name string | seed int64 | weight string | context_sources list | skills list | background string | scenario string | constraints string | seasonal_period int64 | past_time string | future_time string | metric_scaling float64 | region_of_interest list | constraint_min float64 | constraint_max float64 | constraint_variable_max_index list | constraint_variable_max_values list |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
ElectricityIncreaseInPredictionTask | 1 | 1 | [
"c_cov",
"c_f"
] | [
"forecasting",
"natural language processing",
"instruction following"
] | This is the electricity consumption recorded in Kilowatt (kW) in city A. | Suppose that there is a heat wave in city A from 2013-05-28 12:00:00 for 2 hours in city A, leading to excessive use of air conditioning, and 4 times the usual electricity being consumed. | 24 | {"0":{"2013-05-22T04:00:00.000":351.2516211415,"2013-05-22T05:00:00.000":296.4482090403,"2013-05-22T06:00:00.000":231.8647128451,"2013-05-22T07:00:00.000":313.3813388685,"2013-05-22T08:00:00.000":358.7757575781,"2013-05-22T09:00:00.000":388.4802172839,"2013-05-22T10:00:00.000":416.0560981503,"2013-05-22T11:00:00.000":4... | {"0":{"2013-05-28T04:00:00.000":345.3082090214,"2013-05-28T05:00:00.000":297.3952571634,"2013-05-28T06:00:00.000":252.9083406769,"2013-05-28T07:00:00.000":326.4740036796,"2013-05-28T08:00:00.000":332.7867941306,"2013-05-28T09:00:00.000":322.0692551745,"2013-05-28T10:00:00.000":442.4631202637,"2013-05-28T11:00:00.000":3... | 0.00004 | [
8,
9
] | null | null | [] | [] | |
ElectricityIncreaseInPredictionTask | 2 | 1 | [
"c_cov",
"c_f"
] | [
"forecasting",
"natural language processing",
"instruction following"
] | This is the electricity consumption recorded in Kilowatt (kW) in city A. | Suppose that there is a heat wave in city A from 2012-10-09 18:00:00 for 3 hours in city A, leading to excessive use of air conditioning, and 5 times the usual electricity being consumed. | 24 | {"0":{"2012-10-03T13:00:00.000":2849.5772644674,"2012-10-03T14:00:00.000":2912.3948947443,"2012-10-03T15:00:00.000":2995.999075764,"2012-10-03T16:00:00.000":2936.6241219303,"2012-10-03T17:00:00.000":3016.1410780868,"2012-10-03T18:00:00.000":2869.9795725569,"2012-10-03T19:00:00.000":2986.7066804195,"2012-10-03T20:00:00.... | {"0":{"2012-10-09T13:00:00.000":3306.4741429646,"2012-10-09T14:00:00.000":2965.5678242008,"2012-10-09T15:00:00.000":2906.5024781426,"2012-10-09T16:00:00.000":3090.7526672942,"2012-10-09T17:00:00.000":3227.4065834481,"2012-10-09T18:00:00.000":16754.824097567,"2012-10-09T19:00:00.000":16714.5345840893,"2012-10-09T20:00:0... | 0.00004 | [
5,
6,
7
] | null | null | [] | [] | |
ElectricityIncreaseInPredictionTask | 3 | 1 | [
"c_cov",
"c_f"
] | [
"forecasting",
"natural language processing",
"instruction following"
] | This is the electricity consumption recorded in Kilowatt (kW) in city A. | Suppose that there is a heat wave in city A from 2013-10-03 12:00:00 for 1 hour in city A, leading to excessive use of air conditioning, and 4 times the usual electricity being consumed. | 24 | {"0":{"2013-09-29T00:00:00.000":999.6195114715,"2013-09-29T01:00:00.000":784.2344573531,"2013-09-29T02:00:00.000":737.588217289,"2013-09-29T03:00:00.000":753.8795816336,"2013-09-29T04:00:00.000":749.7819284344,"2013-09-29T05:00:00.000":805.625010687,"2013-09-29T06:00:00.000":855.4612387851,"2013-09-29T07:00:00.000":910... | {"0":{"2013-10-03T00:00:00.000":840.2519958766,"2013-10-03T01:00:00.000":782.0773855098,"2013-10-03T02:00:00.000":805.9704632229,"2013-10-03T03:00:00.000":779.3670760035,"2013-10-03T04:00:00.000":702.3938474406,"2013-10-03T05:00:00.000":787.7919464477,"2013-10-03T06:00:00.000":782.4958200928,"2013-10-03T07:00:00.000":8... | 0.00004 | [
12
] | null | null | [] | [] | |
ElectricityIncreaseInPredictionTask | 4 | 1 | [
"c_cov",
"c_f"
] | [
"forecasting",
"natural language processing",
"instruction following"
] | This is the electricity consumption recorded in Kilowatt (kW) in city A. | Suppose that there is a heat wave in city A from 2013-12-19 00:00:00 for 1 hour in city A, leading to excessive use of air conditioning, and 3 times the usual electricity being consumed. | 24 | {"0":{"2013-12-13T05:00:00.000":91.8673086996,"2013-12-13T06:00:00.000":92.0924125542,"2013-12-13T07:00:00.000":93.9442670199,"2013-12-13T08:00:00.000":95.788876666,"2013-12-13T09:00:00.000":92.9137018953,"2013-12-13T10:00:00.000":92.0797519891,"2013-12-13T11:00:00.000":92.0442677915,"2013-12-13T12:00:00.000":91.847114... | {"0":{"2013-12-18T05:00:00.000":83.5794338029,"2013-12-18T06:00:00.000":82.515900143,"2013-12-18T07:00:00.000":85.8644546967,"2013-12-18T08:00:00.000":85.6216877662,"2013-12-18T09:00:00.000":92.8109308545,"2013-12-18T10:00:00.000":96.2353549458,"2013-12-18T11:00:00.000":94.1802269888,"2013-12-18T12:00:00.000":89.633869... | 0.00004 | [
19
] | null | null | [] | [] | |
ElectricityIncreaseInPredictionTask | 5 | 1 | [
"c_cov",
"c_f"
] | [
"forecasting",
"natural language processing",
"instruction following"
] | This is the electricity consumption recorded in Kilowatt (kW) in city A. | Suppose that there is a heat wave in city A from 2012-05-13 16:00:00 for 2 hours in city A, leading to excessive use of air conditioning, and 3 times the usual electricity being consumed. | 24 | {"0":{"2012-05-06T22:00:00.000":576.5585078009,"2012-05-06T23:00:00.000":360.2741590517,"2012-05-07T00:00:00.000":329.540771783,"2012-05-07T01:00:00.000":299.0708102444,"2012-05-07T02:00:00.000":312.7244949413,"2012-05-07T03:00:00.000":305.6137486905,"2012-05-07T04:00:00.000":323.6999245903,"2012-05-07T05:00:00.000":37... | {"0":{"2012-05-12T22:00:00.000":731.4944051872,"2012-05-12T23:00:00.000":480.4839431434,"2012-05-13T00:00:00.000":399.4650331586,"2012-05-13T01:00:00.000":390.030081889,"2012-05-13T02:00:00.000":391.1636328287,"2012-05-13T03:00:00.000":328.4591506993,"2012-05-13T04:00:00.000":375.8035994547,"2012-05-13T05:00:00.000":48... | 0.00004 | [
18,
19
] | null | null | [] | [] | |
ElectricityIncreaseInPredictionWithDistractorText | 1 | 1/3 | [
"c_cov",
"c_f"
] | [
"forecasting",
"natural language processing",
"instruction following",
"retrieval: context"
] | This is the electricity consumption recorded in Kilowatt (kW) in city A. | Suppose that there is a heat wave in city A from 2013-05-28 12:00:00 for 2 hours, leading to excessive use of air conditioning, and 4 times the usual electricity being consumed. A brief technical issue in the electricity grid caused a major dip of 75% in electricity consumption 2 weeks ago. This issue is not expected t... | 24 | {"0":{"2013-05-22T04:00:00.000":351.2516211415,"2013-05-22T05:00:00.000":296.4482090403,"2013-05-22T06:00:00.000":231.8647128451,"2013-05-22T07:00:00.000":313.3813388685,"2013-05-22T08:00:00.000":358.7757575781,"2013-05-22T09:00:00.000":388.4802172839,"2013-05-22T10:00:00.000":416.0560981503,"2013-05-22T11:00:00.000":4... | {"0":{"2013-05-28T04:00:00.000":345.3082090214,"2013-05-28T05:00:00.000":297.3952571634,"2013-05-28T06:00:00.000":252.9083406769,"2013-05-28T07:00:00.000":326.4740036796,"2013-05-28T08:00:00.000":332.7867941306,"2013-05-28T09:00:00.000":322.0692551745,"2013-05-28T10:00:00.000":442.4631202637,"2013-05-28T11:00:00.000":3... | 0.00004 | [
8,
9
] | null | null | [] | [] | |
ElectricityIncreaseInPredictionWithDistractorText | 2 | 1/3 | [
"c_cov",
"c_f"
] | [
"forecasting",
"natural language processing",
"instruction following",
"retrieval: context"
] | This is the electricity consumption recorded in Kilowatt (kW) in city A. | Suppose that there is a heat wave in city A from 2012-10-09 18:00:00 for 3 hours, leading to excessive use of air conditioning, and 5 times the usual electricity being consumed. Historically, over the past 3 years, there have been patterns of increased electricity usage due to extreme cold weather in city A in the mont... | 24 | {"0":{"2012-10-03T13:00:00.000":2849.5772644674,"2012-10-03T14:00:00.000":2912.3948947443,"2012-10-03T15:00:00.000":2995.999075764,"2012-10-03T16:00:00.000":2936.6241219303,"2012-10-03T17:00:00.000":3016.1410780868,"2012-10-03T18:00:00.000":2869.9795725569,"2012-10-03T19:00:00.000":2986.7066804195,"2012-10-03T20:00:00.... | {"0":{"2012-10-09T13:00:00.000":3306.4741429646,"2012-10-09T14:00:00.000":2965.5678242008,"2012-10-09T15:00:00.000":2906.5024781426,"2012-10-09T16:00:00.000":3090.7526672942,"2012-10-09T17:00:00.000":3227.4065834481,"2012-10-09T18:00:00.000":16754.824097567,"2012-10-09T19:00:00.000":16714.5345840893,"2012-10-09T20:00:0... | 0.00004 | [
5,
6,
7
] | null | null | [] | [] | |
ElectricityIncreaseInPredictionWithDistractorText | 3 | 1/3 | [
"c_cov",
"c_f"
] | [
"forecasting",
"natural language processing",
"instruction following",
"retrieval: context"
] | This is the electricity consumption recorded in Kilowatt (kW) in city A. | Suppose that there is a heat wave in city A from 2013-10-03 12:00:00 for 1 hour, leading to excessive use of air conditioning, and 4 times the usual electricity being consumed. A brief technical issue in the electricity grid caused a major dip of 85% in electricity consumption 2 weeks ago. This issue is not expected to... | 24 | {"0":{"2013-09-29T00:00:00.000":999.6195114715,"2013-09-29T01:00:00.000":784.2344573531,"2013-09-29T02:00:00.000":737.588217289,"2013-09-29T03:00:00.000":753.8795816336,"2013-09-29T04:00:00.000":749.7819284344,"2013-09-29T05:00:00.000":805.625010687,"2013-09-29T06:00:00.000":855.4612387851,"2013-09-29T07:00:00.000":910... | {"0":{"2013-10-03T00:00:00.000":840.2519958766,"2013-10-03T01:00:00.000":782.0773855098,"2013-10-03T02:00:00.000":805.9704632229,"2013-10-03T03:00:00.000":779.3670760035,"2013-10-03T04:00:00.000":702.3938474406,"2013-10-03T05:00:00.000":787.7919464477,"2013-10-03T06:00:00.000":782.4958200928,"2013-10-03T07:00:00.000":8... | 0.00004 | [
12
] | null | null | [] | [] | |
ElectricityIncreaseInPredictionWithDistractorText | 4 | 1/3 | [
"c_cov",
"c_f"
] | [
"forecasting",
"natural language processing",
"instruction following",
"retrieval: context"
] | This is the electricity consumption recorded in Kilowatt (kW) in city A. | Suppose that there is a heat wave in city A from 2013-12-19 00:00:00 for 1 hour, leading to excessive use of air conditioning, and 3 times the usual electricity being consumed. A brief technical issue in the electricity grid caused a major dip of 85% in electricity consumption 2 weeks ago. This issue is not expected to... | 24 | {"0":{"2013-12-13T05:00:00.000":91.8673086996,"2013-12-13T06:00:00.000":92.0924125542,"2013-12-13T07:00:00.000":93.9442670199,"2013-12-13T08:00:00.000":95.788876666,"2013-12-13T09:00:00.000":92.9137018953,"2013-12-13T10:00:00.000":92.0797519891,"2013-12-13T11:00:00.000":92.0442677915,"2013-12-13T12:00:00.000":91.847114... | {"0":{"2013-12-18T05:00:00.000":83.5794338029,"2013-12-18T06:00:00.000":82.515900143,"2013-12-18T07:00:00.000":85.8644546967,"2013-12-18T08:00:00.000":85.6216877662,"2013-12-18T09:00:00.000":92.8109308545,"2013-12-18T10:00:00.000":96.2353549458,"2013-12-18T11:00:00.000":94.1802269888,"2013-12-18T12:00:00.000":89.633869... | 0.00004 | [
19
] | null | null | [] | [] | |
ElectricityIncreaseInPredictionWithDistractorText | 5 | 1/3 | [
"c_cov",
"c_f"
] | [
"forecasting",
"natural language processing",
"instruction following",
"retrieval: context"
] | This is the electricity consumption recorded in Kilowatt (kW) in city A. | Suppose that there is a heat wave in city A from 2012-05-13 16:00:00 for 2 hours, leading to excessive use of air conditioning, and 3 times the usual electricity being consumed. There was a festival in neighbouring cities B and C that resulted in 6 times the usual electricity being consumed there. But this did not affe... | 24 | {"0":{"2012-05-06T22:00:00.000":576.5585078009,"2012-05-06T23:00:00.000":360.2741590517,"2012-05-07T00:00:00.000":329.540771783,"2012-05-07T01:00:00.000":299.0708102444,"2012-05-07T02:00:00.000":312.7244949413,"2012-05-07T03:00:00.000":305.6137486905,"2012-05-07T04:00:00.000":323.6999245903,"2012-05-07T05:00:00.000":37... | {"0":{"2012-05-12T22:00:00.000":731.4944051872,"2012-05-12T23:00:00.000":480.4839431434,"2012-05-13T00:00:00.000":399.4650331586,"2012-05-13T01:00:00.000":390.030081889,"2012-05-13T02:00:00.000":391.1636328287,"2012-05-13T03:00:00.000":328.4591506993,"2012-05-13T04:00:00.000":375.8035994547,"2012-05-13T05:00:00.000":48... | 0.00004 | [
18,
19
] | null | null | [] | [] | |
ElectricityIncreaseInPredictionWithDistractorWithDates | 1 | 1/3 | [
"c_cov",
"c_f"
] | [
"forecasting",
"natural language processing",
"instruction following",
"retrieval: context"
] | This is the electricity consumption recorded in Kilowatt (kW) in city A. | There was a festival in neighbouring cities B and C that resulted in 10 times the usual electricity being consumed there from 2013-05-28 12:00:00 for 2 hours. But this did not affect electricity consumption in city A.Suppose that there is a heat wave in city A from 2013-05-28 12:00:00 for 2 hours, leading to excessive ... | 24 | {"0":{"2013-05-22T04:00:00.000":351.2516211415,"2013-05-22T05:00:00.000":296.4482090403,"2013-05-22T06:00:00.000":231.8647128451,"2013-05-22T07:00:00.000":313.3813388685,"2013-05-22T08:00:00.000":358.7757575781,"2013-05-22T09:00:00.000":388.4802172839,"2013-05-22T10:00:00.000":416.0560981503,"2013-05-22T11:00:00.000":4... | {"0":{"2013-05-28T04:00:00.000":345.3082090214,"2013-05-28T05:00:00.000":297.3952571634,"2013-05-28T06:00:00.000":252.9083406769,"2013-05-28T07:00:00.000":326.4740036796,"2013-05-28T08:00:00.000":332.7867941306,"2013-05-28T09:00:00.000":322.0692551745,"2013-05-28T10:00:00.000":442.4631202637,"2013-05-28T11:00:00.000":3... | 0.00004 | [
8,
9
] | null | null | [] | [] | |
ElectricityIncreaseInPredictionWithDistractorWithDates | 2 | 1/3 | [
"c_cov",
"c_f"
] | [
"forecasting",
"natural language processing",
"instruction following",
"retrieval: context"
] | This is the electricity consumption recorded in Kilowatt (kW) in city A. | A brief technical issue in the electricity grid in a nearby city caused a major dip of 85% from 2012-10-09 18:00:00 for 3 hours. This issue has affected many nearby cities, but not this city.Suppose that there is a heat wave in city A from 2012-10-09 18:00:00 for 3 hours, leading to excessive use of air conditioning, a... | 24 | {"0":{"2012-10-03T13:00:00.000":2849.5772644674,"2012-10-03T14:00:00.000":2912.3948947443,"2012-10-03T15:00:00.000":2995.999075764,"2012-10-03T16:00:00.000":2936.6241219303,"2012-10-03T17:00:00.000":3016.1410780868,"2012-10-03T18:00:00.000":2869.9795725569,"2012-10-03T19:00:00.000":2986.7066804195,"2012-10-03T20:00:00.... | {"0":{"2012-10-09T13:00:00.000":3306.4741429646,"2012-10-09T14:00:00.000":2965.5678242008,"2012-10-09T15:00:00.000":2906.5024781426,"2012-10-09T16:00:00.000":3090.7526672942,"2012-10-09T17:00:00.000":3227.4065834481,"2012-10-09T18:00:00.000":16754.824097567,"2012-10-09T19:00:00.000":16714.5345840893,"2012-10-09T20:00:0... | 0.00004 | [
