesis", "category": "Insight Synthesis", "subtask": "Insight Synthesis", "question": "Analyze the behavior of channel 595 for the period 2022-04.\nPlease use ONLY the following phrases for trend description: rapid rise, gradual rise, rapid fall, gradual fall, steady stable, fluctuating stable.\nProvide a structured report covering:\n1. Trend Segmentation: Describe each stage with precise start/end timestamps (HH:MM:SS) using the phrases above.\n2. Outlier Audit: Identify only significant outliers that deviate sharply from the local trend. Ignore minor background noise.\n(Output format: structured natural language report.)", "answer": "1. Trend Segmentation: from 2022-04-01 00:00:00 to 2022-04-19 00:00:00, the trend showed a fluctuating stable; from 2022-04-19 00:00:00 to 2022-04-30 23:45:00, the trend showed a rapid rise.\n2. Outlier Audit: A significant spike was detected at 2022-04-13 15:00:00 (value: 19.85).", "ground_truth": { "trend_segments": [ "from 2022-04-01 00:00:00 to 2022-04-19 00:00:00, the trend showed a fluctuating stable", "from 2022-04-19 00:00:00 to 2022-04-30 23:45:00, the trend showed a rapid rise" ], "segments_meta": [ { "adj": "fluctuating", "kind": "stable", "start_idx": 0, "end_idx": 1728 }, { "adj": "rapid", "kind": "rise", "start_idx": 1728, "end_idx": 2880 } ], "significant_anomaly": { "timestamp": "2022-04-13 15:00:00", "kind": "significant spike", "value": 19.849975894832784 } }, "eval_metric": "report", "channel": "595", "ts_data_path": "ts_data/L4_T1_Insight_Synthesis_00134.csv", "meta": { "target_month": "2022-04", "target_year": 2022, "source": "causal_rivers" } }, { "id": "L4_T1_Insight_Synthesis_00135", "level": 4, "level_name": "Insight Synthesis", "category": "Insight Synthesis", "subtask": "Insight Synthesis", "question": "Analyze the behavior of channel 625 for the period 2021-10.\nPlease use ONLY the following phrases for trend description: rapid rise, gradual rise, rapid fall, gradual fall, steady stable, fluctuating stable.\nProvide a structured report covering:\n1. Trend Segmentation: Describe each stage with precise start/end timestamps (HH:MM:SS) using the phrases above.\n2. Outlier Audit: Identify only significant outliers that deviate sharply from the local trend. Ignore minor background noise.\n(Output format: structured natural language report.)", "answer": "1. Trend Segmentation: from 2021-10-01 00:00:00 to 2021-10-20 09:00:00, the trend showed a rapid fall; from 2021-10-20 09:00:00 to 2021-10-31 23:45:00, the trend showed a gradual rise.\n2. Outlier Audit: A significant drop was detected at 2021-10-09 22:00:00 (value: -8.07).", "ground_truth": { "trend_segments": [ "from 2021-10-01 00:00:00 to 2021-10-20 09:00:00, the trend showed a rapid fall", "from 2021-10-20 09:00:00 to 2021-10-31 23:45:00, the trend showed a gradual rise" ], "segments_meta": [ { "adj": "rapid", "kind": "fall", "start_idx": 0, "end_idx": 1860 }, { "adj": "gradual", "kind": "rise", "start_idx": 1860, "end_idx": 2976 } ], "significant_anomaly": { "timestamp": "2021-10-09 22:00:00", "kind": "significant drop", "value": -8.07481294171743 } }, "eval_metric": "report", "channel": "625", "ts_data_path": "ts_data/L4_T1_Insight_Synthesis_00135.csv", "meta": { "target_month": "2021-10", "target_year": 2021, "source": "causal_rivers" } }, { "id": "L4_T1_Insight_Synthesis_00137", "level": 4, "level_name": "Insight Synthesis", "category": "Insight Synthesis", "subtask": "Insight Synthesis", "question": "Analyze the behavior of channel 627 for the period 2021-08.\nPlease use ONLY the following phrases for trend description: rapid rise, gradual rise, rapid fall, gradual fall, steady stable, fluctuating stable.\nProvide a structured report covering:\n1. Trend Segmentation: Describe each stage with precise start/end timestamps (HH:MM:SS) using the phrases above.\n2. Outlier Audit: Identify only significant outliers that deviate sharply from the local trend. Ignore minor background noise.\n(Output format: structured natural language report.)", "answer": "1. Trend Segmentation: from 2021-08-01 00:00:00 to 2021-08-06 04:00:00, the trend showed a rapid rise; from 2021-08-06 04:00:00 to 2021-08-16 12:00:00, the trend showed a gradual fall; from 2021-08-16 12:00:00 to 2021-08-31 23:45:00, the trend showed a rapid fall.