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 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
SpeedFromLoadTask | 1 | 1 | [
"c_cov",
"c_causal",
"c_i"
] | [
"forecasting",
"natural language processing",
"reasoning: causal",
"reasoning: math",
"instruction following"
] | The wind tunnel is a chamber with one controllable fan that pushes air through it. We can control the load of the fan (corresponding to the duty cycle of the pulse-width-modulation signal) and measure its speed (in revolutions per minute). The fan is designed so its steady-state speed scales broadly linearly with the l... | The load is set to: 0.0 until 05:47:09, 0.1 from 05:47:09 until 05:47:29, 0.0 from 05:47:29 until 05:48:01, 0.2 from 05:48:01 until 05:48:27, 0.1 from 05:48:27 until 05:48:49, 0.0 from 05:48:49 until 05:49:00. | The load is between 0 and 1. At full load (=1), the fan turns at a maximum speed of 3000 rpm. | -1 | {"rpm_in":{"1970-01-01T05:46:27.000":285.5619812012,"1970-01-01T05:46:28.000":285.8994445801,"1970-01-01T05:46:29.000":285.3989868164,"1970-01-01T05:46:30.000":284.9327697754,"1970-01-01T05:46:31.000":284.4248962402,"1970-01-01T05:46:32.000":285.3772583008,"1970-01-01T05:46:33.000":285.681640625,"1970-01-01T05:46:34.00... | {"rpm_in":{"1970-01-01T05:48:02.000":285.3012695312,"1970-01-01T05:48:03.000":432.3763427734,"1970-01-01T05:48:04.000":557.1237792969,"1970-01-01T05:48:05.000":612.3448486328,"1970-01-01T05:48:06.000":654.2219238281,"1970-01-01T05:48:07.000":661.3756713867,"1970-01-01T05:48:08.000":662.0762939453,"1970-01-01T05:48:09.0... | 0.001468 | [] | 0 | 3,000 | [] | [] |
SpeedFromLoadTask | 2 | 1 | [
"c_cov",
"c_causal",
"c_i"
] | [
"forecasting",
"natural language processing",
"reasoning: causal",
"reasoning: math",
"instruction following"
] | The wind tunnel is a chamber with one controllable fan that pushes air through it. We can control the load of the fan (corresponding to the duty cycle of the pulse-width-modulation signal) and measure its speed (in revolutions per minute). The fan is designed so its steady-state speed scales broadly linearly with the l... | The load is set to: 0.0 until 05:25:04, 0.1 from 05:25:04 until 05:25:44, 0.2 from 05:25:44 until 05:26:07, 0.1 from 05:26:07 until 05:26:25, 0.2 from 05:26:25 until 05:27:09, 0.5 from 05:27:09 until 05:27:17. | The load is between 0 and 1. At full load (=1), the fan turns at a maximum speed of 3000 rpm. | -1 | {"rpm_in":{"1970-01-01T05:24:27.000":286.1394042969,"1970-01-01T05:24:28.000":285.2036437988,"1970-01-01T05:24:29.000":285.8776550293,"1970-01-01T05:24:30.000":285.7142944336,"1970-01-01T05:24:31.000":285.3772583008,"1970-01-01T05:24:32.000":285.0843811035,"1970-01-01T05:24:33.000":284.7921142578,"1970-01-01T05:24:34.0... | {"rpm_in":{"1970-01-01T05:25:44.000":297.7135620117,"1970-01-01T05:25:45.000":482.7497558594,"1970-01-01T05:25:46.000":584.4763183594,"1970-01-01T05:25:47.000":632.8045654297,"1970-01-01T05:25:48.000":656.97265625,"1970-01-01T05:25:49.000":661.3756713867,"1970-01-01T05:25:50.000":661.6090087891,"1970-01-01T05:25:51.000... | 0.001468 | [] | 0 | 3,000 | [] | [] |
SpeedFromLoadTask | 3 | 1 | [
"c_cov",
"c_causal",
"c_i"
] | [
"forecasting",
"natural language processing",
"reasoning: causal",
"reasoning: math",
"instruction following"
