diff --git "a/tasks.json" "b/tasks.json" new file mode 100644--- /dev/null +++ "b/tasks.json" @@ -0,0 +1,5597 @@ +[ + { + "id": "L1_T1_Global_Aggregation_00014", + "level": 1, + "level_name": "Basic Operations", + "category": "Atomic Retrieval", + "subtask": "Global Aggregation", + "question": "What is the average value of channel 172 in 2019? (Output format: a single numeric value, rounded to 3 decimal places, e.g., x.xxx)", + "answer": "273.214", + "ground_truth": 273.214, + "eval_metric": "rel_acc", + "channel": "172", + "ts_data_path": "ts_data/L1_T1_Global_Aggregation_00014.csv", + "meta": { + "source": "causal_rivers", + "args": { + "channel": "172", + "time": "2019", + "agg": "average" + } + } + }, + { + "id": "L1_T1_Global_Aggregation_00019", + "level": 1, + "level_name": "Basic Operations", + "category": "Atomic Retrieval", + "subtask": "Global Aggregation", + "question": "What is the median value of channel 312 in 2019-07? (Output format: a single numeric value, rounded to 3 decimal places, e.g., x.xxx)", + "answer": "0.1", + "ground_truth": 0.1, + "eval_metric": "rel_acc", + "channel": "312", + "ts_data_path": "ts_data/L1_T1_Global_Aggregation_00019.csv", + "meta": { + "source": "causal_rivers", + "args": { + "channel": "312", + "time": "2019-07", + "agg": "median" + } + } + }, + { + "id": "L1_T1_Global_Aggregation_00020", + "level": 1, + "level_name": "Basic Operations", + "category": "Atomic Retrieval", + "subtask": "Global Aggregation", + "question": "What is the minimum value of channel 430 in 2021? (Output format: a single numeric value, rounded to 3 decimal places, e.g., x.xxx)", + "answer": "0.057", + "ground_truth": 0.057, + "eval_metric": "rel_acc", + "channel": "430", + "ts_data_path": "ts_data/L1_T1_Global_Aggregation_00020.csv", + "meta": { + "source": "causal_rivers", + "args": { + "channel": "430", + "time": "2021", + "agg": "minimum" + } + } + }, + { + "id": "L1_T1_Global_Aggregation_00026", + "level": 1, + "level_name": "Basic Operations", + "category": "Atomic Retrieval", + "subtask": "Global Aggregation", + "question": "What is the minimum value of channel 580 in 2019-11? (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": "580", + "ts_data_path": "ts_data/L1_T1_Global_Aggregation_00026.csv", + "meta": { + "source": "causal_rivers", + "args": { + "channel": "580", + "time": "2019-11", + "agg": "minimum" + } + } + }, + { + "id": "L1_T1_Global_Aggregation_00027", + "level": 1, + "level_name": "Basic Operations", + "category": "Atomic Retrieval", + "subtask": "Global Aggregation", + "question": "What is the maximum value of channel 589 in 2020? (Output format: a single numeric value, rounded to 3 decimal places, e.g., x.xxx)", + "answer": "1100.0", + "ground_truth": 1100.0, + "eval_metric": "rel_acc", + "channel": "589", + "ts_data_path": "ts_data/L1_T1_Global_Aggregation_00027.csv", + "meta": { + "source": "causal_rivers", + "args": { + "channel": "589", + "time": "2020", + "agg": "maximum" + } + } + }, + { + "id": "L1_T1_Global_Aggregation_00028", + "level": 1, + "level_name": "Basic Operations", + "category": "Atomic Retrieval", + "subtask": "Global Aggregation", + "question": "What is the minimum value of channel 591 in 2020? (Output format: a single numeric value, rounded to 3 decimal places, e.g., x.xxx)", + "answer": "86.6", + "ground_truth": 86.6, + "eval_metric": "rel_acc", + "channel": "591", + "ts_data_path": "ts_data/L1_T1_Global_Aggregation_00028.csv", + "meta": { + "source": "causal_rivers", + "args": { + "channel": "591", + "time": "2020", + "agg": "minimum" + } + } + }, + { + "id": "L1_T1_Global_Aggregation_00029", + "level": 1, + "level_name": "Basic Operations", + "category": "Atomic Retrieval", + "subtask": "Global Aggregation", + "question": "What is the range value of channel 595 in 2022-08? (Output format: a single numeric value, rounded to 3 decimal places, e.g., x.xxx)", + "answer": "0.085", + "ground_truth": 0.085, + "eval_metric": "rel_acc", + "channel": "595", + "ts_data_path": "ts_data/L1_T1_Global_Aggregation_00029.csv", + "meta": { + "source": "causal_rivers", + "args": { + "channel": "595", + "time": "2022-08", + "agg": "range" + } + } + }, + { + "id": "L1_T1_Global_Aggregation_00032", + "level": 1, + "level_name": "Basic Operations", + "category": "Atomic Retrieval", + "subtask": "Global Aggregation", + "question": "What is the median value of channel 627 in 2023-07? (Output format: a single numeric value, rounded to 3 decimal places, e.g., x.xxx)", + "answer": "0.252", + "ground_truth": 0.252, + "eval_metric": "rel_acc", + "channel": "627", + "ts_data_path": "ts_data/L1_T1_Global_Aggregation_00032.csv", + "meta": { + "source": "causal_rivers", + "args": { + "channel": "627", + "time": "2023-07", + "agg": "median" + } + } + }, + { + "id": "L1_T1_Global_Aggregation_00036", + "level": 1, + "level_name": "Basic Operations", + "category": "Atomic Retrieval", + "subtask": "Global Aggregation", + "question": "What is the range value of channel 727 in 2020? (Output format: a single numeric value, rounded to 3 decimal places, e.g., x.xxx)", + "answer": "244.5", + "ground_truth": 244.5, + "eval_metric": "rel_acc", + "channel": "727", + "ts_data_path": "ts_data/L1_T1_Global_Aggregation_00036.csv", + "meta": { + "source": "causal_rivers", + "args": { + "channel": "727", + "time": "2020", + "agg": "range" + } + } + }, + { + "id": "L1_T1_Global_Aggregation_00040", + "level": 1, + "level_name": "Basic Operations", + "category": "Atomic Retrieval", + "subtask": "Global Aggregation", + "question": "What is the maximum value of channel 762 in 2023-12? (Output format: a single numeric value, rounded to 3 decimal places, e.g., x.xxx)", + "answer": "1.77", + "ground_truth": 1.77, + "eval_metric": "rel_acc", + "channel": "762", + "ts_data_path": "ts_data/L1_T1_Global_Aggregation_00040.csv", + "meta": { + "source": "causal_rivers", + "args": { + "channel": "762", + "time": "2023-12", + "agg": "maximum" + } + } + }, + { + "id": "L1_T1_Global_Aggregation_00044", + "level": 1, + "level_name": "Basic Operations", + "category": "Atomic Retrieval", + "subtask": "Global Aggregation", + "question": "What is the range value of channel 891 in 2022-05? (Output format: a single numeric value, rounded to 3 decimal places, e.g., x.xxx)", + "answer": "1.29", + "ground_truth": 1.29, + "eval_metric": "rel_acc", + "channel": "891", + "ts_data_path": "ts_data/L1_T1_Global_Aggregation_00044.csv", + "meta": { + "source": "causal_rivers", + "args": { + "channel": "891", + "time": "2022-05", + "agg": "range" + } + } + }, + { + "id": "L1_T1_Global_Aggregation_00046", + "level": 1, + "level_name": "Basic Operations", + "category": "Atomic Retrieval", + "subtask": "Global Aggregation", + "question": "What is the average value of channel 895 in 2023-10? (Output format: a single numeric value, rounded to 3 decimal places, e.g., x.xxx)", + "answer": "1.823", + "ground_truth": 1.823, + "eval_metric": "rel_acc", + "channel": "895", + "ts_data_path": "ts_data/L1_T1_Global_Aggregation_00046.csv", + "meta": { + "source": "causal_rivers", + "args": { + "channel": "895", + "time": "2023-10", + "agg": "average" + } + } + }, + { + "id": "L1_T1_Global_Aggregation_00064", + "level": 1, + "level_name": "Basic Operations", + "category": "Atomic Retrieval", + "subtask": "Global Aggregation", + "question": "What is the maximum value of channel 151 in 2021-07-09 to 2021-07-22? (Output format: a single numeric value, rounded to 3 decimal places, e.g., x.xxx)", + "answer": "0.897", + "ground_truth": 0.897, + "eval_metric": "rel_acc", + "channel": "151", + "ts_data_path": "ts_data/L1_T1_Global_Aggregation_00064.csv", + "meta": { + "source": "causal_rivers", + "args": { + "channel": "151", + "time": "(Timestamp('2021-07-09 20:15:00'), Timestamp('2021-07-22 20:15:00'))", + "agg": "maximum" + } + } + }, + { + "id": "L1_T1_Global_Aggregation_00073", + "level": 1, + "level_name": "Basic Operations", + "category": "Atomic Retrieval", + "subtask": "Global Aggregation", + "question": "What is the maximum value of channel 237 in 2023? (Output format: a single numeric value, rounded to 3 decimal places, e.g., x.xxx)", + "answer": "29.8", + "ground_truth": 29.8, + "eval_metric": "rel_acc", + "channel": "237", + "ts_data_path": "ts_data/L1_T1_Global_Aggregation_00073.csv", + "meta": { + "source": "causal_rivers", + "args": { + "channel": "237", + "time": "2023", + "agg": "maximum" + } + } + }, + { + "id": "L1_T1_Global_Aggregation_00076", + "level": 1, + "level_name": "Basic Operations", + "category": "Atomic Retrieval", + "subtask": "Global Aggregation", + "question": "What is the range value of channel 430 in 2023? (Output format: a single numeric value, rounded to 3 decimal places, e.g., x.xxx)", + "answer": "2.831", + "ground_truth": 2.831, + "eval_metric": "rel_acc", + "channel": "430", + "ts_data_path": "ts_data/L1_T1_Global_Aggregation_00076.csv", + "meta": { + "source": "causal_rivers", + "args": { + "channel": "430", + "time": "2023", + "agg": "range" + } + } + }, + { + "id": "L1_T1_Global_Aggregation_00081", + "level": 1, + "level_name": "Basic Operations", + "category": "Atomic Retrieval", + "subtask": "Global Aggregation", + "question": "What is the median value of channel 578 in 2021-12? (Output format: a single numeric value, rounded to 3 decimal places, e.g., x.xxx)", + "answer": "0.935", + "ground_truth": 0.935, + "eval_metric": "rel_acc", + "channel": "578", + "ts_data_path": "ts_data/L1_T1_Global_Aggregation_00081.csv", + "meta": { + "source": "causal_rivers", + "args": { + "channel": "578", + "time": "2021-12", + "agg": "median" + } + } + }, + { + "id": "L1_T1_Global_Aggregation_00087", + "level": 1, + "level_name": "Basic Operations", + "category": "Atomic Retrieval", + "subtask": "Global Aggregation", + "question": "What is the maximum value of channel 626 in 2019-12-30 to 2020-01-22? (Output format: a single numeric value, rounded to 3 decimal places, e.g., x.xxx)", + "answer": "1.09", + "ground_truth": 1.09, + "eval_metric": "rel_acc", + "channel": "626", + "ts_data_path": "ts_data/L1_T1_Global_Aggregation_00087.csv", + "meta": { + "source": "causal_rivers", + "args": { + "channel": "626", + "time": "(Timestamp('2019-12-30 14:00:00'), Timestamp('2020-01-22 14:00:00'))", + "agg": "maximum" + } + } + }, + { + "id": "L1_T1_Global_Aggregation_00093", + "level": 1, + "level_name": "Basic Operations", + "category": "Atomic Retrieval", + "subtask": "Global Aggregation", + "question": "What is the median value of channel 728 in 2022-06-09 to 2022-06-29? (Output format: a single numeric value, rounded to 3 decimal places, e.g., x.xxx)", + "answer": "39.0", + "ground_truth": 39.0, + "eval_metric": "rel_acc", + "channel": "728", + "ts_data_path": "ts_data/L1_T1_Global_Aggregation_00093.csv", + "meta": { + "source": "causal_rivers", + "args": { + "channel": "728", + "time": "(Timestamp('2022-06-09 05:45:00'), Timestamp('2022-06-29 05:45:00'))", + "agg": "median" + } + } + }, + { + "id": "L1_T1_Global_Aggregation_00098", + "level": 1, + "level_name": "Basic Operations", + "category": "Atomic Retrieval", + "subtask": "Global Aggregation", + "question": "What is the maximum value of channel 813 in 2021? (Output format: a single numeric value, rounded to 3 decimal places, e.g., x.xxx)", + "answer": "9.28", + "ground_truth": 9.28, + "eval_metric": "rel_acc", + "channel": "813", + "ts_data_path": "ts_data/L1_T1_Global_Aggregation_00098.csv", + "meta": { + "source": "causal_rivers", + "args": { + "channel": "813", + "time": "2021", + "agg": "maximum" + } + } + }, + { + "id": "L1_T1_Global_Aggregation_00101", + "level": 1, + "level_name": "Basic Operations", + "category": "Atomic Retrieval", + "subtask": "Global Aggregation", + "question": "What is the range value of channel 894 in 2021-10? (Output format: a single numeric value, rounded to 3 decimal places, e.g., x.xxx)", + "answer": "1.56", + "ground_truth": 1.56, + "eval_metric": "rel_acc", + "channel": "894", + "ts_data_path": "ts_data/L1_T1_Global_Aggregation_00101.csv", + "meta": { + "source": "causal_rivers", + "args": { + "channel": "894", + "time": "2021-10", + "agg": "range" + } + } + }, + { + "id": "L1_T1_Global_Aggregation_00110", + "level": 1, + "level_name": "Basic Operations", + "category": "Atomic Retrieval", + "subtask": "Global Aggregation", + "question": "What is the maximum value of channel m_02 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_02", + "ts_data_path": "ts_data/L1_T1_Global_Aggregation_00110.csv", + "meta": { + "source": "smd_machine_1_1", + "args": { + "channel": "m_02", + "time": "2020", + "agg": "maximum" + } + } + }, + { + "id": "L1_T1_Global_Aggregation_00123", + "level": 1, + "level_name": "Basic Operations", + "category": "Atomic Retrieval", + "subtask": "Global Aggregation", + "question": "What is the range value of channel 166 in 2021-12? (Output format: a single numeric value, rounded to 3 decimal places, e.g., x.xxx)", + "answer": "156.0", + "ground_truth": 156.0, + "eval_metric": "rel_acc", + "channel": "166", + "ts_data_path": "ts_data/L1_T1_Global_Aggregation_00123.csv", + "meta": { + "source": "causal_rivers", + "args": { + "channel": "166", + "time": "2021-12", + "agg": "range" + } + } + }, + { + "id": "L1_T1_Global_Aggregation_00124", + "level": 1, + "level_name": "Basic Operations", + "category": "Atomic Retrieval", + "subtask": "Global Aggregation", + "question": "What is the range value of channel 169 in 2021-05-29 to 2021-06-17? (Output format: a single numeric value, rounded to 3 decimal places, e.g., x.xxx)", + "answer": "267.0", + "ground_truth": 267.0, + "eval_metric": "rel_acc", + "channel": "169", + "ts_data_path": "ts_data/L1_T1_Global_Aggregation_00124.csv", + "meta": { + "source": "causal_rivers", + "args": { + "channel": "169", + "time": "(Timestamp('2021-05-29 12:00:00'), Timestamp('2021-06-17 12:00:00'))", + "agg": "range" + } + } + }, + { + "id": "L1_T1_Global_Aggregation_00128", + "level": 1, + "level_name": "Basic Operations", + "category": "Atomic Retrieval", + "subtask": "Global Aggregation", + "question": "What is the average value of channel 177 in 2022? (Output format: a single numeric value, rounded to 3 decimal places, e.g., x.xxx)", + "answer": "219.874", + "ground_truth": 219.874, + "eval_metric": "rel_acc", + "channel": "177", + "ts_data_path": "ts_data/L1_T1_Global_Aggregation_00128.csv", + "meta": { + "source": "causal_rivers", + "args": { + "channel": "177", + "time": "2022", + "agg": "average" + } + } + }, + { + "id": "L1_T1_Global_Aggregation_00137", + "level": 1, + "level_name": "Basic Operations", + "category": "Atomic Retrieval", + "subtask": "Global Aggregation", + "question": "What is the minimum value of channel 578 in 2023-01-31 to 2023-02-10? (Output format: a single numeric value, rounded to 3 decimal places, e.g., x.xxx)", + "answer": "0.799", + "ground_truth": 0.799, + "eval_metric": "rel_acc", + "channel": "578", + "ts_data_path": "ts_data/L1_T1_Global_Aggregation_00137.csv", + "meta": { + "source": "causal_rivers", + "args": { + "channel": "578", + "time": "(Timestamp('2023-01-31 19:30:00'), Timestamp('2023-02-10 19:30:00'))", + "agg": "minimum" + } + } + }, + { + "id": "L1_T1_Global_Aggregation_00146", + "level": 1, + "level_name": "Basic Operations", + "category": "Atomic Retrieval", + "subtask": "Global Aggregation", + "question": "What is the median value of channel 680 in 2021-10-15 to 2021-11-03? (Output format: a single numeric value, rounded to 3 decimal places, e.g., x.xxx)", + "answer": "2.39", + "ground_truth": 2.39, + "eval_metric": "rel_acc", + "channel": "680", + "ts_data_path": "ts_data/L1_T1_Global_Aggregation_00146.csv", + "meta": { + "source": "causal_rivers", + "args": { + "channel": "680", + "time": "(Timestamp('2021-10-15 14:00:00'), Timestamp('2021-11-03 14:00:00'))", + "agg": "median" + } + } + }, + { + "id": "L1_T1_Global_Aggregation_00156", + "level": 1, + "level_name": "Basic Operations", + "category": "Atomic Retrieval", + "subtask": "Global Aggregation", + "question": "What is the range value of channel 891 in 2021-04-01 to 2021-04-14? (Output format: a single numeric value, rounded to 3 decimal places, e.g., x.xxx)", + "answer": "1.04", + "ground_truth": 1.04, + "eval_metric": "rel_acc", + "channel": "891", + "ts_data_path": "ts_data/L1_T1_Global_Aggregation_00156.csv", + "meta": { + "source": "causal_rivers", + "args": { + "channel": "891", + "time": "(Timestamp('2021-04-01 00:00:00'), Timestamp('2021-04-14 00:00:00'))", + "agg": "range" + } + } + }, + { + "id": "L1_T1_Global_Aggregation_00169", + "level": 1, + "level_name": "Basic Operations", + "category": "Atomic Retrieval", + "subtask": "Global Aggregation", + "question": "What is the average value of channel m_16 in 2020? (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": "m_16", + "ts_data_path": "ts_data/L1_T1_Global_Aggregation_00169.csv", + "meta": { + "source": "smd_machine_1_1", + "args": { + "channel": "m_16", + "time": "2020", + "agg": "average" + } + } + }, + { + "id": "L1_T3_Interval_Discovery_00343", + "level": 1, + "level_name": "Basic Operations", + "category": "Atomic Retrieval", + "subtask": "Interval Discovery", + "question": "Find the longest period where channel 124 remained above 0.587 in 2021-12-02 to 2022-02-24. (Output format: a time interval, e.g., [YYYY-MM-DD HH:MM:SS, YYYY-MM-DD HH:MM:SS])", + "answer": "[2022-02-20 04:30:00, 2022-02-24 13:30:00]", + "ground_truth": [ + "2022-02-20 04:30:00", + "2022-02-24 13:30:00" + ], + "eval_metric": "iou", + "channel": "124", + "ts_data_path": "ts_data/L1_T3_Interval_Discovery_00343.csv", + "meta": { + "source": "causal_rivers", + "args": { + "channel": "124", + "time": "(Timestamp('2021-12-02 13:30:00'), Timestamp('2022-02-24 13:30:00'))", + "threshold": "0.587" + } + } + }, + { + "id": "L1_T3_Interval_Discovery_00347", + "level": 1, + "level_name": "Basic Operations", + "category": "Atomic Retrieval", + "subtask": "Interval Discovery", + "question": "Find the longest period where channel 154 remained above 0.751 in 2023. (Output format: a time interval, e.g., [YYYY-MM-DD HH:MM:SS, YYYY-MM-DD HH:MM:SS])", + "answer": "[2023-12-19 18:15:00, 2023-12-31 23:45:00]", + "ground_truth": [ + "2023-12-19 18:15:00", + "2023-12-31 23:45:00" + ], + "eval_metric": "iou", + "channel": "154", + "ts_data_path": "ts_data/L1_T3_Interval_Discovery_00347.csv", + "meta": { + "source": "causal_rivers", + "args": { + "channel": "154", + "time": "2023", + "threshold": "0.751" + } + } + }, + { + "id": "L1_T3_Interval_Discovery_00351", + "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_00351.csv", + "meta": { + "source": "causal_rivers", + "args": { + "channel": "170", + "time": "2022", + "threshold": "486.0" + } + } + }, + { + "id": "L1_T3_Interval_Discovery_00353", + "level": 1, + "level_name": "Basic Operations", + "category": "Atomic Retrieval", + "subtask": "Interval Discovery", + "question": "Find the longest period where channel 173 remained above 326.0 in 2020. (Output format: a time interval, e.g., [YYYY-MM-DD HH:MM:SS, YYYY-MM-DD HH:MM:SS])", + "answer": "[2020-02-26 14:30:00, 2020-03-22 09:15:00]", + "ground_truth": [ + "2020-02-26 14:30:00", + "2020-03-22 09:15:00" + ], + "eval_metric": "iou", + "channel": "173", + "ts_data_path": "ts_data/L1_T3_Interval_Discovery_00353.csv", + "meta": { + "source": "causal_rivers", + "args": { + "channel": "173", + "time": "2020", + "threshold": "326.0" + } + } + }, + { + "id": "L1_T3_Interval_Discovery_00357", + "level": 1, + "level_name": "Basic Operations", + "category": "Atomic Retrieval", + "subtask": "Interval Discovery", + "question": "Find the longest period where channel 312 remained above 0.204 in 2019. (Output format: a time interval, e.g., [YYYY-MM-DD HH:MM:SS, YYYY-MM-DD HH:MM:SS])", + "answer": "[2019-01-08 06:15:00, 2019-02-05 08:45:00]", + "ground_truth": [ + "2019-01-08 06:15:00", + "2019-02-05 08:45:00" + ], + "eval_metric": "iou", + "channel": "312", + "ts_data_path": "ts_data/L1_T3_Interval_Discovery_00357.csv", + "meta": { + "source": "causal_rivers", + "args": { + "channel": "312", + "time": "2019", + "threshold": "0.204" + } + } + }, + { + "id": "L1_T3_Interval_Discovery_00358", + "level": 1, + "level_name": "Basic Operations", + "category": "Atomic Retrieval", + "subtask": "Interval Discovery", + "question": "Find the longest period where channel 430 remained above 0.331 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, 2022-03-14 20:45:00]", + "ground_truth": [ + "2022-01-01 00:00:00", + "2022-03-14 