\n2. Outlier Audit: A significant drop was detected at 2021-08-15 10:15:00 (value: -2.03).", "ground_truth": { "trend_segments": [ "from 2021-08-01 00:00:00 to 2021-08-06 04:00:00, the trend showed a rapid rise", "from 2021-08-06 04:00:00 to 2021-08-16 12:00:00, the trend showed a gradual fall", "from 2021-08-16 12:00:00 to 2021-08-31 23:45:00, the trend showed a rapid fall" ], "segments_meta": [ { "adj": "rapid", "kind": "rise", "start_idx": 0, "end_idx": 496 }, { "adj": "gradual", "kind": "fall", "start_idx": 496, "end_idx": 1488 }, { "adj": "rapid", "kind": "fall", "start_idx": 1488, "end_idx": 2976 } ], "significant_anomaly": { "timestamp": "2021-08-15 10:15:00", "kind": "significant drop", "value": -2.0335238178812567 } }, "eval_metric": "report", "channel": "627", "ts_data_path": "ts_data/L4_T1_Insight_Synthesis_00137.csv", "meta": { "target_month": "2021-08", "target_year": 2021, "source": "causal_rivers" } }, { "id": "L4_T1_Insight_Synthesis_00140", "level": 4, "level_name": "Insight Synthesis", "category": "Insight Synthesis", "subtask": "Insight Synthesis", "question": "Analyze the behavior of channel 683 for the period 2020-04.\nPlease use ONLY the following phrases for trend description: rapid rise, gradual rise, rapid fall, gradual fall, steady stable, fluctuating stable.\nProvide a structured report covering:\n1. Trend Segmentation: Describe each stage with precise start/end timestamps (HH:MM:SS) using the phrases above.\n2. Outlier Audit: Identify only significant outliers that deviate sharply from the local trend. Ignore minor background noise.\n(Output format: structured natural language report.)", "answer": "1. Trend Segmentation: from 2020-04-01 00:00:00 to 2020-04-19 00:00:00, the trend showed a rapid fall; from 2020-04-19 00:00:00 to 2020-04-30 23:45:00, the trend showed a rapid rise.\n2. Outlier Audit: A significant spike was detected at 2020-04-19 02:00:00 (value: 5.24).", "ground_truth": { "trend_segments": [ "from 2020-04-01 00:00:00 to 2020-04-19 00:00:00, the trend showed a rapid fall", "from 2020-04-19 00:00:00 to 2020-04-30 23:45:00, the trend showed a rapid rise" ], "segments_meta": [ { "adj": "rapid", "kind": "fall", "start_idx": 0, "end_idx": 1728 }, { "adj": "rapid", "kind": "rise", "start_idx": 1728, "end_idx": 2880 } ], "significant_anomaly": { "timestamp": "2020-04-19 02:00:00", "kind": "significant spike", "value": 5.242123819276829 } }, "eval_metric": "report", "channel": "683", "ts_data_path": "ts_data/L4_T1_Insight_Synthesis_00140.csv", "meta": { "target_month": "2020-04", "target_year": 2020, "source": "causal_rivers" } }, { "id": "L4_T1_Insight_Synthesis_00141", "level": 4, "level_name": "Insight Synthesis", "category": "Insight Synthesis", "subtask": "Insight Synthesis", "question": "Analyze the behavior of channel 727 for the period 2021-10.\nPlease use ONLY the following phrases for trend description: rapid rise, gradual rise, rapid fall, gradual fall, steady stable, fluctuating stable.\nProvide a structured report covering:\n1. Trend Segmentation: Describe each stage with precise start/end timestamps (HH:MM:SS) using the phrases above.\n2. Outlier Audit: Identify only significant outliers that deviate sharply from the local trend. Ignore minor background noise.\n(Output format: structured natural language report.)", "answer": "1. Trend Segmentation: from 2021-10-01 00:00:00 to 2021-10-14 18:45:00, the trend showed a fluctuating stable; from 2021-10-14 18:45:00 to 2021-10-25 02:45:00, the trend showed a gradual fall; from 2021-10-25 02:45:00 to 2021-10-31 23:45:00, the trend showed a fluctuating stable.\n2. Outlier Audit: A significant spike was detected at 2021-10-18 07:30:00 (value: 671.46).", "ground_truth": { "trend_segments": [ "from 2021-10-01 00:00:00 to 2021-10-14 18:45:00, the trend showed a fluctuating stable", "from 2021-10-14 18:45:00 to 2021-10-25 02:45:00, the trend showed a gradual fall", "from 2021-10-25 02:45:00 to 2021-10-31 23:45:00, the trend showed a fluctuating stable" ], "segments_meta": [ { "adj": "fluctuating", "kind": "stable", "start_idx": 0, "end_idx": 1323 }, { "adj": "gradual", "kind": "fall", "start_idx": 1323, "end_idx": 2315 }, { "adj": "fluctuating", "kind": "stable", "start_idx": 2315, "end_idx": 2976 } ], "significant_anomaly": { "timestamp": "2021-10-18 07:30:00", "kind": "significant spike", "value": 671.4634261588876 } }, "eval_metric": "report", "channel": "727", "ts_data_path": "ts_data/L4_T1_Insight_Synthesis_00141.csv", "meta": { "target_month": "2021-10", "target_year": 2021, "source": "causal_rivers" } }, { "id": "L4_T1_Insight_Synthesis_00142", "level": 4, "level_name": "Insight Synthesis", "category": "Insight Synthesis", "subtask": "Insight Synthesis", "question": "Analyze the behavior of channel 728 for the period 2019-08.