] | The wind tunnel is a chamber with one controllable fan that pushes air through it. We can control the load of the fan (corresponding to the duty cycle of the pulse-width-modulation signal) and measure its speed (in revolutions per minute). The fan is designed so its steady-state speed scales broadly linearly with the l... | The load is set to: 0.1 until 05:30:23, 0.0 from 05:30:23 until 05:30:49, 0.1 from 05:30:49 until 05:31:26, 0.0 from 05:31:26 until 05:32:07, 0.5 from 05:32:07 until 05:32:42, 0.1 from 05:32:42 until 05:33:14, 0.5 from 05:33:14 until 05:33:52, 0.1 from 05:33:52 until 05:34:29, 0.2 from 05:34:29 until 05:34:46. | The load is between 0 and 1. At full load (=1), the fan turns at a maximum speed of 3000 rpm. | -1 | {"rpm_in":{"1970-01-01T05:29:42.000":297.7135620117,"1970-01-01T05:29:43.000":288.1290893555,"1970-01-01T05:29:44.000":288.0848083496,"1970-01-01T05:29:45.000":288.2176513672,"1970-01-01T05:29:46.000":297.4891967773,"1970-01-01T05:29:47.000":288.1290893555,"1970-01-01T05:29:48.000":297.5009765625,"1970-01-01T05:29:49.0... | {"rpm_in":{"1970-01-01T05:34:29.000":296.302154541,"1970-01-01T05:34:30.000":415.9272460938,"1970-01-01T05:34:31.000":566.1231689453,"1970-01-01T05:34:32.000":618.8118896484,"1970-01-01T05:34:33.000":654.6787719727,"1970-01-01T05:34:34.000":655.1362915039,"1970-01-01T05:34:35.000":661.6090087891,"1970-01-01T05:34:36.00... | 0.001468 | [] | 0 | 3,000 | [] | [] |
SpeedFromLoadTask | 4 | 1 | [
"c_cov",
"c_causal",
"c_i"
] | [
"forecasting",
"natural language processing",
"reasoning: causal",
"reasoning: math",
"instruction following"
] | The wind tunnel is a chamber with one controllable fan that pushes air through it. We can control the load of the fan (corresponding to the duty cycle of the pulse-width-modulation signal) and measure its speed (in revolutions per minute). The fan is designed so its steady-state speed scales broadly linearly with the l... | The load is set to: 0.2 until 05:36:27, 0.0 from 05:36:27 until 05:37:07, 0.1 from 05:37:07 until 05:37:22. | The load is between 0 and 1. At full load (=1), the fan turns at a maximum speed of 3000 rpm. | -1 | {"rpm_in":{"1970-01-01T05:35:48.000":661.842590332,"1970-01-01T05:35:49.000":661.6090087891,"1970-01-01T05:35:50.000":662.3101196289,"1970-01-01T05:35:51.000":661.725769043,"1970-01-01T05:35:52.000":660.2112426758,"1970-01-01T05:35:53.000":661.6090087891,"1970-01-01T05:35:54.000":661.3756713867,"1970-01-01T05:35:55.000... | {"rpm_in":{"1970-01-01T05:37:07.000":285.4533081055,"1970-01-01T05:37:08.000":284.9111022949,"1970-01-01T05:37:09.000":285.2795715332,"1970-01-01T05:37:10.000":284.6083679199,"1970-01-01T05:37:11.000":285.3229980469,"1970-01-01T05:37:12.000":285.7796020508,"1970-01-01T05:37:13.000":285.4967651367,"1970-01-01T05:37:14.0... | 0.001468 | [] | 0 | 3,000 | [] | [] |
SpeedFromLoadTask | 5 | 1 | [
"c_cov",
"c_causal",
"c_i"
] | [
"forecasting",
"natural language processing",
"reasoning: causal",
"reasoning: math",
"instruction following"