20:45:00" + ], + "eval_metric": "iou", + "channel": "430", + "ts_data_path": "ts_data/L1_T3_Interval_Discovery_00358.csv", + "meta": { + "source": "causal_rivers", + "args": { + "channel": "430", + "time": "2022", + "threshold": "0.331" + } + } + }, + { + "id": "L1_T3_Interval_Discovery_00360", + "level": 1, + "level_name": "Basic Operations", + "category": "Atomic Retrieval", + "subtask": "Interval Discovery", + "question": "Find the longest period where channel 495 remained above 0.787 in 2021-01-11 to 2021-03-21. (Output format: a time interval, e.g., [YYYY-MM-DD HH:MM:SS, YYYY-MM-DD HH:MM:SS])", + "answer": "[2021-01-11 08:15:00, 2021-01-24 15:00:00]", + "ground_truth": [ + "2021-01-11 08:15:00", + "2021-01-24 15:00:00" + ], + "eval_metric": "iou", + "channel": "495", + "ts_data_path": "ts_data/L1_T3_Interval_Discovery_00360.csv", + "meta": { + "source": "causal_rivers", + "args": { + "channel": "495", + "time": "(Timestamp('2021-01-11 08:15:00'), Timestamp('2021-03-21 08:15:00'))", + "threshold": "0.787" + } + } + }, + { + "id": "L1_T3_Interval_Discovery_00362", + "level": 1, + "level_name": "Basic Operations", + "category": "Atomic Retrieval", + "subtask": "Interval Discovery", + "question": "Find the longest period where channel 501 remained above 1.64 in 2023. (Output format: a time interval, e.g., [YYYY-MM-DD HH:MM:SS, YYYY-MM-DD HH:MM:SS])", + "answer": "[2023-02-01 09:00:00, 2023-02-26 23:15:00]", + "ground_truth": [ + "2023-02-01 09:00:00", + "2023-02-26 23:15:00" + ], + "eval_metric": "iou", + "channel": "501", + "ts_data_path": "ts_data/L1_T3_Interval_Discovery_00362.csv", + "meta": { + "source": "causal_rivers", + "args": { + "channel": "501", + "time": "2023", + "threshold": "1.64" + } + } + }, + { + "id": "L1_T3_Interval_Discovery_00367", + "level": 1, + "level_name": "Basic Operations", + "category": "Atomic Retrieval", + "subtask": "Interval Discovery", + "question": "Find the longest period where channel 595 remained above 2.45 in 2022. (Output format: a time interval, e.g., [YYYY-MM-DD HH:MM:SS, YYYY-MM-DD HH:MM:SS])", + "answer": "[2022-01-23 23:30:00, 2022-03-03 13:30:00]", + "ground_truth": [ + "2022-01-23 23:30:00", + "2022-03-03 13:30:00" + ], + "eval_metric": "iou", + "channel": "595", + "ts_data_path": "ts_data/L1_T3_Interval_Discovery_00367.csv", + "meta": { + "source": "causal_rivers", + "args": { + "channel": "595", + "time": "2022", + "threshold": "2.45" + } + } + }, + { + "id": "L1_T3_Interval_Discovery_00386", + "level": 1, + "level_name": "Basic Operations", + "category": "Atomic Retrieval", + "subtask": "Interval Discovery", + "question": "Find the longest period where channel 933 remained above 0.07 in 2020-04-26 to 2020-05-27. (Output format: a time interval, e.g., [YYYY-MM-DD HH:MM:SS, YYYY-MM-DD HH:MM:SS])", + "answer": "[2020-04-28 11:30:00, 2020-05-01 02:15:00]", + "ground_truth": [ + "2020-04-28 11:30:00", + "2020-05-01 02:15:00" + ], + "eval_metric": "iou", + "channel": "933", + "ts_data_path": "ts_data/L1_T3_Interval_Discovery_00386.csv", + "meta": { + "source": "causal_rivers", + "args": { + "channel": "933", + "time": "(Timestamp('2020-04-26 07:00:00'), Timestamp('2020-05-27 07:00:00'))", + "threshold": "0.07" + } + } + }, + { + "id": "L1_T3_Interval_Discovery_00387", + "level": 1, + "level_name": "Basic Operations", + "category": "Atomic Retrieval", + "subtask": "Interval Discovery", + "question": "Find the longest period where channel HUFL remained above 12.324 in 2016-11-27 to 2017-01-24. (Output format: a time interval, e.g., [YYYY-MM-DD HH:MM:SS, YYYY-MM-DD HH:MM:SS])", + "answer": "[2016-12-30 23:15:00, 2017-01-01 07:45:00]", + "ground_truth": [ + "2016-12-30 23:15:00", + "2017-01-01 07:45:00" + ], + "eval_metric": "iou", + "channel": "HUFL", + "ts_data_path": "ts_data/L1_T3_Interval_Discovery_00387.csv", + "meta": { + "source": "ettm1", + "args": { + "channel": "HUFL", + "time": "(Timestamp('2016-11-27 23:30:00'), Timestamp('2017-01-24 23:30:00'))", + "threshold": "12.324" + } + } + }, + { + "id": "L1_T3_Interval_Discovery_00391", + "level": 1, + "level_name": "Basic Operations", + "category": "Atomic Retrieval", + "subtask": "Interval Discovery", + "question": "Find the longest period where channel LUFL remained above 4.417 in 2016-07-24 to 2016-10-18. (Output format: a time interval, e.g., [YYYY-MM-DD HH:MM:SS, YYYY-MM-DD HH:MM:SS])", + "answer": "[2016-07-30 22:30:00, 2016-08-01 00:30:00]", + "ground_truth": [ + "2016-07-30 22:30:00", + "2016-08-01 00:30:00" + ], + "eval_metric": "iou", + "channel": "LUFL", + "ts_data_path": "ts_data/L1_T3_Interval_Discovery_00391.csv", + "meta": { + "source": "ettm1", + "args": { + "channel": "LUFL", + "time": "(Timestamp('2016-07-24 18:15:00'), Timestamp('2016-10-18 18:15:00'))", + "threshold": "4.417" + } + } + }, + { + "id": "L1_T3_Interval_Discovery_00392", + "level": 1, + "level_name": "Basic Operations", + "category": "Atomic Retrieval", + "subtask": "Interval Discovery", + "question": "Find the longest period where channel LULL remained above 1.218 in 2017-09-22 to 2017-11-21. (Output format: a time interval, e.g., [YYYY-MM-DD HH:MM:SS, YYYY-MM-DD HH:MM:SS])", + "answer": "[2017-10-22 18:15:00, 2017-10-23 05:00:00]", + "ground_truth": [ + "2017-10-22 18:15:00", + "2017-10-23 05:00:00" + ], + "eval_metric": "iou", + "channel": "LULL", + "ts_data_path": "ts_data/L1_T3_Interval_Discovery_00392.csv", + "meta": { + "source": "ettm1", + "args": { + "channel": "LULL", + "time": "(Timestamp('2017-09-22 13:45:00'), Timestamp('2017-11-21 13:45:00'))", + "threshold": "1.218" + } + } + }, + { + "id": "L1_T3_Interval_Discovery_00395", + "level": 1, + "level_name": "Basic Operations", + "category": "Atomic Retrieval", + "subtask": "Interval Discovery", + "question": "Find the longest period where channel m_29 remained above 0.021 in 2020. (Output format: a time interval, e.g., [YYYY-MM-DD HH:MM:SS, YYYY-MM-DD HH:MM:SS])", + "answer": "[2020-02-02 11:02:00, 2020-02-05 20:27:00]", + "ground_truth": [ + "2020-02-02 11:02:00", + "2020-02-05 20:27:00" + ], + "eval_metric": "iou", + "channel": "m_29", + "ts_data_path": "ts_data/L1_T3_Interval_Discovery_00395.csv", + "meta": { + "source": "smd_machine_1_1", + "args": { + "channel": "m_29", + "time": "2020", + "threshold": "0.021" + } + } + }, + { + "id": "L1_T3_Interval_Discovery_00402", + "level": 1, + "level_name": "Basic Operations", + "category": "Atomic Retrieval", + "subtask": "Interval Discovery", + "question": "Find the longest period where channel 146 remained above 1.59 in 2023-01-19 to 2023-04-19. (Output format: a time interval, e.g., [YYYY-MM-DD HH:MM:SS, YYYY-MM-DD HH:MM:SS])", + "answer": "[2023-03-23 23:30:00, 2023-03-28 22:30:00]", + "ground_truth": [ + "2023-03-23 23:30:00", + "2023-03-28 22:30:00" + ], + "eval_metric": "iou", + "channel": "146", + "ts_data_path": "ts_data/L1_T3_Interval_Discovery_00402.csv", + "meta": { + "source": "causal_rivers", + "args": { + "channel": "146", + "time": "(Timestamp('2023-01-19 13:45:00'), Timestamp('2023-04-19 13:45:00'))", + "threshold": "1.59" + } + } + }, + { + "id": "L1_T3_Interval_Discovery_00404", + "level": 1, + "level_name": "Basic Operations", + "category": "Atomic Retrieval", + "subtask": "Interval Discovery", + "question": "Find the longest period where channel 151 remained above 1.04 in 2019-12-12 to 2020-03-02. (Output format: a time interval, e.g., [YYYY-MM-DD HH:MM:SS, YYYY-MM-DD HH:MM:SS])", + "answer": "[2020-02-23 20:30:00, 2020-03-02 19:15:00]", + "ground_truth": [ + "2020-02-23 20:30:00", + "2020-03-02 19:15:00" + ], + "eval_metric": "iou", + "channel": "151", + "ts_data_path": "ts_data/L1_T3_Interval_Discovery_00404.csv", + "meta": { + "source": "causal_rivers", + "args": { + "channel": "151", + "time": "(Timestamp('2019-12-12 19:15:00'), Timestamp('2020-03-02 19:15:00'))", + "threshold": "1.04" + } + } + }, + { + "id": "L1_T3_Interval_Discovery_00411", + "level": 1, + "level_name": "Basic Operations", + "category": "Atomic Retrieval", + "subtask": "Interval Discovery", + "question": "Find the longest period where channel 173 remained above 326.0 in 2020. (Output format: a time interval, e.g., [YYYY-MM-DD HH:MM:SS, YYYY-MM-DD HH:MM:SS])", + "answer": "[2020-02-26 14:30:00, 2020-03-22 09:15:00]", + "ground_truth": [ + "2020-02-26 14:30:00", + "2020-03-22 09:15:00" + ], + "eval_metric": "iou", + "channel": "173", + "ts_data_path": "ts_data/L1_T3_Interval_Discovery_00411.csv", + "meta": { + "source": "causal_rivers", + "args": { + "channel": "173", + "time": "2020", + "threshold": "326.0" + } + } + }, + { + "id": "L1_T3_Interval_Discovery_00412", + "level": 1, + "level_name": "Basic Operations", + "category": "Atomic Retrieval", + "subtask": "Interval Discovery", + "question": "Find the longest period where channel 177 remained above 337.0 in 2020-09-05 to 2020-10-25. (Output format: a time interval, e.g., [YYYY-MM-DD HH:MM:SS, YYYY-MM-DD HH:MM:SS])", + "answer": "[2020-10-14 20:45:00, 2020-10-23 03:30:00]", + "ground_truth": [ + "2020-10-14 20:45:00", + "2020-10-23 03:30:00" + ], + "eval_metric": "iou", + "channel": "177", + "ts_data_path": "ts_data/L1_T3_Interval_Discovery_00412.csv", + "meta": { + "source": "causal_rivers", + "args": { + "channel": "177", + "time": "(Timestamp('2020-09-05 04:45:00'), Timestamp('2020-10-25 04:45:00'))", + "threshold": "337.0" + } + } + }, + { + "id": "L1_T3_Interval_Discovery_00415", + "level": 1, + "level_name": "Basic Operations", + "category": "Atomic Retrieval", + "subtask": "Interval Discovery", + "question": "Find the longest period where channel 312 remained above 0.622 in 2021. (Output format: a time interval, e.g., [YYYY-MM-DD HH:MM:SS, YYYY-MM-DD HH:MM:SS])", + "answer": "[2021-12-06 02:15:00, 2021-12-31 23:45:00]", + "ground_truth": [ + "2021-12-06 02:15:00", + "2021-12-31 23:45:00" + ], + "eval_metric": "iou", + "channel": "312", + "ts_data_path": "ts_data/L1_T3_Interval_Discovery_00415.csv", + "meta": { + "source": "causal_rivers", + "args": { + "channel": "312", + "time": "2021", + "threshold": "0.622" + } + } + }, + { + "id": "L1_T3_Interval_Discovery_00443", + "level": 1, + "level_name": "Basic Operations", + "category": "Atomic Retrieval", + "subtask": "Interval Discovery", + "question": "Find the longest period where channel 897 remained above 0.038 in 2020. (Output format: a time interval, e.g., [YYYY-MM-DD HH:MM:SS, YYYY-MM-DD HH:MM:SS])", + "answer": "[2020-02-19 23:00:00, 2020-03-17 04:45:00]", + "ground_truth": [ + "2020-02-19 23:00:00", + "2020-03-17 04:45:00" + ], + "eval_metric": "iou", + "channel": "897", + "ts_data_path": "ts_data/L1_T3_Interval_Discovery_00443.csv", + "meta": { + "source": "causal_rivers", + "args": { + "channel": "897", + "time": "2020", + "threshold": "0.038" + } + } + }, + { + "id": "L1_T3_Interval_Discovery_00447", + "level": 1, + "level_name": "Basic Operations", + "category": "Atomic Retrieval", + "subtask": "Interval Discovery", + "question": "Find the longest period where channel MUFL remained above 9.666 in 2017-06-15 to 2017-08-16. (Output format: a time interval, e.g., [YYYY-MM-DD HH:MM:SS, YYYY-MM-DD HH:MM:SS])", + "answer": "[2017-07-30 19:45:00, 2017-08-01 03:00:00]", + "ground_truth": [ + "2017-07-30 19:45:00", + "2017-08-01 03:00:00" + ], + "eval_metric": "iou", + "channel": "MUFL", + "ts_data_path": "ts_data/L1_T3_Interval_Discovery_00447.csv", + "meta": { + "source": "ettm1", + "args": { + "channel": "MUFL", + "time": "(Timestamp('2017-06-15 19:00:00'), Timestamp('2017-08-16 19:00:00'))", + "threshold": "9.666" + } + } + }, + { + "id": "L1_T3_Interval_Discovery_00455", + "level": 1, + "level_name": "Basic Operations", + "category": "Atomic Retrieval", + "subtask": "Interval Discovery", + "question": "Find the longest period where channel m_31 remained above 0.071 in 2020. (Output format: a time interval, e.g., [YYYY-MM-DD HH:MM:SS, YYYY-MM-DD HH:MM:SS])", + "answer": "[2020-01-31 23:02:00, 2020-02-01 11:52:00]", + "ground_truth": [ + "2020-01-31 23:02:00", + "2020-02-01 11:52:00" + ], + "eval_metric": "iou", + "channel": "m_31", + "ts_data_path": "ts_data/L1_T3_Interval_Discovery_00455.csv", + "meta": { + "source": "smd_machine_1_1", + "args": { + "channel": "m_31", + "time": "2020", + "threshold": "0.071" + } + } + }, + { + "id": "L1_T3_Interval_Discovery_00461", + "level": 1, + "level_name": "Basic Operations", + "category": "Atomic Retrieval", + "subtask": "Interval Discovery", + "question": "Find the longest period where channel 146 remained above 0.926 in 2023-04-16 to 2023-05-22. (Output format: a time interval, e.g., [YYYY-MM-DD HH:MM:SS, YYYY-MM-DD HH:MM:SS])", + "answer": "[2023-04-16 08:15:00, 2023-04-19 20:15:00]", + "ground_truth": [ + "2023-04-16 08:15:00", + "2023-04-19 20:15:00" + ], + "eval_metric": "iou", + "channel": "146", + "ts_data_path": "ts_data/L1_T3_Interval_Discovery_00461.csv", + "meta": { + "source": "causal_rivers", + "args": { + "channel": "146", + "time": "(Timestamp('2023-04-16 08:15:00'), Timestamp('2023-05-22 08:15:00'))", + "threshold": "0.926" + } + } + }, + { + "id": "L1_T3_Interval_Discovery_00464", + "level": 1, + "level_name": "Basic Operations", + "category": "Atomic Retrieval", + "subtask": "Interval Discovery", + "question": "Find the longest period where channel 154 remained above 0.294 in 2021-07-08 to 2021-09-10. (Output format: a time interval, e.g., [YYYY-MM-DD HH:MM:SS, YYYY-MM-DD HH:MM:SS])", + "answer": "[2021-08-29 02:30:00, 2021-08-31 10:30:00]", + "ground_truth": [ + "2021-08-29 02:30:00", + "2021-08-31 10:30:00" + ], + "eval_metric": "iou", + "channel": "154", + "ts_data_path": "ts_data/L1_T3_Interval_Discovery_00464.csv", + "meta": { + "source": "causal_rivers", + "args": { + "channel": "154", + "time": "(Timestamp('2021-07-08 20:15:00'), Timestamp('2021-09-10 20:15:00'))", + "threshold": "0.294" + } + } + }, + { + "id": "L1_T3_Interval_Discovery_00468", + "level": 1, + "level_name": "Basic Operations", + "category": "Atomic Retrieval", + "subtask": "Interval Discovery", + "question": "Find the longest period where channel 170 remained above 492.0 in 2022-11-20 to 2023-01-06. (Output format: a time interval, e.g., [YYYY-MM-DD HH:MM:SS, YYYY-MM-DD HH:MM:SS])", + "answer": "[2022-12-27 13:00:00, 2023-01-05 06:30:00]", + "ground_truth": [ + "2022-12-27 13:00:00", + "2023-01-05 06:30:00" + ], + "eval_metric": "iou", + "channel": "170", + "ts_data_path": "ts_data/L1_T3_Interval_Discovery_00468.csv", + "meta": { + "source": "causal_rivers", + "args": { + "channel": "170", + "time": "(Timestamp('2022-11-20 16:45:00'), Timestamp('2023-01-06 16:45:00'))", + "threshold": "492.0" + } + } + }, + { + "id": "L1_T3_Interval_Discovery_00473", + "level": 1, + "level_name": "Basic Operations", + "category": "Atomic Retrieval", + "subtask": "Interval Discovery", + "question": "Find the longest period where channel 245 remained above 0.577 in 2023. (Output format: a time interval, e.g., [YYYY-MM-DD HH:MM:SS, YYYY-MM-DD HH:MM:SS])", + "answer": "[2023-01-31 02:30:00, 2023-02-14 02:15:00]", + "ground_truth": [ + "2023-01-31 02:30:00", + "2023-02-14 02:15:00" + ], + "eval_metric": "iou", + "channel": "245", + "ts_data_path": "ts_data/L1_T3_Interval_Discovery_00473.csv", + "meta": { + "source": "causal_rivers", + "args": { + "channel": "245", + "time": "2023", + "threshold": "0.577" + } + } + }, + { + "id": "L1_T3_Interval_Discovery_00475", + "level": 1, + "level_name": "Basic Operations", + "category": "Atomic Retrieval", + "subtask": "Interval Discovery", + "question": "Find the longest period where channel 430 remained above 0.26 in 2019. (Output format: a time interval, e.g., [YYYY-MM-DD HH:MM:SS, YYYY-MM-DD HH:MM:SS])", + "answer": "[2019-12-07 20:00:00, 2019-12-31 19:00:00]", + "ground_truth": [ + "2019-12-07 20:00:00", + "2019-12-31 19:00:00" + ], + "eval_metric": "iou", + "channel": "430", + "ts_data_path": "ts_data/L1_T3_Interval_Discovery_00475.csv", + "meta": { + "source": "causal_rivers", + "args": { + "channel": "430", + "time": "2019", + "threshold": "0.26" + } + } + }, + { + "id": "L1_T3_Interval_Discovery_00476", + "level": 1, + "level_name": "Basic Operations", + "category": "Atomic Retrieval", + "subtask": "Interval Discovery", + "question": "Find the longest period where channel 441 remained above 23.5 in 2021. (Output format: a time interval, e.g., [YYYY-MM-DD HH:MM:SS, YYYY-MM-DD HH:MM:SS])", + "answer": "[2021-02-03 23:00:00, 2021-02-15 12:15:00]", + "ground_truth": [ + "2021-02-03 23:00:00", + "2021-02-15 12:15:00" + ], + "eval_metric": "iou", + "channel": "441", + "ts_data_path": "ts_data/L1_T3_Interval_Discovery_00476.csv", + "meta": { + "source": "causal_rivers", + "args": { + "channel": "441", + "time": "2021", + "threshold": "23.5" + } + } + }, + { + "id": "L1_T3_Interval_Discovery_00477", + "level": 1, + "level_name": "Basic Operations", + "category": "Atomic Retrieval", + "subtask": "Interval Discovery", + "question": "Find the longest period where channel 495 remained above 1.21 in 2023. (Output format: a time interval, e.g., [YYYY-MM-DD HH:MM:SS, YYYY-MM-DD HH:MM:SS])", + "answer": "[2023-02-17 18:15:00, 2023-03-30 17:30:00]", + "ground_truth": [ + "2023-02-17 18:15:00", + "2023-03-30 17:30:00" + ], + "eval_metric": "iou", + "channel": "495", + "ts_data_path": "ts_data/L1_T3_Interval_Discovery_00477.csv", + "meta": { + "source": "causal_rivers", + "args": { + "channel": "495", + "time": "2023", + "threshold": "1.21" + } + } + }, + { + "id": "L1_T3_Interval_Discovery_00485", + "level": 1, + "level_name": "Basic Operations", + "category": "Atomic Retrieval", + "subtask": "Interval Discovery", + "question": "Find the longest period where channel 625 remained above 0.978 in 2019. (Output format: a time interval, e.g., [YYYY-MM-DD HH:MM:SS, YYYY-MM-DD HH:MM:SS])", + "answer": "[2019-01-26 15:30:00, 2019-03-01 12:15:00]", + "ground_truth": [ + "2019-01-26 15:30:00", + "2019-03-01 12:15:00" + ], + "eval_metric": "iou", + "channel": "625", + "ts_data_path": "ts_data/L1_T3_Interval_Discovery_00485.csv", + "meta": { + "source": "causal_rivers", + "args": { + "channel": "625", + "time": "2019", + "threshold": "0.978" + } + } + }, + { + "id": "L1_T3_Interval_Discovery_00487", + "level": 1, + "level_name": "Basic Operations", + "category": "Atomic Retrieval", + "subtask": "Interval Discovery", + "question": "Find the longest period where channel 627 remained above 1.04 in 2023-11-25 to 2023-12-26. (Output format: a time interval, e.g., [YYYY-MM-DD HH:MM:SS, YYYY-MM-DD HH:MM:SS])", + "answer": "[2023-12-10 23:15:00, 2023-12-13 22:00:00]", + "ground_truth": [ + "2023-12-10 23:15:00", + "2023-12-13 22:00:00" + ], + "eval_metric": "iou", + "channel": "627", + "ts_data_path": "ts_data/L1_T3_Interval_Discovery_00487.csv", + "meta": { + "source": "causal_rivers", + "args": { + "channel": "627", + "time": "(Timestamp('2023-11-25 17:00:00'), Timestamp('2023-12-26 17:00:00'))", + "threshold": "1.04" + } + } + }, + { + "id": "L1_T3_Interval_Discovery_00493", + "level": 1, + "level_name": "Basic Operations", + "category": "Atomic Retrieval", + "subtask": "Interval Discovery", + "question": "Find the longest period where channel 729 remained above 38.7 in 2019-08-20 to 2019-10-27. (Output format: a time interval, e.g., [YYYY-MM-DD HH:MM:SS, YYYY-MM-DD HH:MM:SS])", + "answer": "[2019-10-04 02:00:00, 2019-10-06 15:15:00]", + "ground_truth": [ + "2019-10-04 02:00:00", + "2019-10-06 15:15:00" + ], + "eval_metric": "iou", + "channel": "729", + "ts_data_path": "ts_data/L1_T3_Interval_Discovery_00493.csv", + "meta": { + "source": "causal_rivers", + "args": { + "channel": "729", + "time": "(Timestamp('2019-08-20 04:30:00'), Timestamp('2019-10-27 04:30:00'))", + "threshold": "38.7" + } + } + }, + { + "id": "L1_T3_Interval_Discovery_00504", + "level": 1, + "level_name": "Basic Operations", + "category": "Atomic Retrieval", + "subtask": "Interval Discovery", + "question": "Find the longest period where channel HUFL remained above 14.066 in 2018. (Output format: a time interval, e.g., [YYYY-MM-DD HH:MM:SS, YYYY-MM-DD HH:MM:SS])", + "answer": "[2018-01-30 17:30:00, 2018-02-01 01:00:00]", + "ground_truth": [ + "2018-01-30 17:30:00", + "2018-02-01 01:00:00" + ], + "eval_metric": "iou", + "channel": "HUFL", + "ts_data_path": "ts_data/L1_T3_Interval_Discovery_00504.csv", + "meta": { + "source": "ettm1", + "args": { + "channel": "HUFL", + "time": "2018", + "threshold": "14.066" + } + } + }, + { + "id": "L1_T2_Temporal_Localization_00175", + "level": 1, + "level_name": "Basic Operations", + "category": "Atomic Retrieval", + "subtask": "Temporal Localization", + "question": "At what exact timestamp did channel 124 first rise above 0.12 in 2023-08? (Output format: a specific