5,
6,
7
] | null | null | [] | [] | |
ElectricityIncreaseInPredictionWithDistractorWithDates | 3 | 1/3 | [
"c_cov",
"c_f"
] | [
"forecasting",
"natural language processing",
"instruction following",
"retrieval: context"
] | This is the electricity consumption recorded in Kilowatt (kW) in city A. | A brief technical issue in the electricity grid in a nearby city caused a major dip of 95% from 2013-10-03 12:00:00 for 1 hour. This issue has affected many nearby cities, but not this city.Suppose that there is a heat wave in city A from 2013-10-03 12:00:00 for 1 hour, leading to excessive use of air conditioning, and... | 24 | {"0":{"2013-09-29T00:00:00.000":999.6195114715,"2013-09-29T01:00:00.000":784.2344573531,"2013-09-29T02:00:00.000":737.588217289,"2013-09-29T03:00:00.000":753.8795816336,"2013-09-29T04:00:00.000":749.7819284344,"2013-09-29T05:00:00.000":805.625010687,"2013-09-29T06:00:00.000":855.4612387851,"2013-09-29T07:00:00.000":910... | {"0":{"2013-10-03T00:00:00.000":840.2519958766,"2013-10-03T01:00:00.000":782.0773855098,"2013-10-03T02:00:00.000":805.9704632229,"2013-10-03T03:00:00.000":779.3670760035,"2013-10-03T04:00:00.000":702.3938474406,"2013-10-03T05:00:00.000":787.7919464477,"2013-10-03T06:00:00.000":782.4958200928,"2013-10-03T07:00:00.000":8... | 0.00004 | [
12
] | null | null | [] | [] | |
ElectricityIncreaseInPredictionWithDistractorWithDates | 4 | 1/3 | [
"c_cov",
"c_f"
] | [
"forecasting",
"natural language processing",
"instruction following",
"retrieval: context"
] | This is the electricity consumption recorded in Kilowatt (kW) in city A. | There was a festival in neighbouring cities B and C that resulted in 7 times the usual electricity being consumed there from 2013-12-19 00:00:00 for 1 hour. But this did not affect electricity consumption in city A.Suppose that there is a heat wave in city A from 2013-12-19 00:00:00 for 1 hour, leading to excessive use... | 24 | {"0":{"2013-12-13T05:00:00.000":91.8673086996,"2013-12-13T06:00:00.000":92.0924125542,"2013-12-13T07:00:00.000":93.9442670199,"2013-12-13T08:00:00.000":95.788876666,"2013-12-13T09:00:00.000":92.9137018953,"2013-12-13T10:00:00.000":92.0797519891,"2013-12-13T11:00:00.000":92.0442677915,"2013-12-13T12:00:00.000":91.847114... | {"0":{"2013-12-18T05:00:00.000":83.5794338029,"2013-12-18T06:00:00.000":82.515900143,"2013-12-18T07:00:00.000":85.8644546967,"2013-12-18T08:00:00.000":85.6216877662,"2013-12-18T09:00:00.000":92.8109308545,"2013-12-18T10:00:00.000":96.2353549458,"2013-12-18T11:00:00.000":94.1802269888,"2013-12-18T12:00:00.000":89.633869... | 0.00004 | [
19
] | null | null | [] | [] | |
ElectricityIncreaseInPredictionWithDistractorWithDates | 5 | 1/3 | [
"c_cov",
"c_f"
] | [
"forecasting",
"natural language processing",
"instruction following",
"retrieval: context"
] | This is the electricity consumption recorded in Kilowatt (kW) in city A. | There was a festival in neighbouring cities B and C that resulted in 6 times the usual electricity being consumed there from 2012-05-13 16:00:00 for 2 hours. But this did not affect electricity consumption in city A.Suppose that there is a heat wave in city A from 2012-05-13 16:00:00 for 2 hours, leading to excessive u... | 24 | {"0":{"2012-05-06T22:00:00.000":576.5585078009,"2012-05-06T23:00:00.000":360.2741590517,"2012-05-07T00:00:00.000":329.540771783,"2012-05-07T01:00:00.000":299.0708102444,"2012-05-07T02:00:00.000":312.7244949413,"2012-05-07T03:00:00.000":305.6137486905,"2012-05-07T04:00:00.000":323.6999245903,"2012-05-07T05:00:00.000":37... | {"0":{"2012-05-12T22:00:00.000":731.4944051872,"2012-05-12T23:00:00.000":480.4839431434,"2012-05-13T00:00:00.000":399.4650331586,"2012-05-13T01:00:00.000":390.030081889,"2012-05-13T02:00:00.000":391.1636328287,"2012-05-13T03:00:00.000":328.4591506993,"2012-05-13T04:00:00.000":375.8035994547,"2012-05-13T05:00:00.000":48... | 0.00004 | [
18,
19
] | null | null | [] | [] | |
ElectricityIncreaseInPredictionWithSplitContext | 1 | 1/3 | [
"c_cov",
"c_f"
] | [
"forecasting",
"natural language processing",
"instruction following"
] | This is the electricity consumption recorded in Kilowatt (kW) in city A. | Suppose that there is a heat wave in city A from 2013-05-28 12:00:00 for 2 hours, which would typically lead to excessive use of air conditioning, and 10 times the usual electricity being consumed. But in this case, residents sought to conserve energy and used lesser air conditioning, resulting in excessive usage of on... | 24 | {"0":{"2013-05-22T04:00:00.000":351.2516211415,"2013-05-22T05:00:00.000":296.4482090403,"2013-05-22T06:00:00.000":231.8647128451,"2013-05-22T07:00:00.000":313.3813388685,"2013-05-22T08:00:00.000":358.7757575781,"2013-05-22T09:00:00.000":388.4802172839,"2013-05-22T10:00:00.000":416.0560981503,"2013-05-22T11:00:00.000":4... | {"0":{"2013-05-28T04:00:00.000":345.3082090214,"2013-05-28T05:00:00.000":297.3952571634,"2013-05-28T06:00:00.000":252.9083406769,"2013-05-28T07:00:00.000":326.4740036796,"2013-05-28T08:00:00.000":332.7867941306,"2013-05-28T09:00:00.000":322.0692551745,"2013-05-28T10:00:00.000":442.4631202637,"2013-05-28T11:00:00.000":3... | 0.00004 | [
8,
9
] | null | null | [] | [] | |
ElectricityIncreaseInPredictionWithSplitContext | 2 | 1/3 | [
"c_cov",
"c_f"
] | [
"forecasting",
"natural language processing",
"instruction following"
] | This is the electricity consumption recorded in Kilowatt (kW) in city A. | Suppose that there is a heat wave in city A from 2012-10-09 18:00:00 for 3 hours, which would typically lead to excessive use of air conditioning, and 9 times the usual electricity being consumed. But in this case, residents sought to conserve energy and used lesser air conditioning, resulting in excessive usage of onl... | 24 | {"0":{"2012-10-03T13:00:00.000":2849.5772644674,"2012-10-03T14:00:00.000":2912.3948947443,"2012-10-03T15:00:00.000":2995.999075764,"2012-10-03T16:00:00.000":2936.6241219303,"2012-10-03T17:00:00.000":3016.1410780868,"2012-10-03T18:00:00.000":2869.9795725569,"2012-10-03T19:00:00.000":2986.7066804195,"2012-10-03T20:00:00.... | {"0":{"2012-10-09T13:00:00.000":3306.4741429646,"2012-10-09T14:00:00.000":2965.5678242008,"2012-10-09T15:00:00.000":2906.5024781426,"2012-10-09T16:00:00.000":3090.7526672942,"2012-10-09T17:00:00.000":3227.4065834481,"2012-10-09T18:00:00.000":16754.824097567,"2012-10-09T19:00:00.000":16714.5345840893,"2012-10-09T20:00:0... | 0.00004 | [
5,
6,
7
] | null | null | [] | [] | |
ElectricityIncreaseInPredictionWithSplitContext | 3 | 1/3 | [
"c_cov",
"c_f"
] | [
"forecasting",
"natural language processing",
"instruction following"
] | This is the electricity consumption recorded in Kilowatt (kW) in city A. | Suppose that there is a heat wave in city A from 2013-10-03 12:00:00 for 1 hour, which would typically lead to excessive use of air conditioning, and 10 times the usual electricity being consumed. But in this case, residents sought to conserve energy and used lesser air conditioning, resulting in excessive usage of onl... | 24 | {"0":{"2013-09-29T00:00:00.000":999.6195114715,"2013-09-29T01:00:00.000":784.2344573531,"2013-09-29T02:00:00.000":737.588217289,"2013-09-29T03:00:00.000":753.8795816336,"2013-09-29T04:00:00.000":749.7819284344,"2013-09-29T05:00:00.000":805.625010687,"2013-09-29T06:00:00.000":855.4612387851,"2013-09-29T07:00:00.000":910... | {"0":{"2013-10-03T00:00:00.000":840.2519958766,"2013-10-03T01:00:00.000":782.0773855098,"2013-10-03T02:00:00.000":805.9704632229,"2013-10-03T03:00:00.000":779.3670760035,"2013-10-03T04:00:00.000":702.3938474406,"2013-10-03T05:00:00.000":787.7919464477,"2013-10-03T06:00:00.000":782.4958200928,"2013-10-03T07:00:00.000":8... | 0.00004 | [
12
] | null | null | [] | [] | |
ElectricityIncreaseInPredictionWithSplitContext | 4 | 1/3 | [
"c_cov",
"c_f"
] | [
"forecasting",
"natural language processing",
"instruction following"
] | This is the electricity consumption recorded in Kilowatt (kW) in city A. | Suppose that there is a heat wave in city A from 2013-12-19 00:00:00 for 1 hour, which would typically lead to excessive use of air conditioning, and 7 times the usual electricity being consumed. But in this case, residents sought to conserve energy and used lesser air conditioning, resulting in excessive usage of only... | 24 | {"0":{"2013-12-13T05:00:00.000":91.8673086996,"2013-12-13T06:00:00.000":92.0924125542,"2013-12-13T07:00:00.000":93.9442670199,"2013-12-13T08:00:00.000":95.788876666,"2013-12-13T09:00:00.000":92.9137018953,"2013-12-13T10:00:00.000":92.0797519891,"2013-12-13T11:00:00.000":92.0442677915,"2013-12-13T12:00:00.000":91.847114... | {"0":{"2013-12-18T05:00:00.000":83.5794338029,"2013-12-18T06:00:00.000":82.515900143,"2013-12-18T07:00:00.000":85.8644546967,"2013-12-18T08:00:00.000":85.6216877662,"2013-12-18T09:00:00.000":92.8109308545,"2013-12-18T10:00:00.000":96.2353549458,"2013-12-18T11:00:00.000":94.1802269888,"2013-12-18T12:00:00.000":89.633869... | 0.00004 | [
19
] | null | null | [] | [] | |
ElectricityIncreaseInPredictionWithSplitContext | 5 | 1/3 | [
"c_cov",
"c_f"
] | [
"forecasting",
"natural language processing",
"instruction following"
] | This is the electricity consumption recorded in Kilowatt (kW) in city A. | Suppose that there is a heat wave in city A from 2012-05-13 16:00:00 for 2 hours, which would typically lead to excessive use of air conditioning, and 6 times the usual electricity being consumed. But in this case, residents sought to conserve energy and used lesser air conditioning, resulting in excessive usage of onl... | 24 | {"0":{"2012-05-06T22:00:00.000":576.5585078009,"2012-05-06T23:00:00.000":360.2741590517,"2012-05-07T00:00:00.000":329.540771783,"2012-05-07T01:00:00.000":299.0708102444,"2012-05-07T02:00:00.000":312.7244949413,"2012-05-07T03:00:00.000":305.6137486905,"2012-05-07T04:00:00.000":323.6999245903,"2012-05-07T05:00:00.000":37... | {"0":{"2012-05-12T22:00:00.000":731.4944051872,"2012-05-12T23:00:00.000":480.4839431434,"2012-05-13T00:00:00.000":399.4650331586,"2012-05-13T01:00:00.000":390.030081889,"2012-05-13T02:00:00.000":391.1636328287,"2012-05-13T03:00:00.000":328.4591506993,"2012-05-13T04:00:00.000":375.8035994547,"2012-05-13T05:00:00.000":48... | 0.00004 | [
18,
19
] | null | null | [] | [] | |
ShortNewsElectricityIncreaseInPredictionTask | 1 | 1/3 | [
"c_cov",
"c_f"
] | [
"forecasting",
"natural language processing",
"instruction following",
"retrieval: context"
] | This is the electricity consumption recorded in Kilowatt (kW) in city A. | A heatwave struck the city, which began on 2013-05-28 12:00:00 and lasted for approximately 2 hours, saw temperatures soar to unprecedented levels. According to the city's electricity provider, power consumption during the peak of the heatwave reached approximately 4 times the typical usage for this time of year. | 24 | {"0":{"2013-05-22T04:00:00.000":351.2516211415,"2013-05-22T05:00:00.000":296.4482090403,"2013-05-22T06:00:00.000":231.8647128451,"2013-05-22T07:00:00.000":313.3813388685,"2013-05-22T08:00:00.000":358.7757575781,"2013-05-22T09:00:00.000":388.4802172839,"2013-05-22T10:00:00.000":416.0560981503,"2013-05-22T11:00:00.000":4... | {"0":{"2013-05-28T04:00:00.000":345.3082090214,"2013-05-28T05:00:00.000":297.3952571634,"2013-05-28T06:00:00.000":252.9083406769,"2013-05-28T07:00:00.000":326.4740036796,"2013-05-28T08:00:00.000":332.7867941306,"2013-05-28T09:00:00.000":322.0692551745,"2013-05-28T10:00:00.000":442.4631202637,"2013-05-28T11:00:00.000":3... | 0.00004 | [
8,
9
] | null | null | [] | [] | |
ShortNewsElectricityIncreaseInPredictionTask | 2 | 1/3 | [
"c_cov",
"c_f"
] | [
"forecasting",
"natural language processing",
"instruction following",
"retrieval: context"
] | This is the electricity consumption recorded in Kilowatt (kW) in city A. | A heatwave struck the city, which began on 2012-10-09 18:00:00 and lasted for approximately 3 hours, saw temperatures soar to unprecedented levels. According to the city's electricity provider, power consumption during the peak of the heatwave reached approximately 5 times the typical usage for this time of year. | 24 | {"0":{"2012-10-03T13:00:00.000":2849.5772644674,"2012-10-03T14:00:00.000":2912.3948947443,"2012-10-03T15:00:00.000":2995.999075764,"2012-10-03T16:00:00.000":2936.6241219303,"2012-10-03T17:00:00.000":3016.1410780868,"2012-10-03T18:00:00.000":2869.9795725569,"2012-10-03T19:00:00.000":2986.7066804195,"2012-10-03T20:00:00.... | {"0":{"2012-10-09T13:00:00.000":3306.4741429646,"2012-10-09T14:00:00.000":2965.5678242008,"2012-10-09T15:00:00.000":2906.5024781426,"2012-10-09T16:00:00.000":3090.7526672942,"2012-10-09T17:00:00.000":3227.4065834481,"2012-10-09T18:00:00.000":16754.824097567,"2012-10-09T19:00:00.000":16714.5345840893,"2012-10-09T20:00:0... | 0.00004 | [
5,
6,
7
] | null | null | [] | [] | |
ShortNewsElectricityIncreaseInPredictionTask | 3 | 1/3 | [
"c_cov",
"c_f"
] | [
"forecasting",
"natural language processing",
"instruction following",
"retrieval: context"
] | This is the electricity consumption recorded in Kilowatt (kW) in city A. | A heatwave struck the city, which began on 2013-10-03 12:00:00 and lasted for approximately 1 hour, saw temperatures soar to unprecedented levels. According to the city's electricity provider, power consumption during the peak of the heatwave reached approximately 4 times the typical usage for this time of year. | 24 | {"0":{"2013-09-29T00:00:00.000":999.6195114715,"2013-09-29T01:00:00.000":784.2344573531,"2013-09-29T02:00:00.000":737.588217289,"2013-09-29T03:00:00.000":753.8795816336,"2013-09-29T04:00:00.000":749.7819284344,"2013-09-29T05:00:00.000":805.625010687,"2013-09-29T06:00:00.000":855.4612387851,"2013-09-29T07:00:00.000":910... | {"0":{"2013-10-03T00:00:00.000":840.2519958766,"2013-10-03T01:00:00.000":782.0773855098,"2013-10-03T02:00:00.000":805.9704632229,"2013-10-03T03:00:00.000":779.3670760035,"2013-10-03T04:00:00.000":702.3938474406,"2013-10-03T05:00:00.000":787.7919464477,"2013-10-03T06:00:00.000":782.4958200928,"2013-10-03T07:00:00.000":8... | 0.00004 | [
12
] | null | null | [] | [] | |
ShortNewsElectricityIncreaseInPredictionTask | 4 | 1/3 | [
"c_cov",
"c_f"
] | [