] | The wind tunnel is a chamber with one controllable fan that pushes air through it. We can control the load of the fan (corresponding to the duty cycle of the pulse-width-modulation signal) and measure its speed (in revolutions per minute). The fan is designed so its steady-state speed scales broadly linearly with the l... | The load is set to: 0.0 until 05:41:49, 0.2 from 05:41:49 until 05:42:17, 0.0 from 05:42:17 until 05:42:35, 0.5 from 05:42:35 until 05:43:00, 0.1 from 05:43:00 until 05:43:30. | The load is between 0 and 1. At full load (=1), the fan turns at a maximum speed of 3000 rpm. | -1 | {"rpm_in":{"1970-01-01T05:41:16.000":285.8013916016,"1970-01-01T05:41:17.000":286.0302734375,"1970-01-01T05:41:18.000":285.4859008789,"1970-01-01T05:41:19.000":285.0518798828,"1970-01-01T05:41:20.000":285.8776550293,"1970-01-01T05:41:21.000":285.9430541992,"1970-01-01T05:41:22.000":285.60546875,"1970-01-01T05:41:23.000... | {"rpm_in":{"1970-01-01T05:43:01.000":980.6485595703,"1970-01-01T05:43:02.000":677.1397705078,"1970-01-01T05:43:03.000":497.2156066895,"1970-01-01T05:43:04.000":367.2869873047,"1970-01-01T05:43:05.000":300.2642211914,"1970-01-01T05:43:06.000":288.9060058594,"1970-01-01T05:43:07.000":297.7608337402,"1970-01-01T05:43:08.0... | 0.001468 | [] | 0 | 3,000 | [] | [] |
ExplicitPressureFromSpeedTask | 1 | 1/2 | [
"c_cov",
"c_causal",
"c_i"
] | [
"forecasting",
"natural language processing",
"reasoning: causal",
"reasoning: math",
"instruction following"
] | The wind tunnel is a chamber with one controllable fan that pushes air through it. We can control the speed of the fan (rpm_in) and measure the gap between the internal pressure and the ambient pressure (in Pascals). The pressure gap can be estimated from the speed using the affinity laws, which state that the pressure... | The speed starts at 661.0. At 05:14:23, it rapidly and smoothly changes to 1590.3. At 05:14:56, it rapidly and smoothly changes to 661.1. | The maximal fan speed is 3000 rpm and the maximal pressure is 37.5 Pa. | -1 | {"pressure_gap":{"1970-01-01T05:14:03.000":-0.1171875,"1970-01-01T05:14:04.000":-0.21875,"1970-01-01T05:14:05.000":-0.2421875,"1970-01-01T05:14:06.000":-0.1171875,"1970-01-01T05:14:07.000":-1.09375,"1970-01-01T05:14:08.000":-0.3515625,"1970-01-01T05:14:09.000":0.0,"1970-01-01T05:14:10.000":-0.203125,"1970-01-01T05:14:1... | {"pressure_gap":{"1970-01-01T05:14:57.000":3.0078125,"1970-01-01T05:14:58.000":2.4609375,"1970-01-01T05:14:59.000":2.015625,"1970-01-01T05:15:00.000":1.765625,"1970-01-01T05:15:01.000":1.515625,"1970-01-01T05:15:02.000":1.7890625,"1970-01-01T05:15:03.000":1.6953125,"1970-01-01T05:15:04.000":1.1640625,"1970-01-01T05:15:... | 0.146105 | [] | null | 37.5 | [] | [] |
ExplicitPressureFromSpeedTask | 2 | 1/2 | [
"c_cov",
"c_causal",
"c_i"
] | [
"forecasting",
"natural language processing",
"reasoning: causal",
"reasoning: math",
"instruction following"
] | The wind tunnel is a chamber with one controllable fan that pushes air through it. We can control the speed of the fan (rpm_in) and measure the gap between the internal pressure and the ambient pressure (in Pascals). The pressure gap can be estimated from the speed using the affinity laws, which state that the pressure... | The speed starts at 657.9. At 05:08:34, it rapidly and smoothly changes to 306.7. At 05:08:55, it rapidly and smoothly changes to 658.1. At 05:09:36, it rapidly and smoothly changes to 1585.0. | The maximal fan speed is 3000 rpm and the maximal pressure is 37.5 Pa. | -1 | {"pressure_gap":{"1970-01-01T05:08:04.000":-0.7734375,"1970-01-01T05:08:05.000":-1.1015625,"1970-01-01T05:08:06.000":-1.0390625,"1970-01-01T05:08:07.000":-1.5546875,"1970-01-01T05:08:08.000":-1.140625,"1970-01-01T05:08:09.000":-1.5625,"1970-01-01T05:08:10.000":-1.6484375,"1970-01-01T05:08:11.000":-1.359375,"1970-01-01T... | {"pressure_gap":{"1970-01-01T05:09:36.000":0.96875,"1970-01-01T05:09:37.000":3.5234375,"1970-01-01T05:09:38.000":4.9765625,"1970-01-01T05:09:39.000":6.3671875,"1970-01-01T05:09:40.000":6.7109375,"1970-01-01T05:09:41.000":7.015625,"1970-01-01T05:09:42.000":6.8203125,"1970-01-01T05:09:43.000":7.2578125,"1970-01-01T05:09:... | 0.146105 | [] | null | 37.5 | [] | [] |
ExplicitPressureFromSpeedTask | 3 | 1/2 | [
"c_cov",
"c_causal",
"c_i"
] | [
"forecasting",
"natural language processing",
"reasoning: causal",
"reasoning: math",
"instruction following"
] | The wind tunnel is a chamber with one controllable fan that pushes air through it. We can control the speed of the fan (rpm_in) and measure the gap between the internal pressure and the ambient pressure (in Pascals). The pressure gap can be estimated from the speed using the affinity laws, which state that the pressure... | The speed starts at 286.1. At 05:18:52, it rapidly and smoothly changes to 661.1. At 05:19:36, it rapidly and smoothly changes to 296.6. At 05:19:57, it rapidly and smoothly changes to 1594.4. At 05:20:17, it rapidly and smoothly changes to 285.7. | The maximal fan speed is 3000 rpm and the maximal pressure is 37.5 Pa. | -1 | {"pressure_gap":{"1970-01-01T05:18:28.000":-1.953125,"1970-01-01T05:18:29.000":-2.6875,"1970-01-01T05:18:30.000":-2.171875,"1970-01-01T05:18:31.000":-2.265625,"1970-01-01T05:18:32.000":-2.2578125,"1970-01-01T05:18:33.000":-2.4140625,"1970-01-01T05:18:34.000":-2.21875,"1970-01-01T05:18:35.000":-2.46875,"1970-01-01T05:18... | {"pressure_gap":{"1970-01-01T05:19:57.000":-0.0703125,"1970-01-01T05:19:58.000":2.453125,"1970-01-01T05:19:59.000":5.421875,"1970-01-01T05:20:00.000":5.625,"1970-01-01T05:20:01.000":7.046875,"1970-01-01T05:20:02.000":6.8828125,"1970-01-01T05:20:03.000":6.875,"1970-01-01T05:20:04.000":7.0390625,"1970-01-01T05:20:05.000"... | 0.146105 | [] | null | 37.5 | [] | [] |
ExplicitPressureFromSpeedTask | 4 | 1/2 | [
"c_cov",
"c_causal",
"c_i"
] | [
"forecasting",
"natural language processing",
"reasoning: causal",
"reasoning: math",
"instruction following"
] | The wind tunnel is a chamber with one controllable fan that pushes air through it. We can control the speed of the fan (rpm_in) and measure the gap between the internal pressure and the ambient pressure (in Pascals). The pressure gap can be estimated from the speed using the affinity laws, which state that the pressure... | The speed starts at 285.3. At 05:25:44, it rapidly and smoothly changes to 661.8. At 05:26:07, it rapidly and smoothly changes to 297.6. At 05:26:25, it rapidly and smoothly changes to 661.8. At 05:27:09, it rapidly and smoothly changes to 1593.0. | The maximal fan speed is 3000 rpm and the maximal pressure is 37.5 Pa. | -1 | {"pressure_gap":{"1970-01-01T05:25:27.000":-2.71875,"1970-01-01T05:25:28.000":-3.625,"1970-01-01T05:25:29.000":-3.2421875,"1970-01-01T05:25:30.000":-3.25,"1970-01-01T05:25:31.000":-3.3984375,"1970-01-01T05:25:32.000":-3.3125,"1970-01-01T05:25:33.000":-3.046875,"1970-01-01T05:25:34.000":-3.390625,"1970-01-01T05:25:35.00... | {"pressure_gap":{"1970-01-01T05:27:09.000":0.03125,"1970-01-01T05:27:10.000":2.421875,"1970-01-01T05:27:11.000":4.4296875,"1970-01-01T05:27:12.000":4.4296875,"1970-01-01T05:27:13.000":5.09375,"1970-01-01T05:27:14.000":5.484375,"1970-01-01T05:27:15.000":5.296875,"1970-01-01T05:27:16.000":5.5546875,"1970-01-01T05:27:17.0... | 0.146105 | [] | null | 37.5 | [] | [] |