\nPlease use ONLY the following phrases for trend description: rapid rise, gradual rise, rapid fall, gradual fall, steady stable, fluctuating stable.\nProvide a structured report covering:\n1. Trend Segmentation: Describe each stage with precise start/end timestamps (HH:MM:SS) using the phrases above.\n2. Outlier Audit: Identify only significant outliers that deviate sharply from the local trend. Ignore minor background noise.\n(Output format: structured natural language report.)", "answer": "1. Trend Segmentation: from 2019-08-01 00:00:00 to 2019-08-11 08:00:00, the trend showed a steady stable; from 2019-08-11 08:00:00 to 2019-08-16 12:00:00, the trend showed a gradual fall; from 2019-08-16 12:00:00 to 2019-08-21 16:00:00, the trend showed a rapid fall; from 2019-08-21 16:00:00 to 2019-08-31 23:45:00, the trend showed a rapid rise.\n2. Outlier Audit: A significant drop was detected at 2019-08-20 03:30:00 (value: -541.19).", "ground_truth": { "trend_segments": [ "from 2019-08-01 00:00:00 to 2019-08-11 08:00:00, the trend showed a steady stable", "from 2019-08-11 08:00:00 to 2019-08-16 12:00:00, the trend showed a gradual fall", "from 2019-08-16 12:00:00 to 2019-08-21 16:00:00, the trend showed a rapid fall", "from 2019-08-21 16:00:00 to 2019-08-31 23:45:00, the trend showed a rapid rise" ], "segments_meta": [ { "adj": "steady", "kind": "stable", "start_idx": 0, "end_idx": 992 }, { "adj": "gradual", "kind": "fall", "start_idx": 992, "end_idx": 1488 }, { "adj": "rapid", "kind": "fall", "start_idx": 1488, "end_idx": 1984 }, { "adj": "rapid", "kind": "rise", "start_idx": 1984, "end_idx": 2976 } ], "significant_anomaly": { "timestamp": "2019-08-20 03:30:00", "kind": "significant drop", "value": -541.1868642819688 } }, "eval_metric": "report", "channel": "728", "ts_data_path": "ts_data/L4_T1_Insight_Synthesis_00142.csv", "meta": { "target_month": "2019-08", "target_year": 2019, "source": "causal_rivers" } }, { "id": "L4_T1_Insight_Synthesis_00144", "level": 4, "level_name": "Insight Synthesis", "category": "Insight Synthesis", "subtask": "Insight Synthesis", "question": "Analyze the behavior of channel 754 for the period 2023-11.\nPlease use ONLY the following phrases for trend description: rapid rise, gradual rise, rapid fall, gradual fall, steady stable, fluctuating stable.\nProvide a structured report covering:\n1. Trend Segmentation: Describe each stage with precise start/end timestamps (HH:MM:SS) using the phrases above.\n2. Outlier Audit: Identify only significant outliers that deviate sharply from the local trend. Ignore minor background noise.\n(Output format: structured natural language report.)", "answer": "1. Trend Segmentation: from 2023-11-01 00:00:00 to 2023-11-08 12:00:00, the trend showed a steady stable; from 2023-11-08 12:00:00 to 2023-11-16 00:00:00, the trend showed a rapid rise; from 2023-11-16 00:00:00 to 2023-11-27 06:00:00, the trend showed a rapid fall; from 2023-11-27 06:00:00 to 2023-11-30 23:45:00, the trend showed a steady stable.\n2. Outlier Audit: A significant spike was detected at 2023-11-24 20:30:00 (value: 3.34).", "ground_truth": { "trend_segments": [ "from 2023-11-01 00:00:00 to 2023-11-08 12:00:00, the trend showed a steady stable", "from 2023-11-08 12:00:00 to 2023-11-16 00:00:00, the trend showed a rapid rise", "from 2023-11-16 00:00:00 to 2023-11-27 06:00:00, the trend showed a rapid fall", "from 2023-11-27 06:00:00 to 2023-11-30 23:45:00, the trend showed a steady stable" ], "segments_meta": [ { "adj": "steady", "kind": "stable", "start_idx": 0, "end_idx": 720 }, { "adj": "rapid", "kind": "rise", "start_idx": 720, "end_idx": 1440 }, { "adj": "rapid", "kind": "fall", "start_idx": 1440, "end_idx": 2520 }, { "adj": "steady", "kind": "stable", "start_idx": 2520, "end_idx": 2880 } ], "significant_anomaly": { "timestamp": "2023-11-24 20:30:00", "kind": "significant spike", "value": 3.344913171782098 } }, "eval_metric": "report", "channel": "754", "ts_data_path": "ts_data/L4_T1_Insight_Synthesis_00144.csv", "meta": { "target_month": "2023-11", "target_year": 2023, "source": "causal_rivers" } }, { "id": "L4_T1_Insight_Synthesis_00145", "level": 4, "level_name": "Insight Synthesis", "category": "Insight Synthesis", "subtask": "Insight Synthesis", "question": "Analyze the behavior of channel 762 for the period 2020-12.\nPlease use ONLY the following phrases for trend description: rapid rise, gradual rise, rapid fall, gradual fall, steady stable, fluctuating stable.\nProvide a structured report covering:\n1. Trend Segmentation: Describe each stage with precise start/end timestamps (HH:MM:SS) using the phrases above.\n2. Outlier Audit: Identify only significant outliers that deviate sharply from the local trend. Ignore minor background noise.\n(Output format: structured natural language report.)", "answer": "1. Trend Segmentation: from 2020-12-01 00:00:00 to 2020-12-18 17:15:00, the trend showed a gradual fall; from 2020-12-18 17:15:00 to 2020-12-31 23:45:00, the trend showed a fluctuating stable.\n2. Outlier Audit: A significant drop was detected at 2020-12-13 03:30:00 (value: -1.18).", "ground_truth": { "trend_segments": [ "from 2020-12-01 00:00:00 to 2020-12-18 17:15:00, the trend showed a gradual fall", "from 2020-12-18 17:15:00 to 2020-12-31 23:45:00, the trend showed a fluctuating stable" ], "segments_meta": [ { "adj": "gradual", "kind": "fall", "start_idx": 0, "end_idx": 1701 }, { "adj": "fluctuating", "kind": "stable", "start_idx": 1701, "end_idx": 2976 } ], "significant_anomaly": { "timestamp": "2020-12-13 03:30:00", "kind": "significant drop", "value": -1.1761030754712691 } }, "eval_metric": "report", "channel": "762", "ts_data_path": "ts_data/L4_T1_Insight_Synthesis_00145.csv", "meta": { "target_month": "2020-12", "target_year": 2020, "source": "causal_rivers" } }, { "id": "L4_T1_Insight_Synthesis_00146", "level": 4, "level_name": "Insight Synthesis", "category": "Insight Synthesis", "subtask": "Insight Synthesis", "question": "Analyze the behavior of channel 811 for the period 2019-11.\nPlease use ONLY the following phrases for trend description: rapid rise, gradual rise, rapid fall, gradual fall, steady stable, fluctuating stable.\nProvide a structured report covering:\n1. Trend Segmentation: Describe each stage with precise start/end timestamps (HH:MM:SS) using the phrases above.\n2. Outlier Audit: Identify only significant outliers that deviate sharply from the local trend. Ignore minor background noise.\n(Output format: structured natural language report.)", "answer": "1. Trend Segmentation: from 2019-11-01 00:00:00 to 2019-11-04 18:00:00, the trend showed a rapid fall; from 2019-11-04 18:00:00 to 2019-11-14 03:00:00, the trend showed a rapid rise; from 2019-11-14 03:00:00 to 2019-11-25 09:00:00, the trend showed a gradual fall; from 2019-11-25 09:00:00 to 2019-11-30 23:45:00, the trend showed a fluctuating stable.\n2. Outlier Audit: A significant spike was detected at 2019-11-25 05:45:00 (value: 76.16).", "ground_truth": { "trend_segments": [ "from 2019-11-01 00:00:00 to 2019-11-04 18:00:00, the trend showed a rapid fall", "from 2019-11-04 18:00:00 to 2019-11-14 03:00:00, the trend showed a rapid rise", "from 2019-11-14 03:00:00 to 2019-11-25 09:00:00, the trend showed a gradual fall", "from 2019-11-25 09:00:00 to 2019-11-30 23:45:00, the trend showed a fluctuating stable" ], "segments_meta": [ { "adj": "rapid", "kind": "fall", "start_idx": 0, "end_idx": 360 }, { "adj": "rapid", "kind": "rise", "start_idx": 360, "end_idx": 1260 }, { "adj": "gradual", "kind": "fall", "start_idx": 1260, "end_idx": 2340 }, { "adj": "fluctuating", "kind": "stable", "start_idx": 2340, "end_idx": 2880 } ], "significant_anomaly": { "timestamp": "2019-11-25 05:45:00", "kind": "significant spike", "value": 76.16370637569132 } }, "eval_metric": "report", "channel": "811", "ts_data_path": "ts_data/L4_T1_Insight_Synthesis_00146.csv", "meta": { "target_month": "2019-11", "target_year": 2019, "source": "causal_rivers" } }, { "id": "L4_T1_Insight_Synthesis_00147", "level": 4, "level_name": "Insight Synthesis", "category": "Insight Synthesis", "subtask": "Insight Synthesis", "question": "Analyze the behavior of channel 813 for the period 2021-05.