"forecasting",
"natural language processing",
"instruction following",
"retrieval: context"
] | This is the electricity consumption recorded in Kilowatt (kW) in city A. | A heatwave struck the city, which began on 2013-12-19 00:00:00 and lasted for approximately 1 hour, saw temperatures soar to unprecedented levels. According to the city's electricity provider, power consumption during the peak of the heatwave reached approximately 3 times the typical usage for this time of year. | 24 | {"0":{"2013-12-13T05:00:00.000":91.8673086996,"2013-12-13T06:00:00.000":92.0924125542,"2013-12-13T07:00:00.000":93.9442670199,"2013-12-13T08:00:00.000":95.788876666,"2013-12-13T09:00:00.000":92.9137018953,"2013-12-13T10:00:00.000":92.0797519891,"2013-12-13T11:00:00.000":92.0442677915,"2013-12-13T12:00:00.000":91.847114... | {"0":{"2013-12-18T05:00:00.000":83.5794338029,"2013-12-18T06:00:00.000":82.515900143,"2013-12-18T07:00:00.000":85.8644546967,"2013-12-18T08:00:00.000":85.6216877662,"2013-12-18T09:00:00.000":92.8109308545,"2013-12-18T10:00:00.000":96.2353549458,"2013-12-18T11:00:00.000":94.1802269888,"2013-12-18T12:00:00.000":89.633869... | 0.00004 | [
19
] | null | null | [] | [] | |
ShortNewsElectricityIncreaseInPredictionTask | 5 | 1/3 | [
"c_cov",
"c_f"
] | [
"forecasting",
"natural language processing",
"instruction following",
"retrieval: context"
] | This is the electricity consumption recorded in Kilowatt (kW) in city A. | A heatwave struck the city, which began on 2012-05-13 16:00:00 and lasted for approximately 2 hours, saw temperatures soar to unprecedented levels. According to the city's electricity provider, power consumption during the peak of the heatwave reached approximately 3 times the typical usage for this time of year. | 24 | {"0":{"2012-05-06T22:00:00.000":576.5585078009,"2012-05-06T23:00:00.000":360.2741590517,"2012-05-07T00:00:00.000":329.540771783,"2012-05-07T01:00:00.000":299.0708102444,"2012-05-07T02:00:00.000":312.7244949413,"2012-05-07T03:00:00.000":305.6137486905,"2012-05-07T04:00:00.000":323.6999245903,"2012-05-07T05:00:00.000":37... | {"0":{"2012-05-12T22:00:00.000":731.4944051872,"2012-05-12T23:00:00.000":480.4839431434,"2012-05-13T00:00:00.000":399.4650331586,"2012-05-13T01:00:00.000":390.030081889,"2012-05-13T02:00:00.000":391.1636328287,"2012-05-13T03:00:00.000":328.4591506993,"2012-05-13T04:00:00.000":375.8035994547,"2012-05-13T05:00:00.000":48... | 0.00004 | [
18,
19
] | null | null | [] | [] | |
MediumNewsElectricityIncreaseInPredictionTask | 1 | 1/3 | [
"c_cov",
"c_f"
] | [
"forecasting",
"natural language processing",
"instruction following",
"retrieval: context"
] | This is the electricity consumption recorded in Kilowatt (kW) in city A. | A sudden and intense heatwave struck the city, causing a dramatic surge in electricity consumption as residents sought refuge from the scorching temperatures. The extreme weather event, which began on 2013-05-28 12:00:00 and lasted for approximately 2 hours, saw temperatures soar to unprecedented levels. In response, c... | 24 | {"0":{"2013-05-22T04:00:00.000":351.2516211415,"2013-05-22T05:00:00.000":296.4482090403,"2013-05-22T06:00:00.000":231.8647128451,"2013-05-22T07:00:00.000":313.3813388685,"2013-05-22T08:00:00.000":358.7757575781,"2013-05-22T09:00:00.000":388.4802172839,"2013-05-22T10:00:00.000":416.0560981503,"2013-05-22T11:00:00.000":4... | {"0":{"2013-05-28T04:00:00.000":345.3082090214,"2013-05-28T05:00:00.000":297.3952571634,"2013-05-28T06:00:00.000":252.9083406769,"2013-05-28T07:00:00.000":326.4740036796,"2013-05-28T08:00:00.000":332.7867941306,"2013-05-28T09:00:00.000":322.0692551745,"2013-05-28T10:00:00.000":442.4631202637,"2013-05-28T11:00:00.000":3... | 0.00004 | [
8,
9
] | null | null | [] | [] | |
MediumNewsElectricityIncreaseInPredictionTask | 2 | 1/3 | [
"c_cov",
"c_f"
] | [
"forecasting",
"natural language processing",
"instruction following",
"retrieval: context"
] | This is the electricity consumption recorded in Kilowatt (kW) in city A. | A sudden and intense heatwave struck the city, causing a dramatic surge in electricity consumption as residents sought refuge from the scorching temperatures. The extreme weather event, which began on 2012-10-09 18:00:00 and lasted for approximately 3 hours, saw temperatures soar to unprecedented levels. In response, c... | 24 | {"0":{"2012-10-03T13:00:00.000":2849.5772644674,"2012-10-03T14:00:00.000":2912.3948947443,"2012-10-03T15:00:00.000":2995.999075764,"2012-10-03T16:00:00.000":2936.6241219303,"2012-10-03T17:00:00.000":3016.1410780868,"2012-10-03T18:00:00.000":2869.9795725569,"2012-10-03T19:00:00.000":2986.7066804195,"2012-10-03T20:00:00.... | {"0":{"2012-10-09T13:00:00.000":3306.4741429646,"2012-10-09T14:00:00.000":2965.5678242008,"2012-10-09T15:00:00.000":2906.5024781426,"2012-10-09T16:00:00.000":3090.7526672942,"2012-10-09T17:00:00.000":3227.4065834481,"2012-10-09T18:00:00.000":16754.824097567,"2012-10-09T19:00:00.000":16714.5345840893,"2012-10-09T20:00:0... | 0.00004 | [
5,
6,
7
] | null | null | [] | [] | |
MediumNewsElectricityIncreaseInPredictionTask | 3 | 1/3 | [
"c_cov",
"c_f"
] | [
"forecasting",
"natural language processing",
"instruction following",
"retrieval: context"
] | This is the electricity consumption recorded in Kilowatt (kW) in city A. | A sudden and intense heatwave struck the city, causing a dramatic surge in electricity consumption as residents sought refuge from the scorching temperatures. The extreme weather event, which began on 2013-10-03 12:00:00 and lasted for approximately 1 hour, saw temperatures soar to unprecedented levels. In response, ci... | 24 | {"0":{"2013-09-29T00:00:00.000":999.6195114715,"2013-09-29T01:00:00.000":784.2344573531,"2013-09-29T02:00:00.000":737.588217289,"2013-09-29T03:00:00.000":753.8795816336,"2013-09-29T04:00:00.000":749.7819284344,"2013-09-29T05:00:00.000":805.625010687,"2013-09-29T06:00:00.000":855.4612387851,"2013-09-29T07:00:00.000":910... | {"0":{"2013-10-03T00:00:00.000":840.2519958766,"2013-10-03T01:00:00.000":782.0773855098,"2013-10-03T02:00:00.000":805.9704632229,"2013-10-03T03:00:00.000":779.3670760035,"2013-10-03T04:00:00.000":702.3938474406,"2013-10-03T05:00:00.000":787.7919464477,"2013-10-03T06:00:00.000":782.4958200928,"2013-10-03T07:00:00.000":8... | 0.00004 | [
12
] | null | null | [] | [] | |
MediumNewsElectricityIncreaseInPredictionTask | 4 | 1/3 | [
"c_cov",
"c_f"
] | [
"forecasting",
"natural language processing",
"instruction following",
"retrieval: context"
] | This is the electricity consumption recorded in Kilowatt (kW) in city A. | A sudden and intense heatwave struck the city, causing a dramatic surge in electricity consumption as residents sought refuge from the scorching temperatures. The extreme weather event, which began on 2013-12-19 00:00:00 and lasted for approximately 1 hour, saw temperatures soar to unprecedented levels. In response, ci... | 24 | {"0":{"2013-12-13T05:00:00.000":91.8673086996,"2013-12-13T06:00:00.000":92.0924125542,"2013-12-13T07:00:00.000":93.9442670199,"2013-12-13T08:00:00.000":95.788876666,"2013-12-13T09:00:00.000":92.9137018953,"2013-12-13T10:00:00.000":92.0797519891,"2013-12-13T11:00:00.000":92.0442677915,"2013-12-13T12:00:00.000":91.847114... | {"0":{"2013-12-18T05:00:00.000":83.5794338029,"2013-12-18T06:00:00.000":82.515900143,"2013-12-18T07:00:00.000":85.8644546967,"2013-12-18T08:00:00.000":85.6216877662,"2013-12-18T09:00:00.000":92.8109308545,"2013-12-18T10:00:00.000":96.2353549458,"2013-12-18T11:00:00.000":94.1802269888,"2013-12-18T12:00:00.000":89.633869... | 0.00004 | [
19
] | null | null | [] | [] | |
MediumNewsElectricityIncreaseInPredictionTask | 5 | 1/3 | [
"c_cov",
"c_f"
] | [
"forecasting",
"natural language processing",
"instruction following",
"retrieval: context"
] | This is the electricity consumption recorded in Kilowatt (kW) in city A. | A sudden and intense heatwave struck the city, causing a dramatic surge in electricity consumption as residents sought refuge from the scorching temperatures. The extreme weather event, which began on 2012-05-13 16:00:00 and lasted for approximately 2 hours, saw temperatures soar to unprecedented levels. In response, c... | 24 | {"0":{"2012-05-06T22:00:00.000":576.5585078009,"2012-05-06T23:00:00.000":360.2741590517,"2012-05-07T00:00:00.000":329.540771783,"2012-05-07T01:00:00.000":299.0708102444,"2012-05-07T02:00:00.000":312.7244949413,"2012-05-07T03:00:00.000":305.6137486905,"2012-05-07T04:00:00.000":323.6999245903,"2012-05-07T05:00:00.000":37... | {"0":{"2012-05-12T22:00:00.000":731.4944051872,"2012-05-12T23:00:00.000":480.4839431434,"2012-05-13T00:00:00.000":399.4650331586,"2012-05-13T01:00:00.000":390.030081889,"2012-05-13T02:00:00.000":391.1636328287,"2012-05-13T03:00:00.000":328.4591506993,"2012-05-13T04:00:00.000":375.8035994547,"2012-05-13T05:00:00.000":48... | 0.00004 | [
18,
19
] | null | null | [] | [] | |
LongNewsElectricityIncreaseInPredictionTask | 1 | 1/3 | [
"c_cov",
"c_f"
] | [
"forecasting",
"natural language processing",
"instruction following",
"retrieval: context"
] | This is the electricity consumption recorded in Kilowatt (kW) in city A. | A sudden and intense heatwave struck the city, causing a dramatic surge in electricity consumption as residents sought refuge from the scorching temperatures. The extreme weather event, which began on 2013-05-28 12:00:00 and lasted for approximately 2 hours, saw temperatures soar to unprecedented levels. In response, c... | 24 | {"0":{"2013-05-22T04:00:00.000":351.2516211415,"2013-05-22T05:00:00.000":296.4482090403,"2013-05-22T06:00:00.000":231.8647128451,"2013-05-22T07:00:00.000":313.3813388685,"2013-05-22T08:00:00.000":358.7757575781,"2013-05-22T09:00:00.000":388.4802172839,"2013-05-22T10:00:00.000":416.0560981503,"2013-05-22T11:00:00.000":4... | {"0":{"2013-05-28T04:00:00.000":345.3082090214,"2013-05-28T05:00:00.000":297.3952571634,"2013-05-28T06:00:00.000":252.9083406769,"2013-05-28T07:00:00.000":326.4740036796,"2013-05-28T08:00:00.000":332.7867941306,"2013-05-28T09:00:00.000":322.0692551745,"2013-05-28T10:00:00.000":442.4631202637,"2013-05-28T11:00:00.000":3... | 0.00004 | [
8,
9
] | null | null | [] | [] | |
LongNewsElectricityIncreaseInPredictionTask | 2 | 1/3 | [
"c_cov",
"c_f"
] | [
"forecasting",
"natural language processing",
"instruction following",
"retrieval: context"
] | This is the electricity consumption recorded in Kilowatt (kW) in city A. | A sudden and intense heatwave struck the city, causing a dramatic surge in electricity consumption as residents sought refuge from the scorching temperatures. The extreme weather event, which began on 2012-10-09 18:00:00 and lasted for approximately 3 hours, saw temperatures soar to unprecedented levels. In response, c... | 24 | {"0":{"2012-10-03T13:00:00.000":2849.5772644674,"2012-10-03T14:00:00.000":2912.3948947443,"2012-10-03T15:00:00.000":2995.999075764,"2012-10-03T16:00:00.000":2936.6241219303,"2012-10-03T17:00:00.000":3016.1410780868,"2012-10-03T18:00:00.000":2869.9795725569,"2012-10-03T19:00:00.000":2986.7066804195,"2012-10-03T20:00:00.... | {"0":{"2012-10-09T13:00:00.000":3306.4741429646,"2012-10-09T14:00:00.000":2965.5678242008,"2012-10-09T15:00:00.000":2906.5024781426,"2012-10-09T16:00:00.000":3090.7526672942,"2012-10-09T17:00:00.000":3227.4065834481,"2012-10-09T18:00:00.000":16754.824097567,"2012-10-09T19:00:00.000":16714.5345840893,"2012-10-09T20:00:0... | 0.00004 | [
5,
6,
7
] | null | null | [] | [] | |
LongNewsElectricityIncreaseInPredictionTask | 3 | 1/3 | [
"c_cov",
"c_f"
] | [
"forecasting",
"natural language processing",
"instruction following",
"retrieval: context"
] | This is the electricity consumption recorded in Kilowatt (kW) in city A. | A sudden and intense heatwave struck the city, causing a dramatic surge in electricity consumption as residents sought refuge from the scorching temperatures. The extreme weather event, which began on 2013-10-03 12:00:00 and lasted for approximately 1 hour, saw temperatures soar to unprecedented levels. In response, ci... | 24 | {"0":{"2013-09-29T00:00:00.000":999.6195114715,"2013-09-29T01:00:00.000":784.2344573531,"2013-09-29T02:00:00.000":737.588217289,"2013-09-29T03:00:00.000":753.8795816336,"2013-09-29T04:00:00.000":749.7819284344,"2013-09-29T05:00:00.000":805.625010687,"2013-09-29T06:00:00.000":855.4612387851,"2013-09-29T07:00:00.000":910... | {"0":{"2013-10-03T00:00:00.000":840.2519958766,"2013-10-03T01:00:00.000":782.0773855098,"2013-10-03T02:00:00.000":805.9704632229,"2013-10-03T03:00:00.000":779.3670760035,"2013-10-03T04:00:00.000":702.3938474406,"2013-10-03T05:00:00.000":787.7919464477,"2013-10-03T06:00:00.000":782.4958200928,"2013-10-03T07:00:00.000":8... | 0.00004 | [
12
] | null | null | [] | [] | |
LongNewsElectricityIncreaseInPredictionTask | 4 | 1/3 | [
"c_cov",
"c_f"
] | [
"forecasting",
"natural language processing",
"instruction following",
"retrieval: context"
] | This is the electricity consumption recorded in Kilowatt (kW) in city A. | A sudden and intense heatwave struck the city, causing a dramatic surge in electricity consumption as residents sought refuge from the scorching temperatures. The extreme weather event, which began on 2013-12-19 00:00:00 and lasted for approximately 1 hour, saw temperatures soar to unprecedented levels. In response, ci... | 24 | {"0":{"2013-12-13T05:00:00.000":91.8673086996,"2013-12-13T06:00:00.000":92.0924125542,"2013-12-13T07:00:00.000":93.9442670199,"2013-12-13T08:00:00.000":95.788876666,"2013-12-13T09:00:00.000":92.9137018953,"2013-12-13T10:00:00.000":92.0797519891,"2013-12-13T11:00:00.000":92.0442677915,"2013-12-13T12:00:00.000":91.847114... | {"0":{"2013-12-18T05:00:00.000":83.5794338029,"2013-12-18T06:00:00.000":82.515900143,"2013-12-18T07:00:00.000":85.8644546967,"2013-12-18T08:00:00.000":85.6216877662,"2013-12-18T09:00:00.000":92.8109308545,"2013-12-18T10:00:00.000":96.2353549458,"2013-12-18T11:00:00.000":94.1802269888,"2013-12-18T12:00:00.000":89.633869... | 0.00004 | [
19
] | null | null | [] | [] | |
LongNewsElectricityIncreaseInPredictionTask | 5 | 1/3 | [
"c_cov",
"c_f"
] | [
"forecasting",
"natural language processing",
"instruction following",
"retrieval: context"