ExplicitPressureFromSpeedTask | 5 | 1/2 | [
"c_cov",
"c_causal",
"c_i"
] | [
"forecasting",
"natural language processing",
"reasoning: causal",
"reasoning: math",
"instruction following"
] | The wind tunnel is a chamber with one controllable fan that pushes air through it. We can control the speed of the fan (rpm_in) and measure the gap between the internal pressure and the ambient pressure (in Pascals). The pressure gap can be estimated from the speed using the affinity laws, which state that the pressure... | The speed starts at 285.5. At 05:31:26, it rapidly and smoothly changes to 285.9. At 05:32:07, it rapidly and smoothly changes to 1591.7. At 05:32:42, it rapidly and smoothly changes to 298.0. | The maximal fan speed is 3000 rpm and the maximal pressure is 37.5 Pa. | -1 | {"pressure_gap":{"1970-01-01T05:31:13.000":-1.984375,"1970-01-01T05:31:14.000":-2.1953125,"1970-01-01T05:31:15.000":-2.2265625,"1970-01-01T05:31:16.000":-2.3984375,"1970-01-01T05:31:17.000":-2.1640625,"1970-01-01T05:31:18.000":-2.3671875,"1970-01-01T05:31:19.000":-2.109375,"1970-01-01T05:31:20.000":-2.0546875,"1970-01-... | {"pressure_gap":{"1970-01-01T05:32:08.000":-0.890625,"1970-01-01T05:32:09.000":2.7109375,"1970-01-01T05:32:10.000":4.6875,"1970-01-01T05:32:11.000":5.4609375,"1970-01-01T05:32:12.000":5.59375,"1970-01-01T05:32:13.000":5.9921875,"1970-01-01T05:32:14.000":6.25,"1970-01-01T05:32:15.000":6.0078125,"1970-01-01T05:32:16.000"... | 0.146105 | [] | null | 37.5 | [] | [] |
ImplicitPressureFromSpeedTask | 1 | 1/2 | [
"c_cov",
"c_causal",
"c_i"
] | [
"forecasting",
"natural language processing",
"reasoning: causal",
"retrieval: memory",
"reasoning: math",
"instruction following"
] | The wind tunnel is a chamber with one controllable fan that pushes air through it. We can control the speed of the fan (in revolutions per minute) and measure the gap between the internal pressure and the ambient pressure (in Pascals). The pressure gap can be estimated from the speed using the affinity laws. The task i... | The speed starts at 661.0. At 05:14:23, it rapidly and smoothly changes to 1590.3. At 05:14:56, it rapidly and smoothly changes to 661.1. | The maximal fan speed is 3000 rpm and the maximal pressure is 37.5 Pa. | -1 | {"pressure_gap":{"1970-01-01T05:14:03.000":-0.1171875,"1970-01-01T05:14:04.000":-0.21875,"1970-01-01T05:14:05.000":-0.2421875,"1970-01-01T05:14:06.000":-0.1171875,"1970-01-01T05:14:07.000":-1.09375,"1970-01-01T05:14:08.000":-0.3515625,"1970-01-01T05:14:09.000":0.0,"1970-01-01T05:14:10.000":-0.203125,"1970-01-01T05:14:1... | {"pressure_gap":{"1970-01-01T05:14:57.000":3.0078125,"1970-01-01T05:14:58.000":2.4609375,"1970-01-01T05:14:59.000":2.015625,"1970-01-01T05:15:00.000":1.765625,"1970-01-01T05:15:01.000":1.515625,"1970-01-01T05:15:02.000":1.7890625,"1970-01-01T05:15:03.000":1.6953125,"1970-01-01T05:15:04.000":1.1640625,"1970-01-01T05:15:... | 0.146105 | [] | null | 37.5 | [] | [] |
ImplicitPressureFromSpeedTask | 2 | 1/2 | [
"c_cov",
"c_causal",
"c_i"
] | [