\nPlease use ONLY the following phrases for trend description: rapid rise, gradual rise, rapid fall, gradual fall, steady stable, fluctuating stable.\nProvide a structured report covering:\n1. Trend Segmentation: Describe each stage with precise start/end timestamps (HH:MM:SS) using the phrases above.\n2. Outlier Audit: Identify only significant outliers that deviate sharply from the local trend. Ignore minor background noise.\n(Output format: structured natural language report.)", "answer": "1. Trend Segmentation: from 2021-05-01 00:00:00 to 2021-05-09 03:45:00, the trend showed a steady stable; from 2021-05-09 03:45:00 to 2021-05-18 22:45:00, the trend showed a rapid rise; from 2021-05-18 22:45:00 to 2021-05-28 17:45:00, the trend showed a gradual fall; from 2021-05-28 17:45:00 to 2021-05-31 23:45:00, the trend showed a fluctuating stable.\n2. Outlier Audit: A significant drop was detected at 2021-05-25 05:45:00 (value: -4.03).", "ground_truth": { "trend_segments": [ "from 2021-05-01 00:00:00 to 2021-05-09 03:45:00, the trend showed a steady stable", "from 2021-05-09 03:45:00 to 2021-05-18 22:45:00, the trend showed a rapid rise", "from 2021-05-18 22:45:00 to 2021-05-28 17:45:00, the trend showed a gradual fall", "from 2021-05-28 17:45:00 to 2021-05-31 23:45:00, the trend showed a fluctuating stable" ], "segments_meta": [ { "adj": "steady", "kind": "stable", "start_idx": 0, "end_idx": 783 }, { "adj": "rapid", "kind": "rise", "start_idx": 783, "end_idx": 1723 }, { "adj": "gradual", "kind": "fall", "start_idx": 1723, "end_idx": 2663 }, { "adj": "fluctuating", "kind": "stable", "start_idx": 2663, "end_idx": 2976 } ], "significant_anomaly": { "timestamp": "2021-05-25 05:45:00", "kind": "significant drop", "value": -4.03269602889429 } }, "eval_metric": "report", "channel": "813", "ts_data_path": "ts_data/L4_T1_Insight_Synthesis_00147.csv", "meta": { "target_month": "2021-05", "target_year": 2021, "source": "causal_rivers" } }, { "id": "L4_T1_Insight_Synthesis_00148", "level": 4, "level_name": "Insight Synthesis", "category": "Insight Synthesis", "subtask": "Insight Synthesis", "question": "Analyze the behavior of channel 865 for the period 2021-10.\nPlease use ONLY the following phrases for trend description: rapid rise, gradual rise, rapid fall, gradual fall, steady stable, fluctuating stable.\nProvide a structured report covering:\n1. Trend Segmentation: Describe each stage with precise start/end timestamps (HH:MM:SS) using the phrases above.\n2. Outlier Audit: Identify only significant outliers that deviate sharply from the local trend. Ignore minor background noise.\n(Output format: structured natural language report.)", "answer": "1. Trend Segmentation: from 2021-10-01 00:00:00 to 2021-10-17 21:45:00, the trend showed a rapid fall; from 2021-10-17 21:45:00 to 2021-10-31 23:45:00, the trend showed a fluctuating stable.\n2. Outlier Audit: A significant drop was detected at 2021-10-18 10:30:00 (value: -65.46).", "ground_truth": { "trend_segments": [ "from 2021-10-01 00:00:00 to 2021-10-17 21:45:00, the trend showed a rapid fall", "from 2021-10-17 21:45:00 to 2021-10-31 23:45:00, the trend showed a fluctuating stable" ], "segments_meta": [ { "adj": "rapid", "kind": "fall", "start_idx": 0, "end_idx": 1623 }, { "adj": "fluctuating", "kind": "stable", "start_idx": 1623, "end_idx": 2976 } ], "significant_anomaly": { "timestamp": "2021-10-18 10:30:00", "kind": "significant drop", "value": -65.46143723321703 } }, "eval_metric": "report", "channel": "865", "ts_data_path": "ts_data/L4_T1_Insight_Synthesis_00148.csv", "meta": { "target_month": "2021-10", "target_year": 2021, "source": "causal_rivers" } }, { "id": "L4_T1_Insight_Synthesis_00149", "level": 4, "level_name": "Insight Synthesis", "category": "Insight Synthesis", "subtask": "Insight Synthesis", "question": "Analyze the behavior of channel 891 for the period 2023-02.