] | This is the electricity consumption recorded in Kilowatt (kW) in city A. | A sudden and intense heatwave struck the city, causing a dramatic surge in electricity consumption as residents sought refuge from the scorching temperatures. The extreme weather event, which began on 2012-05-13 16:00:00 and lasted for approximately 2 hours, saw temperatures soar to unprecedented levels. In response, c... | 24 | {"0":{"2012-05-06T22:00:00.000":576.5585078009,"2012-05-06T23:00:00.000":360.2741590517,"2012-05-07T00:00:00.000":329.540771783,"2012-05-07T01:00:00.000":299.0708102444,"2012-05-07T02:00:00.000":312.7244949413,"2012-05-07T03:00:00.000":305.6137486905,"2012-05-07T04:00:00.000":323.6999245903,"2012-05-07T05:00:00.000":37... | {"0":{"2012-05-12T22:00:00.000":731.4944051872,"2012-05-12T23:00:00.000":480.4839431434,"2012-05-13T00:00:00.000":399.4650331586,"2012-05-13T01:00:00.000":390.030081889,"2012-05-13T02:00:00.000":391.1636328287,"2012-05-13T03:00:00.000":328.4591506993,"2012-05-13T04:00:00.000":375.8035994547,"2012-05-13T05:00:00.000":48... | 0.00004 | [
18,
19
] | null | null | [] | [] | |
CashDepletedinATMScenarioTask | 1 | 1/2 | [
"c_i",
"c_f"
] | [
"forecasting",
"natural language processing",
"instruction following"
] | This is the number of cash withdrawals from an automated teller machine (ATM) in an arbitrary location in England. | Consider that cash is depleted in the ATM from 1996-11-24 00:00:00, for 10 days, resulting in no withdrawals during that period. | 7 | {"0":{"1996-05-29T00:00:00.000":31.3269446476,"1996-05-30T00:00:00.000":41.8490827396,"1996-05-31T00:00:00.000":27.9978737412,"1996-06-01T00:00:00.000":19.766357063,"1996-06-02T00:00:00.000":22.2057944367,"1996-06-03T00:00:00.000":19.033263005,"1996-06-04T00:00:00.000":24.65537344,"1996-06-05T00:00:00.000":31.586049974... | {"0":{"1996-11-13T00:00:00.000":29.8801811284,"1996-11-14T00:00:00.000":39.2428278071,"1996-11-15T00:00:00.000":37.0494174402,"1996-11-16T00:00:00.000":24.2551993681,"1996-11-17T00:00:00.000":20.945981097,"1996-11-18T00:00:00.000":14.9020674982,"1996-11-19T00:00:00.000":23.568944611,"1996-11-20T00:00:00.000":32.1968335... | 0.02701 | [
11,
12,
13,
14,
15,
16,
17,
18,
19,
20
] | null | null | [] | [] | |
CashDepletedinATMScenarioTask | 2 | 1/2 | [
"c_i",
"c_f"
] | [
"forecasting",
"natural language processing",
"instruction following"
] | This is the number of cash withdrawals from an automated teller machine (ATM) in an arbitrary location in England. | Consider that cash is depleted in the ATM from 1998-01-30 00:00:00, for 9 days, resulting in no withdrawals during that period. | 7 | {"0":{"1997-07-24T00:00:00.000":28.3105609601,"1997-07-25T00:00:00.000":22.2355782892,"1997-07-26T00:00:00.000":11.2811871586,"1997-07-27T00:00:00.000":18.9865533482,"1997-07-28T00:00:00.000":17.3113897691,"1997-07-29T00:00:00.000":20.8268859616,"1997-07-30T00:00:00.000":20.2688368308,"1997-07-31T00:00:00.000":26.21034... | {"0":{"1998-01-08T00:00:00.000":27.1831380635,"1998-01-09T00:00:00.000":22.7916733623,"1998-01-10T00:00:00.000":13.6601516444,"1998-01-11T00:00:00.000":13.9889084879,"1998-01-12T00:00:00.000":8.7372423594,"1998-01-13T00:00:00.000":11.7828544777,"1998-01-14T00:00:00.000":20.5998141981,"1998-01-15T00:00:00.000":32.041397... | 0.02701 | [
22,
23,
24,
25,
26,
27,
28,
29,
30
] | null | null | [] | [] | |
CashDepletedinATMScenarioTask | 3 | 1/2 | [
"c_i",
"c_f"
] | [
"forecasting",
"natural language processing",
"instruction following"
] | This is the number of cash withdrawals from an automated teller machine (ATM) in an arbitrary location in England. | Consider that cash is depleted in the ATM from 1997-03-22 00:00:00, for 4 days, resulting in no withdrawals during that period. | 7 | {"0":{"1996-11-22T00:00:00.000":26.7624570764,"1996-11-23T00:00:00.000":7.5606339282,"1996-11-24T00:00:00.000":13.8868448306,"1996-11-25T00:00:00.000":13.6334266131,"1996-11-26T00:00:00.000":14.4243172137,"1996-11-27T00:00:00.000":16.359903538,"1996-11-28T00:00:00.000":27.2622949758,"1996-11-29T00:00:00.000":29.0547458... | {"0":{"1997-03-14T00:00:00.000":21.0868678929,"1997-03-15T00:00:00.000":6.7695739771,"1997-03-16T00:00:00.000":12.6453883486,"1997-03-17T00:00:00.000":11.85902715,"1997-03-18T00:00:00.000":11.1551024467,"1997-03-19T00:00:00.000":17.0780967472,"1997-03-20T00:00:00.000":21.7362062967,"1997-03-21T00:00:00.000":23.57542911... | 0.02701 | [
8,
9,
10,
11
] | null | null | [] | [] | |
CashDepletedinATMScenarioTask | 4 | 1/2 | [
"c_i",
"c_f"
] | [
"forecasting",
"natural language processing",
"instruction following"
] | This is the number of cash withdrawals from an automated teller machine (ATM) in an arbitrary location in England. | Consider that cash is depleted in the ATM from 1997-09-05 00:00:00, for 8 days, resulting in no withdrawals during that period. | 7 | {"0":{"1997-03-13T00:00:00.000":38.1224366896,"1997-03-14T00:00:00.000":36.0782575814,"1997-03-15T00:00:00.000":21.17553163,"1997-03-16T00:00:00.000":18.2037741708,"1997-03-17T00:00:00.000":13.6095466555,"1997-03-18T00:00:00.000":18.8303621684,"1997-03-19T00:00:00.000":30.5276783081,"1997-03-20T00:00:00.000":37.1557636... | {"0":{"1997-08-28T00:00:00.000":50.1586236768,"1997-08-29T00:00:00.000":44.3568113206,"1997-08-30T00:00:00.000":29.3499896282,"1997-08-31T00:00:00.000":29.0911061849,"1997-09-01T00:00:00.000":24.0012570173,"1997-09-02T00:00:00.000":24.3941304397,"1997-09-03T00:00:00.000":37.5570892066,"1997-09-04T00:00:00.000":60.61528... | 0.02701 | [
8,
9,
10,
11,
12,
13,
14,
15
] | null | null | [] | [] | |
CashDepletedinATMScenarioTask | 5 | 1/2 | [
"c_i",
"c_f"
] | [
"forecasting",
"natural language processing",
"instruction following"
] | This is the number of cash withdrawals from an automated teller machine (ATM) in an arbitrary location in England. | Consider that cash is depleted in the ATM from 1996-11-12 00:00:00, for 1 day, resulting in no withdrawals during that period. | 7 | {"0":{"1996-07-14T00:00:00.000":23.0573194768,"1996-07-15T00:00:00.000":12.9165042205,"1996-07-16T00:00:00.000":11.9178069141,"1996-07-17T00:00:00.000":21.6696334344,"1996-07-18T00:00:00.000":19.8026874487,"1996-07-19T00:00:00.000":15.8385136193,"1996-07-20T00:00:00.000":11.0613535947,"1996-07-21T00:00:00.000":14.10176... | {"0":{"1996-11-03T00:00:00.000":11.6830578152,"1996-11-04T00:00:00.000":9.4384059809,"1996-11-05T00:00:00.000":10.7786436095,"1996-11-06T00:00:00.000":13.064487694,"1996-11-07T00:00:00.000":16.9101641171,"1996-11-08T00:00:00.000":17.7642332262,"1996-11-09T00:00:00.000":12.6483036001,"1996-11-10T00:00:00.000":11.2366710... | 0.02701 | [
9
] | null | null | [] | [] | |
ATMBuildingClosedTask | 1 | 1 | [
"c_i",
"c_cov"
] | [
"forecasting",
"natural language processing",
"reasoning: deduction"
] | This is the number of cash withdrawals from an automated teller machine (ATM) in an arbitrary location in England. | Consider that the building which contains the ATM is closed from 1996-11-24 00:00:00, for 10 days. | 7 | {"0":{"1996-05-29T00:00:00.000":31.3269446476,"1996-05-30T00:00:00.000":41.8490827396,"1996-05-31T00:00:00.000":27.9978737412,"1996-06-01T00:00:00.000":19.766357063,"1996-06-02T00:00:00.000":22.2057944367,"1996-06-03T00:00:00.000":19.033263005,"1996-06-04T00:00:00.000":24.65537344,"1996-06-05T00:00:00.000":31.586049974... | {"0":{"1996-11-13T00:00:00.000":29.8801811284,"1996-11-14T00:00:00.000":39.2428278071,"1996-11-15T00:00:00.000":37.0494174402,"1996-11-16T00:00:00.000":24.2551993681,"1996-11-17T00:00:00.000":20.945981097,"1996-11-18T00:00:00.000":14.9020674982,"1996-11-19T00:00:00.000":23.568944611,"1996-11-20T00:00:00.000":32.1968335... | 0.02701 | [
11,
12,
13,
14,
15,
16,
17,
18,
19,
20
] | null | null | [] | [] | |
ATMBuildingClosedTask | 2 | 1 | [
"c_i",
"c_cov"
] | [
"forecasting",
"natural language processing",
"reasoning: deduction"
] | This is the number of cash withdrawals from an automated teller machine (ATM) in an arbitrary location in England. | Consider that the building which contains the ATM is closed from 1998-01-30 00:00:00, for 9 days. | 7 | {"0":{"1997-07-24T00:00:00.000":28.3105609601,"1997-07-25T00:00:00.000":22.2355782892,"1997-07-26T00:00:00.000":11.2811871586,"1997-07-27T00:00:00.000":18.9865533482,"1997-07-28T00:00:00.000":17.3113897691,"1997-07-29T00:00:00.000":20.8268859616,"1997-07-30T00:00:00.000":20.2688368308,"1997-07-31T00:00:00.000":26.21034... | {"0":{"1998-01-08T00:00:00.000":27.1831380635,"1998-01-09T00:00:00.000":22.7916733623,"1998-01-10T00:00:00.000":13.6601516444,"1998-01-11T00:00:00.000":13.9889084879,"1998-01-12T00:00:00.000":8.7372423594,"1998-01-13T00:00:00.000":11.7828544777,"1998-01-14T00:00:00.000":20.5998141981,"1998-01-15T00:00:00.000":32.041397... | 0.02701 | [
22,
23,
24,
25,
26,
27,
28,
29,
30
] | null | null | [] | [] | |
ATMBuildingClosedTask | 3 | 1 | [
"c_i",
"c_cov"
] | [
"forecasting",
"natural language processing",
"reasoning: deduction"
] | This is the number of cash withdrawals from an automated teller machine (ATM) in an arbitrary location in England. | Consider that the building which contains the ATM is closed from 1997-03-22 00:00:00, for 4 days. | 7 | {"0":{"1996-11-22T00:00:00.000":26.7624570764,"1996-11-23T00:00:00.000":7.5606339282,"1996-11-24T00:00:00.000":13.8868448306,"1996-11-25T00:00:00.000":13.6334266131,"1996-11-26T00:00:00.000":14.4243172137,"1996-11-27T00:00:00.000":16.359903538,"1996-11-28T00:00:00.000":27.2622949758,"1996-11-29T00:00:00.000":29.0547458... | {"0":{"1997-03-14T00:00:00.000":21.0868678929,"1997-03-15T00:00:00.000":6.7695739771,"1997-03-16T00:00:00.000":12.6453883486,"1997-03-17T00:00:00.000":11.85902715,"1997-03-18T00:00:00.000":11.1551024467,"1997-03-19T00:00:00.000":17.0780967472,"1997-03-20T00:00:00.000":21.7362062967,"1997-03-21T00:00:00.000":23.57542911... | 0.02701 | [
8,
9,
10,
11
] | null | null | [] | [] | |
ATMBuildingClosedTask | 4 | 1 | [
"c_i",
"c_cov"
] | [
"forecasting",
"natural language processing",
"reasoning: deduction"
] | This is the number of cash withdrawals from an automated teller machine (ATM) in an arbitrary location in England. | Consider that the building which contains the ATM is closed from 1997-09-05 00:00:00, for 8 days. | 7 | {"0":{"1997-03-13T00:00:00.000":38.1224366896,"1997-03-14T00:00:00.000":36.0782575814,"1997-03-15T00:00:00.000":21.17553163,"1997-03-16T00:00:00.000":18.2037741708,"1997-03-17T00:00:00.000":13.6095466555,"1997-03-18T00:00:00.000":18.8303621684,"1997-03-19T00:00:00.000":30.5276783081,"1997-03-20T00:00:00.000":37.1557636... | {"0":{"1997-08-28T00:00:00.000":50.1586236768,"1997-08-29T00:00:00.000":44.3568113206,"1997-08-30T00:00:00.000":29.3499896282,"1997-08-31T00:00:00.000":29.0911061849,"1997-09-01T00:00:00.000":24.0012570173,"1997-09-02T00:00:00.000":24.3941304397,"1997-09-03T00:00:00.000":37.5570892066,"1997-09-04T00:00:00.000":60.61528... | 0.02701 | [
8,
9,
10,
11,
12,
13,
14,
15
] | null | null | [] | [] | |
ATMBuildingClosedTask | 5 | 1 | [
"c_i",
"c_cov"
] | [
"forecasting",
"natural language processing",
"reasoning: deduction"
] | This is the number of cash withdrawals from an automated teller machine (ATM) in an arbitrary location in England. | Consider that the building which contains the ATM is closed from 1996-11-12 00:00:00, for 1 day. | 7 | {"0":{"1996-07-14T00:00:00.000":23.0573194768,"1996-07-15T00:00:00.000":12.9165042205,"1996-07-16T00:00:00.000":11.9178069141,"1996-07-17T00:00:00.000":21.6696334344,"1996-07-18T00:00:00.000":19.8026874487,"1996-07-19T00:00:00.000":15.8385136193,"1996-07-20T00:00:00.000":11.0613535947,"1996-07-21T00:00:00.000":14.10176... | {"0":{"1996-11-03T00:00:00.000":11.6830578152,"1996-11-04T00:00:00.000":9.4384059809,"1996-11-05T00:00:00.000":10.7786436095,"1996-11-06T00:00:00.000":13.064487694,"1996-11-07T00:00:00.000":16.9101641171,"1996-11-08T00:00:00.000":17.7642332262,"1996-11-09T00:00:00.000":12.6483036001,"1996-11-10T00:00:00.000":11.2366710... | 0.02701 | [
9
] | null | null | [] | [] | |
ATMUnderPeriodicMaintenanceTaskWithConclusion | 1 | 1/3 | [
"c_i",
"c_cov"
] | [
"forecasting",
"natural language processing",
"instruction following",
"reasoning: math"
] | This is the number of cash withdrawals from an automated teller machine (ATM) in an arbitrary location in England. The ATM was under maintenance for 7 days, periodically every 15 days, starting from 1996-08-12 00:00:00, resulting in no withdrawals recorded. Assume that the ATM will not be in maintenance in the future. | 7 | {"0":{"1996-05-29T00:00:00.000":31.122501499,"1996-05-30T00:00:00.000":41.6604530533,"1996-05-31T00:00:00.000":28.0595869941,"1996-06-01T00:00:00.000":19.79040905,"1996-06-02T00:00:00.000":22.1464820253,"1996-06-03T00:00:00.000":19.1432663383,"1996-06-04T00:00:00.000":24.3165843031,"1996-06-05T00:00:00.000":31.73312469... | {"0":{"1996-11-13T00:00:00.000":29.8624128638,"1996-11-14T00:00:00.000":39.2815935556,"1996-11-15T00:00:00.000":37.0513358742,"1996-11-16T00:00:00.000":24.2783073932,"1996-11-17T00:00:00.000":20.9533626081,"1996-11-18T00:00:00.000":14.8815312198,"1996-11-19T00:00:00.000":23.6438709762,"1996-11-20T00:00:00.000":32.20213... | 0.029119 | [
0,
1,
2,
3,
12,
13,
14,
15,
16,
17,
18,
27,
28,
29,
30,
31,
32,
33,
42,
43,
44,
45,
46,
47,
48
] | null | null | [] | [] | ||
ATMUnderPeriodicMaintenanceTaskWithConclusion | 2 | 1/3 | [
"c_i",
"c_cov"
] | [
"forecasting",
"natural language processing",
"instruction following",
"reasoning: math"
] | This is the number of cash withdrawals from an automated teller machine (ATM) in an arbitrary location in England. The ATM was under maintenance for 4 days, periodically every 12 days, starting from 1997-09-05 00:00:00, resulting in no withdrawals recorded. Assume that the ATM will not be in maintenance in the future. | 7 | {"0":{"1997-07-24T00:00:00.000":27.6740903266,"1997-07-25T00:00:00.000":21.5446995009,"1997-07-26T00:00:00.000":11.1547503362,"1997-07-27T00:00:00.000":18.9014206013,"1997-07-28T00:00:00.000":16.9952641406,"1997-07-29T00:00:00.000":21.0278605018,"1997-07-30T00:00:00.000":19.9284706507,"1997-07-31T00:00:00.000":25.80756... | {"0":{"1998-01-08T00:00:00.000":27.1767751563,"1998-01-09T00:00:00.000":22.9276935053,"1998-01-10T00:00:00.000":13.515230708,"1998-01-11T00:00:00.000":13.6311084267,"1998-01-12T00:00:00.000":8.4650116439,"1998-01-13T00:00:00.000":12.1123825331,"1998-01-14T00:00:00.000":20.3628655634,"1998-01-15T00:00:00.000":31.7413473... | 0.029119 | [
7,
8,
9,
10,
19,
20,
21,
22,
31,
32,
33,
34,
43,
44,
45,
46
] | null | null | [] | [] | ||
ATMUnderPeriodicMaintenanceTaskWithConclusion | 3 | 1/3 | [
"c_i",
"c_cov"
] | [
"forecasting",
"natural language processing",
"instruction following",
"reasoning: math"