timestamp, e.g., YYYY-MM-DD HH:MM:SS)", + "answer": "2023-08-07 01:45:00", + "ground_truth": "2023-08-07 01:45:00", + "eval_metric": "hit", + "channel": "124", + "ts_data_path": "ts_data/L1_T2_Temporal_Localization_00175.csv", + "meta": { + "source": "causal_rivers", + "args": { + "channel": "124", + "time": "2023-08", + "threshold_high": "0.12", + "threshold_low": "0.085", + "action": "first rise above 0.12" + } + } + }, + { + "id": "L1_T2_Temporal_Localization_00179", + "level": 1, + "level_name": "Basic Operations", + "category": "Atomic Retrieval", + "subtask": "Temporal Localization", + "question": "At what exact timestamp did channel 154 reach its maximum value in 2021-08? (Output format: a specific timestamp, e.g., YYYY-MM-DD HH:MM:SS)", + "answer": "2021-08-27 06:15:00", + "ground_truth": "2021-08-27 06:15:00", + "eval_metric": "hit", + "channel": "154", + "ts_data_path": "ts_data/L1_T2_Temporal_Localization_00179.csv", + "meta": { + "source": "causal_rivers", + "args": { + "channel": "154", + "time": "2021-08", + "threshold_high": "0.297", + "threshold_low": "0.194", + "action": "reach its maximum value" + } + } + }, + { + "id": "L1_T2_Temporal_Localization_00190", + "level": 1, + "level_name": "Basic Operations", + "category": "Atomic Retrieval", + "subtask": "Temporal Localization", + "question": "At what exact timestamp did channel 430 reach its maximum value in 2023? (Output format: a specific timestamp, e.g., YYYY-MM-DD HH:MM:SS)", + "answer": "2023-12-21 21:00:00", + "ground_truth": "2023-12-21 21:00:00", + "eval_metric": "hit", + "channel": "430", + "ts_data_path": "ts_data/L1_T2_Temporal_Localization_00190.csv", + "meta": { + "source": "causal_rivers", + "args": { + "channel": "430", + "time": "2023", + "threshold_high": "0.451", + "threshold_low": "0.091", + "action": "reach its maximum value" + } + } + }, + { + "id": "L1_T2_Temporal_Localization_00193", + "level": 1, + "level_name": "Basic Operations", + "category": "Atomic Retrieval", + "subtask": "Temporal Localization", + "question": "At what exact timestamp did channel 496 first rise above 0.614 in 2019-04? (Output format: a specific timestamp, e.g., YYYY-MM-DD HH:MM:SS)", + "answer": "2019-04-01 00:00:00", + "ground_truth": "2019-04-01 00:00:00", + "eval_metric": "hit", + "channel": "496", + "ts_data_path": "ts_data/L1_T2_Temporal_Localization_00193.csv", + "meta": { + "source": "causal_rivers", + "args": { + "channel": "496", + "time": "2019-04", + "threshold_high": "0.614", + "threshold_low": "0.321", + "action": "first rise above 0.614" + } + } + }, + { + "id": "L1_T2_Temporal_Localization_00199", + "level": 1, + "level_name": "Basic Operations", + "category": "Atomic Retrieval", + "subtask": "Temporal Localization", + "question": "At what exact timestamp did channel 595 last fall below 1.35 in 2021-11? (Output format: a specific timestamp, e.g., YYYY-MM-DD HH:MM:SS)", + "answer": "2021-11-26 08:15:00", + "ground_truth": "2021-11-26 08:15:00", + "eval_metric": "hit", + "channel": "595", + "ts_data_path": "ts_data/L1_T2_Temporal_Localization_00199.csv", + "meta": { + "source": "causal_rivers", + "args": { + "channel": "595", + "time": "2021-11", + "threshold_high": "2.08", + "threshold_low": "1.35", + "action": "last fall below 1.35" + } + } + }, + { + "id": "L1_T2_Temporal_Localization_00203", + "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 2023-04? (Output format: a specific timestamp, e.g., YYYY-MM-DD HH:MM:SS)", + "answer": "2023-04-16 11:00:00", + "ground_truth": "2023-04-16 11:00:00", + "eval_metric": "hit", + "channel": "647", + "ts_data_path": "ts_data/L1_T2_Temporal_Localization_00203.csv", + "meta": { + "source": "causal_rivers", + "args": { + "channel": "647", + "time": "2023-04", + "threshold_high": "3.1", + "threshold_low": "2.26", + "action": "reach its maximum value" + } + } + }, + { + "id": "L1_T2_Temporal_Localization_00205", + "level": 1, + "level_name": "Basic Operations", + "category": "Atomic Retrieval", + "subtask": "Temporal Localization", + "question": "At what exact timestamp did channel 683 last fall below 0.776 in 2023? (Output format: a specific timestamp, e.g., YYYY-MM-DD HH:MM:SS)", + "answer": "2023-11-23 18:30:00", + "ground_truth": "2023-11-23 18:30:00", + "eval_metric": "hit", + "channel": "683", + "ts_data_path": "ts_data/L1_T2_Temporal_Localization_00205.csv", + "meta": { + "source": "causal_rivers", + "args": { + "channel": "683", + "time": "2023", + "threshold_high": "1.25", + "threshold_low": "0.776", + "action": "last fall below 0.776" + } + } + }, + { + "id": "L1_T2_Temporal_Localization_00206", + "level": 1, + "level_name": "Basic Operations", + "category": "Atomic Retrieval", + "subtask": "Temporal Localization", + "question": "At what exact timestamp did channel 727 last fall below 32.0 in 2020-08? (Output format: a specific timestamp, e.g., YYYY-MM-DD HH:MM:SS)", + "answer": "2020-08-27 09:00:00", + "ground_truth": "2020-08-27 09:00:00", + "eval_metric": "hit", + "channel": "727", + "ts_data_path": "ts_data/L1_T2_Temporal_Localization_00206.csv", + "meta": { + "source": "causal_rivers", + "args": { + "channel": "727", + "time": "2020-08", + "threshold_high": "44.0", + "threshold_low": "32.0", + "action": "last fall below 32.0" + } + } + }, + { + "id": "L1_T2_Temporal_Localization_00212", + "level": 1, + "level_name": "Basic Operations", + "category": "Atomic Retrieval", + "subtask": "Temporal Localization", + "question": "At what exact timestamp did channel 813 reach its minimum value in 2020? (Output format: a specific timestamp, e.g., YYYY-MM-DD HH:MM:SS)", + "answer": "2020-10-17 18:45:00", + "ground_truth": "2020-10-17 18:45:00", + "eval_metric": "hit", + "channel": "813", + "ts_data_path": "ts_data/L1_T2_Temporal_Localization_00212.csv", + "meta": { + "source": "causal_rivers", + "args": { + "channel": "813", + "time": "2020", + "threshold_high": "0.821", + "threshold_low": "0.482", + "action": "reach its minimum value" + } + } + }, + { + "id": "L1_T2_Temporal_Localization_00219", + "level": 1, + "level_name": "Basic Operations", + "category": "Atomic Retrieval", + "subtask": "Temporal Localization", + "question": "At what exact timestamp did channel HUFL reach its maximum value in 2018? (Output format: a specific timestamp, e.g., YYYY-MM-DD HH:MM:SS)", + "answer": "2018-02-10 21:15:00", + "ground_truth": "2018-02-10 21:15:00", + "eval_metric": "hit", + "channel": "HUFL", + "ts_data_path": "ts_data/L1_T2_Temporal_Localization_00219.csv", + "meta": { + "source": "ettm1", + "args": { + "channel": "HUFL", + "time": "2018", + "threshold_high": "14.066", + "threshold_low": "1.072", + "action": "reach its maximum value" + } + } + }, + { + "id": "L1_T2_Temporal_Localization_00228", + "level": 1, + "level_name": "Basic Operations", + "category": "Atomic Retrieval", + "subtask": "Temporal Localization", + "question": "At what exact timestamp did channel 67 first rise above 0.236 in 2021? (Output format: a specific timestamp, e.g., YYYY-MM-DD HH:MM:SS)", + "answer": "2021-01-05 14:45:00", + "ground_truth": "2021-01-05 14:45:00", + "eval_metric": "hit", + "channel": "67", + "ts_data_path": "ts_data/L1_T2_Temporal_Localization_00228.csv", + "meta": { + "source": "causal_rivers", + "args": { + "channel": "67", + "time": "2021", + "threshold_high": "0.236", + "threshold_low": "0.178", + "action": "first rise above 0.236" + } + } + }, + { + "id": "L1_T2_Temporal_Localization_00235", + "level": 1, + "level_name": "Basic Operations", + "category": "Atomic Retrieval", + "subtask": "Temporal Localization", + "question": "At what exact timestamp did channel 151 reach its minimum value in 2023? (Output format: a specific timestamp, e.g., YYYY-MM-DD HH:MM:SS)", + "answer": "2023-09-08 06:45:00", + "ground_truth": "2023-09-08 06:45:00", + "eval_metric": "hit", + "channel": "151", + "ts_data_path": "ts_data/L1_T2_Temporal_Localization_00235.csv", + "meta": { + "source": "causal_rivers", + "args": { + "channel": "151", + "time": "2023", + "threshold_high": "0.89", + "threshold_low": "0.197", + "action": "reach its minimum value" + } + } + }, + { + "id": "L1_T2_Temporal_Localization_00242", + "level": 1, + "level_name": "Basic Operations", + "category": "Atomic Retrieval", + "subtask": "Temporal Localization", + "question": "At what exact timestamp did channel 173 reach its maximum value in 2019-02? (Output format: a specific timestamp, e.g., YYYY-MM-DD HH:MM:SS)", + "answer": "2019-02-15 17:00:00", + "ground_truth": "2019-02-15 17:00:00", + "eval_metric": "hit", + "channel": "173", + "ts_data_path": "ts_data/L1_T2_Temporal_Localization_00242.csv", + "meta": { + "source": "causal_rivers", + "args": { + "channel": "173", + "time": "2019-02", + "threshold_high": "413.0", + "threshold_low": "320.0", + "action": "reach its maximum value" + } + } + }, + { + "id": "L1_T2_Temporal_Localization_00244", + "level": 1, + "level_name": "Basic Operations", + "category": "Atomic Retrieval", + "subtask": "Temporal Localization", + "question": "At what exact timestamp did channel 237 last fall below 0.113 in 2022? (Output format: a specific timestamp, e.g., YYYY-MM-DD HH:MM:SS)", + "answer": "2022-08-28 13:45:00", + "ground_truth": "2022-08-28 13:45:00", + "eval_metric": "hit", + "channel": "237", + "ts_data_path": "ts_data/L1_T2_Temporal_Localization_00244.csv", + "meta": { + "source": "causal_rivers", + "args": { + "channel": "237", + "time": "2022", + "threshold_high": "1.67", + "threshold_low": "0.113", + "action": "last fall below 0.113" + } + } + }, + { + "id": "L1_T2_Temporal_Localization_00245", + "level": 1, + "level_name": "Basic Operations", + "category": "Atomic Retrieval", + "subtask": "Temporal Localization", + "question": "At what exact timestamp did channel 245 first rise above 0.433 in 2021? (Output format: a specific timestamp, e.g., YYYY-MM-DD HH:MM:SS)", + "answer": "2021-01-01 00:00:00", + "ground_truth": "2021-01-01 00:00:00", + "eval_metric": "hit", + "channel": "245", + "ts_data_path": "ts_data/L1_T2_Temporal_Localization_00245.csv", + "meta": { + "source": "causal_rivers", + "args": { + "channel": "245", + "time": "2021", + "threshold_high": "0.433", + "threshold_low": "0.252", + "action": "first rise above 0.433" + } + } + }, + { + "id": "L1_T2_Temporal_Localization_00252", + "level": 1, + "level_name": "Basic Operations", + "category": "Atomic Retrieval", + "subtask": "Temporal Localization", + "question": "At what exact timestamp did channel 578 reach its maximum value in 2023? (Output format: a specific timestamp, e.g., YYYY-MM-DD HH:MM:SS)", + "answer": "2023-12-24 20:00:00", + "ground_truth": "2023-12-24 20:00:00", + "eval_metric": "hit", + "channel": "578", + "ts_data_path": "ts_data/L1_T2_Temporal_Localization_00252.csv", + "meta": { + "source": "causal_rivers", + "args": { + "channel": "578", + "time": "2023", + "threshold_high": "1.11", + "threshold_low": "0.313", + "action": "reach its maximum value" + } + } + }, + { + "id": "L1_T2_Temporal_Localization_00256", + "level": 1, + "level_name": "Basic Operations", + "category": "Atomic Retrieval", + "subtask": "Temporal Localization", + "question": "At what exact timestamp did channel 595 reach its minimum value in 2021-03? (Output format: a specific timestamp, e.g., YYYY-MM-DD HH:MM:SS)", + "answer": "2021-03-10 15:45:00", + "ground_truth": "2021-03-10 15:45:00", + "eval_metric": "hit", + "channel": "595", + "ts_data_path": "ts_data/L1_T2_Temporal_Localization_00256.csv", + "meta": { + "source": "causal_rivers", + "args": { + "channel": "595", + "time": "2021-03", + "threshold_high": "4.02", + "threshold_low": "1.84", + "action": "reach its minimum value" + } + } + }, + { + "id": "L1_T2_Temporal_Localization_00263", + "level": 1, + "level_name": "Basic Operations", + "category": "Atomic Retrieval", + "subtask": "Temporal Localization", + "question": "At what exact timestamp did channel 727 reach its maximum value in 2021-09? (Output format: a specific timestamp, e.g., YYYY-MM-DD HH:MM:SS)", + "answer": "2021-09-01 13:30:00", + "ground_truth": "2021-09-01 13:30:00", + "eval_metric": "hit", + "channel": "727", + "ts_data_path": "ts_data/L1_T2_Temporal_Localization_00263.csv", + "meta": { + "source": "causal_rivers", + "args": { + "channel": "727", + "time": "2021-09", + "threshold_high": "141.0", + "threshold_low": "62.7", + "action": "reach its maximum value" + } + } + }, + { + "id": "L1_T2_Temporal_Localization_00265", + "level": 1, + "level_name": "Basic Operations", + "category": "Atomic Retrieval", + "subtask": "Temporal Localization", + "question": "At what exact timestamp did channel 729 first rise above 98.6 in 2021? (Output format: a specific timestamp, e.g., YYYY-MM-DD HH:MM:SS)", + "answer": "2021-01-25 08:45:00", + "ground_truth": "2021-01-25 08:45:00", + "eval_metric": "hit", + "channel": "729", + "ts_data_path": "ts_data/L1_T2_Temporal_Localization_00265.csv", + "meta": { + "source": "causal_rivers", + "args": { + "channel": "729", + "time": "2021", + "threshold_high": "98.6", + "threshold_low": "48.2", + "action": "first rise above 98.6" + } + } + }, + { + "id": "L1_T2_Temporal_Localization_00273", + "level": 1, + "level_name": "Basic Operations", + "category": "Atomic Retrieval", + "subtask": "Temporal Localization", + "question": "At what exact timestamp did channel 895 last fall below 1.09 in 2020-10? (Output format: a specific timestamp, e.g., YYYY-MM-DD HH:MM:SS)", + "answer": "2020-10-18 20:00:00", + "ground_truth": "2020-10-18 20:00:00", + "eval_metric": "hit", + "channel": "895", + "ts_data_path": "ts_data/L1_T2_Temporal_Localization_00273.csv", + "meta": { + "source": "causal_rivers", + "args": { + "channel": "895", + "time": "2020-10", + "threshold_high": "1.41", + "threshold_low": "1.09", + "action": "last fall below 1.09" + } + } + }, + { + "id": "L1_T2_Temporal_Localization_00275", + "level": 1, + "level_name": "Basic Operations", + "category": "Atomic Retrieval", + "subtask": "Temporal Localization", + "question": "At what exact timestamp did channel 933 last fall below 0.02 in 2023? (Output format: a specific timestamp, e.g., YYYY-MM-DD HH:MM:SS)", + "answer": "2023-12-11 03:00:00", + "ground_truth": "2023-12-11 03:00:00", + "eval_metric": "hit", + "channel": "933", + "ts_data_path": "ts_data/L1_T2_Temporal_Localization_00275.csv", + "meta": { + "source": "causal_rivers", + "args": { + "channel": "933", + "time": "2023", + "threshold_high": "0.204", + "threshold_low": "0.02", + "action": "last fall below 0.02" + } + } + }, + { + "id": "L1_T2_Temporal_Localization_00279", + "level": 1, + "level_name": "Basic Operations", + "category": "Atomic Retrieval", + "subtask": "Temporal Localization", + "question": "At what exact timestamp did channel LUFL last fall below 1.888 in 2016? (Output format: a specific timestamp, e.g., YYYY-MM-DD HH:MM:SS)", + "answer": "2016-12-29 04:15:00", + "ground_truth": "2016-12-29 04:15:00", + "eval_metric": "hit", + "channel": "LUFL", + "ts_data_path": "ts_data/L1_T2_Temporal_Localization_00279.csv", + "meta": { + "source": "ettm1", + "args": { + "channel": "LUFL", + "time": "2016", + "threshold_high": "3.899", + "threshold_low": "1.888", + "action": "last fall below 1.888" + } + } + }, + { + "id": "L1_T2_Temporal_Localization_00292", + "level": 1, + "level_name": "Basic Operations", + "category": "Atomic Retrieval", + "subtask": "Temporal Localization", + "question": "At what exact timestamp did channel 154 reach its maximum value in 2019-01? (Output format: a specific timestamp, e.g., YYYY-MM-DD HH:MM:SS)", + "answer": "2019-01-13 20:15:00", + "ground_truth": "2019-01-13 20:15:00", + "eval_metric": "hit", + "channel": "154", + "ts_data_path": "ts_data/L1_T2_Temporal_Localization_00292.csv", + "meta": { + "source": "causal_rivers", + "args": { + "channel": "154", + "time": "2019-01", + "threshold_high": "0.799", + "threshold_low": "0.524", + "action": "reach its maximum value" + } + } + }, + { + "id": "L1_T2_Temporal_Localization_00295", + "level": 1, + "level_name": "Basic Operations", + "category": "Atomic Retrieval", + "subtask": "Temporal Localization", + "question": "At what exact timestamp did channel 169 reach its maximum value in 2022-04? (Output format: a specific timestamp, e.g., YYYY-MM-DD HH:MM:SS)", + "answer": "2022-04-13 16:00:00", + "ground_truth": "2022-04-13 16:00:00", + "eval_metric": "hit", + "channel": "169", + "ts_data_path": "ts_data/L1_T2_Temporal_Localization_00295.csv", + "meta": { + "source": "causal_rivers", + "args": { + "channel": "169", + "time": "2022-04", + "threshold_high": "420.4", + "threshold_low": "363.0", + "action": "reach its maximum value" + } + } + }, + { + "id": "L1_T2_Temporal_Localization_00302", + "level": 1, + "level_name": "Basic Operations", + "category": "Atomic Retrieval", + "subtask": "Temporal Localization", + "question": "At what exact timestamp did channel 312 reach its maximum value in 2021-10? (Output format: a specific timestamp, e.g., YYYY-MM-DD HH:MM:SS)", + "answer": "2021-10-21 23:00:00", + "ground_truth": "2021-10-21 23:00:00", + "eval_metric": "hit", + "channel": "312", + "ts_data_path": "ts_data/L1_T2_Temporal_Localization_00302.csv", + "meta": { + "source": "causal_rivers", + "args": { + "channel": "312", + "time": "2021-10", + "threshold_high": "0.324", + "threshold_low": "0.291", + "action": "reach its maximum value" + } + } + }, + { + "id": "L1_T2_Temporal_Localization_00307", + "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 2021? (Output format: a specific timestamp, e.g., YYYY-MM-DD HH:MM:SS)", + "answer": "2021-07-29 13:15:00", + "ground_truth": "2021-07-29 13:15:00", + "eval_metric": "hit", + "channel": "501", + "ts_data_path": "ts_data/L1_T2_Temporal_Localization_00307.csv", + "meta": { + "source": "causal_rivers", + "args": { + "channel": "501", + "time": "2021", + "threshold_high": "1.24", + "threshold_low": "0.238", + "action": "reach its minimum value" + } + } + }, + { + "id": "L1_T2_Temporal_Localization_00309", + "level": 1, + "level_name": "Basic Operations", + "category": "Atomic Retrieval", + "subtask": "Temporal Localization", + "question": "At what exact timestamp did channel 580 first rise above 0.132 in 2020-08? (Output format: a specific timestamp, e.g., YYYY-MM-DD HH:MM:SS)", + "answer": "2020-08-10 01:45:00", + "ground_truth": "2020-08-10 01:45:00", + "eval_metric": "hit", + "channel": "580", + "ts_data_path": "ts_data/L1_T2_Temporal_Localization_00309.csv", + "meta": { + "source": "causal_rivers", + "args": { + "channel": "580", + "time": "2020-08", + "threshold_high": "0.132", + "threshold_low": "0.113", + "action": "first rise above 0.132" + } + } + }, + { + "id": "L1_T2_Temporal_Localization_00320", + "level": 1, + "level_name": "Basic Operations", + "category": "Atomic Retrieval", + "subtask": "Temporal Localization", + "question": "At what exact timestamp did channel 728 reach its minimum value in 2022-04? (Output format: a specific timestamp, e.g., YYYY-MM-DD HH:MM:SS)", + "answer": "2022-04-30 11:30:00", + "ground_truth": "2022-04-30 11:30:00", + "eval_metric": "hit", + "channel": "728", + "ts_data_path": "ts_data/L1_T2_Temporal_Localization_00320.csv", + "meta": { + "source": "causal_rivers", + "args": { + "channel": "728", + "time": "2022-04", + "threshold_high": "92.2", + "threshold_low": "71.1", + "action": "reach its minimum value" + } + } + }, + { + "id": "L1_T2_Temporal_Localization_00323", + "level": 1, + "level_name": "Basic Operations", + "category": "Atomic Retrieval", + "subtask": "Temporal Localization", + "question": "At what exact timestamp did channel 762 reach its minimum value in 2023? (Output format: a specific timestamp, e.g., YYYY-MM-DD HH:MM:SS)", + "answer": "2023-07-18 03:30:00", + "ground_truth": "2023-07-18 03:30:00", + "eval_metric": "hit", + "channel": "762", + "ts_data_path": "ts_data/L1_T2_Temporal_Localization_00323.csv", + "meta": { + "source": "causal_rivers", + "args": { + "channel": "762", + "time": "2023", + "threshold_high": "0.691", + "threshold_low": "0.075", + "action": "reach its minimum value" + } + } + }, + { + "id": "L1_T2_Temporal_Localization_00325", + "level": 1, + "level_name": "Basic Operations", + "category": "Atomic Retrieval", + "subtask": "Temporal Localization", + "question": "At what exact timestamp did channel 813 last fall below 0.568 in 2023? (Output format: a specific timestamp, e.g., YYYY-MM-DD HH:MM:SS)", + "answer": "2023-11-17 11:30:00", + "ground_truth": "2023-11-17 11:30:00", + "eval_metric": "hit", + "channel": "813", + "ts_data_path": "ts_data/L1_T2_Temporal_Localization_00325.csv", + "meta": { + "source": "causal_rivers", + "args": { + "channel": "813", + "time": "2023", + "threshold_high": "2.15", + "threshold_low": "0.568", + "action": "last