\nPlease use ONLY the following phrases for trend description: rapid rise, gradual rise, rapid fall, gradual fall, steady stable, fluctuating stable.\nProvide a structured report covering:\n1. Trend Segmentation: Describe each stage with precise start/end timestamps (HH:MM:SS) using the phrases above.\n2. Outlier Audit: Identify only significant outliers that deviate sharply from the local trend. Ignore minor background noise.\n(Output format: structured natural language report.)", "answer": "1. Trend Segmentation: from 2023-02-01 00:00:00 to 2023-02-16 06:30:00, the trend showed a fluctuating stable; from 2023-02-16 06:30:00 to 2023-02-28 23:45:00, the trend showed a gradual rise.\n2. Outlier Audit: A significant spike was detected at 2023-02-22 15:15:00 (value: 34.64).", "ground_truth": { "trend_segments": [ "from 2023-02-01 00:00:00 to 2023-02-16 06:30:00, the trend showed a fluctuating stable", "from 2023-02-16 06:30:00 to 2023-02-28 23:45:00, the trend showed a gradual rise" ], "segments_meta": [ { "adj": "fluctuating", "kind": "stable", "start_idx": 0, "end_idx": 1466 }, { "adj": "gradual", "kind": "rise", "start_idx": 1466, "end_idx": 2688 } ], "significant_anomaly": { "timestamp": "2023-02-22 15:15:00", "kind": "significant spike", "value": 34.637589089062764 } }, "eval_metric": "report", "channel": "891", "ts_data_path": "ts_data/L4_T1_Insight_Synthesis_00149.csv", "meta": { "target_month": "2023-02", "target_year": 2023, "source": "causal_rivers" } }, { "id": "L4_T1_Insight_Synthesis_00150", "level": 4, "level_name": "Insight Synthesis", "category": "Insight Synthesis", "subtask": "Insight Synthesis", "question": "Analyze the behavior of channel 894 for the period 2022-03.\nPlease use ONLY the following phrases for trend description: rapid rise, gradual rise, rapid fall, gradual fall, steady stable, fluctuating stable.\nProvide a structured report covering:\n1. Trend Segmentation: Describe each stage with precise start/end timestamps (HH:MM:SS) using the phrases above.\n2. Outlier Audit: Identify only significant outliers that deviate sharply from the local trend. Ignore minor background noise.\n(Output format: structured natural language report.)", "answer": "1. Trend Segmentation: from 2022-03-01 00:00:00 to 2022-03-13 09:30:00, the trend showed a gradual fall; from 2022-03-13 09:30:00 to 2022-03-31 23:45:00, the trend showed a rapid rise.\n2. Outlier Audit: A significant spike was detected at 2022-03-06 17:30:00 (value: 70.24).", "ground_truth": { "trend_segments": [ "from 2022-03-01 00:00:00 to 2022-03-13 09:30:00, the trend showed a gradual fall", "from 2022-03-13 09:30:00 to 2022-03-31 23:45:00, the trend showed a rapid rise" ], "segments_meta": [ { "adj": "gradual", "kind": "fall", "start_idx": 0, "end_idx": 1190 }, { "adj": "rapid", "kind": "rise", "start_idx": 1190, "end_idx": 2976 } ], "significant_anomaly": { "timestamp": "2022-03-06 17:30:00", "kind": "significant spike", "value": 70.24342661965348 } }, "eval_metric": "report", "channel": "894", "ts_data_path": "ts_data/L4_T1_Insight_Synthesis_00150.csv", "meta": { "target_month": "2022-03", "target_year": 2022, "source": "causal_rivers" } }, { "id": "L4_T1_Insight_Synthesis_00151", "level": 4, "level_name": "Insight Synthesis", "category": "Insight Synthesis", "subtask": "Insight Synthesis", "question": "Analyze the behavior of channel 895 for the period 2022-05.\nPlease use ONLY the following phrases for trend description: rapid rise, gradual rise, rapid fall, gradual fall, steady stable, fluctuating stable.\nProvide a structured report covering:\n1. Trend Segmentation: Describe each stage with precise start/end timestamps (HH:MM:SS) using the phrases above.\n2. Outlier Audit: Identify only significant outliers that deviate sharply from the local trend. Ignore minor background noise.\n(Output format: structured natural language report.)", "answer": "1. Trend Segmentation: from 2022-05-01 00:00:00 to 2022-05-06 04:00:00, the trend showed a gradual rise; from 2022-05-06 04:00:00 to 2022-05-16 12:00:00, the trend showed a steady stable; from 2022-05-16 12:00:00 to 2022-05-24 06:00:00, the trend showed a rapid fall; from 2022-05-24 06:00:00 to 2022-05-31 23:45:00, the trend showed a rapid rise.