] | This is the number of cash withdrawals from an automated teller machine (ATM) in an arbitrary location in England. The ATM was under maintenance for 7 days, periodically every 14 days, starting from 1996-11-30 00:00:00, resulting in no withdrawals recorded. Assume that the ATM will not be in maintenance in the future. | 7 | {"0":{"1996-11-22T00:00:00.000":26.8124041887,"1996-11-23T00:00:00.000":7.5620102148,"1996-11-24T00:00:00.000":13.8634113649,"1996-11-25T00:00:00.000":13.6304762615,"1996-11-26T00:00:00.000":14.5301439633,"1996-11-27T00:00:00.000":16.329274092,"1996-11-28T00:00:00.000":27.3101374727,"1996-11-29T00:00:00.000":28.9946186... | {"0":{"1997-03-14T00:00:00.000":21.05471373,"1997-03-15T00:00:00.000":6.809713593,"1997-03-16T00:00:00.000":12.656364801,"1997-03-17T00:00:00.000":11.8317726301,"1997-03-18T00:00:00.000":11.117821392,"1997-03-19T00:00:00.000":17.1104362941,"1997-03-20T00:00:00.000":21.7183650914,"1997-03-21T00:00:00.000":23.6067226341,... | 0.029119 | [
0,
8,
9,
10,
11,
12,
13,
14,
22,
23,
24,
25,
26,
27,
28,
36,
37,
38,
39,
40,
41,
42
] | null | null | [] | [] | ||
ATMUnderPeriodicMaintenanceTaskWithConclusion | 4 | 1/3 | [
"c_i",
"c_cov"
] | [
"forecasting",
"natural language processing",
"instruction following",
"reasoning: math"
] | This is the number of cash withdrawals from an automated teller machine (ATM) in an arbitrary location in England. The ATM was under maintenance for 7 days, periodically every 14 days, starting from 1997-05-02 00:00:00, resulting in no withdrawals recorded. Assume that the ATM will not be in maintenance in the future. | 7 | {"0":{"1997-03-13T00:00:00.000":37.9192009009,"1997-03-14T00:00:00.000":36.0067750248,"1997-03-15T00:00:00.000":21.6482194388,"1997-03-16T00:00:00.000":18.1970730377,"1997-03-17T00:00:00.000":13.9248175294,"1997-03-18T00:00:00.000":19.0175829269,"1997-03-19T00:00:00.000":31.0186859852,"1997-03-20T00:00:00.000":36.71434... | {"0":{"1997-08-28T00:00:00.000":50.1575043585,"1997-08-29T00:00:00.000":43.662540009,"1997-08-30T00:00:00.000":27.9600228085,"1997-08-31T00:00:00.000":28.4164242699,"1997-09-01T00:00:00.000":23.1253135356,"1997-09-02T00:00:00.000":24.6558910111,"1997-09-03T00:00:00.000":36.3943277021,"1997-09-04T00:00:00.000":60.455834... | 0.029119 | [
0,
8,
9,
10,
11,
12,
13,
14,
22,
23,
24,
25,
26,
27,
28,
36,
37,
38,
39,
40,
41,
42
] | null | null | [] | [] | ||
ATMUnderPeriodicMaintenanceTaskWithConclusion | 5 | 1/3 | [
"c_i",
"c_cov"
] | [
"forecasting",
"natural language processing",
"instruction following",
"reasoning: math"
] | This is the number of cash withdrawals from an automated teller machine (ATM) in an arbitrary location in England. The ATM was under maintenance for 4 days, periodically every 15 days, starting from 1996-07-22 00:00:00, resulting in no withdrawals recorded. Assume that the ATM will not be in maintenance in the future. | 7 | {"0":{"1996-07-14T00:00:00.000":22.6553089193,"1996-07-15T00:00:00.000":12.7818297352,"1996-07-16T00:00:00.000":11.857827131,"1996-07-17T00:00:00.000":20.957922512,"1996-07-18T00:00:00.000":20.1687491151,"1996-07-19T00:00:00.000":16.0343068332,"1996-07-20T00:00:00.000":11.2047675642,"1996-07-21T00:00:00.000":13.9838200... | {"0":{"1996-11-03T00:00:00.000":12.0719284125,"1996-11-04T00:00:00.000":9.649466624,"1996-11-05T00:00:00.000":10.8386282045,"1996-11-06T00:00:00.000":12.6642069977,"1996-11-07T00:00:00.000":17.3357106673,"1996-11-08T00:00:00.000":17.8100702993,"1996-11-09T00:00:00.000":12.2797308106,"1996-11-10T00:00:00.000":11.2419985... | 0.029119 | [
1,
2,
3,
4,
16,
17,
18,
19,
31,
32,
33,
34,
46,
47,
48,
49
] | null | null | [] | [] | ||
ATMUnderPeriodicMaintenanceTaskWithConclusionLessExplicit | 1 | 1/3 | [
"c_i",
"c_cov"
] | [
"forecasting",
"natural language processing",
"instruction following",
"reasoning: deduction"
] | This is the number of cash withdrawals from an automated teller machine (ATM) in an arbitrary location in England. The ATM was under maintenance for various periods, resulting in no withdrawals recorded. Assume that the ATM will not be in maintenance in the future. | 7 | {"0":{"1996-05-29T00:00:00.000":31.122501499,"1996-05-30T00:00:00.000":41.6604530533,"1996-05-31T00:00:00.000":28.0595869941,"1996-06-01T00:00:00.000":19.79040905,"1996-06-02T00:00:00.000":22.1464820253,"1996-06-03T00:00:00.000":19.1432663383,"1996-06-04T00:00:00.000":24.3165843031,"1996-06-05T00:00:00.000":31.73312469... | {"0":{"1996-11-13T00:00:00.000":29.8624128638,"1996-11-14T00:00:00.000":39.2815935556,"1996-11-15T00:00:00.000":37.0513358742,"1996-11-16T00:00:00.000":24.2783073932,"1996-11-17T00:00:00.000":20.9533626081,"1996-11-18T00:00:00.000":14.8815312198,"1996-11-19T00:00:00.000":23.6438709762,"1996-11-20T00:00:00.000":32.20213... | 0.029119 | [
0,
1,
2,
3,
12,
13,
14,
15,
16,
17,
18,
27,
28,
29,
30,
31,
32,
33,
42,
43,
44,
45,
46,
47,
48
] | null | null | [] | [] | ||
ATMUnderPeriodicMaintenanceTaskWithConclusionLessExplicit | 2 | 1/3 | [
"c_i",
"c_cov"
] | [
"forecasting",
"natural language processing",
"instruction following",
"reasoning: deduction"
] | This is the number of cash withdrawals from an automated teller machine (ATM) in an arbitrary location in England. The ATM was under maintenance for various periods, resulting in no withdrawals recorded. Assume that the ATM will not be in maintenance in the future. | 7 | {"0":{"1997-07-24T00:00:00.000":27.6740903266,"1997-07-25T00:00:00.000":21.5446995009,"1997-07-26T00:00:00.000":11.1547503362,"1997-07-27T00:00:00.000":18.9014206013,"1997-07-28T00:00:00.000":16.9952641406,"1997-07-29T00:00:00.000":21.0278605018,"1997-07-30T00:00:00.000":19.9284706507,"1997-07-31T00:00:00.000":25.80756... | {"0":{"1998-01-08T00:00:00.000":27.1767751563,"1998-01-09T00:00:00.000":22.9276935053,"1998-01-10T00:00:00.000":13.515230708,"1998-01-11T00:00:00.000":13.6311084267,"1998-01-12T00:00:00.000":8.4650116439,"1998-01-13T00:00:00.000":12.1123825331,"1998-01-14T00:00:00.000":20.3628655634,"1998-01-15T00:00:00.000":31.7413473... | 0.029119 | [
7,
8,
9,
10,
19,
20,
21,
22,
31,
32,
33,
34,
43,
44,
45,
46
] | null | null | [] | [] | ||
ATMUnderPeriodicMaintenanceTaskWithConclusionLessExplicit | 3 | 1/3 | [
"c_i",
"c_cov"
] | [
"forecasting",
"natural language processing",
"instruction following",
"reasoning: deduction"
] | This is the number of cash withdrawals from an automated teller machine (ATM) in an arbitrary location in England. The ATM was under maintenance for various periods, resulting in no withdrawals recorded. Assume that the ATM will not be in maintenance in the future. | 7 | {"0":{"1996-11-22T00:00:00.000":26.8124041887,"1996-11-23T00:00:00.000":7.5620102148,"1996-11-24T00:00:00.000":13.8634113649,"1996-11-25T00:00:00.000":13.6304762615,"1996-11-26T00:00:00.000":14.5301439633,"1996-11-27T00:00:00.000":16.329274092,"1996-11-28T00:00:00.000":27.3101374727,"1996-11-29T00:00:00.000":28.9946186... | {"0":{"1997-03-14T00:00:00.000":21.05471373,"1997-03-15T00:00:00.000":6.809713593,"1997-03-16T00:00:00.000":12.656364801,"1997-03-17T00:00:00.000":11.8317726301,"1997-03-18T00:00:00.000":11.117821392,"1997-03-19T00:00:00.000":17.1104362941,"1997-03-20T00:00:00.000":21.7183650914,"1997-03-21T00:00:00.000":23.6067226341,... | 0.029119 | [
0,
8,
9,
10,
11,
12,
13,
14,
22,
23,
24,
25,
26,
27,
28,
36,
37,
38,
39,
40,
41,
42
] | null | null | [] | [] | ||
ATMUnderPeriodicMaintenanceTaskWithConclusionLessExplicit | 4 | 1/3 | [
"c_i",
"c_cov"
] | [
"forecasting",
"natural language processing",
"instruction following",
"reasoning: deduction"
] | This is the number of cash withdrawals from an automated teller machine (ATM) in an arbitrary location in England. The ATM was under maintenance for various periods, resulting in no withdrawals recorded. Assume that the ATM will not be in maintenance in the future. | 7 | {"0":{"1997-03-13T00:00:00.000":37.9192009009,"1997-03-14T00:00:00.000":36.0067750248,"1997-03-15T00:00:00.000":21.6482194388,"1997-03-16T00:00:00.000":18.1970730377,"1997-03-17T00:00:00.000":13.9248175294,"1997-03-18T00:00:00.000":19.0175829269,"1997-03-19T00:00:00.000":31.0186859852,"1997-03-20T00:00:00.000":36.71434... | {"0":{"1997-08-28T00:00:00.000":50.1575043585,"1997-08-29T00:00:00.000":43.662540009,"1997-08-30T00:00:00.000":27.9600228085,"1997-08-31T00:00:00.000":28.4164242699,"1997-09-01T00:00:00.000":23.1253135356,"1997-09-02T00:00:00.000":24.6558910111,"1997-09-03T00:00:00.000":36.3943277021,"1997-09-04T00:00:00.000":60.455834... | 0.029119 | [
0,
8,
9,
10,
11,
12,
13,
14,
22,
23,
24,
25,
26,
27,
28,
36,
37,
38,
39,
40,
41,
42
] | null | null | [] | [] | ||
ATMUnderPeriodicMaintenanceTaskWithConclusionLessExplicit | 5 | 1/3 | [
"c_i",
"c_cov"
] | [
"forecasting",
"natural language processing",
"instruction following",
"reasoning: deduction"
] | This is the number of cash withdrawals from an automated teller machine (ATM) in an arbitrary location in England. The ATM was under maintenance for various periods, resulting in no withdrawals recorded. Assume that the ATM will not be in maintenance in the future. | 7 | {"0":{"1996-07-14T00:00:00.000":22.6553089193,"1996-07-15T00:00:00.000":12.7818297352,"1996-07-16T00:00:00.000":11.857827131,"1996-07-17T00:00:00.000":20.957922512,"1996-07-18T00:00:00.000":20.1687491151,"1996-07-19T00:00:00.000":16.0343068332,"1996-07-20T00:00:00.000":11.2047675642,"1996-07-21T00:00:00.000":13.9838200... | {"0":{"1996-11-03T00:00:00.000":12.0719284125,"1996-11-04T00:00:00.000":9.649466624,"1996-11-05T00:00:00.000":10.8386282045,"1996-11-06T00:00:00.000":12.6642069977,"1996-11-07T00:00:00.000":17.3357106673,"1996-11-08T00:00:00.000":17.8100702993,"1996-11-09T00:00:00.000":12.2797308106,"1996-11-10T00:00:00.000":11.2419985... | 0.029119 | [
1,
2,
3,
4,
16,
17,
18,
19,
31,
32,
33,
34,
46,
47,
48,
49
] | null | null | [] | [] | ||
ATMUnderPeriodicMaintenanceTaskWithoutConclusion | 1 | 1/3 | [
"c_i",
"c_cov"
] | [
"forecasting",
"natural language processing",
"instruction following",
"reasoning: deduction"
] | This is the number of cash withdrawals from an automated teller machine (ATM) in an arbitrary location in England. The ATM was under maintenance for 7 days, periodically every 15 days, starting from 1996-08-12 00:00:00. Assume that the ATM will not be in maintenance in the future. | 7 | {"0":{"1996-05-29T00:00:00.000":31.122501499,"1996-05-30T00:00:00.000":41.6604530533,"1996-05-31T00:00:00.000":28.0595869941,"1996-06-01T00:00:00.000":19.79040905,"1996-06-02T00:00:00.000":22.1464820253,"1996-06-03T00:00:00.000":19.1432663383,"1996-06-04T00:00:00.000":24.3165843031,"1996-06-05T00:00:00.000":31.73312469... | {"0":{"1996-11-13T00:00:00.000":29.8624128638,"1996-11-14T00:00:00.000":39.2815935556,"1996-11-15T00:00:00.000":37.0513358742,"1996-11-16T00:00:00.000":24.2783073932,"1996-11-17T00:00:00.000":20.9533626081,"1996-11-18T00:00:00.000":14.8815312198,"1996-11-19T00:00:00.000":23.6438709762,"1996-11-20T00:00:00.000":32.20213... | 0.029119 | [
0,
1,
2,
3,
12,
13,
14,
15,
16,
17,
18,
27,
28,
29,
30,
31,
32,
33,
42,
43,
44,
45,
46,
47,
48
] | null | null | [] | [] | ||
ATMUnderPeriodicMaintenanceTaskWithoutConclusion | 2 | 1/3 | [
"c_i",
"c_cov"
] | [
"forecasting",
"natural language processing",
"instruction following",
"reasoning: deduction"
] | This is the number of cash withdrawals from an automated teller machine (ATM) in an arbitrary location in England. The ATM was under maintenance for 4 days, periodically every 12 days, starting from 1997-09-05 00:00:00. Assume that the ATM will not be in maintenance in the future. | 7 | {"0":{"1997-07-24T00:00:00.000":27.6740903266,"1997-07-25T00:00:00.000":21.5446995009,"1997-07-26T00:00:00.000":11.1547503362,"1997-07-27T00:00:00.000":18.9014206013,"1997-07-28T00:00:00.000":16.9952641406,"1997-07-29T00:00:00.000":21.0278605018,"1997-07-30T00:00:00.000":19.9284706507,"1997-07-31T00:00:00.000":25.80756... | {"0":{"1998-01-08T00:00:00.000":27.1767751563,"1998-01-09T00:00:00.000":22.9276935053,"1998-01-10T00:00:00.000":13.515230708,"1998-01-11T00:00:00.000":13.6311084267,"1998-01-12T00:00:00.000":8.4650116439,"1998-01-13T00:00:00.000":12.1123825331,"1998-01-14T00:00:00.000":20.3628655634,"1998-01-15T00:00:00.000":31.7413473... | 0.029119 | [
7,
8,
9,
10,
19,
20,
21,
22,
31,
32,
33,
34,
43,
44,
45,
46
] | null | null | [] | [] | ||
ATMUnderPeriodicMaintenanceTaskWithoutConclusion | 3 | 1/3 | [
"c_i",
"c_cov"
] | [
"forecasting",
"natural language processing",
"instruction following",
"reasoning: deduction"
] | This is the number of cash withdrawals from an automated teller machine (ATM) in an arbitrary location in England. The ATM was under maintenance for 7 days, periodically every 14 days, starting from 1996-11-30 00:00:00. Assume that the ATM will not be in maintenance in the future. | 7 | {"0":{"1996-11-22T00:00:00.000":26.8124041887,"1996-11-23T00:00:00.000":7.5620102148,"1996-11-24T00:00:00.000":13.8634113649,"1996-11-25T00:00:00.000":13.6304762615,"1996-11-26T00:00:00.000":14.5301439633,"1996-11-27T00:00:00.000":16.329274092,"1996-11-28T00:00:00.000":27.3101374727,"1996-11-29T00:00:00.000":28.9946186... | {"0":{"1997-03-14T00:00:00.000":21.05471373,"1997-03-15T00:00:00.000":6.809713593,"1997-03-16T00:00:00.000":12.656364801,"1997-03-17T00:00:00.000":11.8317726301,"1997-03-18T00:00:00.000":11.117821392,"1997-03-19T00:00:00.000":17.1104362941,"1997-03-20T00:00:00.000":21.7183650914,"1997-03-21T00:00:00.000":23.6067226341,... | 0.029119 | [
0,
8,
9,
10,
11,
12,
13,
14,
22,
23,
24,
25,
26,
27,
28,
36,
37,
38,
39,
40,
41,
42
] | null | null | [] | [] | ||
ATMUnderPeriodicMaintenanceTaskWithoutConclusion | 4 | 1/3 | [
"c_i",
"c_cov"
] | [
"forecasting",
"natural language processing",
"instruction following",
"reasoning: deduction"
] | This is the number of cash withdrawals from an automated teller machine (ATM) in an arbitrary location in England. The ATM was under maintenance for 7 days, periodically every 14 days, starting from 1997-05-02 00:00:00. Assume that the ATM will not be in maintenance in the future. | 7 | {"0":{"1997-03-13T00:00:00.000":37.9192009009,"1997-03-14T00:00:00.000":36.0067750248,"1997-03-15T00:00:00.000":21.6482194388,"1997-03-16T00:00:00.000":18.1970730377,"1997-03-17T00:00:00.000":13.9248175294,"1997-03-18T00:00:00.000":19.0175829269,"1997-03-19T00:00:00.000":31.0186859852,"1997-03-20T00:00:00.000":36.71434... | {"0":{"1997-08-28T00:00:00.000":50.1575043585,"1997-08-29T00:00:00.000":43.662540009,"1997-08-30T00:00:00.000":27.9600228085,"1997-08-31T00:00:00.000":28.4164242699,"1997-09-01T00:00:00.000":23.1253135356,"1997-09-02T00:00:00.000":24.6558910111,"1997-09-03T00:00:00.000":36.3943277021,"1997-09-04T00:00:00.000":60.455834... | 0.029119 | [
0,
8,
9,
10,
11,
12,
13,
14,
22,
23,
24,
25,
26,
27,
28,
36,
37,
38,
39,
40,
41,
42