fall below 0.568" + } + } + }, + { + "id": "L1_T2_Temporal_Localization_00333", + "level": 1, + "level_name": "Basic Operations", + "category": "Atomic Retrieval", + "subtask": "Temporal Localization", + "question": "At what exact timestamp did channel MULL reach its minimum value in 2017? (Output format: a specific timestamp, e.g., YYYY-MM-DD HH:MM:SS)", + "answer": "2017-08-30 20:30:00", + "ground_truth": "2017-08-30 20:30:00", + "eval_metric": "hit", + "channel": "MULL", + "ts_data_path": "ts_data/L1_T2_Temporal_Localization_00333.csv", + "meta": { + "source": "ettm1", + "args": { + "channel": "MULL", + "time": "2017", + "threshold_high": "1.777", + "threshold_low": "-1.528", + "action": "reach its minimum value" + } + } + }, + { + "id": "L1_T4_Sliding_Window_00524", + "level": 1, + "level_name": "Basic Operations", + "category": "Sliding Window", + "subtask": "Sliding Window", + "question": "Which 47-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": "[2023-08-21 11:45:00, 2023-10-07 11:30:00]", + "ground_truth": [ + "2023-08-21 11:45:00", + "2023-10-07 11:30:00" + ], + "eval_metric": "iou", + "channel": "155", + "ts_data_path": "ts_data/L1_T4_Sliding_Window_00524.csv", + "meta": { + "source": "causal_rivers", + "args": { + "channel": "155", + "time": "2023", + "metric": "lowest average", + "window": "47D" + } + } + }, + { + "id": "L1_T4_Sliding_Window_00526", + "level": 1, + "level_name": "Basic Operations", + "category": "Sliding Window", + "subtask": "Sliding Window", + "question": "Which 3-day period in 2023 had the highest variance for channel 169? (Output format: [YYYY-MM-DD HH:MM:SS, YYYY-MM-DD HH:MM:SS])", + "answer": "[2023-12-24 06:15:00, 2023-12-27 06:00:00]", + "ground_truth": [ + "2023-12-24 06:15:00", + "2023-12-27 06:00:00" + ], + "eval_metric": "iou", + "channel": "169", + "ts_data_path": "ts_data/L1_T4_Sliding_Window_00526.csv", + "meta": { + "source": "causal_rivers", + "args": { + "channel": "169", + "time": "2023", + "metric": "highest variance", + "window": "3D" + } + } + }, + { + "id": "L1_T4_Sliding_Window_00538", + "level": 1, + "level_name": "Basic Operations", + "category": "Sliding Window", + "subtask": "Sliding Window", + "question": "Which 42-day period in 2023 had the largest range for channel 501? (Output format: [YYYY-MM-DD HH:MM:SS, YYYY-MM-DD HH:MM:SS])", + "answer": "[2023-11-13 05:30:00, 2023-12-25 05:15:00]", + "ground_truth": [ + "2023-11-13 05:30:00", + "2023-12-25 05:15:00" + ], + "eval_metric": "iou", + "channel": "501", + "ts_data_path": "ts_data/L1_T4_Sliding_Window_00538.csv", + "meta": { + "source": "causal_rivers", + "args": { + "channel": "501", + "time": "2023", + "metric": "largest range", + "window": "42D" + } + } + }, + { + "id": "L1_T4_Sliding_Window_00539", + "level": 1, + "level_name": "Basic Operations", + "category": "Sliding Window", + "subtask": "Sliding Window", + "question": "Which 30-day period in 2021 had the highest average for channel 578? (Output format: [YYYY-MM-DD HH:MM:SS, YYYY-MM-DD HH:MM:SS])", + "answer": "[2021-02-02 23:45:00, 2021-03-04 23:30:00]", + "ground_truth": [ + "2021-02-02 23:45:00", + "2021-03-04 23:30:00" + ], + "eval_metric": "iou", + "channel": "578", + "ts_data_path": "ts_data/L1_T4_Sliding_Window_00539.csv", + "meta": { + "source": "causal_rivers", + "args": { + "channel": "578", + "time": "2021", + "metric": "highest average", + "window": "30D" + } + } + }, + { + "id": "L1_T4_Sliding_Window_00549", + "level": 1, + "level_name": "Basic Operations", + "category": "Sliding Window", + "subtask": "Sliding Window", + "question": "Which 4-day period in 2021 had the lowest average for channel 683? (Output format: [YYYY-MM-DD HH:MM:SS, YYYY-MM-DD HH:MM:SS])", + "answer": "[2021-02-21 23:15:00, 2021-02-25 23:00:00]", + "ground_truth": [ + "2021-02-21 23:15:00", + "2021-02-25 23:00:00" + ], + "eval_metric": "iou", + "channel": "683", + "ts_data_path": "ts_data/L1_T4_Sliding_Window_00549.csv", + "meta": { + "source": "causal_rivers", + "args": { + "channel": "683", + "time": "2021", + "metric": "lowest average", + "window": "4D" + } + } + }, + { + "id": "L1_T4_Sliding_Window_00553", + "level": 1, + "level_name": "Basic Operations", + "category": "Sliding Window", + "subtask": "Sliding Window", + "question": "Which 52-day period in 2019 had the highest average for channel 754? (Output format: [YYYY-MM-DD HH:MM:SS, YYYY-MM-DD HH:MM:SS])", + "answer": "[2019-01-08 10:15:00, 2019-03-01 10:00:00]", + "ground_truth": [ + "2019-01-08 10:15:00", + "2019-03-01 10:00:00" + ], + "eval_metric": "iou", + "channel": "754", + "ts_data_path": "ts_data/L1_T4_Sliding_Window_00553.csv", + "meta": { + "source": "causal_rivers", + "args": { + "channel": "754", + "time": "2019", + "metric": "highest average", + "window": "52D" + } + } + }, + { + "id": "L1_T4_Sliding_Window_00559", + "level": 1, + "level_name": "Basic Operations", + "category": "Sliding Window", + "subtask": "Sliding Window", + "question": "Which 36-day period in 2022 had the lowest average for channel 894? (Output format: [YYYY-MM-DD HH:MM:SS, YYYY-MM-DD HH:MM:SS])", + "answer": "[2022-07-29 01:00:00, 2022-09-03 00:45:00]", + "ground_truth": [ + "2022-07-29 01:00:00", + "2022-09-03 00:45:00" + ], + "eval_metric": "iou", + "channel": "894", + "ts_data_path": "ts_data/L1_T4_Sliding_Window_00559.csv", + "meta": { + "source": "causal_rivers", + "args": { + "channel": "894", + "time": "2022", + "metric": "lowest average", + "window": "36D" + } + } + }, + { + "id": "L1_T4_Sliding_Window_00575", + "level": 1, + "level_name": "Basic Operations", + "category": "Sliding Window", + "subtask": "Sliding Window", + "question": "Which 59-day period in 2019 had the highest variance for channel 71? (Output format: [YYYY-MM-DD HH:MM:SS, YYYY-MM-DD HH:MM:SS])", + "answer": "[2019-03-10 21:45:00, 2019-05-08 21:30:00]", + "ground_truth": [ + "2019-03-10 21:45:00", + "2019-05-08 21:30:00" + ], + "eval_metric": "iou", + "channel": "71", + "ts_data_path": "ts_data/L1_T4_Sliding_Window_00575.csv", + "meta": { + "source": "causal_rivers", + "args": { + "channel": "71", + "time": "2019", + "metric": "highest variance", + "window": "59D" + } + } + }, + { + "id": "L1_T4_Sliding_Window_00576", + "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 99? (Output format: [YYYY-MM-DD HH:MM:SS, YYYY-MM-DD HH:MM:SS])", + "answer": "[2020-09-21 04:30:00, 2020-09-25 04:15:00]", + "ground_truth": [ + "2020-09-21 04:30:00", + "2020-09-25 04:15:00" + ], + "eval_metric": "iou", + "channel": "99", + "ts_data_path": "ts_data/L1_T4_Sliding_Window_00576.csv", + "meta": { + "source": "causal_rivers", + "args": { + "channel": "99", + "time": "2020", + "metric": "lowest average", + "window": "4D" + } + } + }, + { + "id": "L1_T4_Sliding_Window_00589", + "level": 1, + "level_name": "Basic Operations", + "category": "Sliding Window", + "subtask": "Sliding Window", + "question": "Which 33-day period in 2019 had the highest variance for channel 177? (Output format: [YYYY-MM-DD HH:MM:SS, YYYY-MM-DD HH:MM:SS])", + "answer": "[2019-03-15 21:30:00, 2019-04-17 21:15:00]", + "ground_truth": [ + "2019-03-15 21:30:00", + "2019-04-17 21:15:00" + ], + "eval_metric": "iou", + "channel": "177", + "ts_data_path": "ts_data/L1_T4_Sliding_Window_00589.csv", + "meta": { + "source": "causal_rivers", + "args": { + "channel": "177", + "time": "2019", + "metric": "highest variance", + "window": "33D" + } + } + }, + { + "id": "L1_T4_Sliding_Window_00593", + "level": 1, + "level_name": "Basic Operations", + "category": "Sliding Window", + "subtask": "Sliding Window", + "question": "Which 33-day period in 2019 had the largest range for channel 430? (Output format: [YYYY-MM-DD HH:MM:SS, YYYY-MM-DD HH:MM:SS])", + "answer": "[2019-11-09 04:45:00, 2019-12-12 04:30:00]", + "ground_truth": [ + "2019-11-09 04:45:00", + "2019-12-12 04:30:00" + ], + "eval_metric": "iou", + "channel": "430", + "ts_data_path": "ts_data/L1_T4_Sliding_Window_00593.csv", + "meta": { + "source": "causal_rivers", + "args": { + "channel": "430", + "time": "2019", + "metric": "largest range", + "window": "33D" + } + } + }, + { + "id": "L1_T4_Sliding_Window_00594", + "level": 1, + "level_name": "Basic Operations", + "category": "Sliding Window", + "subtask": "Sliding Window", + "question": "Which 51-day period in 2022 had the largest range for channel 441? (Output format: [YYYY-MM-DD HH:MM:SS, YYYY-MM-DD HH:MM:SS])", + "answer": "[2022-02-12 17:45:00, 2022-04-04 17:30:00]", + "ground_truth": [ + "2022-02-12 17:45:00", + "2022-04-04 17:30:00" + ], + "eval_metric": "iou", + "channel": "441", + "ts_data_path": "ts_data/L1_T4_Sliding_Window_00594.csv", + "meta": { + "source": "causal_rivers", + "args": { + "channel": "441", + "time": "2022", + "metric": "largest range", + "window": "51D" + } + } + }, + { + "id": "L1_T4_Sliding_Window_00595", + "level": 1, + "level_name": "Basic Operations", + "category": "Sliding Window", + "subtask": "Sliding Window", + "question": "Which 12-day period in 2020 had the highest average for channel 495? (Output format: [YYYY-MM-DD HH:MM:SS, YYYY-MM-DD HH:MM:SS])", + "answer": "[2020-03-03 18:00:00, 2020-03-15 17:45:00]", + "ground_truth": [ + "2020-03-03 18:00:00", + "2020-03-15 17:45:00" + ], + "eval_metric": "iou", + "channel": "495", + "ts_data_path": "ts_data/L1_T4_Sliding_Window_00595.csv", + "meta": { + "source": "causal_rivers", + "args": { + "channel": "495", + "time": "2020", + "metric": "highest average", + "window": "12D" + } + } + }, + { + "id": "L1_T4_Sliding_Window_00597", + "level": 1, + "level_name": "Basic Operations", + "category": "Sliding Window", + "subtask": "Sliding Window", + "question": "Which 26-day period in 2021 had the highest average for channel 501? (Output format: [YYYY-MM-DD HH:MM:SS, YYYY-MM-DD HH:MM:SS])", + "answer": "[2021-01-12 11:00:00, 2021-02-07 10:45:00]", + "ground_truth": [ + "2021-01-12 11:00:00", + "2021-02-07 10:45:00" + ], + "eval_metric": "iou", + "channel": "501", + "ts_data_path": "ts_data/L1_T4_Sliding_Window_00597.csv", + "meta": { + "source": "causal_rivers", + "args": { + "channel": "501", + "time": "2021", + "metric": "highest average", + "window": "26D" + } + } + }, + { + "id": "L1_T4_Sliding_Window_00602", + "level": 1, + "level_name": "Basic Operations", + "category": "Sliding Window", + "subtask": "Sliding Window", + "question": "Which 47-day period in 2023 had the highest average for channel 595? (Output format: [YYYY-MM-DD HH:MM:SS, YYYY-MM-DD HH:MM:SS])", + "answer": "[2023-11-15 00:00:00, 2023-12-31 23:45:00]", + "ground_truth": [ + "2023-11-15 00:00:00", + "2023-12-31 23:45:00" + ], + "eval_metric": "iou", + "channel": "595", + "ts_data_path": "ts_data/L1_T4_Sliding_Window_00602.csv", + "meta": { + "source": "causal_rivers", + "args": { + "channel": "595", + "time": "2023", + "metric": "highest average", + "window": "47D" + } + } + }, + { + "id": "L1_T4_Sliding_Window_00615", + "level": 1, + "level_name": "Basic Operations", + "category": "Sliding Window", + "subtask": "Sliding Window", + "question": "Which 23-day period in 2023 had the lowest average for channel 813? (Output format: [YYYY-MM-DD HH:MM:SS, YYYY-MM-DD HH:MM:SS])", + "answer": "[2023-01-01 00:00:00, 2023-01-01 10:15:00]", + "ground_truth": [ + "2023-01-01 00:00:00", + "2023-01-01 10:15:00" + ], + "eval_metric": "iou", + "channel": "813", + "ts_data_path": "ts_data/L1_T4_Sliding_Window_00615.csv", + "meta": { + "source": "causal_rivers", + "args": { + "channel": "813", + "time": "2023", + "metric": "lowest average", + "window": "23D" + } + } + }, + { + "id": "L1_T4_Sliding_Window_00617", + "level": 1, + "level_name": "Basic Operations", + "category": "Sliding Window", + "subtask": "Sliding Window", + "question": "Which 54-day period in 2020 had the largest range for channel 891? (Output format: [YYYY-MM-DD HH:MM:SS, YYYY-MM-DD HH:MM:SS])", + "answer": "[2020-02-02 13:00:00, 2020-03-27 12:45:00]", + "ground_truth": [ + "2020-02-02 13:00:00", + "2020-03-27 12:45:00" + ], + "eval_metric": "iou", + "channel": "891", + "ts_data_path": "ts_data/L1_T4_Sliding_Window_00617.csv", + "meta": { + "source": "causal_rivers", + "args": { + "channel": "891", + "time": "2020", + "metric": "largest range", + "window": "54D" + } + } + }, + { + "id": "L1_T4_Sliding_Window_00618", + "level": 1, + "level_name": "Basic Operations", + "category": "Sliding Window", + "subtask": "Sliding Window", + "question": "Which 34-day period in 2021 had the highest variance for channel 894? (Output format: [YYYY-MM-DD HH:MM:SS, YYYY-MM-DD HH:MM:SS])", + "answer": "[2021-01-20 07:30:00, 2021-02-23 07:15:00]", + "ground_truth": [ + "2021-01-20 07:30:00", + "2021-02-23 07:15:00" + ], + "eval_metric": "iou", + "channel": "894", + "ts_data_path": "ts_data/L1_T4_Sliding_Window_00618.csv", + "meta": { + "source": "causal_rivers", + "args": { + "channel": "894", + "time": "2021", + "metric": "highest variance", + "window": "34D" + } + } + }, + { + "id": "L1_T4_Sliding_Window_00620", + "level": 1, + "level_name": "Basic Operations", + "category": "Sliding Window", + "subtask": "Sliding Window", + "question": "Which 28-day period in 2019 had the lowest average for channel 897? (Output format: [YYYY-MM-DD HH:MM:SS, YYYY-MM-DD HH:MM:SS])", + "answer": "[2019-06-22 21:45:00, 2019-07-20 21:30:00]", + "ground_truth": [ + "2019-06-22 21:45:00", + "2019-07-20 21:30:00" + ], + "eval_metric": "iou", + "channel": "897", + "ts_data_path": "ts_data/L1_T4_Sliding_Window_00620.csv", + "meta": { + "source": "causal_rivers", + "args": { + "channel": "897", + "time": "2019", + "metric": "lowest average", + "window": "28D" + } + } + }, + { + "id": "L1_T4_Sliding_Window_00623", + "level": 1, + "level_name": "Basic Operations", + "category": "Sliding Window", + "subtask": "Sliding Window", + "question": "Which 33-day period in 2018 had the highest variance for channel HULL? (Output format: [YYYY-MM-DD HH:MM:SS, YYYY-MM-DD HH:MM:SS])", + "answer": "[2018-03-22 22:30:00, 2018-04-24 22:15:00]", + "ground_truth": [ + "2018-03-22 22:30:00", + "2018-04-24 22:15:00" + ], + "eval_metric": "iou", + "channel": "HULL", + "ts_data_path": "ts_data/L1_T4_Sliding_Window_00623.csv", + "meta": { + "source": "ettm1", + "args": { + "channel": "HULL", + "time": "2018", + "metric": "highest variance", + "window": "33D" + } + } + }, + { + "id": "L1_T4_Sliding_Window_00630", + "level": 1, + "level_name": "Basic Operations", + "category": "Sliding Window", + "subtask": "Sliding Window", + "question": "Which 57-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_00630.csv", + "meta": { + "source": "smd_machine_1_1", + "args": { + "channel": "m_14", + "time": "2020", + "metric": "lowest average", + "window": "57D" + } + } + }, + { + "id": "L1_T4_Sliding_Window_00631", + "level": 1, + "level_name": "Basic Operations", + "category": "Sliding Window", + "subtask": "Sliding Window", + "question": "Which 9-day period in 2020 had the lowest average for channel m_18? (Output format: [YYYY-MM-DD HH:MM:SS, YYYY-MM-DD HH:MM:SS])", + "answer": "[2020-01-01 00:00:00, 2020-01-01 06:06:00]", + "ground_truth": [ + "2020-01-01 00:00:00", + "2020-01-01 06:06:00" + ], + "eval_metric": "iou", + "channel": "m_18", + "ts_data_path": "ts_data/L1_T4_Sliding_Window_00631.csv", + "meta": { + "source": "smd_machine_1_1", + "args": { + "channel": "m_18", + "time": "2020", + "metric": "lowest average", + "window": "9D" + } + } + }, + { + "id": "L1_T4_Sliding_Window_00634", + "level": 1, + "level_name": "Basic Operations", + "category": "Sliding Window", + "subtask": "Sliding Window", + "question": "Which 57-day period in 2019 had the highest variance for channel 71? (Output format: [YYYY-MM-DD HH:MM:SS, YYYY-MM-DD HH:MM:SS])", + "answer": "[2019-03-10 23:00:00, 2019-05-06 22:45:00]", + "ground_truth": [ + "2019-03-10 23:00:00", + "2019-05-06 22:45:00" + ], + "eval_metric": "iou", + "channel": "71", + "ts_data_path": "ts_data/L1_T4_Sliding_Window_00634.csv", + "meta": { + "source": "causal_rivers", + "args": { + "channel": "71", + "time": "2019", + "metric": "highest variance", + "window": "57D" + } + } + }, + { + "id": "L1_T4_Sliding_Window_00641", + "level": 1, + "level_name": "Basic Operations", + "category": "Sliding Window", + "subtask": "Sliding Window", + "question": "Which 21-day period in 2020 had the lowest average for channel 154? (Output format: [YYYY-MM-DD HH:MM:SS, YYYY-MM-DD HH:MM:SS])", + "answer": "[2020-09-28 19:00:00, 2020-10-19 18:45:00]", + "ground_truth": [ + "2020-09-28 19:00:00", + "2020-10-19 18:45:00" + ], + "eval_metric": "iou", + "channel": "154", + "ts_data_path": "ts_data/L1_T4_Sliding_Window_00641.csv", + "meta": { + "source": "causal_rivers", + "args": { + "channel": "154", + "time": "2020", + "metric": "lowest average", + "window": "21D" + } + } + }, + { + "id": "L1_T4_Sliding_Window_00644", + "level": 1, + "level_name": "Basic Operations", + "category": "Sliding Window", + "subtask": "Sliding Window", + "question": "Which 21-day period in 2022 had the largest range for channel 169? (Output format: [YYYY-MM-DD HH:MM:SS, YYYY-MM-DD HH:MM:SS])", + "answer": "[2022-02-23 22:45:00, 2022-03-16 22:30:00]", + "ground_truth": [ + "2022-02-23 22:45:00", + "2022-03-16 22:30:00" + ], + "eval_metric": "iou", + "channel": "169", + "ts_data_path": "ts_data/L1_T4_Sliding_Window_00644.csv", + "meta": { + "source": "causal_rivers", + "args": { + "channel": "169", + "time": "2022", + "metric": "largest range", + "window": "21D" + } + } + }, + { + "id": "L1_T4_Sliding_Window_00648", + "level": 1, + "level_name": "Basic Operations", + "category": "Sliding Window", + "subtask": "Sliding Window", + "question": "Which 7-day period in 2020 had the highest average for channel 177? (Output format: [YYYY-MM-DD HH:MM:SS, YYYY-MM-DD HH:MM:SS])", + "answer": "[2020-06-20 01:45:00, 2020-06-27 01:30:00]", + "ground_truth": [ + "2020-06-20 01:45:00", + "2020-06-27 01:30:00" + ], + "eval_metric": "iou", + "channel": "177", + "ts_data_path": "ts_data/L1_T4_Sliding_Window_00648.csv", + "meta": { + "source": "causal_rivers", + "args": { + "channel": "177", + "time": "2020", + "metric": "highest average", + "window": "7D" + } + } + }, + { + "id": "L1_T4_Sliding_Window_00658", + "level": 1, + "level_name": "Basic Operations", + "category": "Sliding Window", + "subtask": "Sliding Window", + "question": "Which 17-day period in 2021 had the highest variance for channel 580? (Output format: [YYYY-MM-DD HH:MM:SS, YYYY-MM-DD HH:MM:SS])", + "answer": "[2021-10-21 10:30:00, 2021-11-07 10:15:00]", + "ground_truth": [ + "2021-10-21 10:30:00", + "2021-11-07 10:15:00" + ], + "eval_metric": "iou", + "channel": "580", + "ts_data_path": "ts_data/L1_T4_Sliding_Window_00658.csv", + "meta": { + "source": "causal_rivers", + "args": { + "channel": "580", + "time": "2021", + "metric": "highest variance", + "window": "17D" + } + } + }, + { + "id": "L1_T4_Sliding_Window_00659", + "level": 1, + "level_name": "Basic Operations", + "category": "Sliding Window", + "subtask": "Sliding Window", + "question": "Which 27-day period in 2019 had the highest variance for channel 589? (Output format: [YYYY-MM-DD HH:MM:SS, YYYY-MM-DD HH:MM:SS])", + "answer": "[2019-05-31 09:00:00, 2019-06-27 08:45:00]", + "ground_truth": [ + "2019-05-31 09:00:00", + "2019-06-27 08:45:00" + ], + "eval_metric": "iou", + "channel": "589", + "ts_data_path": "ts_data/L1_T4_Sliding_Window_00659.csv", + "meta": { + "source": "causal_rivers", + "args": { + "channel": "589", + "time": "2019", + "metric": "highest variance", + "window": "27D" + } + } + }, + { + "id": "L1_T4_Sliding_Window_00660", + "level": 1, + "level_name": "Basic Operations", + "category": "Sliding Window", + "subtask": "Sliding Window", + "question": "Which 45-day period in 2019 had the largest range for channel 591? (Output format: [YYYY-MM-DD HH:MM:SS, YYYY-MM-DD HH:MM:SS])", + "answer": "[2019-05-27 01:45:00, 2019-07-11 01:30:00]", + "ground_truth": [ + "2019-05-27 01:45:00", + "2019-07-11 01:30:00" + ], + "eval_metric": "iou", + "channel": "591", + "ts_data_path": "ts_data/L1_T4_Sliding_Window_00660.csv", + "meta": { + "source": "causal_rivers", + "args": { + "channel": "591", + "time": "2019", + "metric": "largest range", + "window": "45D" + } + } + }, + { + "id": "L1_T4_Sliding_Window_00661", + "level": 1, + "level_name": "Basic Operations", + "category": "Sliding Window", + "subtask": "Sliding Window", + "question": "Which 48-day period in 2019 had the largest range for channel 595? (Output format: [YYYY-MM-DD HH:MM:SS, YYYY-MM-DD