\n2. Outlier Audit: A significant drop was detected at 2022-05-19 18:00:00 (value: -19.27).", "ground_truth": { "trend_segments": [ "from 2022-05-01 00:00:00 to 2022-05-06 04:00:00, the trend showed a gradual rise", "from 2022-05-06 04:00:00 to 2022-05-16 12:00:00, the trend showed a steady stable", "from 2022-05-16 12:00:00 to 2022-05-24 06:00:00, the trend showed a rapid fall", "from 2022-05-24 06:00:00 to 2022-05-31 23:45:00, the trend showed a rapid rise" ], "segments_meta": [ { "adj": "gradual", "kind": "rise", "start_idx": 0, "end_idx": 496 }, { "adj": "steady", "kind": "stable", "start_idx": 496, "end_idx": 1488 }, { "adj": "rapid", "kind": "fall", "start_idx": 1488, "end_idx": 2232 }, { "adj": "rapid", "kind": "rise", "start_idx": 2232, "end_idx": 2976 } ], "significant_anomaly": { "timestamp": "2022-05-19 18:00:00", "kind": "significant drop", "value": -19.273676177095282 } }, "eval_metric": "report", "channel": "895", "ts_data_path": "ts_data/L4_T1_Insight_Synthesis_00151.csv", "meta": { "target_month": "2022-05", "target_year": 2022, "source": "causal_rivers" } }, { "id": "L4_T1_Insight_Synthesis_00152", "level": 4, "level_name": "Insight Synthesis", "category": "Insight Synthesis", "subtask": "Insight Synthesis", "question": "Analyze the behavior of channel 897 for the period 2023-09.\nPlease use ONLY the following phrases for trend description: rapid rise, gradual rise, rapid fall, gradual fall, steady stable, fluctuating stable.\nProvide a structured report covering:\n1. Trend Segmentation: Describe each stage with precise start/end timestamps (HH:MM:SS) using the phrases above.\n2. Outlier Audit: Identify only significant outliers that deviate sharply from the local trend. Ignore minor background noise.\n(Output format: structured natural language report.)", "answer": "1. Trend Segmentation: from 2023-09-01 00:00:00 to 2023-09-08 12:00:00, the trend showed a rapid fall; from 2023-09-08 12:00:00 to 2023-09-16 00:00:00, the trend showed a steady stable; from 2023-09-16 00:00:00 to 2023-09-30 23:45:00, the trend showed a rapid rise.\n2. Outlier Audit: A significant spike was detected at 2023-09-18 12:30:00 (value: 1.58).", "ground_truth": { "trend_segments": [ "from 2023-09-01 00:00:00 to 2023-09-08 12:00:00, the trend showed a rapid fall", "from 2023-09-08 12:00:00 to 2023-09-16 00:00:00, the trend showed a steady stable", "from 2023-09-16 00:00:00 to 2023-09-30 23:45:00, the trend showed a rapid rise" ], "segments_meta": [ { "adj": "rapid", "kind": "fall", "start_idx": 0, "end_idx": 720 }, { "adj": "steady", "kind": "stable", "start_idx": 720, "end_idx": 1440 }, { "adj": "rapid", "kind": "rise", "start_idx": 1440, "end_idx": 2880 } ], "significant_anomaly": { "timestamp": "2023-09-18 12:30:00", "kind": "significant spike", "value": 1.5805081154055862 } }, "eval_metric": "report", "channel": "897", "ts_data_path": "ts_data/L4_T1_Insight_Synthesis_00152.csv", "meta": { "target_month": "2023-09", "target_year": 2023, "source": "causal_rivers" } }, { "id": "L4_T1_Insight_Synthesis_00153", "level": 4, "level_name": "Insight Synthesis", "category": "Insight Synthesis", "subtask": "Insight Synthesis", "question": "Analyze the behavior of channel 933 for the period 2021-10.\nPlease use ONLY the following phrases for trend description: rapid rise, gradual rise, rapid fall, gradual fall, steady stable, fluctuating stable.\nProvide a structured report covering:\n1. Trend Segmentation: Describe each stage with precise start/end timestamps (HH:MM:SS) using the phrases above.\n2. Outlier Audit: Identify only significant outliers that deviate sharply from the local trend. Ignore minor background noise.\n(Output format: structured natural language report.)", "answer": "1. Trend Segmentation: from 2021-10-01 00:00:00 to 2021-10-13 22:00:00, the trend showed a gradual fall; from 2021-10-13 22:00:00 to 2021-10-21 16:00:00, the trend showed a fluctuating stable; from 2021-10-21 16:00:00 to 2021-10-31 23:45:00, the trend showed a gradual fall.