] | null | null | [] | [] | ||
ATMUnderPeriodicMaintenanceTaskWithoutConclusion | 5 | 1/3 | [
"c_i",
"c_cov"
] | [
"forecasting",
"natural language processing",
"instruction following",
"reasoning: deduction"
] | This is the number of cash withdrawals from an automated teller machine (ATM) in an arbitrary location in England. The ATM was under maintenance for 4 days, periodically every 15 days, starting from 1996-07-22 00:00:00. Assume that the ATM will not be in maintenance in the future. | 7 | {"0":{"1996-07-14T00:00:00.000":22.6553089193,"1996-07-15T00:00:00.000":12.7818297352,"1996-07-16T00:00:00.000":11.857827131,"1996-07-17T00:00:00.000":20.957922512,"1996-07-18T00:00:00.000":20.1687491151,"1996-07-19T00:00:00.000":16.0343068332,"1996-07-20T00:00:00.000":11.2047675642,"1996-07-21T00:00:00.000":13.9838200... | {"0":{"1996-11-03T00:00:00.000":12.0719284125,"1996-11-04T00:00:00.000":9.649466624,"1996-11-05T00:00:00.000":10.8386282045,"1996-11-06T00:00:00.000":12.6642069977,"1996-11-07T00:00:00.000":17.3357106673,"1996-11-08T00:00:00.000":17.8100702993,"1996-11-09T00:00:00.000":12.2797308106,"1996-11-10T00:00:00.000":11.2419985... | 0.029119 | [
1,
2,
3,
4,
16,
17,
18,
19,
31,
32,
33,
34,
46,
47,
48,
49
] | null | null | [] | [] | ||
IncreasedWithdrawalScenario | 1 | 1/2 | [
"c_cov",
"c_i",
"c_f"
] | [
"forecasting",
"natural language processing",
"instruction following"
] | This is the number of cash withdrawals from an automated teller machine (ATM) in an arbitrary location in England. | Suppose that there is a carnival from 1996-11-22 00:00:00, for 11 days leading to more people in the area, and 4 times the number of usual withdrawals during that period. | 7 | {"0":{"1996-05-29T00:00:00.000":31.6268342674,"1996-05-30T00:00:00.000":41.9621782458,"1996-05-31T00:00:00.000":28.1045585336,"1996-06-01T00:00:00.000":19.0707972221,"1996-06-02T00:00:00.000":22.8230046303,"1996-06-03T00:00:00.000":18.5798780728,"1996-06-04T00:00:00.000":25.4859474707,"1996-06-05T00:00:00.000":31.19451... | {"0":{"1996-11-13T00:00:00.000":28.9474825502,"1996-11-14T00:00:00.000":40.624613057,"1996-11-15T00:00:00.000":40.2990301851,"1996-11-16T00:00:00.000":24.0721281798,"1996-11-17T00:00:00.000":21.0451620571,"1996-11-18T00:00:00.000":14.4918742936,"1996-11-19T00:00:00.000":25.7133157852,"1996-11-20T00:00:00.000":31.859895... | 0.008049 | [
9,
10,
11,
12,
13,
14,
15,
16,
17,
18,
19
] | null | null | [] | [] | |
IncreasedWithdrawalScenario | 2 | 1/2 | [
"c_cov",
"c_i",
"c_f"
] | [
"forecasting",
"natural language processing",
"instruction following"
] | This is the number of cash withdrawals from an automated teller machine (ATM) in an arbitrary location in England. | Suppose that there is a celebration from 1998-01-30 00:00:00, for 4 days leading to more people in the area, and 5 times the number of usual withdrawals during that period. | 7 | {"0":{"1997-07-24T00:00:00.000":27.8018432358,"1997-07-25T00:00:00.000":22.0226051271,"1997-07-26T00:00:00.000":11.6292005472,"1997-07-27T00:00:00.000":18.8935826373,"1997-07-28T00:00:00.000":17.4782461203,"1997-07-29T00:00:00.000":20.4670736981,"1997-07-30T00:00:00.000":19.9728767327,"1997-07-31T00:00:00.000":26.39543... | {"0":{"1998-01-08T00:00:00.000":27.4897399913,"1998-01-09T00:00:00.000":22.6842310172,"1998-01-10T00:00:00.000":13.4649078228,"1998-01-11T00:00:00.000":13.5856095457,"1998-01-12T00:00:00.000":8.2805475988,"1998-01-13T00:00:00.000":11.7505171259,"1998-01-14T00:00:00.000":19.893298489,"1998-01-15T00:00:00.000":32.9347878... | 0.008049 | [
22,
23,
24,
25
] | null | null | [] | [] | |
IncreasedWithdrawalScenario | 3 | 1/2 | [
"c_cov",
"c_i",
"c_f"
] | [
"forecasting",
"natural language processing",
"instruction following"
] | This is the number of cash withdrawals from an automated teller machine (ATM) in an arbitrary location in England. | Suppose that there is a festival from 1997-03-22 00:00:00, for 7 days leading to more people in the area, and 3 times the number of usual withdrawals during that period. | 7 | {"0":{"1996-11-22T00:00:00.000":26.0798168168,"1996-11-23T00:00:00.000":8.0346910942,"1996-11-24T00:00:00.000":13.91242301,"1996-11-25T00:00:00.000":13.5922298444,"1996-11-26T00:00:00.000":13.7007281051,"1996-11-27T00:00:00.000":16.6219267426,"1996-11-28T00:00:00.000":26.65918957,"1996-11-29T00:00:00.000":29.290408331,... | {"0":{"1997-03-14T00:00:00.000":21.0760352577,"1997-03-15T00:00:00.000":7.1849032618,"1997-03-16T00:00:00.000":12.5901917537,"1997-03-17T00:00:00.000":11.4258807583,"1997-03-18T00:00:00.000":11.3366569105,"1997-03-19T00:00:00.000":17.3356002772,"1997-03-20T00:00:00.000":21.5277782498,"1997-03-21T00:00:00.000":23.339912... | 0.008049 | [
8,
9,
10,
11,
12,
13,
14
] | null | null | [] | [] | |
IncreasedWithdrawalScenario | 4 | 1/2 | [
"c_cov",
"c_i",
"c_f"
] | [
"forecasting",
"natural language processing",
"instruction following"
] | This is the number of cash withdrawals from an automated teller machine (ATM) in an arbitrary location in England. | Suppose that there is a carnival from 1997-09-05 00:00:00, for 11 days leading to more people in the area, and 5 times the number of usual withdrawals during that period. | 7 | {"0":{"1997-03-13T00:00:00.000":38.1057650281,"1997-03-14T00:00:00.000":35.9560116507,"1997-03-15T00:00:00.000":21.5191862274,"1997-03-16T00:00:00.000":18.3762275741,"1997-03-17T00:00:00.000":13.8798630323,"1997-03-18T00:00:00.000":18.881269264,"1997-03-19T00:00:00.000":30.7991655743,"1997-03-20T00:00:00.000":37.006327... | {"0":{"1997-08-28T00:00:00.000":49.8959705361,"1997-08-29T00:00:00.000":41.9406732434,"1997-08-30T00:00:00.000":26.6144379479,"1997-08-31T00:00:00.000":27.7775227744,"1997-09-01T00:00:00.000":22.6822661365,"1997-09-02T00:00:00.000":25.764847446,"1997-09-03T00:00:00.000":35.7511982188,"1997-09-04T00:00:00.000":59.426268... | 0.008049 | [
8,
9,
10,
11,
12,
13,
14,
15,
16,
17,
18
] | null | null | [] | [] | |
IncreasedWithdrawalScenario | 5 | 1/2 | [
"c_cov",
"c_i",
"c_f"
] | [
"forecasting",
"natural language processing",
"instruction following"
] | This is the number of cash withdrawals from an automated teller machine (ATM) in an arbitrary location in England. | Suppose that there is a festival from 1996-11-12 00:00:00, for 4 days leading to more people in the area, and 3 times the number of usual withdrawals during that period. | 7 | {"0":{"1996-07-14T00:00:00.000":22.6691065662,"1996-07-15T00:00:00.000":12.836636228,"1996-07-16T00:00:00.000":11.8717945432,"1996-07-17T00:00:00.000":21.11039322,"1996-07-18T00:00:00.000":20.094811945,"1996-07-19T00:00:00.000":15.9997168239,"1996-07-20T00:00:00.000":11.1502091371,"1996-07-21T00:00:00.000":14.036951807... | {"0":{"1996-11-03T00:00:00.000":12.0784116234,"1996-11-04T00:00:00.000":9.6613853757,"1996-11-05T00:00:00.000":10.9080696967,"1996-11-06T00:00:00.000":12.6436277099,"1996-11-07T00:00:00.000":17.4327271044,"1996-11-08T00:00:00.000":17.8324529605,"1996-11-09T00:00:00.000":12.2097433347,"1996-11-10T00:00:00.000":11.295545... | 0.008049 | [
9,
10,
11,
12
] | null | null | [] | [] | |
DecreaseInTrafficInPredictionTask | 1 | 1 | [
"c_cov",
"c_f"
] | [
"forecasting",
"natural language processing",
"instruction following"
] | This is hourly traffic data. | Suppose that there is an accident on the road and there is 20.0% of the usual traffic from 2024-01-18 06:00:00 for 5 hours. | 24 | {"Occupancy (%)":{"2024-01-11T00:00:00.000":0.8,"2024-01-11T01:00:00.000":1.3,"2024-01-11T02:00:00.000":2.1,"2024-01-11T03:00:00.000":5.3,"2024-01-11T04:00:00.000":19.0,"2024-01-11T05:00:00.000":35.0,"2024-01-11T06:00:00.000":38.5,"2024-01-11T07:00:00.000":35.3,"2024-01-11T08:00:00.000":8.6,"2024-01-11T09:00:00.000":7.... | {"Occupancy (%)":{"2024-01-18T00:00:00.000":1.0,"2024-01-18T01:00:00.000":1.3,"2024-01-18T02:00:00.000":2.1,"2024-01-18T03:00:00.000":5.5,"2024-01-18T04:00:00.000":17.7,"2024-01-18T05:00:00.000":34.1,"2024-01-18T06:00:00.000":7.52,"2024-01-18T07:00:00.000":7.52,"2024-01-18T08:00:00.000":7.52,"2024-01-18T09:00:00.000":7... | 0.10845 | [
6,
7,
8,
9,
10
] | null | null | [] | [] | |
DecreaseInTrafficInPredictionTask | 2 | 1 | [
"c_cov",
"c_f"
] | [
"forecasting",
"natural language processing",
"instruction following"
] | This is hourly traffic data. | Suppose that there is an accident on the road and there is 40.0% of the usual traffic from 2024-04-24 17:00:00 for 6 hours. | 24 | {"Occupancy (%)":{"2024-04-17T13:00:00.000":0.6,"2024-04-17T14:00:00.000":0.8,"2024-04-17T15:00:00.000":0.9,"2024-04-17T16:00:00.000":1.3,"2024-04-17T17:00:00.000":1.3,"2024-04-17T18:00:00.000":0.6,"2024-04-17T19:00:00.000":0.5,"2024-04-17T20:00:00.000":0.3,"2024-04-17T21:00:00.000":0.3,"2024-04-17T22:00:00.000":0.2,"2... | {"Occupancy (%)":{"2024-04-24T13:00:00.000":0.6,"2024-04-24T14:00:00.000":0.8,"2024-04-24T15:00:00.000":0.9,"2024-04-24T16:00:00.000":1.3,"2024-04-24T17:00:00.000":0.56,"2024-04-24T18:00:00.000":0.56,"2024-04-24T19:00:00.000":0.56,"2024-04-24T20:00:00.000":0.56,"2024-04-24T21:00:00.000":0.56,"2024-04-24T22:00:00.000":0... | 0.10845 | [
4,
5,
6,
7,
8,
9
] | null | null | [] | [] | |
DecreaseInTrafficInPredictionTask | 3 | 1 | [
"c_cov",
"c_f"
] | [
"forecasting",
"natural language processing",
"instruction following"
] | This is hourly traffic data. | Suppose that there is an accident on the road and there is 10.0% of the usual traffic from 2024-03-19 09:00:00 for 2 hours. | 24 | {"Occupancy (%)":{"2024-03-11T14:00:00.000":1.3,"2024-03-11T15:00:00.000":1.0,"2024-03-11T16:00:00.000":1.1,"2024-03-11T17:00:00.000":0.8,"2024-03-11T18:00:00.000":0.8,"2024-03-11T19:00:00.000":0.4,"2024-03-11T20:00:00.000":0.5,"2024-03-11T21:00:00.000":0.5,"2024-03-11T22:00:00.000":0.4,"2024-03-11T23:00:00.000":0.4,"2... | {"Occupancy (%)":{"2024-03-18T14:00:00.000":1.5,"2024-03-18T15:00:00.000":1.3,"2024-03-18T16:00:00.000":1.1,"2024-03-18T17:00:00.000":0.8,"2024-03-18T18:00:00.000":0.7,"2024-03-18T19:00:00.000":0.6,"2024-03-18T20:00:00.000":0.5,"2024-03-18T21:00:00.000":0.6,"2024-03-18T22:00:00.000":0.5,"2024-03-18T23:00:00.000":0.4,"2... | 0.10845 | [
19,
20
] | null | null | [] | [] | |
DecreaseInTrafficInPredictionTask | 4 | 1 | [
"c_cov",
"c_f"
] | [
"forecasting",
"natural language processing",
"instruction following"
] | This is hourly traffic data. | Suppose that there is an accident on the road and there is 10.0% of the usual traffic from 2024-01-15 16:00:00 for 6 hours. | 24 | {"Occupancy (%)":{"2024-01-08T11:00:00.000":9.6,"2024-01-08T12:00:00.000":9.8,"2024-01-08T13:00:00.000":10.0,"2024-01-08T14:00:00.000":10.1,"2024-01-08T15:00:00.000":10.3,"2024-01-08T16:00:00.000":10.9,"2024-01-08T17:00:00.000":13.2,"2024-01-08T18:00:00.000":9.0,"2024-01-08T19:00:00.000":7.3,"2024-01-08T20:00:00.000":6... | {"Occupancy (%)":{"2024-01-15T11:00:00.000":9.6,"2024-01-15T12:00:00.000":9.8,"2024-01-15T13:00:00.000":10.0,"2024-01-15T14:00:00.000":10.1,"2024-01-15T15:00:00.000":10.3,"2024-01-15T16:00:00.000":1.09,"2024-01-15T17:00:00.000":1.09,"2024-01-15T18:00:00.000":1.09,"2024-01-15T19:00:00.000":1.09,"2024-01-15T20:00:00.000"... | 0.10845 | [
5,
6,
7,
8,
9,
10
] | null | null | [] | [] | |
DecreaseInTrafficInPredictionTask | 5 | 1 | [
"c_cov",
"c_f"
] | [
"forecasting",
"natural language processing",
"instruction following"
] | This is hourly traffic data. | Suppose that there is an accident on the road and there is 20.0% of the usual traffic from 2024-04-11 17:00:00 for 6 hours. | 24 | {"Occupancy (%)":{"2024-04-04T04:00:00.000":2.7,"2024-04-04T05:00:00.000":5.2,"2024-04-04T06:00:00.000":7.7,"2024-04-04T07:00:00.000":14.4,"2024-04-04T08:00:00.000":8.0,"2024-04-04T09:00:00.000":7.2,"2024-04-04T10:00:00.000":6.9,"2024-04-04T11:00:00.000":7.2,"2024-04-04T12:00:00.000":7.9,"2024-04-04T13:00:00.000":9.0,"... | {"Occupancy (%)":{"2024-04-11T04:00:00.000":2.8,"2024-04-11T05:00:00.000":5.3,"2024-04-11T06:00:00.000":8.5,"2024-04-11T07:00:00.000":10.8,"2024-04-11T08:00:00.000":7.7,"2024-04-11T09:00:00.000":7.6,"2024-04-11T10:00:00.000":7.3,"2024-04-11T11:00:00.000":7.5,"2024-04-11T12:00:00.000":7.7,"2024-04-11T13:00:00.000":8.5,"... | 0.10845 | [
13,
14,
15,
16,
17,
18
] | null | null | [] | [] | |
OraclePredUnivariateConstraintsTask | 1 | 1/2 | [
"c_f"
] | [
"forecasting",
"natural language processing",
"instruction following"
] | Suppose that in the forecast, the values are bounded below by 0.60. | 24 | {"Occupancy (%)":{"2024-04-12T22:00:00.000":3.4,"2024-04-12T23:00:00.000":3.0,"2024-04-13T00:00:00.000":2.9,"2024-04-13T01:00:00.000":2.9,"2024-04-13T02:00:00.000":2.9,"2024-04-13T03:00:00.000":2.9,"2024-04-13T04:00:00.000":3.0,"2024-04-13T05:00:00.000":3.1,"2024-04-13T06:00:00.000":3.1,"2024-04-13T07:00:00.000":3.3,"2... | {"Occupancy (%)":{"2024-04-19T22:00:00.000":1.8,"2024-04-19T23:00:00.000":1.4,"2024-04-20T00:00:00.000":1.0,"2024-04-20T01:00:00.000":0.8,"2024-04-20T02:00:00.000":0.6,"2024-04-20T03:00:00.000":0.9,"2024-04-20T04:00:00.000":1.0,"2024-04-20T05:00:00.000":1.1,"2024-04-20T06:00:00.000":2.8,"2024-04-20T07:00:00.000":3.5,"2... | 0.078518 | [] | 0.6 | null | [] | [] | ||
OraclePredUnivariateConstraintsTask | 2 | 1/2 | [
"c_f"
] | [
"forecasting",
"natural language processing",
"instruction following"
] | Suppose that in the forecast, the values are bounded above by 13.20, the values are bounded below by 2.40. | 24 | {"Occupancy (%)":{"2024-04-27T13:00:00.000":13.3,"2024-04-27T14:00:00.000":13.5,"2024-04-27T15:00:00.000":12.8,"2024-04-27T16:00:00.000":13.5,"2024-04-27T17:00:00.000":13.1,"2024-04-27T18:00:00.000":13.7,"2024-04-27T19:00:00.000":12.1,"2024-04-27T20:00:00.000":10.9,"2024-04-27T21:00:00.000":11.5,"2024-04-27T22:00:00.00... | {"Occupancy (%)":{"2024-05-04T13:00:00.000":8.3,"2024-05-04T14:00:00.000":13.2,"2024-05-04T15:00:00.000":9.2,"2024-05-04T16:00:00.000":6.1,"2024-05-04T17:00:00.000":5.2,"2024-05-04T18:00:00.000":4.8,"2024-05-04T19:00:00.000":4.5,"2024-05-04T20:00:00.000":4.4,"2024-05-04T21:00:00.000":4.4,"2024-05-04T22:00:00.000":4.4,"... | 0.078518 | [] | 2.4 | 13.2 | [] | [] | ||
OraclePredUnivariateConstraintsTask | 3 | 1/2 | [
"c_f"
] | [
"forecasting",
"natural language processing",
"instruction following"
] | Suppose that in the forecast, the values are bounded below by 0.80. | 24 | {"Occupancy (%)":{"2024-01-29T23:00:00.000":0.9,"2024-01-30T00:00:00.000":0.1,"2024-01-30T01:00:00.000":0.0,"2024-01-30T02:00:00.000":0.0,"2024-01-30T03:00:00.000":0.0,"2024-01-30T04:00:00.000":0.1,"2024-01-30T05:00:00.000":0.2,"2024-01-30T06:00:00.000":0.7,"2024-01-30T07:00:00.000":1.3,"2024-01-30T08:00:00.000":1.1,"2... | {"Occupancy (%)":{"2024-02-05T23:00:00.000":0.8,"2024-02-06T00:00:00.000":0.8,"2024-02-06T01:00:00.000":0.8,"2024-02-06T02:00:00.000":0.8,"2024-02-06T03:00:00.000":0.8,"2024-02-06T04:00:00.000":0.8,"2024-02-06T05:00:00.000":1.0,"2024-02-06T06:00:00.000":1.7,"2024-02-06T07:00:00.000":2.2,"2024-02-06T08:00:00.000":2.0,"2... | 0.078518 | [] | 0.8 | null | [] | [] | ||