HH:MM:SS])", + "answer": "[2019-03-02 16:15:00, 2019-04-19 16:00:00]", + "ground_truth": [ + "2019-03-02 16:15:00", + "2019-04-19 16:00:00" + ], + "eval_metric": "iou", + "channel": "595", + "ts_data_path": "ts_data/L1_T4_Sliding_Window_00661.csv", + "meta": { + "source": "causal_rivers", + "args": { + "channel": "595", + "time": "2019", + "metric": "largest range", + "window": "48D" + } + } + }, + { + "id": "L1_T4_Sliding_Window_00669", + "level": 1, + "level_name": "Basic Operations", + "category": "Sliding Window", + "subtask": "Sliding Window", + "question": "Which 22-day period in 2022 had the lowest average for channel 728? (Output format: [YYYY-MM-DD HH:MM:SS, YYYY-MM-DD HH:MM:SS])", + "answer": "[2022-07-27 09:00:00, 2022-08-18 08:45:00]", + "ground_truth": [ + "2022-07-27 09:00:00", + "2022-08-18 08:45:00" + ], + "eval_metric": "iou", + "channel": "728", + "ts_data_path": "ts_data/L1_T4_Sliding_Window_00669.csv", + "meta": { + "source": "causal_rivers", + "args": { + "channel": "728", + "time": "2022", + "metric": "lowest average", + "window": "22D" + } + } + }, + { + "id": "L1_T4_Sliding_Window_00671", + "level": 1, + "level_name": "Basic Operations", + "category": "Sliding Window", + "subtask": "Sliding Window", + "question": "Which 35-day period in 2019 had the largest range for channel 754? (Output format: [YYYY-MM-DD HH:MM:SS, YYYY-MM-DD HH:MM:SS])", + "answer": "[2019-01-29 13:00:00, 2019-03-05 12:45:00]", + "ground_truth": [ + "2019-01-29 13:00:00", + "2019-03-05 12:45:00" + ], + "eval_metric": "iou", + "channel": "754", + "ts_data_path": "ts_data/L1_T4_Sliding_Window_00671.csv", + "meta": { + "source": "causal_rivers", + "args": { + "channel": "754", + "time": "2019", + "metric": "largest range", + "window": "35D" + } + } + }, + { + "id": "L1_T4_Sliding_Window_00673", + "level": 1, + "level_name": "Basic Operations", + "category": "Sliding Window", + "subtask": "Sliding Window", + "question": "Which 54-day period in 2021 had the highest variance for channel 811? (Output format: [YYYY-MM-DD HH:MM:SS, YYYY-MM-DD HH:MM:SS])", + "answer": "[2021-01-01 00:00:00, 2021-02-23 13:15:00]", + "ground_truth": [ + "2021-01-01 00:00:00", + "2021-02-23 13:15:00" + ], + "eval_metric": "iou", + "channel": "811", + "ts_data_path": "ts_data/L1_T4_Sliding_Window_00673.csv", + "meta": { + "source": "causal_rivers", + "args": { + "channel": "811", + "time": "2021", + "metric": "highest variance", + "window": "54D" + } + } + }, + { + "id": "L1_T4_Sliding_Window_00678", + "level": 1, + "level_name": "Basic Operations", + "category": "Sliding Window", + "subtask": "Sliding Window", + "question": "Which 5-day period in 2022 had the highest average for channel 895? (Output format: [YYYY-MM-DD HH:MM:SS, YYYY-MM-DD HH:MM:SS])", + "answer": "[2022-02-19 01:15:00, 2022-02-24 01:00:00]", + "ground_truth": [ + "2022-02-19 01:15:00", + "2022-02-24 01:00:00" + ], + "eval_metric": "iou", + "channel": "895", + "ts_data_path": "ts_data/L1_T4_Sliding_Window_00678.csv", + "meta": { + "source": "causal_rivers", + "args": { + "channel": "895", + "time": "2022", + "metric": "highest average", + "window": "5D" + } + } + }, + { + "id": "L1_T4_Sliding_Window_00679", + "level": 1, + "level_name": "Basic Operations", + "category": "Sliding Window", + "subtask": "Sliding Window", + "question": "Which 44-day period in 2023 had the highest average for channel 897? (Output format: [YYYY-MM-DD HH:MM:SS, YYYY-MM-DD HH:MM:SS])", + "answer": "[2023-11-18 00:00:00, 2023-12-31 23:45:00]", + "ground_truth": [ + "2023-11-18 00:00:00", + "2023-12-31 23:45:00" + ], + "eval_metric": "iou", + "channel": "897", + "ts_data_path": "ts_data/L1_T4_Sliding_Window_00679.csv", + "meta": { + "source": "causal_rivers", + "args": { + "channel": "897", + "time": "2023", + "metric": "highest average", + "window": "44D" + } + } + }, + { + "id": "L1_T4_Sliding_Window_00683", + "level": 1, + "level_name": "Basic Operations", + "category": "Sliding Window", + "subtask": "Sliding Window", + "question": "Which 9-day period in 2016 had the lowest average for channel MUFL? (Output format: [YYYY-MM-DD HH:MM:SS, YYYY-MM-DD HH:MM:SS])", + "answer": "[2016-07-01 00:00:00, 2016-07-01 03:00:00]", + "ground_truth": [ + "2016-07-01 00:00:00", + "2016-07-01 03:00:00" + ], + "eval_metric": "iou", + "channel": "MUFL", + "ts_data_path": "ts_data/L1_T4_Sliding_Window_00683.csv", + "meta": { + "source": "ettm1", + "args": { + "channel": "MUFL", + "time": "2016", + "metric": "lowest average", + "window": "9D" + } + } + }, + { + "id": "L1_T4_Sliding_Window_00684", + "level": 1, + "level_name": "Basic Operations", + "category": "Sliding Window", + "subtask": "Sliding Window", + "question": "Which 16-day period in 2018 had the highest average for channel MULL? (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": "MULL", + "ts_data_path": "ts_data/L1_T4_Sliding_Window_00684.csv", + "meta": { + "source": "ettm1", + "args": { + "channel": "MULL", + "time": "2018", + "metric": "highest average", + "window": "16D" + } + } + }, + { + "id": "L1_T4_Sliding_Window_00686", + "level": 1, + "level_name": "Basic Operations", + "category": "Sliding Window", + "subtask": "Sliding Window", + "question": "Which 10-day period in 2016 had the largest range for channel LULL? (Output format: [YYYY-MM-DD HH:MM:SS, YYYY-MM-DD HH:MM:SS])", + "answer": "[2016-10-17 14:30:00, 2016-10-27 14:15:00]", + "ground_truth": [ + "2016-10-17 14:30:00", + "2016-10-27 14:15:00" + ], + "eval_metric": "iou", + "channel": "LULL", + "ts_data_path": "ts_data/L1_T4_Sliding_Window_00686.csv", + "meta": { + "source": "ettm1", + "args": { + "channel": "LULL", + "time": "2016", + "metric": "largest range", + "window": "10D" + } + } + }, + { + "id": "L2_T2_Periodicity_Detection_00199", + "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 [2019-10-12 09:00:00 to 2019-10-21 20:45:00]? (Output format: integer)", + "answer": "30", + "ground_truth": 30, + "eval_metric": "rel_acc", + "channel": "680", + "ts_data_path": "ts_data/L2_T2_Periodicity_Detection_00199.csv", + "meta": { + "period": 30, + "wave_type": "cosine", + "sub_period": 15, + "amp": 1.0, + "search_window": [ + "2019-10-12 09:00:00", + "2019-10-21 20:45:00" + ], + "source": "causal_rivers" + } + }, + { + "id": "L2_T2_Periodicity_Detection_00206", + "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 [2023-10-15 11:45:00 to 2023-10-18 13:15:00]? (Output format: integer)", + "answer": "34", + "ground_truth": 34, + "eval_metric": "rel_acc", + "channel": "811", + "ts_data_path": "ts_data/L2_T2_Periodicity_Detection_00206.csv", + "meta": { + "period": 34, + "wave_type": "sine", + "sub_period": 11, + "amp": 1.0, + "search_window": [ + "2023-10-15 11:45:00", + "2023-10-18 13:15:00" + ], + "source": "causal_rivers" + } + }, + { + "id": "L2_T2_Periodicity_Detection_00212", + "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 [2020-11-06 20:45:00 to 2020-11-15 06:30:00]? (Output format: integer)", + "answer": "44", + "ground_truth": 44, + "eval_metric": "rel_acc", + "channel": "897", + "ts_data_path": "ts_data/L2_T2_Periodicity_Detection_00212.csv", + "meta": { + "period": 44, + "wave_type": "cosine", + "sub_period": 22, + "amp": 1.0, + "search_window": [ + "2020-11-06 20:45:00", + "2020-11-15 06:30:00" + ], + "source": "causal_rivers" + } + }, + { + "id": "L2_T2_Periodicity_Detection_00221", + "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 [2020-05-23 20:00:00 to 2020-05-31 05:30:00]? (Output format: integer)", + "answer": "80", + "ground_truth": 80, + "eval_metric": "rel_acc", + "channel": "67", + "ts_data_path": "ts_data/L2_T2_Periodicity_Detection_00221.csv", + "meta": { + "period": 80, + "wave_type": "cosine", + "sub_period": 40, + "amp": 1.0, + "search_window": [ + "2020-05-23 20:00:00", + "2020-05-31 05:30:00" + ], + "source": "causal_rivers" + } + }, + { + "id": "L2_T2_Periodicity_Detection_00224", + "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 [2019-07-01 03:00:00 to 2019-07-10 23:15:00]? (Output format: integer)", + "answer": "89", + "ground_truth": 89, + "eval_metric": "rel_acc", + "channel": "123", + "ts_data_path": "ts_data/L2_T2_Periodicity_Detection_00224.csv", + "meta": { + "period": 89, + "wave_type": "sine", + "sub_period": 22, + "amp": 1.0, + "search_window": [ + "2019-07-01 03:00:00", + "2019-07-10 23:15:00" + ], + "source": "causal_rivers" + } + }, + { + "id": "L2_T2_Periodicity_Detection_00240", + "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 [2019-04-16 12:00:00 to 2019-04-18 23:45:00]? (Output format: integer)", + "answer": "80", + "ground_truth": 80, + "eval_metric": "rel_acc", + "channel": "430", + "ts_data_path": "ts_data/L2_T2_Periodicity_Detection_00240.csv", + "meta": { + "period": 80, + "wave_type": "cosine", + "sub_period": 40, + "amp": 3.0, + "search_window": [ + "2019-04-16 12:00:00", + "2019-04-18 23:45:00" + ], + "source": "causal_rivers" + } + }, + { + "id": "L2_T2_Periodicity_Detection_00246", + "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 [2020-01-23 23:15:00 to 2020-01-27 09:15:00]? (Output format: integer)", + "answer": "90", + "ground_truth": 90, + "eval_metric": "rel_acc", + "channel": "580", + "ts_data_path": "ts_data/L2_T2_Periodicity_Detection_00246.csv", + "meta": { + "period": 90, + "wave_type": "composite", + "sub_period": 45, + "amp": 3.0, + "search_window": [ + "2020-01-23 23:15:00", + "2020-01-27 09:15:00" + ], + "source": "causal_rivers" + } + }, + { + "id": "L2_T2_Periodicity_Detection_00269", + "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 [2016-07-18 06:00:00 to 2016-07-21 23:45:00]? (Output format: integer)", + "answer": "61", + "ground_truth": 61, + "eval_metric": "rel_acc", + "channel": "HUFL", + "ts_data_path": "ts_data/L2_T2_Periodicity_Detection_00269.csv", + "meta": { + "period": 61, + "wave_type": "composite", + "sub_period": 30, + "amp": 8.676983675663347, + "search_window": [ + "2016-07-18 06:00:00", + "2016-07-21 23:45:00" + ], + "source": "ettm1" + } + }, + { + "id": "L2_T2_Periodicity_Detection_00271", + "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 [2016-07-22 04:00:00 to 2016-07-28 18:30:00]? (Output format: integer)", + "answer": "83", + "ground_truth": 83, + "eval_metric": "rel_acc", + "channel": "MUFL", + "ts_data_path": "ts_data/L2_T2_Periodicity_Detection_00271.csv", + "meta": { + "period": 83, + "wave_type": "sine", + "sub_period": 41, + "amp": 5.412897416801251, + "search_window": [ + "2016-07-22 04:00:00", + "2016-07-28 18:30:00" + ], + "source": "ettm1" + } + }, + { + "id": "L2_T2_Periodicity_Detection_00280", + "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 [2022-06-29 13:00:00 to 2022-07-04 07:45:00]? (Output format: integer)", + "answer": "80", + "ground_truth": 80, + "eval_metric": "rel_acc", + "channel": "124", + "ts_data_path": "ts_data/L2_T2_Periodicity_Detection_00280.csv", + "meta": { + "period": 80, + "wave_type": "sine", + "sub_period": 40, + "amp": 1.0, + "search_window": [ + "2022-06-29 13:00:00", + "2022-07-04 07:45:00" + ], + "source": "causal_rivers" + } + }, + { + "id": "L2_T2_Periodicity_Detection_00297", + "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 [2022-08-06 15:45:00 to 2022-08-13 07:15:00]? (Output format: integer)", + "answer": "80", + "ground_truth": 80, + "eval_metric": "rel_acc", + "channel": "495", + "ts_data_path": "ts_data/L2_T2_Periodicity_Detection_00297.csv", + "meta": { + "period": 80, + "wave_type": "sine", + "sub_period": 20, + "amp": 3.0, + "search_window": [ + "2022-08-06 15:45:00", + "2022-08-13 07:15:00" + ], + "source": "causal_rivers" + } + }, + { + "id": "L2_T2_Periodicity_Detection_00315", + "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 [2022-11-11 22:30:00 to 2022-11-15 20:45:00]? (Output format: integer)", + "answer": "33", + "ground_truth": 33, + "eval_metric": "rel_acc", + "channel": "762", + "ts_data_path": "ts_data/L2_T2_Periodicity_Detection_00315.csv", + "meta": { + "period": 33, + "wave_type": "composite", + "sub_period": 11, + "amp": 1.0, + "search_window": [ + "2022-11-11 22:30:00", + "2022-11-15 20:45:00" + ], + "source": "causal_rivers" + } + }, + { + "id": "L2_T2_Periodicity_Detection_00317", + "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 [2022-06-22 04:00:00 to 2022-06-28 00:30:00]? (Output format: integer)", + "answer": "71", + "ground_truth": 71, + "eval_metric": "rel_acc", + "channel": "813", + "ts_data_path": "ts_data/L2_T2_Periodicity_Detection_00317.csv", + "meta": { + "period": 71, + "wave_type": "sine", + "sub_period": 17, + "amp": 1.0, + "search_window": [ + "2022-06-22 04:00:00", + "2022-06-28 00:30:00" + ], + "source": "causal_rivers" + } + }, + { + "id": "L2_T2_Periodicity_Detection_00322", + "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 [2020-05-17 14:00:00 to 2020-05-26 02:00:00]? (Output format: integer)", + "answer": "37", + "ground_truth": 37, + "eval_metric": "rel_acc", + "channel": "897", + "ts_data_path": "ts_data/L2_T2_Periodicity_Detection_00322.csv", + "meta": { + "period": 37, + "wave_type": "sine", + "sub_period": 18, + "amp": 1.0, + "search_window": [ + "2020-05-17 14:00:00", + "2020-05-26 02:00:00" + ], + "source": "causal_rivers" + } + }, + { + "id": "L2_T1_Shape_Identification_00005", + "level": 2, + "level_name": "Pattern Recognition", + "category": "Shape Identification", + "subtask": "Shape Identification", + "question": "Identify the time range of the deepest deep valley in channel 124 within [2020-09-20 11:45:00 to 2020-09-23 08:15:00]. (Output format: [YYYY-MM-DD HH:MM:SS, YYYY-MM-DD HH:MM:SS])", + "answer": "['2020-09-22 18:30:00', '2020-09-23 04:30:00']", + "ground_truth": [ + "2020-09-22 18:30:00", + "2020-09-23 04:30:00" + ], + "eval_metric": "iou", + "channel": "124", + "ts_data_path": "ts_data/L2_T1_Shape_Identification_00005.csv", + "meta": { + "pattern": "valley", + "superlative": "deepest", + "direction": "down", + "primary_amp": 0.06121986117662915, + "local_std": 0.003706449221645664, + "search_window": [ + "2020-09-20 11:45:00", + "2020-09-23 08:15:00" + ], + "source": "causal_rivers" + } + }, + { + "id": "L2_T1_Shape_Identification_00034", + "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 680 within [2022-07-13 13:30:00 to 2022-07-16 01:00:00]. (Output format: [YYYY-MM-DD HH:MM:SS, YYYY-MM-DD HH:MM:SS])", + "answer": "['2022-07-15 05:45:00', '2022-07-15 14:15:00']", + "ground_truth": [ + "2022-07-15 05:45:00", + "2022-07-15 14:15:00" + ], + "eval_metric": "iou", + "channel": "680", + "ts_data_path": "ts_data/L2_T1_Shape_Identification_00034.csv", + "meta": { + "pattern": "depression", + "superlative": "longest", + "direction": "down", + "primary_amp": 0.11059295820305343, + "local_std": 0.005781830316378313, + "search_window": [ + "2022-07-13 13:30:00", + "2022-07-16 01:00:00" + ], + "source": "causal_rivers" + } + }, + { + "id": "L2_T1_Shape_Identification_00050", + "level": 2, + "level_name": "Pattern Recognition", + "category": "Shape Identification", + "subtask": "Shape Identification", + "question": "Identify the time range of the highest upward spike in channel HULL within [2016-07-02 04:00:00 to 2016-07-08 00:15:00]. (Output format: [YYYY-MM-DD HH:MM:SS, YYYY-MM-DD HH:MM:SS])", + "answer": "['2016-07-02 08:00:00', '2016-07-03 04:45:00']", + "ground_truth": [ + "2016-07-02 08:00:00", + "2016-07-03 04:45:00" + ], + "eval_metric": "iou", + "channel": "HULL", + "ts_data_path": "ts_data/L2_T1_Shape_Identification_00050.csv", + "meta": { + "pattern": "spike", + "superlative": "highest", + "direction": "up", + "primary_amp": 11.208676500507917, + "local_std": 0.694588787737734, + "search_window": [ + "2016-07-02 04:00:00", + "2016-07-08 00:15:00" + ], + "source": "ettm1" + } + }, + { + "id": "L2_T1_Shape_Identification_00057", + "level": 2, + "level_name": "Pattern Recognition", + "category": "Shape Identification", + "subtask": "Shape Identification", + "question": "Identify the time range of the highest upward spike in channel 71 within [2022-09-21 11:00:00 to 2022-09-24 12:30:00]. (Output format: [YYYY-MM-DD HH:MM:SS, YYYY-MM-DD HH:MM:SS])", + "answer": "['2022-09-21 14:30:00', '2022-09-22 01:15:00']", + "ground_truth": [ + "2022-09-21 14:30:00", + "2022-09-22 01:15:00" + ], + "eval_metric": "iou", + "channel": "71", + "ts_data_path": "ts_data/L2_T1_Shape_Identification_00057.csv", + "meta": { + "pattern": "spike", + "superlative": "highest", + "direction": "up", + "primary_amp": 0.011119347664937, + "local_std": 0.0007412898443291333, + "search_window": [ + "2022-09-21 11:00:00", + "2022-09-24 12:30:00" + ], + "source": "causal_rivers" + } + }, + { + "id": "L2_T1_Shape_Identification_00077", + "level": 2, + "level_name": "Pattern Recognition", + "category": "Shape Identification", + "subtask": "Shape Identification", + "question": "Identify the time range of the highest upward spike in channel 495 within [2021-06-05 01:45:00 to 2021-06-11 08:45:00]. (Output format: [YYYY-MM-DD HH:MM:SS, YYYY-MM-DD HH:MM:SS])", + "answer": "['2021-06-07 00:30:00', '2021-06-07 22:45:00']", + "ground_truth": [ + "2021-06-07 00:30:00", + "2021-06-07 22:45:00" + ], + "eval_metric": "iou", + "channel": "495", + "ts_data_path": "ts_data/L2_T1_Shape_Identification_00077.csv", + "meta": { + "pattern": "spike", + "superlative": "highest", + "direction": "up", + "primary_amp": 0.011119347664937, + "local_std": 0.0007412898443291333, + "search_window": [ + "2021-06-05 01:45:00", + "2021-06-11 08:45:00" + ], + "source": "causal_rivers" + } + }, + { + "id": "L2_T1_Shape_Identification_00089", + "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 680 within [2020-10-11 10:15:00 to 2020-10-21 02:15:00]. (Output format: [YYYY-MM-DD HH:MM:SS, YYYY-MM-DD HH:MM:SS])", + "answer": "['2020-10-11 17:15:00', '2020-10-13 03:45:00']", + "ground_truth": [ + "2020-10-11 17:15:00", + "2020-10-13 03:45:00" + ], + "eval_metric": "iou", + "channel": "680", + "ts_data_path": "ts_data/L2_T1_Shape_Identification_00089.csv", + "meta": { + "pattern": "depression", + "superlative": "longest", + "direction": "down", + "primary_amp": 0.5782060785767233, + "local_std": 0.038547071905114895, + "search_window": [ + "2020-10-11 10:15:00", + "2020-10-21 02:15:00" + ], + "source": "causal_rivers" + } + }, + { + "id": "L2_T1_Shape_Identification_00096", + "level": 2, + "level_name": "Pattern Recognition", + "category": "Shape Identification", + "subtask": "Shape Identification", + "question": "Identify the time range of the largest step descent in channel 811 within [2023-09-28 07:00:00 to 2023-10-06 00:00:00]. (Output format: [YYYY-MM-DD HH:MM:SS, YYYY-MM-DD HH:MM:SS])", + "answer": "['2023-09-30 19:45:00', '2023-10-01 23:15:00']", + "ground_truth": [ + "2023-09-30 19:45:00", + "2023-10-01 23:15:00" + ], + "eval_metric": "iou", + "channel": "811", + "ts_data_path": "ts_data/L2_T1_Shape_Identification_00096.csv", + "meta": { + "pattern": "step_down", + "superlative": "largest", + "direction": "down", + "primary_amp": 1.389918458117123, + "local_std": 0.11119347664936984, + "search_window": [ + "2023-09-28 07:00:00", + "2023-10-06 00:00:00" + ], + "source": "causal_rivers" + } + }, + { + "id": "L2_T1_Shape_Identification_00131", + "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 [2020-09-10 21:30:00 to 2020-09-17 06:15:00]. (Output format: [YYYY-MM-DD HH:MM:SS, YYYY-MM-DD HH:MM:SS])", + "answer": "['2020-09-15 02:15:00', '2020-09-16 00:45:00']", + "ground_truth": [ + "2020-09-15 02:15:00", + "2020-09-16 00:45:00" + ], + "eval_metric": "iou", + "channel": "441", + "ts_data_path": "ts_data/L2_T1_Shape_Identification_00131.csv", + "meta": { + "pattern": "plateau", + "superlative": "longest", + "direction": "up", + "primary_amp": 15.003912520530825, + "local_std": 0.7412898443291327, + "search_window": [ + "2020-09-10 21:30:00", + "2020-09-17 06:15:00" + ], + "source": "causal_rivers" + } + }, + { + "id": "L2_T1_Shape_Identification_00134", + "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 501 within [2022-07-03 06:00:00 to 2022-07-10 08:15:00]. (Output format: [YYYY-MM-DD HH:MM:SS, YYYY-MM-DD HH:MM:SS])", + "answer": "['2022-07-05 04:30:00', '2022-07-06 05:45:00']", + "ground_truth": [ + "2022-07-05 04:30:00", + "2022-07-06 05:45:00" + ], + "eval_metric": "iou", + "channel": "501", + "ts_data_path": "ts_data/L2_T1_Shape_Identification_00134.csv", + "meta": { + "pattern": "depression", + "superlative": "longest", + "direction": "down", + "primary_amp": 0.33358042994811005, + "local_std": 0.022238695329874002, + "search_window": [ + "2022-07-03 06:00:00", + "2022-07-10 