| [ |
| { |
| "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" |
| } |
| } |
| ] |