OraclePredUnivariateConstraintsTask | 4 | 1/2 | [
"c_f"
] | [
"forecasting",
"natural language processing",
"instruction following"
] | Suppose that in the forecast, the values are bounded below by 0.00. | 24 | {"Occupancy (%)":{"2024-03-12T14:00:00.000":0.1,"2024-03-12T15:00:00.000":0.2,"2024-03-12T16:00:00.000":0.1,"2024-03-12T17:00:00.000":0.1,"2024-03-12T18:00:00.000":0.1,"2024-03-12T19:00:00.000":0.1,"2024-03-12T20:00:00.000":0.1,"2024-03-12T21:00:00.000":0.1,"2024-03-12T22:00:00.000":0.1,"2024-03-12T23:00:00.000":0.0,"2... | {"Occupancy (%)":{"2024-03-19T14:00:00.000":0.1,"2024-03-19T15:00:00.000":0.2,"2024-03-19T16:00:00.000":0.1,"2024-03-19T17:00:00.000":0.2,"2024-03-19T18:00:00.000":0.1,"2024-03-19T19:00:00.000":0.1,"2024-03-19T20:00:00.000":0.1,"2024-03-19T21:00:00.000":0.1,"2024-03-19T22:00:00.000":0.1,"2024-03-19T23:00:00.000":0.0,"2... | 0.078518 | [] | 0 | null | [] | [] | ||
OraclePredUnivariateConstraintsTask | 5 | 1/2 | [
"c_f"
] | [
"forecasting",
"natural language processing",
"instruction following"
] | Suppose that in the forecast, the values are bounded above by 1.80, the values are bounded below by 1.30. | 24 | {"Occupancy (%)":{"2024-05-04T21:00:00.000":0.1,"2024-05-04T22:00:00.000":0.1,"2024-05-04T23:00:00.000":0.0,"2024-05-05T00:00:00.000":2.3,"2024-05-05T01:00:00.000":2.3,"2024-05-05T02:00:00.000":2.3,"2024-05-05T03:00:00.000":2.3,"2024-05-05T04:00:00.000":2.3,"2024-05-05T05:00:00.000":2.3,"2024-05-05T06:00:00.000":2.3,"2... | {"Occupancy (%)":{"2024-05-11T21:00:00.000":1.4,"2024-05-11T22:00:00.000":1.4,"2024-05-11T23:00:00.000":1.4,"2024-05-12T00:00:00.000":1.4,"2024-05-12T01:00:00.000":1.3,"2024-05-12T02:00:00.000":1.3,"2024-05-12T03:00:00.000":1.3,"2024-05-12T04:00:00.000":1.3,"2024-05-12T05:00:00.000":1.3,"2024-05-12T06:00:00.000":1.4,"2... | 0.078518 | [] | 1.3 | 1.8 | [] | [] | ||
BoundedPredConstraintsBasedOnPredQuantilesTask | 1 | 1/2 | [
"c_f"
] | [
"forecasting",
"natural language processing",
"instruction following"
] | Suppose that in the forecast, the values are bounded above by 6.29. | 24 | {"Occupancy (%)":{"2024-01-11T00:00:00.000":0.8,"2024-01-11T01:00:00.000":1.3,"2024-01-11T02:00:00.000":2.1,"2024-01-11T03:00:00.000":5.3,"2024-01-11T04:00:00.000":19.0,"2024-01-11T05:00:00.000":35.0,"2024-01-11T06:00:00.000":38.5,"2024-01-11T07:00:00.000":35.3,"2024-01-11T08:00:00.000":8.6,"2024-01-11T09:00:00.000":7.... | {"Occupancy (%)":{"2024-01-18T00:00:00.000":1.0,"2024-01-18T01:00:00.000":1.3,"2024-01-18T02:00:00.000":2.1,"2024-01-18T03:00:00.000":5.5,"2024-01-18T04:00:00.000":6.29,"2024-01-18T05:00:00.000":6.29,"2024-01-18T06:00:00.000":6.29,"2024-01-18T07:00:00.000":6.29,"2024-01-18T08:00:00.000":6.29,"2024-01-18T09:00:00.000":6... | 0.244882 | [] | null | 6.29 | [] | [] | ||
BoundedPredConstraintsBasedOnPredQuantilesTask | 2 | 1/2 | [
"c_f"
] | [
"forecasting",
"natural language processing",
"instruction following"
] | Suppose that in the forecast, the values are bounded above by 0.60, the values are bounded below by 0.30. | 24 | {"Occupancy (%)":{"2024-04-17T13:00:00.000":0.6,"2024-04-17T14:00:00.000":0.8,"2024-04-17T15:00:00.000":0.9,"2024-04-17T16:00:00.000":1.3,"2024-04-17T17:00:00.000":1.3,"2024-04-17T18:00:00.000":0.6,"2024-04-17T19:00:00.000":0.5,"2024-04-17T20:00:00.000":0.3,"2024-04-17T21:00:00.000":0.3,"2024-04-17T22:00:00.000":0.2,"2... | {"Occupancy (%)":{"2024-04-24T13:00:00.000":0.6,"2024-04-24T14:00:00.000":0.6,"2024-04-24T15:00:00.000":0.6,"2024-04-24T16:00:00.000":0.6,"2024-04-24T17:00:00.000":0.6,"2024-04-24T18:00:00.000":0.6,"2024-04-24T19:00:00.000":0.4,"2024-04-24T20:00:00.000":0.4,"2024-04-24T21:00:00.000":0.5,"2024-04-24T22:00:00.000":0.3,"2... | 0.244882 | [] | 0.3 | 0.6 | [] | [] | ||
BoundedPredConstraintsBasedOnPredQuantilesTask | 3 | 1/2 | [
"c_f"
] | [
"forecasting",
"natural language processing",
"instruction following"
] | Suppose that in the forecast, the values are bounded below by 0.50, the values are bounded above by 1.10. | 24 | {"Occupancy (%)":{"2024-03-11T14:00:00.000":1.3,"2024-03-11T15:00:00.000":1.0,"2024-03-11T16:00:00.000":1.1,"2024-03-11T17:00:00.000":0.8,"2024-03-11T18:00:00.000":0.8,"2024-03-11T19:00:00.000":0.4,"2024-03-11T20:00:00.000":0.5,"2024-03-11T21:00:00.000":0.5,"2024-03-11T22:00:00.000":0.4,"2024-03-11T23:00:00.000":0.4,"2... | {"Occupancy (%)":{"2024-03-18T14:00:00.000":1.1,"2024-03-18T15:00:00.000":1.1,"2024-03-18T16:00:00.000":1.1,"2024-03-18T17:00:00.000":0.8,"2024-03-18T18:00:00.000":0.7,"2024-03-18T19:00:00.000":0.6,"2024-03-18T20:00:00.000":0.5,"2024-03-18T21:00:00.000":0.6,"2024-03-18T22:00:00.000":0.5,"2024-03-18T23:00:00.000":0.5,"2... | 0.244882 | [] | 0.5 | 1.1 | [] | [] | ||
BoundedPredConstraintsBasedOnPredQuantilesTask | 4 | 1/2 | [
"c_f"
] | [
"forecasting",
"natural language processing",
"instruction following"
] | Suppose that in the forecast, the values are bounded below by 5.28, the values are bounded above by 9.82. | 24 | {"Occupancy (%)":{"2024-01-08T11:00:00.000":9.6,"2024-01-08T12:00:00.000":9.8,"2024-01-08T13:00:00.000":10.0,"2024-01-08T14:00:00.000":10.1,"2024-01-08T15:00:00.000":10.3,"2024-01-08T16:00:00.000":10.9,"2024-01-08T17:00:00.000":13.2,"2024-01-08T18:00:00.000":9.0,"2024-01-08T19:00:00.000":7.3,"2024-01-08T20:00:00.000":6... | {"Occupancy (%)":{"2024-01-15T11:00:00.000":9.6,"2024-01-15T12:00:00.000":9.8,"2024-01-15T13:00:00.000":9.82,"2024-01-15T14:00:00.000":9.82,"2024-01-15T15:00:00.000":9.82,"2024-01-15T16:00:00.000":9.82,"2024-01-15T17:00:00.000":9.82,"2024-01-15T18:00:00.000":9.0,"2024-01-15T19:00:00.000":7.3,"2024-01-15T20:00:00.000":6... | 0.244882 | [] | 5.28 | 9.82 | [] | [] | ||
BoundedPredConstraintsBasedOnPredQuantilesTask | 5 | 1/2 | [
"c_f"
] | [
"forecasting",
"natural language processing",
"instruction following"
] | Suppose that in the forecast, the values are bounded below by 5.27, the values are bounded above by 7.78. | 24 | {"Occupancy (%)":{"2024-04-04T04:00:00.000":2.7,"2024-04-04T05:00:00.000":5.2,"2024-04-04T06:00:00.000":7.7,"2024-04-04T07:00:00.000":14.4,"2024-04-04T08:00:00.000":8.0,"2024-04-04T09:00:00.000":7.2,"2024-04-04T10:00:00.000":6.9,"2024-04-04T11:00:00.000":7.2,"2024-04-04T12:00:00.000":7.9,"2024-04-04T13:00:00.000":9.0,"... | {"Occupancy (%)":{"2024-04-11T04:00:00.000":5.27,"2024-04-11T05:00:00.000":5.3,"2024-04-11T06:00:00.000":7.78,"2024-04-11T07:00:00.000":7.78,"2024-04-11T08:00:00.000":7.7,"2024-04-11T09:00:00.000":7.6,"2024-04-11T10:00:00.000":7.3,"2024-04-11T11:00:00.000":7.5,"2024-04-11T12:00:00.000":7.7,"2024-04-11T13:00:00.000":7.7... | 0.244882 | [] | 5.27 | 7.78 | [] | [] | ||
SensorMaintenanceInPredictionTask | 1 | 1/4 | [
"c_cov",
"c_f"
] | [
"forecasting",
"natural language processing",
"instruction following"
] | This series represents the occupancy rate (%) captured by a highway sensor. | Consider that the meter will be offline for maintenance between 2024-01-18 08:00:00 and 2024-01-18 14:00:00, which results in zero readings. | 24 | {"Occupancy (%)":{"2024-01-11T00:00:00.000":0.8,"2024-01-11T01:00:00.000":1.3,"2024-01-11T02:00:00.000":2.1,"2024-01-11T03:00:00.000":5.3,"2024-01-11T04:00:00.000":19.0,"2024-01-11T05:00:00.000":35.0,"2024-01-11T06:00:00.000":38.5,"2024-01-11T07:00:00.000":35.3,"2024-01-11T08:00:00.000":8.6,"2024-01-11T09:00:00.000":7.... | {"Occupancy (%)":{"2024-01-18T00:00:00.000":1.0,"2024-01-18T01:00:00.000":1.3,"2024-01-18T02:00:00.000":2.1,"2024-01-18T03:00:00.000":5.5,"2024-01-18T04:00:00.000":17.7,"2024-01-18T05:00:00.000":34.1,"2024-01-18T06:00:00.000":37.6,"2024-01-18T07:00:00.000":29.5,"2024-01-18T08:00:00.000":0.0,"2024-01-18T09:00:00.000":0.... | 0.086595 | [
8,
9,
10,
11,
12,
13
] | null | null | [] | [] | |
SensorMaintenanceInPredictionTask | 2 | 1/4 | [
"c_cov",
"c_f"
] | [
"forecasting",
"natural language processing",
"instruction following"
] | This series represents the occupancy rate (%) captured by a highway sensor. | Consider that the meter will be offline for maintenance between 2024-04-25 00:00:00 and 2024-04-25 02:00:00, which results in zero readings. | 24 | {"Occupancy (%)":{"2024-04-17T13:00:00.000":0.6,"2024-04-17T14:00:00.000":0.8,"2024-04-17T15:00:00.000":0.9,"2024-04-17T16:00:00.000":1.3,"2024-04-17T17:00:00.000":1.3,"2024-04-17T18:00:00.000":0.6,"2024-04-17T19:00:00.000":0.5,"2024-04-17T20:00:00.000":0.3,"2024-04-17T21:00:00.000":0.3,"2024-04-17T22:00:00.000":0.2,"2... | {"Occupancy (%)":{"2024-04-24T13:00:00.000":0.6,"2024-04-24T14:00:00.000":0.8,"2024-04-24T15:00:00.000":0.9,"2024-04-24T16:00:00.000":1.3,"2024-04-24T17:00:00.000":1.4,"2024-04-24T18:00:00.000":0.7,"2024-04-24T19:00:00.000":0.4,"2024-04-24T20:00:00.000":0.4,"2024-04-24T21:00:00.000":0.5,"2024-04-24T22:00:00.000":0.2,"2... | 0.086595 | [
11,
12
] | null | null | [] | [] | |
SensorMaintenanceInPredictionTask | 3 | 1/4 | [
"c_cov",
"c_f"
] | [
"forecasting",
"natural language processing",
"instruction following"
] | This series represents the occupancy rate (%) captured by a highway sensor. | Consider that the meter will be offline for maintenance between 2024-03-18 17:00:00 and 2024-03-18 20:00:00, which results in zero readings. | 24 | {"Occupancy (%)":{"2024-03-11T14:00:00.000":1.3,"2024-03-11T15:00:00.000":1.0,"2024-03-11T16:00:00.000":1.1,"2024-03-11T17:00:00.000":0.8,"2024-03-11T18:00:00.000":0.8,"2024-03-11T19:00:00.000":0.4,"2024-03-11T20:00:00.000":0.5,"2024-03-11T21:00:00.000":0.5,"2024-03-11T22:00:00.000":0.4,"2024-03-11T23:00:00.000":0.4,"2... | {"Occupancy (%)":{"2024-03-18T14:00:00.000":1.5,"2024-03-18T15:00:00.000":1.3,"2024-03-18T16:00:00.000":1.1,"2024-03-18T17:00:00.000":0.0,"2024-03-18T18:00:00.000":0.0,"2024-03-18T19:00:00.000":0.0,"2024-03-18T20:00:00.000":0.5,"2024-03-18T21:00:00.000":0.6,"2024-03-18T22:00:00.000":0.5,"2024-03-18T23:00:00.000":0.4,"2... | 0.086595 | [
3,
4,
5
] | null | null | [] | [] | |
SensorMaintenanceInPredictionTask | 4 | 1/4 | [
"c_cov",
"c_f"
] | [
"forecasting",
"natural language processing",
"instruction following"
] | This series represents the occupancy rate (%) captured by a highway sensor. | Consider that the meter will be offline for maintenance between 2024-01-15 19:00:00 and 2024-01-15 22:00:00, which results in zero readings. | 24 | {"Occupancy (%)":{"2024-01-08T11:00:00.000":9.6,"2024-01-08T12:00:00.000":9.8,"2024-01-08T13:00:00.000":10.0,"2024-01-08T14:00:00.000":10.1,"2024-01-08T15:00:00.000":10.3,"2024-01-08T16:00:00.000":10.9,"2024-01-08T17:00:00.000":13.2,"2024-01-08T18:00:00.000":9.0,"2024-01-08T19:00:00.000":7.3,"2024-01-08T20:00:00.000":6... | {"Occupancy (%)":{"2024-01-15T11:00:00.000":9.6,"2024-01-15T12:00:00.000":9.8,"2024-01-15T13:00:00.000":10.0,"2024-01-15T14:00:00.000":10.1,"2024-01-15T15:00:00.000":10.3,"2024-01-15T16:00:00.000":10.9,"2024-01-15T17:00:00.000":13.2,"2024-01-15T18:00:00.000":9.0,"2024-01-15T19:00:00.000":0.0,"2024-01-15T20:00:00.000":0... | 0.086595 | [
8,
9,
10
] | null | null | [] | [] | |
SensorMaintenanceInPredictionTask | 5 | 1/4 | [
"c_cov",
"c_f"
] | [
"forecasting",
"natural language processing",
"instruction following"
] | This series represents the occupancy rate (%) captured by a highway sensor. | Consider that the meter will be offline for maintenance between 2024-04-11 13:00:00 and 2024-04-11 15:00:00, which results in zero readings. | 24 | {"Occupancy (%)":{"2024-04-04T04:00:00.000":2.7,"2024-04-04T05:00:00.000":5.2,"2024-04-04T06:00:00.000":7.7,"2024-04-04T07:00:00.000":14.4,"2024-04-04T08:00:00.000":8.0,"2024-04-04T09:00:00.000":7.2,"2024-04-04T10:00:00.000":6.9,"2024-04-04T11:00:00.000":7.2,"2024-04-04T12:00:00.000":7.9,"2024-04-04T13:00:00.000":9.0,"... | {"Occupancy (%)":{"2024-04-11T04:00:00.000":2.8,"2024-04-11T05:00:00.000":5.3,"2024-04-11T06:00:00.000":8.5,"2024-04-11T07:00:00.000":10.8,"2024-04-11T08:00:00.000":7.7,"2024-04-11T09:00:00.000":7.6,"2024-04-11T10:00:00.000":7.3,"2024-04-11T11:00:00.000":7.5,"2024-04-11T12:00:00.000":7.7,"2024-04-11T13:00:00.000":0.0,"... | 0.086595 | [
9,
10
] | null | null | [] | [] | |
SensorPeriodicMaintenanceTask | 1 | 1/4 | [
"c_cov"
] | [
"forecasting",
"natural language processing",
"instruction following"
] | This series represents the occupancy rate (%) captured by a highway sensor. The sensor was offline for maintenance every day between 08:00 and 14:00, which resulted in zero readings. Assume that the sensor will not be in maintenance in the future. | 24 | {"Occupancy (%)":{"2024-01-11T00:00:00.000":0.8,"2024-01-11T01:00:00.000":1.3,"2024-01-11T02:00:00.000":2.1,"2024-01-11T03:00:00.000":5.3,"2024-01-11T04:00:00.000":19.0,"2024-01-11T05:00:00.000":35.0,"2024-01-11T06:00:00.000":38.5,"2024-01-11T07:00:00.000":35.3,"2024-01-11T08:00:00.000":0.0,"2024-01-11T09:00:00.000":0.... | {"Occupancy (%)":{"2024-01-18T00:00:00.000":1.0,"2024-01-18T01:00:00.000":1.3,"2024-01-18T02:00:00.000":2.1,"2024-01-18T03:00:00.000":5.5,"2024-01-18T04:00:00.000":17.7,"2024-01-18T05:00:00.000":34.1,"2024-01-18T06:00:00.000":37.6,"2024-01-18T07:00:00.000":29.5,"2024-01-18T08:00:00.000":8.0,"2024-01-18T09:00:00.000":8.... | 0.094769 | [
8,
9,
10,
11,
12,
13,
14
] | null | null | [] | [] | ||
SensorPeriodicMaintenanceTask | 2 | 1/4 | [
"c_cov"
] | [
"forecasting",
"natural language processing",
"instruction following"
] | This series represents the occupancy rate (%) captured by a highway sensor. The sensor was offline for maintenance every day between 00:00 and 02:00, which resulted in zero readings. Assume that the sensor will not be in maintenance in the future. | 24 | {"Occupancy (%)":{"2024-04-17T13:00:00.000":0.6,"2024-04-17T14:00:00.000":0.8,"2024-04-17T15:00:00.000":0.9,"2024-04-17T16:00:00.000":1.3,"2024-04-17T17:00:00.000":1.3,"2024-04-17T18:00:00.000":0.6,"2024-04-17T19:00:00.000":0.5,"2024-04-17T20:00:00.000":0.3,"2024-04-17T21:00:00.000":0.3,"2024-04-17T22:00:00.000":0.2,"2... | {"Occupancy (%)":{"2024-04-24T13:00:00.000":0.6,"2024-04-24T14:00:00.000":0.8,"2024-04-24T15:00:00.000":0.9,"2024-04-24T16:00:00.000":1.3,"2024-04-24T17:00:00.000":1.4,"2024-04-24T18:00:00.000":0.7,"2024-04-24T19:00:00.000":0.4,"2024-04-24T20:00:00.000":0.4,"2024-04-24T21:00:00.000":0.5,"2024-04-24T22:00:00.000":0.2,"2... | 0.094769 | [
11,
12,
13
] | null | null | [] | [] | ||
SensorPeriodicMaintenanceTask | 3 | 1/4 | [
"c_cov"
] | [