08:15:00" + ], + "source": "causal_rivers" + } + }, + { + "id": "L2_T1_Shape_Identification_00141", + "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 626 within [2022-07-17 14:15:00 to 2022-07-20 06:30:00]. (Output format: [YYYY-MM-DD HH:MM:SS, YYYY-MM-DD HH:MM:SS])", + "answer": "['2022-07-19 09:15:00', '2022-07-19 18:30:00']", + "ground_truth": [ + "2022-07-19 09:15:00", + "2022-07-19 18:30:00" + ], + "eval_metric": "iou", + "channel": "626", + "ts_data_path": "ts_data/L2_T1_Shape_Identification_00141.csv", + "meta": { + "pattern": "plateau", + "superlative": "longest", + "direction": "up", + "primary_amp": 0.062081783233058074, + "local_std": 0.0029651593773165285, + "search_window": [ + "2022-07-17 14:15:00", + "2022-07-20 06:30:00" + ], + "source": "causal_rivers" + } + }, + { + "id": "L2_T1_Shape_Identification_00144", + "level": 2, + "level_name": "Pattern Recognition", + "category": "Shape Identification", + "subtask": "Shape Identification", + "question": "Identify the time range of the largest step descent in channel 680 within [2022-11-06 05:00:00 to 2022-11-10 04:15:00]. (Output format: [YYYY-MM-DD HH:MM:SS, YYYY-MM-DD HH:MM:SS])", + "answer": "['2022-11-06 18:30:00', '2022-11-07 08:30:00']", + "ground_truth": [ + "2022-11-06 18:30:00", + "2022-11-07 08:30:00" + ], + "eval_metric": "iou", + "channel": "680", + "ts_data_path": "ts_data/L2_T1_Shape_Identification_00144.csv", + "meta": { + "pattern": "step_down", + "superlative": "largest", + "direction": "down", + "primary_amp": 0.2965159377316531, + "local_std": 0.02372127501853225, + "search_window": [ + "2022-11-06 05:00:00", + "2022-11-10 04:15:00" + ], + "source": "causal_rivers" + } + }, + { + "id": "L2_T1_Shape_Identification_00155", + "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 894 within [2019-10-23 16:30:00 to 2019-11-01 14:00:00]. (Output format: [YYYY-MM-DD HH:MM:SS, YYYY-MM-DD HH:MM:SS])", + "answer": "['2019-10-28 09:45:00', '2019-10-29 17:30:00']", + "ground_truth": [ + "2019-10-28 09:45:00", + "2019-10-29 17:30:00" + ], + "eval_metric": "iou", + "channel": "894", + "ts_data_path": "ts_data/L2_T1_Shape_Identification_00155.csv", + "meta": { + "pattern": "plateau", + "superlative": "longest", + "direction": "up", + "primary_amp": 1.5123602668643423, + "local_std": 0.05930318754633067, + "search_window": [ + "2019-10-23 16:30:00", + "2019-11-01 14:00:00" + ], + "source": "causal_rivers" + } + }, + { + "id": "L2_T1_Shape_Identification_00158", + "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 933 within [2022-08-30 04:00:00 to 2022-09-06 14:15:00]. (Output format: [YYYY-MM-DD HH:MM:SS, YYYY-MM-DD HH:MM:SS])", + "answer": "['2022-09-04 19:30:00', '2022-09-05 22:00:00']", + "ground_truth": [ + "2022-09-04 19:30:00", + "2022-09-05 22:00:00" + ], + "eval_metric": "iou", + "channel": "933", + "ts_data_path": "ts_data/L2_T1_Shape_Identification_00158.csv", + "meta": { + "pattern": "plateau", + "superlative": "longest", + "direction": "up", + "primary_amp": 0.12098079187197489, + "local_std": 0.0074128984432913275, + "search_window": [ + "2022-08-30 04:00:00", + "2022-09-06 14:15:00" + ], + "source": "causal_rivers" + } + }, + { + "id": "L2_T1_Shape_Identification_00160", + "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 [2016-09-29 00:30:00 to 2016-10-06 18:45:00]. (Output format: [YYYY-MM-DD HH:MM:SS, YYYY-MM-DD HH:MM:SS])", + "answer": "['2016-09-29 07:45:00', '2016-09-30 11:15:00']", + "ground_truth": [ + "2016-09-29 07:45:00", + "2016-09-30 11:15:00" + ], + "eval_metric": "iou", + "channel": "HULL", + "ts_data_path": "ts_data/L2_T1_Shape_Identification_00160.csv", + "meta": { + "pattern": "plateau", + "superlative": "longest", + "direction": "up", + "primary_amp": 19.769645310059218, + "local_std": 1.191994099373107, + "search_window": [ + "2016-09-29 00:30:00", + "2016-10-06 18:45:00" + ], + "source": "ettm1" + } + }, + { + "id": "L2_T3_Subsequence_Matching_00347", + "level": 2, + "level_name": "Pattern Recognition", + "category": "Subsequence Matching", + "subtask": "Subsequence Matching", + "question": "Analyze the reference pattern in [2019-07-04 09:00:00 to 2019-07-04 16:15:00]. Find the time interval where channel 237 exhibits the most similar pattern within the search context [2019-07-05 17:30:00 to 2019-07-09 14:45:00]. (Output format: [YYYY-MM-DD HH:MM:SS, YYYY-MM-DD HH:MM:SS])", + "answer": "['2019-07-09 04:00:00', '2019-07-09 11:15:00']", + "ground_truth": [ + "2019-07-09 04:00:00", + "2019-07-09 11:15:00" + ], + "eval_metric": "iou", + "channel": "237", + "ts_data_path": "ts_data/L2_T3_Subsequence_Matching_00347.csv", + "meta": { + "prototype": "sharp spike", + "query_window": "[2019-07-04 09:00:00 to 2019-07-04 16:15:00]", + "search_window": "[2019-07-05 17:30:00 to 2019-07-09 14:45:00]", + "source": "causal_rivers" + } + }, + { + "id": "L2_T3_Subsequence_Matching_00349", + "level": 2, + "level_name": "Pattern Recognition", + "category": "Subsequence Matching", + "subtask": "Subsequence Matching", + "question": "Analyze the reference pattern in [2020-07-05 04:45:00 to 2020-07-05 17:00:00]. Find the time interval where channel 312 exhibits the most similar pattern within the search context [2020-07-06 17:00:00 to 2020-07-10 04:00:00]. (Output format: [YYYY-MM-DD HH:MM:SS, YYYY-MM-DD HH:MM:SS])", + "answer": "['2020-07-08 06:30:00', '2020-07-08 18:45:00']", + "ground_truth": [ + "2020-07-08 06:30:00", + "2020-07-08 18:45:00" + ], + "eval_metric": "iou", + "channel": "312", + "ts_data_path": "ts_data/L2_T3_Subsequence_Matching_00349.csv", + "meta": { + "prototype": "bell curve", + "query_window": "[2020-07-05 04:45:00 to 2020-07-05 17:00:00]", + "search_window": "[2020-07-06 17:00:00 to 2020-07-10 04:00:00]", + "source": "causal_rivers" + } + }, + { + "id": "L2_T3_Subsequence_Matching_00357", + "level": 2, + "level_name": "Pattern Recognition", + "category": "Subsequence Matching", + "subtask": "Subsequence Matching", + "question": "Analyze the reference pattern in [2023-10-04 11:00:00 to 2023-10-04 22:00:00]. Find the time interval where channel 589 exhibits the most similar pattern within the search context [2023-10-06 14:30:00 to 2023-10-12 20:15:00]. (Output format: [YYYY-MM-DD HH:MM:SS, YYYY-MM-DD HH:MM:SS])", + "answer": "['2023-10-10 16:30:00', '2023-10-11 03:30:00']", + "ground_truth": [ + "2023-10-10 16:30:00", + "2023-10-11 03:30:00" + ], + "eval_metric": "iou", + "channel": "589", + "ts_data_path": "ts_data/L2_T3_Subsequence_Matching_00357.csv", + "meta": { + "prototype": "double-peak pattern", + "query_window": "[2023-10-04 11:00:00 to 2023-10-04 22:00:00]", + "search_window": "[2023-10-06 14:30:00 to 2023-10-12 20:15:00]", + "source": "causal_rivers" + } + }, + { + "id": "L2_T3_Subsequence_Matching_00380", + "level": 2, + "level_name": "Pattern Recognition", + "category": "Subsequence Matching", + "subtask": "Subsequence Matching", + "question": "Analyze the reference pattern in [2016-09-29 01:15:00 to 2016-09-29 12:15:00]. Find the time interval where channel HULL exhibits the most similar pattern within the search context [2016-09-30 12:15:00 to 2016-10-04 00:00:00]. (Output format: [YYYY-MM-DD HH:MM:SS, YYYY-MM-DD HH:MM:SS])", + "answer": "['2016-10-02 04:00:00', '2016-10-02 15:00:00']", + "ground_truth": [ + "2016-10-02 04:00:00", + "2016-10-02 15:00:00" + ], + "eval_metric": "iou", + "channel": "HULL", + "ts_data_path": "ts_data/L2_T3_Subsequence_Matching_00380.csv", + "meta": { + "prototype": "double-peak pattern", + "query_window": "[2016-09-29 01:15:00 to 2016-09-29 12:15:00]", + "search_window": "[2016-09-30 12:15:00 to 2016-10-04 00:00:00]", + "source": "ettm1" + } + }, + { + "id": "L2_T3_Subsequence_Matching_00386", + "level": 2, + "level_name": "Pattern Recognition", + "category": "Subsequence Matching", + "subtask": "Subsequence Matching", + "question": "Analyze the reference pattern in [2020-05-16 13:45:00 to 2020-05-17 02:00:00]. Find the time interval where channel 67 exhibits the most similar pattern within the search context [2020-05-18 23:00:00 to 2020-05-25 22:15:00]. (Output format: [YYYY-MM-DD HH:MM:SS, YYYY-MM-DD HH:MM:SS])", + "answer": "['2020-05-25 08:15:00', '2020-05-25 20:30:00']", + "ground_truth": [ + "2020-05-25 08:15:00", + "2020-05-25 20:30:00" + ], + "eval_metric": "iou", + "channel": "67", + "ts_data_path": "ts_data/L2_T3_Subsequence_Matching_00386.csv", + "meta": { + "prototype": "bell curve", + "query_window": "[2020-05-16 13:45:00 to 2020-05-17 02:00:00]", + "search_window": "[2020-05-18 23:00:00 to 2020-05-25 22:15:00]", + "source": "causal_rivers" + } + }, + { + "id": "L2_T3_Subsequence_Matching_00390", + "level": 2, + "level_name": "Pattern Recognition", + "category": "Subsequence Matching", + "subtask": "Subsequence Matching", + "question": "Analyze the reference pattern in [2022-06-30 00:00:00 to 2022-06-30 12:15:00]. Find the time interval where channel 124 exhibits the most similar pattern within the search context [2022-07-01 10:30:00 to 2022-07-04 14:45:00]. (Output format: [YYYY-MM-DD HH:MM:SS, YYYY-MM-DD HH:MM:SS])", + "answer": "['2022-07-01 12:00:00', '2022-07-02 00:15:00']", + "ground_truth": [ + "2022-07-01 12:00:00", + "2022-07-02 00:15:00" + ], + "eval_metric": "iou", + "channel": "124", + "ts_data_path": "ts_data/L2_T3_Subsequence_Matching_00390.csv", + "meta": { + "prototype": "bell curve", + "query_window": "[2022-06-30 00:00:00 to 2022-06-30 12:15:00]", + "search_window": "[2022-07-01 10:30:00 to 2022-07-04 14:45:00]", + "source": "causal_rivers" + } + }, + { + "id": "L2_T3_Subsequence_Matching_00401", + "level": 2, + "level_name": "Pattern Recognition", + "category": "Subsequence Matching", + "subtask": "Subsequence Matching", + "question": "Analyze the reference pattern in [2019-07-13 13:30:00 to 2019-07-14 01:45:00]. Find the time interval where channel 177 exhibits the most similar pattern within the search context [2019-07-15 06:30:00 to 2019-07-19 12:00:00]. (Output format: [YYYY-MM-DD HH:MM:SS, YYYY-MM-DD HH:MM:SS])", + "answer": "['2019-07-16 04:15:00', '2019-07-16 16:30:00']", + "ground_truth": [ + "2019-07-16 04:15:00", + "2019-07-16 16:30:00" + ], + "eval_metric": "iou", + "channel": "177", + "ts_data_path": "ts_data/L2_T3_Subsequence_Matching_00401.csv", + "meta": { + "prototype": "bell curve", + "query_window": "[2019-07-13 13:30:00 to 2019-07-14 01:45:00]", + "search_window": "[2019-07-15 06:30:00 to 2019-07-19 12:00:00]", + "source": "causal_rivers" + } + }, + { + "id": "L2_T3_Subsequence_Matching_00406", + "level": 2, + "level_name": "Pattern Recognition", + "category": "Subsequence Matching", + "subtask": "Subsequence Matching", + "question": "Analyze the reference pattern in [2022-09-06 21:00:00 to 2022-09-07 11:45:00]. Find the time interval where channel 441 exhibits the most similar pattern within the search context [2022-09-08 10:30:00 to 2022-09-09 21:30:00]. (Output format: [YYYY-MM-DD HH:MM:SS, YYYY-MM-DD HH:MM:SS])", + "answer": "['2022-09-08 15:00:00', '2022-09-09 05:45:00']", + "ground_truth": [ + "2022-09-08 15:00:00", + "2022-09-09 05:45:00" + ], + "eval_metric": "iou", + "channel": "441", + "ts_data_path": "ts_data/L2_T3_Subsequence_Matching_00406.csv", + "meta": { + "prototype": "step pattern", + "query_window": "[2022-09-06 21:00:00 to 2022-09-07 11:45:00]", + "search_window": "[2022-09-08 10:30:00 to 2022-09-09 21:30:00]", + "source": "causal_rivers" + } + }, + { + "id": "L2_T3_Subsequence_Matching_00429", + "level": 2, + "level_name": "Pattern Recognition", + "category": "Subsequence Matching", + "subtask": "Subsequence Matching", + "question": "Analyze the reference pattern in [2019-03-24 12:00:00 to 2019-03-25 02:45:00]. Find the time interval where channel 891 exhibits the most similar pattern within the search context [2019-03-26 15:00:00 to 2019-04-01 00:15:00]. (Output format: [YYYY-MM-DD HH:MM:SS, YYYY-MM-DD HH:MM:SS])", + "answer": "['2019-03-27 07:15:00', '2019-03-27 22:00:00']", + "ground_truth": [ + "2019-03-27 07:15:00", + "2019-03-27 22:00:00" + ], + "eval_metric": "iou", + "channel": "891", + "ts_data_path": "ts_data/L2_T3_Subsequence_Matching_00429.csv", + "meta": { + "prototype": "step pattern", + "query_window": "[2019-03-24 12:00:00 to 2019-03-25 02:45:00]", + "search_window": "[2019-03-26 15:00:00 to 2019-04-01 00:15:00]", + "source": "causal_rivers" + } + }, + { + "id": "L2_T3_Subsequence_Matching_00431", + "level": 2, + "level_name": "Pattern Recognition", + "category": "Subsequence Matching", + "subtask": "Subsequence Matching", + "question": "Analyze the reference pattern in [2022-10-17 14:15:00 to 2022-10-18 05:00:00]. Find the time interval where channel 895 exhibits the most similar pattern within the search context [2022-10-19 06:30:00 to 2022-10-22 20:45:00]. (Output format: [YYYY-MM-DD HH:MM:SS, YYYY-MM-DD HH:MM:SS])", + "answer": "['2022-10-20 17:45:00', '2022-10-21 08:30:00']", + "ground_truth": [ + "2022-10-20 17:45:00", + "2022-10-21 08:30:00" + ], + "eval_metric": "iou", + "channel": "895", + "ts_data_path": "ts_data/L2_T3_Subsequence_Matching_00431.csv", + "meta": { + "prototype": "step pattern", + "query_window": "[2022-10-17 14:15:00 to 2022-10-18 05:00:00]", + "search_window": "[2022-10-19 06:30:00 to 2022-10-22 20:45:00]", + "source": "causal_rivers" + } + }, + { + "id": "L2_T3_Subsequence_Matching_00440", + "level": 2, + "level_name": "Pattern Recognition", + "category": "Subsequence Matching", + "subtask": "Subsequence Matching", + "question": "Analyze the reference pattern in [2017-11-05 09:00:00 to 2017-11-05 21:15:00]. Find the time interval where channel OT exhibits the most similar pattern within the search context [2017-11-06 17:00:00 to 2017-11-09 10:30:00]. (Output format: [YYYY-MM-DD HH:MM:SS, YYYY-MM-DD HH:MM:SS])", + "answer": "['2017-11-07 08:45:00', '2017-11-07 21:00:00']", + "ground_truth": [ + "2017-11-07 08:45:00", + "2017-11-07 21:00:00" + ], + "eval_metric": "iou", + "channel": "OT", + "ts_data_path": "ts_data/L2_T3_Subsequence_Matching_00440.csv", + "meta": { + "prototype": "bell curve", + "query_window": "[2017-11-05 09:00:00 to 2017-11-05 21:15:00]", + "search_window": "[2017-11-06 17:00:00 to 2017-11-09 10:30:00]", + "source": "ettm1" + } + }, + { + "id": "L2_T3_Subsequence_Matching_00454", + "level": 2, + "level_name": "Pattern Recognition", + "category": "Subsequence Matching", + "subtask": "Subsequence Matching", + "question": "Analyze the reference pattern in [2019-05-12 14:45:00 to 2019-05-12 22:00:00]. Find the time interval where channel 172 exhibits the most similar pattern within the search context [2019-05-13 22:00:00 to 2019-05-17 13:45:00]. (Output format: [YYYY-MM-DD HH:MM:SS, YYYY-MM-DD HH:MM:SS])", + "answer": "['2019-05-14 04:45:00', '2019-05-14 12:00:00']", + "ground_truth": [ + "2019-05-14 04:45:00", + "2019-05-14 12:00:00" + ], + "eval_metric": "iou", + "channel": "172", + "ts_data_path": "ts_data/L2_T3_Subsequence_Matching_00454.csv", + "meta": { + "prototype": "sharp spike", + "query_window": "[2019-05-12 14:45:00 to 2019-05-12 22:00:00]", + "search_window": "[2019-05-13 22:00:00 to 2019-05-17 13:45:00]", + "source": "causal_rivers" + } + }, + { + "id": "L2_T3_Subsequence_Matching_00455", + "level": 2, + "level_name": "Pattern Recognition", + "category": "Subsequence Matching", + "subtask": "Subsequence Matching", + "question": "Analyze the reference pattern in [2019-08-11 06:00:00 to 2019-08-11 20:45:00]. Find the time interval where channel 173 exhibits the most similar pattern within the search context [2019-08-13 04:45:00 to 2019-08-17 21:30:00]. (Output format: [YYYY-MM-DD HH:MM:SS, YYYY-MM-DD HH:MM:SS])", + "answer": "['2019-08-14 22:15:00', '2019-08-15 13:00:00']", + "ground_truth": [ + "2019-08-14 22:15:00", + "2019-08-15 13:00:00" + ], + "eval_metric": "iou", + "channel": "173", + "ts_data_path": "ts_data/L2_T3_Subsequence_Matching_00455.csv", + "meta": { + "prototype": "step pattern", + "query_window": "[2019-08-11 06:00:00 to 2019-08-11 20:45:00]", + "search_window": "[2019-08-13 04:45:00 to 2019-08-17 21:30:00]", + "source": "causal_rivers" + } + }, + { + "id": "L2_T3_Subsequence_Matching_00466", + "level": 2, + "level_name": "Pattern Recognition", + "category": "Subsequence Matching", + "subtask": "Subsequence Matching", + "question": "Analyze the reference pattern in [2020-01-11 00:00:00 to 2020-01-11 12:15:00]. Find the time interval where channel 589 exhibits the most similar pattern within the search context [2020-01-13 02:00:00 to 2020-01-18 19:15:00]. (Output format: [YYYY-MM-DD HH:MM:SS, YYYY-MM-DD HH:MM:SS])", + "answer": "['2020-01-14 20:00:00', '2020-01-15 08:15:00']", + "ground_truth": [ + "2020-01-14 20:00:00", + "2020-01-15 08:15:00" + ], + "eval_metric": "iou", + "channel": "589", + "ts_data_path": "ts_data/L2_T3_Subsequence_Matching_00466.csv", + "meta": { + "prototype": "bell curve", + "query_window": "[2020-01-11 00:00:00 to 2020-01-11 12:15:00]", + "search_window": "[2020-01-13 02:00:00 to 2020-01-18 19:15:00]", + "source": "causal_rivers" + } + }, + { + "id": "L2_T3_Subsequence_Matching_00477", + "level": 2, + "level_name": "Pattern Recognition", + "category": "Subsequence Matching", + "subtask": "Subsequence Matching", + "question": "Analyze the reference pattern in [2022-03-12 00:00:00 to 2022-03-12 11:00:00]. Find the time interval where channel 754 exhibits the most similar pattern within the search context [2022-03-13 13:45:00 to 2022-03-17 12:30:00]. (Output format: [YYYY-MM-DD HH:MM:SS, YYYY-MM-DD HH:MM:SS])", + "answer": "['2022-03-15 00:15:00', '2022-03-15 11:15:00']", + "ground_truth": [ + "2022-03-15 00:15:00", + "2022-03-15 11:15:00" + ], + "eval_metric": "iou", + "channel": "754", + "ts_data_path": "ts_data/L2_T3_Subsequence_Matching_00477.csv", + "meta": { + "prototype": "double-peak pattern", + "query_window": "[2022-03-12 00:00:00 to 2022-03-12 11:00:00]", + "search_window": "[2022-03-13 13:45:00 to 2022-03-17 12:30:00]", + "source": "causal_rivers" + } + }, + { + "id": "L2_T3_Subsequence_Matching_00492", + "level": 2, + "level_name": "Pattern Recognition", + "category": "Subsequence Matching", + "subtask": "Subsequence Matching", + "question": "Analyze the reference pattern in [2017-01-11 03:45:00 to 2017-01-11 18:30:00]. Find the time interval where channel LULL exhibits the most similar pattern within the search context [2017-01-13 13:30:00 to 2017-01-20 02:00:00]. (Output format: [YYYY-MM-DD HH:MM:SS, YYYY-MM-DD HH:MM:SS])", + "answer": "['2017-01-14 18:30:00', '2017-01-15 09:15:00']", + "ground_truth": [ + "2017-01-14 18:30:00", + "2017-01-15 09:15:00" + ], + "eval_metric": "iou", + "channel": "LULL", + "ts_data_path": "ts_data/L2_T3_Subsequence_Matching_00492.csv", + "meta": { + "prototype": "step pattern", + "query_window": "[2017-01-11 03:45:00 to 2017-01-11 18:30:00]", + "search_window": "[2017-01-13 13:30:00 to 2017-01-20 02:00:00]", + "source": "ettm1" + } + }, + { + "id": "L3_T3_Causal_Anomaly_00402", + "level": 3, + "level_name": "Semantic Reasoning", + "category": "Causal Anomaly", + "subtask": "Causal Anomaly", + "question": "Given that channel 237_up_2023 is the upstream source of channel 237_down_2023, identify the time period in 2023 where 237_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": "[2023-09-26 14:45:00, 2023-10-05 05:15:00]", + "ground_truth": [ + "2023-09-26 14:45:00", + "2023-10-05 05:15:00" + ], + "eval_metric": "iou", + "channel": "237_down_2023", + "ts_data_path": "ts_data/L3_T3_Causal_Anomaly_00402.csv", + "meta": { + "pair_upstream": "237_up_2023", + "pair_downstream": "237_down_2023", + "upstream_channel_raw": "237", + "downstream_channel_raw": "237_synth", + "break_kind": "flat_line", + "year": 2023, + "source": "causal_rivers" + } + }, + { + "id": "L3_T3_Causal_Anomaly_00418", + "level": 3, + "level_name": "Semantic Reasoning", + "category": "Causal Anomaly", + "subtask": "Causal Anomaly", + "question": "Given that channel 647_up_2022 is the upstream source of channel 647_down_2022, identify the time period in 2022 where 647_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": "[2022-03-31 09:45:00, 2022-04-10 06:15:00]", + "ground_truth": [ + "2022-03-31 09:45:00", + "2022-04-10 06:15:00" + ], + "eval_metric": "iou", + "channel": "647_down_2022", + "ts_data_path": "ts_data/L3_T3_Causal_Anomaly_00418.csv", + "meta": { + "pair_upstream": "647_up_2022", + "pair_downstream": "647_down_2022", + "upstream_channel_raw": "647", + "downstream_channel_raw": "647_synth", + "break_kind": "flat_line", + "year": 2022, + "source": "causal_rivers" + } + }, + { + "id": "L3_T3_Causal_Anomaly_00425", + "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 flat line during high activity. (Output format: [YYYY-MM-DD