\n2. Outlier Audit: A significant drop was detected at 2021-10-20 11:30:00 (value: -2.45).", "ground_truth": { "trend_segments": [ "from 2021-10-01 00:00:00 to 2021-10-13 22:00:00, the trend showed a gradual fall", "from 2021-10-13 22:00:00 to 2021-10-21 16:00:00, the trend showed a fluctuating stable", "from 2021-10-21 16:00:00 to 2021-10-31 23:45:00, the trend showed a gradual fall" ], "segments_meta": [ { "adj": "gradual", "kind": "fall", "start_idx": 0, "end_idx": 1240 }, { "adj": "fluctuating", "kind": "stable", "start_idx": 1240, "end_idx": 1984 }, { "adj": "gradual", "kind": "fall", "start_idx": 1984, "end_idx": 2976 } ], "significant_anomaly": { "timestamp": "2021-10-20 11:30:00", "kind": "significant drop", "value": -2.446343298886553 } }, "eval_metric": "report", "channel": "933", "ts_data_path": "ts_data/L4_T1_Insight_Synthesis_00153.csv", "meta": { "target_month": "2021-10", "target_year": 2021, "source": "causal_rivers" } }, { "id": "L4_T1_Insight_Synthesis_00154", "level": 4, "level_name": "Insight Synthesis", "category": "Insight Synthesis", "subtask": "Insight Synthesis", "question": "Analyze the behavior of channel HULL for the period 2016-08.\nPlease use ONLY the following phrases for trend description: rapid rise, gradual rise, rapid fall, gradual fall, steady stable, fluctuating stable.\nProvide a structured report covering:\n1. Trend Segmentation: Describe each stage with precise start/end timestamps (HH:MM:SS) using the phrases above.\n2. Outlier Audit: Identify only significant outliers that deviate sharply from the local trend. Ignore minor background noise.\n(Output format: structured natural language report.)", "answer": "1. Trend Segmentation: from 2016-08-01 00:00:00 to 2016-08-17 21:45:00, the trend showed a gradual rise; from 2016-08-17 21:45:00 to 2016-08-31 23:45:00, the trend showed a gradual fall.\n2. Outlier Audit: A significant drop was detected at 2016-08-20 15:00:00 (value: -30.52).", "ground_truth": { "trend_segments": [ "from 2016-08-01 00:00:00 to 2016-08-17 21:45:00, the trend showed a gradual rise", "from 2016-08-17 21:45:00 to 2016-08-31 23:45:00, the trend showed a gradual fall" ], "segments_meta": [ { "adj": "gradual", "kind": "rise", "start_idx": 0, "end_idx": 1623 }, { "adj": "gradual", "kind": "fall", "start_idx": 1623, "end_idx": 2976 } ], "significant_anomaly": { "timestamp": "2016-08-20 15:00:00", "kind": "significant drop", "value": -30.52436002025605 } }, "eval_metric": "report", "channel": "HULL", "ts_data_path": "ts_data/L4_T1_Insight_Synthesis_00154.csv", "meta": { "target_month": "2016-08", "target_year": 2016, "source": "ettm1" } }, { "id": "L4_T1_Insight_Synthesis_00155", "level": 4, "level_name": "Insight Synthesis", "category": "Insight Synthesis", "subtask": "Insight Synthesis", "question": "Analyze the behavior of channel MULL for the period 2018-06.\nPlease use ONLY the following phrases for trend description: rapid rise, gradual rise, rapid fall, gradual fall, steady stable, fluctuating stable.\nProvide a structured report covering:\n1. Trend Segmentation: Describe each stage with precise start/end timestamps (HH:MM:SS) using the phrases above.\n2. Outlier Audit: Identify only significant outliers that deviate sharply from the local trend. Ignore minor background noise.\n(Output format: structured natural language report.)", "answer": "1. Trend Segmentation: from 2018-06-01 00:00:00 to 2018-06-10 16:30:00, the trend showed a rapid fall; from 2018-06-10 16:30:00 to 2018-06-20 09:00:00, the trend showed a fluctuating stable; from 2018-06-20 09:00:00 to 2018-06-26 19:45:00, the trend showed a rapid rise.\n2. Outlier Audit: A significant drop was detected at 2018-06-04 02:30:00 (value: -24.52).", "ground_truth": { "trend_segments": [ "from 2018-06-01 00:00:00 to 2018-06-10 16:30:00, the trend showed a rapid fall", "from 2018-06-10 16:30:00 to 2018-06-20 09:00:00, the trend showed a fluctuating stable", "from 2018-06-20 09:00:00 to 2018-06-26 19:45:00, the trend showed a rapid rise" ], "segments_meta": [ { "adj": "rapid", "kind": "fall", "start_idx": 0, "end_idx": 930 }, { "adj": "fluctuating", "kind": "stable", "start_idx": 930, "end_idx": 1860 }, { "adj": "rapid", "kind": "rise", "start_idx": 1860, "end_idx": 2480 } ], "significant_anomaly": { "timestamp": "2018-06-04 02:30:00", "kind": "significant drop", "value": -24.520856743835477 } }, "eval_metric": "report", "channel": "MULL", "ts_data_path": "ts_data/L4_T1_Insight_Synthesis_00155.csv", "meta": { "target_month": "2018-06", "target_year": 2018, "source": "ettm1" } } ]