"forecasting",
"natural language processing",
"instruction following"
] | This series represents the occupancy rate (%) captured by a highway sensor. The sensor was offline for maintenance every day between 17:00 and 20:00, which resulted in zero readings. Assume that the sensor will not be in maintenance in the future. | 24 | {"Occupancy (%)":{"2024-03-11T14:00:00.000":1.3,"2024-03-11T15:00:00.000":1.0,"2024-03-11T16:00:00.000":1.1,"2024-03-11T17:00:00.000":0.0,"2024-03-11T18:00:00.000":0.0,"2024-03-11T19:00:00.000":0.0,"2024-03-11T20:00:00.000":0.0,"2024-03-11T21:00:00.000":0.5,"2024-03-11T22:00:00.000":0.4,"2024-03-11T23:00:00.000":0.4,"2... | {"Occupancy (%)":{"2024-03-18T14:00:00.000":1.5,"2024-03-18T15:00:00.000":1.3,"2024-03-18T16:00:00.000":1.1,"2024-03-18T17:00:00.000":0.8,"2024-03-18T18:00:00.000":0.7,"2024-03-18T19:00:00.000":0.6,"2024-03-18T20:00:00.000":0.5,"2024-03-18T21:00:00.000":0.6,"2024-03-18T22:00:00.000":0.5,"2024-03-18T23:00:00.000":0.4,"2... | 0.094769 | [
3,
4,
5,
6
] | null | null | [] | [] | ||
SensorPeriodicMaintenanceTask | 4 | 1/4 | [
"c_cov"
] | [
"forecasting",
"natural language processing",
"instruction following"
] | This series represents the occupancy rate (%) captured by a highway sensor. The sensor was offline for maintenance every day between 19:00 and 22:00, which resulted in zero readings. Assume that the sensor will not be in maintenance in the future. | 24 | {"Occupancy (%)":{"2024-01-08T11:00:00.000":9.6,"2024-01-08T12:00:00.000":9.8,"2024-01-08T13:00:00.000":10.0,"2024-01-08T14:00:00.000":10.1,"2024-01-08T15:00:00.000":10.3,"2024-01-08T16:00:00.000":10.9,"2024-01-08T17:00:00.000":13.2,"2024-01-08T18:00:00.000":9.0,"2024-01-08T19:00:00.000":0.0,"2024-01-08T20:00:00.000":0... | {"Occupancy (%)":{"2024-01-15T11:00:00.000":9.6,"2024-01-15T12:00:00.000":9.8,"2024-01-15T13:00:00.000":10.0,"2024-01-15T14:00:00.000":10.1,"2024-01-15T15:00:00.000":10.3,"2024-01-15T16:00:00.000":10.9,"2024-01-15T17:00:00.000":13.2,"2024-01-15T18:00:00.000":9.0,"2024-01-15T19:00:00.000":7.3,"2024-01-15T20:00:00.000":6... | 0.094769 | [
8,
9,
10,
11
] | null | null | [] | [] | ||
SensorPeriodicMaintenanceTask | 5 | 1/4 | [
"c_cov"
] | [
"forecasting",
"natural language processing",
"instruction following"
] | This series represents the occupancy rate (%) captured by a highway sensor. The sensor was offline for maintenance every day between 13:00 and 15:00, which resulted in zero readings. Assume that the sensor will not be in maintenance in the future. | 24 | {"Occupancy (%)":{"2024-04-04T04:00:00.000":2.7,"2024-04-04T05:00:00.000":5.2,"2024-04-04T06:00:00.000":7.7,"2024-04-04T07:00:00.000":14.4,"2024-04-04T08:00:00.000":8.0,"2024-04-04T09:00:00.000":7.2,"2024-04-04T10:00:00.000":6.9,"2024-04-04T11:00:00.000":7.2,"2024-04-04T12:00:00.000":7.9,"2024-04-04T13:00:00.000":0.0,"... | {"Occupancy (%)":{"2024-04-11T04:00:00.000":2.8,"2024-04-11T05:00:00.000":5.3,"2024-04-11T06:00:00.000":8.5,"2024-04-11T07:00:00.000":10.8,"2024-04-11T08:00:00.000":7.7,"2024-04-11T09:00:00.000":7.6,"2024-04-11T10:00:00.000":7.3,"2024-04-11T11:00:00.000":7.5,"2024-04-11T12:00:00.000":7.7,"2024-04-11T13:00:00.000":8.5,"... | 0.094769 | [
9,
10,
11
] | null | null | [] | [] | ||
SensorTrendAccumulationTask | 1 | 1/4 | [
"c_cov",
"c_f"
] | [
"forecasting",
"natural language processing",
"instruction following",
"reasoning: math"
] | This series represents the occupancy rate (%) captured by a highway sensor. The sensor had a calibration problem starting from 2024-01-11 12:00:00 which resulted in an additive trend in the series that increases by 0.0874 at every hour. At timestep 2024-01-18 00:00:00, the sensor was repaired and this additive trend wi... | 24 | {"Occupancy (%)":{"2024-01-11T00:00:00.000":0.8,"2024-01-11T01:00:00.000":1.3,"2024-01-11T02:00:00.000":2.1,"2024-01-11T03:00:00.000":5.3,"2024-01-11T04:00:00.000":19.0,"2024-01-11T05:00:00.000":35.0,"2024-01-11T06:00:00.000":38.5,"2024-01-11T07:00:00.000":35.3,"2024-01-11T08:00:00.000":8.6,"2024-01-11T09:00:00.000":7.... | {"Occupancy (%)":{"2024-01-18T00:00:00.000":1.0,"2024-01-18T01:00:00.000":1.3,"2024-01-18T02:00:00.000":2.1,"2024-01-18T03:00:00.000":5.5,"2024-01-18T04:00:00.000":17.7,"2024-01-18T05:00:00.000":34.1,"2024-01-18T06:00:00.000":37.6,"2024-01-18T07:00:00.000":29.5,"2024-01-18T08:00:00.000":8.0,"2024-01-18T09:00:00.000":8.... | 0.094769 | [] | null | null | [] | [] | ||
SensorTrendAccumulationTask | 2 | 1/4 | [
"c_cov",
"c_f"
] | [
"forecasting",
"natural language processing",
"instruction following",
"reasoning: math"
] | This series represents the occupancy rate (%) captured by a highway sensor. The sensor had a calibration problem starting from 2024-04-20 13:00:00 which resulted in an additive trend in the series that increases by 0.0072 at every hour. At timestep 2024-04-24 13:00:00, the sensor was repaired and this additive trend wi... | 24 | {"Occupancy (%)":{"2024-04-17T13:00:00.000":0.6,"2024-04-17T14:00:00.000":0.8,"2024-04-17T15:00:00.000":0.9,"2024-04-17T16:00:00.000":1.3,"2024-04-17T17:00:00.000":1.3,"2024-04-17T18:00:00.000":0.6,"2024-04-17T19:00:00.000":0.5,"2024-04-17T20:00:00.000":0.3,"2024-04-17T21:00:00.000":0.3,"2024-04-17T22:00:00.000":0.2,"2... | {"Occupancy (%)":{"2024-04-24T13:00:00.000":0.6,"2024-04-24T14:00:00.000":0.8,"2024-04-24T15:00:00.000":0.9,"2024-04-24T16:00:00.000":1.3,"2024-04-24T17:00:00.000":1.4,"2024-04-24T18:00:00.000":0.7,"2024-04-24T19:00:00.000":0.4,"2024-04-24T20:00:00.000":0.4,"2024-04-24T21:00:00.000":0.5,"2024-04-24T22:00:00.000":0.2,"2... | 0.094769 | [] | null | null | [] | [] | ||
SensorTrendAccumulationTask | 3 | 1/4 | [
"c_cov",
"c_f"
] | [
"forecasting",
"natural language processing",
"instruction following",
"reasoning: math"
] | This series represents the occupancy rate (%) captured by a highway sensor. The sensor had a calibration problem starting from 2024-03-11 17:00:00 which resulted in an additive trend in the series that increases by 0.0097 at every hour. At timestep 2024-03-18 14:00:00, the sensor was repaired and this additive trend wi... | 24 | {"Occupancy (%)":{"2024-03-11T14:00:00.000":1.3,"2024-03-11T15:00:00.000":1.0,"2024-03-11T16:00:00.000":1.1,"2024-03-11T17:00:00.000":0.809681928,"2024-03-11T18:00:00.000":0.8193638559,"2024-03-11T19:00:00.000":0.429045783,"2024-03-11T20:00:00.000":0.5387277119,"2024-03-11T21:00:00.000":0.5484096408,"2024-03-11T22:00:0... | {"Occupancy (%)":{"2024-03-18T14:00:00.000":1.5,"2024-03-18T15:00:00.000":1.3,"2024-03-18T16:00:00.000":1.1,"2024-03-18T17:00:00.000":0.8,"2024-03-18T18:00:00.000":0.7,"2024-03-18T19:00:00.000":0.6,"2024-03-18T20:00:00.000":0.5,"2024-03-18T21:00:00.000":0.6,"2024-03-18T22:00:00.000":0.5,"2024-03-18T23:00:00.000":0.4,"2... | 0.094769 | [] | null | null | [] | [] | ||
SensorTrendAccumulationTask | 4 | 1/4 | [
"c_cov",
"c_f"
] | [
"forecasting",
"natural language processing",
"instruction following",
"reasoning: math"
] | This series represents the occupancy rate (%) captured by a highway sensor. The sensor had a calibration problem starting from 2024-01-10 18:00:00 which resulted in an additive trend in the series that increases by 0.0870 at every hour. At timestep 2024-01-15 11:00:00, the sensor was repaired and this additive trend wi... | 24 | {"Occupancy (%)":{"2024-01-08T11:00:00.000":9.6,"2024-01-08T12:00:00.000":9.8,"2024-01-08T13:00:00.000":10.0,"2024-01-08T14:00:00.000":10.1,"2024-01-08T15:00:00.000":10.3,"2024-01-08T16:00:00.000":10.9,"2024-01-08T17:00:00.000":13.2,"2024-01-08T18:00:00.000":9.0,"2024-01-08T19:00:00.000":7.3,"2024-01-08T20:00:00.000":6... | {"Occupancy (%)":{"2024-01-15T11:00:00.000":9.6,"2024-01-15T12:00:00.000":9.8,"2024-01-15T13:00:00.000":10.0,"2024-01-15T14:00:00.000":10.1,"2024-01-15T15:00:00.000":10.3,"2024-01-15T16:00:00.000":10.9,"2024-01-15T17:00:00.000":13.2,"2024-01-15T18:00:00.000":9.0,"2024-01-15T19:00:00.000":7.3,"2024-01-15T20:00:00.000":6... | 0.094769 | [] | null | null | [] | [] | ||
SensorTrendAccumulationTask | 5 | 1/4 | [
"c_cov",
"c_f"
] | [
"forecasting",
"natural language processing",
"instruction following",
"reasoning: math"
] | This series represents the occupancy rate (%) captured by a highway sensor. The sensor had a calibration problem starting from 2024-04-06 17:00:00 which resulted in an additive trend in the series that increases by 0.0866 at every hour. At timestep 2024-04-11 04:00:00, the sensor was repaired and this additive trend wi... | 24 | {"Occupancy (%)":{"2024-04-04T04:00:00.000":2.7,"2024-04-04T05:00:00.000":5.2,"2024-04-04T06:00:00.000":7.7,"2024-04-04T07:00:00.000":14.4,"2024-04-04T08:00:00.000":8.0,"2024-04-04T09:00:00.000":7.2,"2024-04-04T10:00:00.000":6.9,"2024-04-04T11:00:00.000":7.2,"2024-04-04T12:00:00.000":7.9,"2024-04-04T13:00:00.000":9.0,"... | {"Occupancy (%)":{"2024-04-11T04:00:00.000":2.8,"2024-04-11T05:00:00.000":5.3,"2024-04-11T06:00:00.000":8.5,"2024-04-11T07:00:00.000":10.8,"2024-04-11T08:00:00.000":7.7,"2024-04-11T09:00:00.000":7.6,"2024-04-11T10:00:00.000":7.3,"2024-04-11T11:00:00.000":7.5,"2024-04-11T12:00:00.000":7.7,"2024-04-11T13:00:00.000":8.5,"... | 0.094769 | [] | null | null | [] | [] | ||
SensorSpikeTask | 1 | 1/4 | [
"c_cov"
] | [
"forecasting",
"natural language processing",
"instruction following"
] | This series represents the occupancy rate (%) captured by a highway sensor. The sensor experienced an unexpected glitch resulting in a spike starting from 2024-01-16 06:00:00 for 1 hour. Assume that the sensor will not have this glitch in the future. | 24 | {"Occupancy (%)":{"2024-01-11T00:00:00.000":0.8,"2024-01-11T01:00:00.000":1.3,"2024-01-11T02:00:00.000":2.1,"2024-01-11T03:00:00.000":5.3,"2024-01-11T04:00:00.000":19.0,"2024-01-11T05:00:00.000":35.0,"2024-01-11T06:00:00.000":38.5,"2024-01-11T07:00:00.000":35.3,"2024-01-11T08:00:00.000":8.6,"2024-01-11T09:00:00.000":7.... | {"Occupancy (%)":{"2024-01-18T00:00:00.000":1.0,"2024-01-18T01:00:00.000":1.3,"2024-01-18T02:00:00.000":2.1,"2024-01-18T03:00:00.000":5.5,"2024-01-18T04:00:00.000":17.7,"2024-01-18T05:00:00.000":34.1,"2024-01-18T06:00:00.000":37.6,"2024-01-18T07:00:00.000":29.5,"2024-01-18T08:00:00.000":8.0,"2024-01-18T09:00:00.000":8.... | 0.094769 | [] | null | null | [] | [] | ||
SensorSpikeTask | 2 | 1/4 | [
"c_cov"
] | [
"forecasting",
"natural language processing",
"instruction following"
] | This series represents the occupancy rate (%) captured by a highway sensor. The sensor experienced an unexpected glitch resulting in a spike starting from 2024-04-23 17:00:00 for 2 hours. Assume that the sensor will not have this glitch in the future. | 24 | {"Occupancy (%)":{"2024-04-17T13:00:00.000":0.6,"2024-04-17T14:00:00.000":0.8,"2024-04-17T15:00:00.000":0.9,"2024-04-17T16:00:00.000":1.3,"2024-04-17T17:00:00.000":1.3,"2024-04-17T18:00:00.000":0.6,"2024-04-17T19:00:00.000":0.5,"2024-04-17T20:00:00.000":0.3,"2024-04-17T21:00:00.000":0.3,"2024-04-17T22:00:00.000":0.2,"2... | {"Occupancy (%)":{"2024-04-24T13:00:00.000":0.6,"2024-04-24T14:00:00.000":0.8,"2024-04-24T15:00:00.000":0.9,"2024-04-24T16:00:00.000":1.3,"2024-04-24T17:00:00.000":1.4,"2024-04-24T18:00:00.000":0.7,"2024-04-24T19:00:00.000":0.4,"2024-04-24T20:00:00.000":0.4,"2024-04-24T21:00:00.000":0.5,"2024-04-24T22:00:00.000":0.2,"2... | 0.094769 | [] | null | null | [] | [] | ||
SensorSpikeTask | 3 | 1/4 | [
"c_cov"
] | [
"forecasting",
"natural language processing",
"instruction following"
] | This series represents the occupancy rate (%) captured by a highway sensor. The sensor experienced an unexpected glitch resulting in a spike starting from 2024-03-15 11:00:00 for 2 hours. Assume that the sensor will not have this glitch in the future. | 24 | {"Occupancy (%)":{"2024-03-11T14:00:00.000":1.3,"2024-03-11T15:00:00.000":1.0,"2024-03-11T16:00:00.000":1.1,"2024-03-11T17:00:00.000":0.8,"2024-03-11T18:00:00.000":0.8,"2024-03-11T19:00:00.000":0.4,"2024-03-11T20:00:00.000":0.5,"2024-03-11T21:00:00.000":0.5,"2024-03-11T22:00:00.000":0.4,"2024-03-11T23:00:00.000":0.4,"2... | {"Occupancy (%)":{"2024-03-18T14:00:00.000":1.5,"2024-03-18T15:00:00.000":1.3,"2024-03-18T16:00:00.000":1.1,"2024-03-18T17:00:00.000":0.8,"2024-03-18T18:00:00.000":0.7,"2024-03-18T19:00:00.000":0.6,"2024-03-18T20:00:00.000":0.5,"2024-03-18T21:00:00.000":0.6,"2024-03-18T22:00:00.000":0.5,"2024-03-18T23:00:00.000":0.4,"2... | 0.094769 | [] | null | null | [] | [] | ||
SensorSpikeTask | 4 | 1/4 | [
"c_cov"
] | [
"forecasting",
"natural language processing",
"instruction following"
] | This series represents the occupancy rate (%) captured by a highway sensor. The sensor experienced an unexpected glitch resulting in a spike starting from 2024-01-10 17:00:00 for 2 hours. Assume that the sensor will not have this glitch in the future. | 24 | {"Occupancy (%)":{"2024-01-08T11:00:00.000":9.6,"2024-01-08T12:00:00.000":9.8,"2024-01-08T13:00:00.000":10.0,"2024-01-08T14:00:00.000":10.1,"2024-01-08T15:00:00.000":10.3,"2024-01-08T16:00:00.000":10.9,"2024-01-08T17:00:00.000":13.2,"2024-01-08T18:00:00.000":9.0,"2024-01-08T19:00:00.000":7.3,"2024-01-08T20:00:00.000":6... | {"Occupancy (%)":{"2024-01-15T11:00:00.000":9.6,"2024-01-15T12:00:00.000":9.8,"2024-01-15T13:00:00.000":10.0,"2024-01-15T14:00:00.000":10.1,"2024-01-15T15:00:00.000":10.3,"2024-01-15T16:00:00.000":10.9,"2024-01-15T17:00:00.000":13.2,"2024-01-15T18:00:00.000":9.0,"2024-01-15T19:00:00.000":7.3,"2024-01-15T20:00:00.000":6... | 0.094769 | [] | null | null | [] | [] | ||
SensorSpikeTask | 5 | 1/4 | [
"c_cov"
] | [
"forecasting",
"natural language processing",
"instruction following"
] | This series represents the occupancy rate (%) captured by a highway sensor. The sensor experienced an unexpected glitch resulting in a spike starting from 2024-04-04 16:00:00 for 2 hours. Assume that the sensor will not have this glitch in the future. | 24 | {"Occupancy (%)":{"2024-04-04T04:00:00.000":2.7,"2024-04-04T05:00:00.000":5.2,"2024-04-04T06:00:00.000":7.7,"2024-04-04T07:00:00.000":14.4,"2024-04-04T08:00:00.000":8.0,"2024-04-04T09:00:00.000":7.2,"2024-04-04T10:00:00.000":6.9,"2024-04-04T11:00:00.000":7.2,"2024-04-04T12:00:00.000":7.9,"2024-04-04T13:00:00.000":9.0,"... | {"Occupancy (%)":{"2024-04-11T04:00:00.000":2.8,"2024-04-11T05:00:00.000":5.3,"2024-04-11T06:00:00.000":8.5,"2024-04-11T07:00:00.000":10.8,"2024-04-11T08:00:00.000":7.7,"2024-04-11T09:00:00.000":7.6,"2024-04-11T10:00:00.000":7.3,"2024-04-11T11:00:00.000":7.5,"2024-04-11T12:00:00.000":7.7,"2024-04-11T13:00:00.000":8.5,"... | 0.094769 | [] | null | null | [] | [] |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.