HH:MM:SS, YYYY-MM-DD HH:MM:SS])", + "answer": "[2022-08-13 21:00:00, 2022-08-22 16:30:00]", + "ground_truth": [ + "2022-08-13 21:00:00", + "2022-08-22 16:30:00" + ], + "eval_metric": "iou", + "channel": "762_down_2022", + "ts_data_path": "ts_data/L3_T3_Causal_Anomaly_00425.csv", + "meta": { + "pair_upstream": "762_up_2022", + "pair_downstream": "762_down_2022", + "upstream_channel_raw": "762", + "downstream_channel_raw": "762_synth", + "break_kind": "flat_line", + "year": 2022, + "source": "causal_rivers" + } + }, + { + "id": "L3_T3_Causal_Anomaly_00426", + "level": 3, + "level_name": "Semantic Reasoning", + "category": "Causal Anomaly", + "subtask": "Causal Anomaly", + "question": "Given that channel 811_up_2023 is the upstream source of channel 811_down_2023, identify the time period in 2023 where 811_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": "[2023-03-12 11:00:00, 2023-03-20 04:00:00]", + "ground_truth": [ + "2023-03-12 11:00:00", + "2023-03-20 04:00:00" + ], + "eval_metric": "iou", + "channel": "811_down_2023", + "ts_data_path": "ts_data/L3_T3_Causal_Anomaly_00426.csv", + "meta": { + "pair_upstream": "811_up_2023", + "pair_downstream": "811_down_2023", + "upstream_channel_raw": "811", + "downstream_channel_raw": "811_synth", + "break_kind": "flat_line", + "year": 2023, + "source": "causal_rivers" + } + }, + { + "id": "L3_T3_Causal_Anomaly_00430", + "level": 3, + "level_name": "Semantic Reasoning", + "category": "Causal Anomaly", + "subtask": "Causal Anomaly", + "question": "Given that channel 894_up_2019 is the upstream source of channel 894_down_2019, identify the time period in 2019 where 894_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": "[2019-09-12 04:00:00, 2019-09-20 21:30:00]", + "ground_truth": [ + "2019-09-12 04:00:00", + "2019-09-20 21:30:00" + ], + "eval_metric": "iou", + "channel": "894_down_2019", + "ts_data_path": "ts_data/L3_T3_Causal_Anomaly_00430.csv", + "meta": { + "pair_upstream": "894_up_2019", + "pair_downstream": "894_down_2019", + "upstream_channel_raw": "894", + "downstream_channel_raw": "894_synth", + "break_kind": "flat_line", + "year": 2019, + "source": "causal_rivers" + } + }, + { + "id": "L3_T3_Causal_Anomaly_00434", + "level": 3, + "level_name": "Semantic Reasoning", + "category": "Causal Anomaly", + "subtask": "Causal Anomaly", + "question": "Given that channel 67_up_2019 is the upstream source of channel 67_down_2019, identify the time period in 2019 where 67_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": "[2019-08-07 23:00:00, 2019-08-17 21:45:00]", + "ground_truth": [ + "2019-08-07 23:00:00", + "2019-08-17 21:45:00" + ], + "eval_metric": "iou", + "channel": "67_down_2019", + "ts_data_path": "ts_data/L3_T3_Causal_Anomaly_00434.csv", + "meta": { + "pair_upstream": "67_up_2019", + "pair_downstream": "67_down_2019", + "upstream_channel_raw": "67", + "downstream_channel_raw": "67_synth", + "break_kind": "flat_line", + "year": 2019, + "source": "causal_rivers" + } + }, + { + "id": "L3_T3_Causal_Anomaly_00441", + "level": 3, + "level_name": "Semantic Reasoning", + "category": "Causal Anomaly", + "subtask": "Causal Anomaly", + "question": "Given that channel 151_up_2020 is the upstream source of channel 151_down_2020, identify the time period in 2020 where 151_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": "[2020-09-22 08:45:00, 2020-09-28 17:30:00]", + "ground_truth": [ + "2020-09-22 08:45:00", + "2020-09-28 17:30:00" + ], + "eval_metric": "iou", + "channel": "151_down_2020", + "ts_data_path": "ts_data/L3_T3_Causal_Anomaly_00441.csv", + "meta": { + "pair_upstream": "151_up_2020", + "pair_downstream": "151_down_2020", + "upstream_channel_raw": "151", + "downstream_channel_raw": "151_synth", + "break_kind": "inverse", + "year": 2020, + "source": "causal_rivers" + } + }, + { + "id": "L3_T3_Causal_Anomaly_00446", + "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": "[2020-10-21 00:45:00, 2020-10-29 10:45:00]", + "ground_truth": [ + "2020-10-21 00:45:00", + "2020-10-29 10:45:00" + ], + "eval_metric": "iou", + "channel": "170_down_2020", + "ts_data_path": "ts_data/L3_T3_Causal_Anomaly_00446.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_00456", + "level": 3, + "level_name": "Semantic Reasoning", + "category": "Causal Anomaly", + "subtask": "Causal Anomaly", + "question": "Given that channel 496_up_2023 is the upstream source of channel 496_down_2023, identify the time period in 2023 where 496_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": "[2023-05-24 08:30:00, 2023-05-29 13:30:00]", + "ground_truth": [ + "2023-05-24 08:30:00", + "2023-05-29 13:30:00" + ], + "eval_metric": "iou", + "channel": "496_down_2023", + "ts_data_path": "ts_data/L3_T3_Causal_Anomaly_00456.csv", + "meta": { + "pair_upstream": "496_up_2023", + "pair_downstream": "496_down_2023", + "upstream_channel_raw": "496", + "downstream_channel_raw": "496_synth", + "break_kind": "flat_line", + "year": 2023, + "source": "causal_rivers" + } + }, + { + "id": "L3_T3_Causal_Anomaly_00467", + "level": 3, + "level_name": "Semantic Reasoning", + "category": "Causal Anomaly", + "subtask": "Causal Anomaly", + "question": "Given that channel 680_up_2021 is the upstream source of channel 680_down_2021, identify the time period in 2021 where 680_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": "[2021-07-19 00:15:00, 2021-07-28 14:30:00]", + "ground_truth": [ + "2021-07-19 00:15:00", + "2021-07-28 14:30:00" + ], + "eval_metric": "iou", + "channel": "680_down_2021", + "ts_data_path": "ts_data/L3_T3_Causal_Anomaly_00467.csv", + "meta": { + "pair_upstream": "680_up_2021", + "pair_downstream": "680_down_2021", + "upstream_channel_raw": "680", + "downstream_channel_raw": "680_synth", + "break_kind": "inverse", + "year": 2021, + "source": "causal_rivers" + } + }, + { + "id": "L3_T3_Causal_Anomaly_00468", + "level": 3, + "level_name": "Semantic Reasoning", + "category": "Causal Anomaly", + "subtask": "Causal Anomaly", + "question": "Given that channel 683_up_2021 is the upstream source of channel 683_down_2021, identify the time period in 2021 where 683_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": "[2021-02-25 13:45:00, 2021-03-02 00:30:00]", + "ground_truth": [ + "2021-02-25 13:45:00", + "2021-03-02 00:30:00" + ], + "eval_metric": "iou", + "channel": "683_down_2021", + "ts_data_path": "ts_data/L3_T3_Causal_Anomaly_00468.csv", + "meta": { + "pair_upstream": "683_up_2021", + "pair_downstream": "683_down_2021", + "upstream_channel_raw": "683", + "downstream_channel_raw": "683_synth", + "break_kind": "inverse", + "year": 2021, + "source": "causal_rivers" + } + }, + { + "id": "L3_T3_Causal_Anomaly_00477", + "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": "[2019-11-14 18:00:00, 2019-11-21 17:30:00]", + "ground_truth": [ + "2019-11-14 18:00:00", + "2019-11-21 17:30:00" + ], + "eval_metric": "iou", + "channel": "891_down_2019", + "ts_data_path": "ts_data/L3_T3_Causal_Anomaly_00477.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_00484", + "level": 3, + "level_name": "Semantic Reasoning", + "category": "Causal Anomaly", + "subtask": "Causal Anomaly", + "question": "Given that channel 99_up_2019 is the upstream source of channel 99_down_2019, identify the time period in 2019 where 99_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": "[2019-05-03 23:00:00, 2019-05-09 11:00:00]", + "ground_truth": [ + "2019-05-03 23:00:00", + "2019-05-09 11:00:00" + ], + "eval_metric": "iou", + "channel": "99_down_2019", + "ts_data_path": "ts_data/L3_T3_Causal_Anomaly_00484.csv", + "meta": { + "pair_upstream": "99_up_2019", + "pair_downstream": "99_down_2019", + "upstream_channel_raw": "99", + "downstream_channel_raw": "99_synth", + "break_kind": "inverse", + "year": 2019, + "source": "causal_rivers" + } + }, + { + "id": "L3_T3_Causal_Anomaly_00485", + "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": "[2021-05-16 18:15:00, 2021-05-25 19:30:00]", + "ground_truth": [ + "2021-05-16 18:15:00", + "2021-05-25 19:30:00" + ], + "eval_metric": "iou", + "channel": "123_down_2021", + "ts_data_path": "ts_data/L3_T3_Causal_Anomaly_00485.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_00497", + "level": 3, + "level_name": "Semantic Reasoning", + "category": "Causal Anomaly", + "subtask": "Causal Anomaly", + "question": "Given that channel 177_up_2023 is the upstream source of channel 177_down_2023, identify the time period in 2023 where 177_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": "[2023-12-15 07:15:00, 2023-12-20 18:15:00]", + "ground_truth": [ + "2023-12-15 07:15:00", + "2023-12-20 18:15:00" + ], + "eval_metric": "iou", + "channel": "177_down_2023", + "ts_data_path": "ts_data/L3_T3_Causal_Anomaly_00497.csv", + "meta": { + "pair_upstream": "177_up_2023", + "pair_downstream": "177_down_2023", + "upstream_channel_raw": "177", + "downstream_channel_raw": "177_synth", + "break_kind": "inverse", + "year": 2023, + "source": "causal_rivers" + } + }, + { + "id": "L3_T3_Causal_Anomaly_00508", + "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": "[2021-05-26 19:15:00, 2021-06-04 18:00:00]", + "ground_truth": [ + "2021-05-26 19:15:00", + "2021-06-04 18:00:00" + ], + "eval_metric": "iou", + "channel": "589_down_2021", + "ts_data_path": "ts_data/L3_T3_Causal_Anomaly_00508.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_00526", + "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": "[2020-07-12 16:30:00, 2020-07-15 17:00:00]", + "ground_truth": [ + "2020-07-12 16:30:00", + "2020-07-15 17:00:00" + ], + "eval_metric": "iou", + "channel": "894_down_2020", + "ts_data_path": "ts_data/L3_T3_Causal_Anomaly_00526.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_00527", + "level": 3, + "level_name": "Semantic Reasoning", + "category": "Causal Anomaly", + "subtask": "Causal Anomaly", + "question": "Given that channel 895_up_2023 is the upstream source of channel 895_down_2023, identify the time period in 2023 where 895_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": "[2023-07-24 17:45:00, 2023-07-29 12:00:00]", + "ground_truth": [ + "2023-07-24 17:45:00", + "2023-07-29 12:00:00" + ], + "eval_metric": "iou", + "channel": "895_down_2023", + "ts_data_path": "ts_data/L3_T3_Causal_Anomaly_00527.csv", + "meta": { + "pair_upstream": "895_up_2023", + "pair_downstream": "895_down_2023", + "upstream_channel_raw": "895", + "downstream_channel_raw": "895_synth", + "break_kind": "inverse", + "year": 2023, + "source": "causal_rivers" + } + }, + { + "id": "L3_T2_Contextual_Anomaly_00229", + "level": 3, + "level_name": "Semantic Reasoning", + "category": "Contextual Anomaly", + "subtask": "Contextual Anomaly", + "question": "Identify the period in channel 154 during 2020 that experienced the most significant severe drought. (Output format: [YYYY-MM-DD, YYYY-MM-DD])", + "answer": "[2020-04-30 19:45:00, 2020-07-25 19:30:00]", + "ground_truth": [ + "2020-04-30 19:45:00", + "2020-07-25 19:30:00" + ], + "eval_metric": "iou", + "channel": "154", + "ts_data_path": "ts_data/L3_T2_Contextual_Anomaly_00229.csv", + "meta": { + "event": "drought", + "intensity": 0.07168589832256497, + "year": 2020, + "duration_days": 86, + "source": "causal_rivers" + } + }, + { + "id": "L3_T2_Contextual_Anomaly_00233", + "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 high water level. (Output format: [YYYY-MM-DD, YYYY-MM-DD])", + "answer": "[2022-08-04 12:00:00, 2022-10-16 11:45:00]", + "ground_truth": [ + "2022-08-04 12:00:00", + "2022-10-16 11:45:00" + ], + "eval_metric": "iou", + "channel": "170", + "ts_data_path": "ts_data/L3_T2_Contextual_Anomaly_00233.csv", + "meta": { + "event": "flood", + "intensity": 3.9983955118168133, + "year": 2022, + "duration_days": 73, + "source": "causal_rivers" + } + }, + { + "id": "L3_T2_Contextual_Anomaly_00243", + "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 high water level. (Output format: [YYYY-MM-DD, YYYY-MM-DD])", + "answer": "[2020-04-14 23:30:00, 2020-07-09 23:15:00]", + "ground_truth": [ + "2020-04-14 23:30:00", + "2020-07-09 23:15:00" + ], + "eval_metric": "iou", + "channel": "496", + "ts_data_path": "ts_data/L3_T2_Contextual_Anomaly_00243.csv", + "meta": { + "event": "flood", + "intensity": 3.827926753204879, + "year": 2020, + "duration_days": 86, + "source": "causal_rivers" + } + }, + { + "id": "L3_T2_Contextual_Anomaly_00245", + "level": 3, + "level_name": "Semantic Reasoning", + "category": "Contextual Anomaly", + "subtask": "Contextual Anomaly", + "question": "Identify the period in channel 578 during 2023 that experienced the most significant historically low water level. (Output format: [YYYY-MM-DD, YYYY-MM-DD])", + "answer": "[2023-02-27 09:15:00, 2023-05-14 09:00:00]", + "ground_truth": [ + "2023-02-27 09:15:00", + "2023-05-14 09:00:00" + ], + "eval_metric": "iou", + "channel": "578", + "ts_data_path": "ts_data/L3_T2_Contextual_Anomaly_00245.csv", + "meta": { + "event": "drought", + "intensity": 0.06718527334317108, + "year": 2023, + "duration_days": 76, + "source": "causal_rivers" + } + }, + { + "id": "L3_T2_Contextual_Anomaly_00267", + "level": 3, + "level_name": "Semantic Reasoning", + "category": "Contextual Anomaly", + "subtask": "Contextual Anomaly", + "question": "Identify the period in channel 897 during 2020 that experienced the most significant extreme surge in flow. (Output format: [YYYY-MM-DD, YYYY-MM-DD])", + "answer": "[2020-01-14 01:00:00, 2020-03-15 00:45:00]", + "ground_truth": [ + "2020-01-14 01:00:00", + "2020-03-15 00:45:00" + ], + "eval_metric": "iou", + "channel": "897", + "ts_data_path": "ts_data/L3_T2_Contextual_Anomaly_00267.csv", + "meta": { + "event": "flood", + "intensity": 4.324556501720294, + "year": 2020, + "duration_days": 61, + "source": "causal_rivers" + } + }, + { + "id": "L3_T2_Contextual_Anomaly_00269", + "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 historically low water level. (Output format: [YYYY-MM-DD, YYYY-MM-DD])", + "answer": "[2017-02-08 22:30:00, 2017-05-02 22:15:00]", + "ground_truth": [ + "2017-02-08 22:30:00", + "2017-05-02 22:15:00" + ], + "eval_metric": "iou", + "channel": "HUFL", + "ts_data_path": "ts_data/L3_T2_Contextual_Anomaly_00269.csv", + "meta": { + "event": "drought", + "intensity": 0.0700470146874097, + "year": 2017, + "duration_days": 83, + "source": "ettm1" + } + }, + { + "id": "L3_T2_Contextual_Anomaly_00291", + "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 historically low water level. (Output format: [YYYY-MM-DD, YYYY-MM-DD])", + "answer": "[2020-05-12 06:15:00, 2020-07-26 06:00:00]", + "ground_truth": [ + "2020-05-12 06:15:00", + "2020-07-26 06:00:00" + ], + "eval_metric": "iou", + "channel": "177", + "ts_data_path": "ts_data/L3_T2_Contextual_Anomaly_00291.csv", + "meta": { + "event": "drought", + "intensity": 0.06326019400915465, + "year": 2020, + "duration_days": 75, + "source": "causal_rivers" + } + }, + { + "id": "L3_T2_Contextual_Anomaly_00301", + "level": 3, + "level_name": "Semantic Reasoning", + "category": "Contextual Anomaly", + "subtask": "Contextual Anomaly", + "question": "Identify the period in channel 580 during 2022 that experienced the most significant dry-out period. (Output format: [YYYY-MM-DD, YYYY-MM-DD])", + "answer": "[2022-05-06 05:30:00, 2022-07-21 05:15:00]", + "ground_truth": [ + "2022-05-06 05:30:00", + "2022-07-21 05:15:00" + ], + "eval_metric": "iou", + "channel": "580", + "ts_data_path": "ts_data/L3_T2_Contextual_Anomaly_00301.csv", + "meta": { + "event": "drought", + "intensity": 0.033803437305069975, + "year": 2022, + "duration_days": 76, + "source": "causal_rivers" + } + }, + { + "id": "L3_T2_Contextual_Anomaly_00306", + "level": 3, + "level_name": "Semantic Reasoning", + "category": "Contextual Anomaly", + "subtask": "Contextual Anomaly", + "question": "Identify the period in channel 626 during 2022 that experienced the most significant severe flood. (Output format: [YYYY-MM-DD, YYYY-MM-DD])", + "answer": "[2022-07-28 10:30:00, 2022-09-27 10:15:00]", + "ground_truth": [ + "2022-07-28 10:30:00", + "2022-09-27 10:15:00" + ], + "eval_metric": "iou", + "channel": "626", + "ts_data_path": "ts_data/L3_T2_Contextual_Anomaly_00306.csv", + "meta": { + "event": "flood", + "intensity": 4.405420601821618, + "year": 2022, + "duration_days": 61, + "source": "causal_rivers" + } + }, + { + "id": "L3_T2_Contextual_Anomaly_00321", + "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 severe flood. (Output format: [YYYY-MM-DD, YYYY-MM-DD])", + "answer": "[2019-06-29 09:00:00, 2019-09-07 08:45:00]", + "ground_truth": [ + "2019-06-29 09:00:00", + "2019-09-07 08:45:00" + ], + "eval_metric": "iou", + "channel": "895", + "ts_data_path": "ts_data/L3_T2_Contextual_Anomaly_00321.csv", + "meta": { + "event": "flood", + "intensity": 3.5388994629131436, + "year": 2019, + "duration_days": 70, + "source": "causal_rivers" + } + }, + { + "id": "L3_T2_Contextual_Anomaly_00332", + "level": 3, + "level_name": "Semantic Reasoning", + "category": "Contextual Anomaly", + "subtask": "Contextual Anomaly", + "question": "Identify the period in channel 71 during 2022 that experienced the most significant historically low water level. (Output format: [YYYY-MM-DD, YYYY-MM-DD])", + "answer": "[2022-02-21 20:15:00, 2022-05-04 20:00:00]", + "ground_truth": [ + "2022-02-21 20:15:00", + "2022-05-04 20:00:00" + ], + "eval_metric": "iou", + "channel": "71", + "ts_data_path": "ts_data/L3_T2_Contextual_Anomaly_00332.csv", + "meta": { + "event": "drought", + "intensity": 0.039724951809058884, + "year": 2022, + "duration_days": 72, + "source": "causal_rivers" + } + }, + { + "id": "L3_T2_Contextual_Anomaly_00333", + "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 dry-out period. (Output format: [YYYY-MM-DD, YYYY-MM-DD])", + "answer": "[2023-03-21 17:15:00, 2023-06-10 17:00:00]", + "ground_truth": [ + "2023-03-21 17:15:00", + "2023-06-10 17:00:00" + ], + "eval_metric": "iou", + "channel": "99", + "ts_data_path": "ts_data/L3_T2_Contextual_Anomaly_00333.csv", + "meta": { + "event": "drought", + "intensity": 0.06539748459194335, + "year": 2023, + "duration_days": 81, + "source": "causal_rivers" + } + }, + { + "id": "L3_T2_Contextual_Anomaly_00374", + "level": 3, + "level_name": "Semantic Reasoning", + "category": "Contextual Anomaly", + "subtask": "Contextual Anomaly", + "question": "Identify the period in channel 891 during 2020 that experienced the most significant severe flood. (Output format: [YYYY-MM-DD, YYYY-MM-DD])", + "answer": "[2020-06-01 06:15:00, 2020-08-27 06:00:00]", + "ground_truth": [ + "2020-06-01 06:15:00", + "2020-08-27 06:00:00" + ], + "eval_metric": "iou", + "channel": "891", + "ts_data_path": "ts_data/L3_T2_Contextual_Anomaly_00374.csv", + "meta": { + "event": "flood", + "intensity": 4.715497785442555, + "year": 2020, + "duration_days": 87, + "source": "causal_rivers" + } + }, + { + "id": "L3_T2_Contextual_Anomaly_00379", + "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 historically high water level. (Output format: [YYYY-MM-DD, YYYY-MM-DD])", + "answer": "[2017-03-04 17:45:00, 2017-05-22 17:30:00]", + "ground_truth": [ + "2017-03-04 17:45:00", + "2017-05-22 17:30:00" + ], + "eval_metric": "iou", + "channel": "HUFL", + "ts_data_path": "ts_data/L3_T2_Contextual_Anomaly_00379.csv", + "meta": { + "event": "flood", + "intensity": 3.48318719586889, + "year": 2017, + "duration_days": 79, + "source": "ettm1" + } + }, + { + "id": "L4_T1_Insight_Synthesis_00017", + "level": 4, + "level_name": "Insight Synthesis", + "category": "Insight Synthesis", + "subtask": "Insight Synthesis", + "question": "Analyze the behavior of channel 237 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 steady stable; from 2021-09-06 00:00:00 to 2021-09-13 12:00:00, the trend showed a rapid rise; from 2021-09-13 12:00:00 to 2021-09-18 12:00:00, the trend showed a gradual fall; from 2021-09-18 12:00:00 to 2021-09-30 23:45:00, the trend showed a rapid fall.\n2. Outlier Audit: A significant spike was detected at 2021-09-04 02:30:00 (value: 30.04).", + "ground_truth": { + "trend_segments": [ + "from 2021-09-01 00:00:00 to 2021-09-06 00:00:00, the trend showed a steady stable", + "from 2021-09-06 00:00:00 to 2021-09-13 12:00:00, the trend showed a rapid rise", + "from 2021-09-13 12:00:00 to 2021-09-18 12:00:00, the trend showed a gradual fall", + "from 2021-09-18 12:00:00 to 2021-09-30 23:45:00, the trend showed a rapid fall" + ], + "segments_meta": [ + { + "adj": "steady", + "kind": "stable", + "start_idx": 0, + "end_idx": 480 + }, + { + "adj": "rapid", + "kind": "rise", + "start_idx": 480, + "end_idx": 1200 + }, + { + "adj": "gradual", + "kind": "fall", + "start_idx": 1200, + "end_idx": 1680 + }, + { + "adj": "rapid", + "kind": "fall", + "start_idx": 1680, + "end_idx": 2880 + } + ], + "significant_anomaly": { + "timestamp": "2021-09-04 02:30:00", + "kind": "significant spike", + "value": 30.039442029479677 + } + }, + "eval_metric": "report", + "channel": "237", + "ts_data_path": "ts_data/L4_T1_Insight_Synthesis_00017.csv", + "meta": { + "target_month": "2021-09", + "target_year": 2021, + "source": "causal_rivers" + } + }, + { + "id": "L4_T1_Insight_Synthesis_00023", + "level": 4, + "level_name": "Insight Synthesis", + "category": "Insight Synthesis", + "subtask": "Insight Synthesis", + "question": "Analyze the behavior of channel 496 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 09:30:00, the trend showed a steady stable; from 2021-07-13 09:30:00 to 2021-07-31 23:45:00, the trend showed a gradual fall.\n2. Outlier Audit: A significant drop was detected at 2021-07-18 09:30:00 (value: -3.14).", + "ground_truth": { + "trend_segments": [ + "from 2021-07-01 00:00:00 to 2021-07-13 09:30:00, the trend showed a steady stable", + "from 2021-07-13 09:30:00 to 2021-07-31 23:45:00, the trend showed a gradual fall" + ], + "segments_meta": [ + { + "adj": "steady", + "kind": "stable", + "start_idx": 0, + "end_idx": 1190 + }, + { + "adj": "gradual", + "kind": "fall", + "start_idx": 1190, + "end_idx": 2976 + } + ], + "significant_anomaly": { + "timestamp": "2021-07-18 09:30:00", + "kind": "significant drop", + "value": -3.1442665461691477 + } + }, + "eval_metric": "report", + "channel": "496", + "ts_data_path": "ts_data/L4_T1_Insight_Synthesis_00023.csv", + "meta": { + "target_month": "2021-07", + "target_year": 2021, + "source": "causal_rivers" + } + }, + { + "id": "L4_T1_Insight_Synthesis_00025", + "level": 4, + "level_name": "Insight Synthesis", + "category": "Insight Synthesis", + "subtask": "Insight Synthesis", + "question": "Analyze the behavior of channel 578 for the period 2022-10.\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 2022-10-01 00:00:00 to 2022-10-08 18:00:00, the trend showed a fluctuating stable; from 2022-10-08 18:00:00 to 2022-10-16 12:00:00, the trend showed a steady stable; from 2022-10-16 12:00:00 to 2022-10-31 23:45:00, the trend showed a gradual fall.\n2. Outlier Audit: A significant drop was detected at 2022-10-14 16:00:00 (value: -6.32).", + "ground_truth": { + "trend_segments": [ + "from 2022-10-01 00:00:00 to 2022-10-08 18:00:00, the trend showed a fluctuating stable", + "from 2022-10-08 18:00:00 to 2022-10-16 12:00:00, the trend showed a steady stable", + "from 2022-10-16 12:00:00 to 2022-10-31 23:45:00, the trend showed a gradual fall" + ], + "segments_meta": [ + { + "adj": "fluctuating", + "kind": "stable", + "start_idx": 0, + "end_idx": 744 + }, + { + "adj": "steady", + "kind": "stable", + "start_idx": 744, + "end_idx": 1488 + }, + { + "adj": "gradual", + "kind": "fall", + "start_idx": 1488, + "end_idx": 2976 + } + ], + "significant_anomaly": { + "timestamp": "2022-10-14 16:00:00", + "kind": "significant drop", + "value": -6.319799307494132 + } + }, + "eval_metric": "report", + "channel": "578", + "ts_data_path": "ts_data/L4_T1_Insight_Synthesis_00025.csv", + "meta": { + "target_month": "2022-10", + "target_year": 2022, + "source": "causal_rivers" + } + }, + { + "id": "L4_T1_Insight_Synthesis_00059", + "level": 4, + "level_name": "Insight Synthesis", + "category": "Insight Synthesis", + "subtask": "Insight Synthesis", + "question": "Analyze the behavior of channel 147 for the period 2023-08.\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 2023-08-01 00:00:00 to 2023-08-24 06:00:00, the trend showed a gradual rise; from 2023-08-24 06:00:00 to 2023-08-31 23:45:00, the trend showed a steady stable.\n2. Outlier Audit: A significant spike was detected at 2023-08-22 00:00:00 (value: 2.06).", + "ground_truth": { + "trend_segments": [ + "from 2023-08-01 00:00:00 to 2023-08-24 06:00:00, the trend showed a gradual rise", + "from 2023-08-24 06:00:00 to 2023-08-31 23:45:00, the trend showed a steady stable" + ], + "segments_meta": [ + { + "adj": "gradual", + "kind": "rise", + "start_idx": 0, + "end_idx": 2232 + }, + { + "adj": "steady", + "kind": "stable", + "start_idx": 2232, + "end_idx": 2976 + } + ], + "significant_anomaly": { + "timestamp": "2023-08-22 00:00:00", + "kind": "significant spike", + "value": 2.058313282808669 + } + }, + "eval_metric": "report", + "channel": "147", + "ts_data_path": "ts_data/L4_T1_Insight_Synthesis_00059.csv", + "meta": { + "target_month": "2023-08", + "target_year": 2023, + "source": "causal_rivers" + } + }, + { + "id": "L4_T1_Insight_Synthesis_00063", + "level": 4, + "level_name": "Insight Synthesis", + "category": "Insight Synthesis", + "subtask": "Insight Synthesis", + "question": "Analyze the behavior of channel 166 for the period 2020-09.\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 2020-09-01 00:00:00 to 2020-09-09 19:45:00, the trend showed a gradual rise; from 2020-09-09 19:45:00 to 2020-09-15 02:45:00, the trend showed a fluctuating stable; from 2020-09-15 02:45:00 to 2020-09-20 09:45:00, the trend showed a gradual fall; from 2020-09-20 09:45:00 to 2020-09-30 23:45:00, the trend showed a rapid rise.\n2. Outlier Audit: A significant spike was detected at 2020-09-27 00:00:00 (value: 3692.06).", + "ground_truth": { + "trend_segments": [ + "from 2020-09-01 00:00:00 to 2020-09-09 19:45:00, the trend showed a gradual rise", + "from 2020-09-09 19:45:00 to 2020-09-15 02:45:00, the trend showed a fluctuating stable", + "from 2020-09-15 02:45:00 to 2020-09-20 09:45:00, the trend showed a gradual fall", + "from 2020-09-20 09:45:00 to 2020-09-30 23:45:00, the trend showed a rapid rise" + ], + "segments_meta": [ + { + "adj": "gradual", + "kind": "rise", + "start_idx": 0, + "end_idx": 847 + }, + { + "adj": "fluctuating", + "kind": "stable", + "start_idx": 847, + "end_idx": 1355 + }, + { + "adj": "gradual", + "kind": "fall", + "start_idx": 1355, + "end_idx": 1863 + }, + { + "adj": "rapid", + "kind": "rise", + "start_idx": 1863, + "end_idx": 2880 + } + ], + "significant_anomaly": { + "timestamp": "2020-09-27 00:00:00", + "kind": "significant spike", + "value": 3692.0579317245274 + } + }, + "eval_metric": "report", + "channel": "166", + "ts_data_path": "ts_data/L4_T1_Insight_Synthesis_00063.csv", + "meta": { + "target_month": "2020-09", + "target_year": 2020, + "source": "causal_rivers" + } + }, + { + "id": "L4_T1_Insight_Synthesis_00072", + "level": 4, + "level_name": "Insight Synthesis", + "category": "Insight Synthesis", + "subtask": "Insight Synthesis", + "question": "Analyze the behavior of channel 430 for the period 2023-08.\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 2023-08-01 00:00:00 to 2023-08-08 18:00:00, the trend showed a fluctuating stable; from 2023-08-08 18:00:00 to 2023-08-16 12:00:00, the trend showed a gradual fall; from 2023-08-16 12:00:00 to 2023-08-20 09:00:00, the trend showed a steady stable; from 2023-08-20 09:00:00 to 2023-08-31 23:45:00, the trend showed a fluctuating stable.\n2. Outlier Audit: A significant drop was detected at 2023-08-25 09:15:00 (value: -3.95).", + "ground_truth": { + "trend_segments": [ + "from 2023-08-01 00:00:00 to 2023-08-08 18:00:00, the trend showed a fluctuating stable", + "from 2023-08-08 18:00:00 to 2023-08-16 12:00:00, the trend showed a gradual fall", + "from 2023-08-16 12:00:00 to 2023-08-20 09:00:00, the trend showed a steady stable", + "from 2023-08-20 09:00:00 to 2023-08-31 23:45:00, the trend showed a fluctuating stable" + ], + "segments_meta": [ + { + "adj": "fluctuating", + "kind": "stable", + "start_idx": 0, + "end_idx": 744 + }, + { + "adj": "gradual", + "kind": "fall", + "start_idx": 744, + "end_idx": 1488 + }, + { + "adj": "steady", + "kind": "stable", + "start_idx": 1488, + "end_idx": 1860 + }, + { + "adj": "fluctuating", + "kind": "stable", + "start_idx": 1860, + "end_idx": 2976 + } + ], + "significant_anomaly": { + "timestamp": "2023-08-25 09:15:00", + "kind": "significant drop", + "value": -3.950903248467273 + } + }, + "eval_metric": "report", + "channel": "430", + "ts_data_path": "ts_data/L4_T1_Insight_Synthesis_00072.csv", + "meta": { + "target_month": "2023-08", + "target_year": 2023, + "source": "causal_rivers" + } + }, + { + "id": "L4_T1_Insight_Synthesis_00082", + "level": 4, + "level_name": "Insight Synthesis", + "category": "Insight Synthesis", + "subtask": "Insight Synthesis", + "question": "Analyze the behavior of channel 625 for the period 2021-03.\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 2021-03-01 00:00:00 to 2021-03-07 21:15:00, the trend showed a steady stable; from 2021-03-07 21:15:00 to 2021-03-14 18:30:00, the trend showed a rapid fall; from 2021-03-14 18:30:00 to 2021-03-25 02:30:00, the trend showed a steady stable; from 2021-03-25 02:30:00 to 2021-03-31 23:45:00, the trend showed a gradual fall.\n2. Outlier Audit: A significant spike was detected at 2021-03-16 10:30:00 (value: 7.97).", + "ground_truth": { + "trend_segments": [ + "from 2021-03-01 00:00:00 to 2021-03-07 21:15:00, the trend showed a steady stable", + "from 2021-03-07 21:15:00 to 2021-03-14 18:30:00, the trend showed a rapid fall", + "from 2021-03-14 18:30:00 to 2021-03-25 02:30:00, the trend showed a steady stable", + "from 2021-03-25 02:30:00 to 2021-03-31 23:45:00, the trend showed a gradual fall" + ], + "segments_meta": [ + { + "adj": "steady", + "kind": "stable", + "start_idx": 0, + "end_idx": 661 + }, + { + "adj": "rapid", + "kind": "fall", + "start_idx": 661, + "end_idx": 1322 + }, + { + "adj": "steady", + "kind": "stable", + "start_idx": 1322, + "end_idx": 2314 + }, + { + "adj": "gradual", + "kind": "fall", + "start_idx": 2314, + "end_idx": 2976 + } + ], + "significant_anomaly": { + "timestamp": "2021-03-16 10:30:00", + "kind": "significant spike", + "value": 7.972063202194917 + } + }, + "eval_metric": "report", + "channel": "625", + "ts_data_path": "ts_data/L4_T1_Insight_Synthesis_00082.csv", + "meta": { + "target_month": "2021-03", + "target_year": 2021, + "source": "causal_rivers" + } + }, + { + "id": "L4_T1_Insight_Synthesis_00084", + "level": 4, + "level_name": "Insight Synthesis", + "category": "Insight Synthesis", + "subtask": "Insight Synthesis", + "question": "Analyze the behavior of channel 627 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 2022-07-09 20:30:00, the trend showed a rapid fall; from 2022-07-09 20:30:00 to 2022-07-31 23:45:00, the trend showed a fluctuating stable.\n2. Outlier Audit: A significant drop was detected at 2022-07-14 02:15:00 (value: -1.97).", + "ground_truth": { + "trend_segments": [ + "from 2022-07-01 00:00:00 to 2022-07-09 20:30:00, the trend showed a rapid fall", + "from 2022-07-09 20:30:00 to 2022-07-31 23:45:00, the trend showed a fluctuating stable" + ], + "segments_meta": [ + { + "adj": "rapid", + "kind": "fall", + "start_idx": 0, + "end_idx": 850 + }, + { + "adj": "fluctuating", + "kind": "stable", + "start_idx": 850, + "end_idx": 2976 + } + ], + "significant_anomaly": { + "timestamp": "2022-07-14 02:15:00", + "kind": "significant drop", + "value": -1.9656435442058697 + } + }, + "eval_metric": "report", + "channel": "627", + "ts_data_path": "ts_data/L4_T1_Insight_Synthesis_00084.csv", + "meta": { + "target_month": "2022-07", + "target_year": 2022, + "source": "causal_rivers" + } + }, + { + "id": "L4_T1_Insight_Synthesis_00088", + "level": 4, + "level_name": "Insight Synthesis", + "category": "Insight Synthesis", + "subtask": "Insight Synthesis", + "question": "Analyze the behavior of channel 727 for the period 2020-09.\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 2020-09-01 00:00:00 to 2020-09-22 10:15:00, the trend showed a steady stable; from 2020-09-22 10:15:00 to 2020-09-30 23:45:00, the trend showed a gradual rise.\n2. Outlier Audit: A significant spike was detected at 2020-09-17 20:30:00 (value: 465.21).", + "ground_truth": { + "trend_segments": [ + "from 2020-09-01 00:00:00 to 2020-09-22 10:15:00, the trend showed a steady stable", + "from 2020-09-22 10:15:00 to 2020-09-30 23:45:00, the trend showed a gradual rise" + ], + "segments_meta": [ + { + "adj": "steady", + "kind": "stable", + "start_idx": 0, + "end_idx": 2057 + }, + { + "adj": "gradual", + "kind": "rise", + "start_idx": 2057, + "end_idx": 2880 + } + ], + "significant_anomaly": { + "timestamp": "2020-09-17 20:30:00", + "kind": "significant spike", + "value": 465.2105332833945 + } + }, + "eval_metric": "report", + "channel": "727", + "ts_data_path": "ts_data/L4_T1_Insight_Synthesis_00088.csv", + "meta": { + "target_month": "2020-09", + "target_year": 2020, + "source": "causal_rivers" + } + }, + { + "id": "L4_T1_Insight_Synthesis_00089", + "level": 4, + "level_name": "Insight Synthesis", + "category": "Insight Synthesis", + "subtask": "Insight Synthesis", + "question": "Analyze the behavior of channel 728 for the period 2023-08.\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 2023-08-01 00:00:00 to 2023-08-07 04:45:00, the trend showed a gradual rise; from 2023-08-07 04:45:00 to 2023-08-19 14:15:00, the trend showed a rapid fall; from 2023-08-19 14:15:00 to 2023-08-31 23:45:00, the trend showed a rapid rise.\n2. Outlier Audit: A significant spike was detected at 2023-08-26 15:30:00 (value: 955.16).", + "ground_truth": { + "trend_segments": [ + "from 2023-08-01 00:00:00 to 2023-08-07 04:45:00, the trend showed a gradual rise", + "from 2023-08-07 04:45:00 to 2023-08-19 14:15:00, the trend showed a rapid fall", + "from 2023-08-19 14:15:00 to 2023-08-31 23:45:00, the trend showed a rapid rise" + ], + "segments_meta": [ + { + "adj": "gradual", + "kind": "rise", + "start_idx": 0, + "end_idx": 595 + }, + { + "adj": "rapid", + "kind": "fall", + "start_idx": 595, + "end_idx": 1785 + }, + { + "adj": "rapid", + "kind": "rise", + "start_idx": 1785, + "end_idx": 2976 + } + ], + "significant_anomaly": { + "timestamp": "2023-08-26 15:30:00", + "kind": "significant spike", + "value": 955.1590594263216 + } + }, + "eval_metric": "report", + "channel": "728", + "ts_data_path": "ts_data/L4_T1_Insight_Synthesis_00089.csv", + "meta": { + "target_month": "2023-08", + "target_year": 2023, + "source": "causal_rivers" + } + }, + { + "id": "L4_T1_Insight_Synthesis_00101", + "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 2017-04-05 12:00:00, the trend showed a gradual rise; from 2017-04-05 12:00:00 to 2017-04-14 12:00:00, the trend showed a fluctuating stable; from 2017-04-14 12:00:00 to 2017-04-23 12:00:00, the trend showed a rapid fall; from 2017-04-23 12:00:00 to 2017-04-30 23:45:00, the trend showed a fluctuating stable.\n2. Outlier Audit: A significant drop was detected at 2017-04-24 23:00:00 (value: -76.31).", + "ground_truth": { + "trend_segments": [ + "from 2017-04-01 00:00:00 to 2017-04-05 12:00:00, the trend showed a gradual rise", + "from 2017-04-05 12:00:00 to 2017-04-14 12:00:00, the trend showed a fluctuating stable", + "from 2017-04-14 12:00:00 to 2017-04-23 12:00:00, the trend showed a rapid fall", + "from 2017-04-23 12:00:00 to 2017-04-30 23:45:00, the trend showed a fluctuating stable" + ], + "segments_meta": [ + { + "adj": "gradual", + "kind": "rise", + "start_idx": 0, + "end_idx": 432 + }, + { + "adj": "fluctuating", + "kind": "stable", + "start_idx": 432, + "end_idx": 1296 + }, + { + "adj": "rapid", + "kind": "fall", + "start_idx": 1296, + "end_idx": 2160 + }, + { + "adj": "fluctuating", + "kind": "stable", + "start_idx": 2160, + "end_idx": 2880 + } + ], + "significant_anomaly": { + "timestamp": "2017-04-24 23:00:00", + "kind": "significant drop", + "value": -76.31295765808908 + } + }, + "eval_metric": "report", + "channel": "HUFL", + "ts_data_path": "ts_data/L4_T1_Insight_Synthesis_00101.csv", + "meta": { + "target_month": "2017-04", + "target_year": 2017, + "source": "ettm1" + } + }, + { + "id": "L4_T1_Insight_Synthesis_00103", + "level": 4, + "level_name": "Insight Synthesis", + "category": "Insight Synthesis", + "subtask": "Insight Synthesis", + "question": "Analyze the behavior of channel MULL for the period 2017-02.\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 2017-02-01 00:00:00 to 2017-02-09 09:30:00, the trend showed a rapid fall; from 2017-02-09 09:30:00 to 2017-02-23 09:30:00, the trend showed a gradual rise; from 2017-02-23 09:30:00 to 2017-02-28 23:45:00, the trend showed a rapid fall.\n2. Outlier Audit: A significant drop was detected at 2017-02-21 22:45:00 (value: -40.76).", + "ground_truth": { + "trend_segments": [ + "from 2017-02-01 00:00:00 to 2017-02-09 09:30:00, the trend showed a rapid fall", + "from 2017-02-09 09:30:00 to 2017-02-23 09:30:00, the trend showed a gradual rise", + "from 2017-02-23 09:30:00 to 2017-02-28 23:45:00, the trend showed a rapid fall" + ], + "segments_meta": [ + { + "adj": "rapid", + "kind": "fall", + "start_idx": 0, + "end_idx": 806 + }, + { + "adj": "gradual", + "kind": "rise", + "start_idx": 806, + "end_idx": 2150 + }, + { + "adj": "rapid", + "kind": "fall", + "start_idx": 2150, + "end_idx": 2688 + } + ], + "significant_anomaly": { + "timestamp": "2017-02-21 22:45:00", + "kind": "significant drop", + "value": -40.75827399185372 + } + }, + "eval_metric": "report", + "channel": "MULL", + "ts_data_path": "ts_data/L4_T1_Insight_Synthesis_00103.csv", + "meta": { + "target_month": "2017-02", + "target_year": 2017, + "source": "ettm1" + } + }, + { + "id": "L4_T1_Insight_Synthesis_00121", + "level": 4, + "level_name": "Insight Synthesis", + "category": "Insight Synthesis", + "subtask": "Insight Synthesis", + "question": "Analyze the behavior of channel 177 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 17:15:00, the trend showed a rapid rise; from 2023-09-11 17:15:00 to 2023-09-18 03:30:00, the trend showed a gradual rise; from 2023-09-18 03:30:00 to 2023-09-30 23:45:00, the trend showed a rapid fall.\n2. Outlier Audit: A significant drop was detected at 2023-09-27 14:45:00 (value: -2538.47).", + "ground_truth": { + "trend_segments": [ + "from 2023-09-01 00:00:00 to 2023-09-11 17:15:00, the trend showed a rapid rise", + "from 2023-09-11 17:15:00 to 2023-09-18 03:30:00, the trend showed a gradual rise", + "from 2023-09-18 03:30:00 to 2023-09-30 23:45:00, the trend showed a rapid fall" + ], + "segments_meta": [ + { + "adj": "rapid", + "kind": "rise", + "start_idx": 0, + "end_idx": 1029 + }, + { + "adj": "gradual", + "kind": "rise", + "start_idx": 1029, + "end_idx": 1646 + }, + { + "adj": "rapid", + "kind": "fall", + "start_idx": 1646, + "end_idx": 2880 + } + ], + "significant_anomaly": { + "timestamp": "2023-09-27 14:45:00", + "kind": "significant drop", + "value": -2538.46750993684 + } + }, + "eval_metric": "report", + "channel": "177", + "ts_data_path": "ts_data/L4_T1_Insight_Synthesis_00121.csv", + "meta": { + "target_month": "2023-09", + "target_year": 2023, + "source": "causal_rivers" + } + }, + { + "id": "L4_T1_Insight_Synthesis_00128", + "level": 4, + "level_name": "Insight Synthesis", + "category": "Insight Synthesis", + "subtask": "Insight Synthesis", + "question": "Analyze the behavior of channel 496 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 2020-03-10 07:15:00, the trend showed a rapid rise; from 2020-03-10 07:15:00 to 2020-03-14 22:45:00, the trend showed a gradual fall; from 2020-03-14 22:45:00 to 2020-03-22 16:45:00, the trend showed a steady stable; from 2020-03-22 16:45:00 to 2020-03-31 23:45:00, the trend showed a rapid fall.\n2. Outlier Audit: A significant spike was detected at 2020-03-15 06:00:00 (value: 4.40).", + "ground_truth": { + "trend_segments": [ + "from 2020-03-01 00:00:00 to 2020-03-10 07:15:00, the trend showed a rapid rise", + "from 2020-03-10 07:15:00 to 2020-03-14 22:45:00, the trend showed a gradual fall", + "from 2020-03-14 22:45:00 to 2020-03-22 16:45:00, the trend showed a steady stable", + "from 2020-03-22 16:45:00 to 2020-03-31 23:45:00, the trend showed a rapid fall" + ], + "segments_meta": [ + { + "adj": "rapid", + "kind": "rise", + "start_idx": 0, + "end_idx": 893 + }, + { + "adj": "gradual", + "kind": "fall", + "start_idx": 893, + "end_idx": 1339 + }, + { + "adj": "steady", + "kind": "stable", + "start_idx": 1339, + "end_idx": 2083 + }, + { + "adj": "rapid", + "kind": "fall", + "start_idx": 2083, + "end_idx": 2976 + } + ], + "significant_anomaly": { + "timestamp": "2020-03-15 06:00:00", + "kind": "significant spike", + "value": 4.396052467609054 + } + }, + "eval_metric": "report", + "channel": "496", + "ts_data_path": "ts_data/L4_T1_Insight_Synthesis_00128.csv", + "meta": { + "target_month": "2020-03", + "target_year": 2020, + "source": "causal_rivers" + } + } +] \ No newline at end of file