"forecasting",
"natural language processing",
"reasoning: causal",
"retrieval: memory",
"reasoning: math",
"instruction following"
] | The wind tunnel is a chamber with one controllable fan that pushes air through it. We can control the speed of the fan (in revolutions per minute) and measure the gap between the internal pressure and the ambient pressure (in Pascals). The pressure gap can be estimated from the speed using the affinity laws. The task i... | The speed starts at 657.9. At 05:08:34, it rapidly and smoothly changes to 306.7. At 05:08:55, it rapidly and smoothly changes to 658.1. At 05:09:36, it rapidly and smoothly changes to 1585.0. | The maximal fan speed is 3000 rpm and the maximal pressure is 37.5 Pa. | -1 | {"pressure_gap":{"1970-01-01T05:08:04.000":-0.7734375,"1970-01-01T05:08:05.000":-1.1015625,"1970-01-01T05:08:06.000":-1.0390625,"1970-01-01T05:08:07.000":-1.5546875,"1970-01-01T05:08:08.000":-1.140625,"1970-01-01T05:08:09.000":-1.5625,"1970-01-01T05:08:10.000":-1.6484375,"1970-01-01T05:08:11.000":-1.359375,"1970-01-01T... | {"pressure_gap":{"1970-01-01T05:09:36.000":0.96875,"1970-01-01T05:09:37.000":3.5234375,"1970-01-01T05:09:38.000":4.9765625,"1970-01-01T05:09:39.000":6.3671875,"1970-01-01T05:09:40.000":6.7109375,"1970-01-01T05:09:41.000":7.015625,"1970-01-01T05:09:42.000":6.8203125,"1970-01-01T05:09:43.000":7.2578125,"1970-01-01T05:09:... | 0.146105 | [] | null | 37.5 | [] | [] |
ImplicitPressureFromSpeedTask | 3 | 1/2 | [
"c_cov",
"c_causal",
"c_i"
] | [
"forecasting",
"natural language processing",
"reasoning: causal",
"retrieval: memory",
"reasoning: math",
"instruction following"
] | The wind tunnel is a chamber with one controllable fan that pushes air through it. We can control the speed of the fan (in revolutions per minute) and measure the gap between the internal pressure and the ambient pressure (in Pascals). The pressure gap can be estimated from the speed using the affinity laws. The task i... | The speed starts at 286.1. At 05:18:52, it rapidly and smoothly changes to 661.1. At 05:19:36, it rapidly and smoothly changes to 296.6. At 05:19:57, it rapidly and smoothly changes to 1594.4. At 05:20:17, it rapidly and smoothly changes to 285.7. | The maximal fan speed is 3000 rpm and the maximal pressure is 37.5 Pa. | -1 | {"pressure_gap":{"1970-01-01T05:18:28.000":-1.953125,"1970-01-01T05:18:29.000":-2.6875,"1970-01-01T05:18:30.000":-2.171875,"1970-01-01T05:18:31.000":-2.265625,"1970-01-01T05:18:32.000":-2.2578125,"1970-01-01T05:18:33.000":-2.4140625,"1970-01-01T05:18:34.000":-2.21875,"1970-01-01T05:18:35.000":-2.46875,"1970-01-01T05:18... | {"pressure_gap":{"1970-01-01T05:19:57.000":-0.0703125,"1970-01-01T05:19:58.000":2.453125,"1970-01-01T05:19:59.000":5.421875,"1970-01-01T05:20:00.000":5.625,"1970-01-01T05:20:01.000":7.046875,"1970-01-01T05:20:02.000":6.8828125,"1970-01-01T05:20:03.000":6.875,"1970-01-01T05:20:04.000":7.0390625,"1970-01-01T05:20:05.000"... | 0.146105 | [] | null | 37.5 | [] | [] |
ImplicitPressureFromSpeedTask | 4 | 1/2 | [
"c_cov",
"c_causal",
"c_i"
] | [
"forecasting",
"natural language processing",
"reasoning: causal",
"retrieval: memory",
"reasoning: math",
"instruction following"
] | The wind tunnel is a chamber with one controllable fan that pushes air through it. We can control the speed of the fan (in revolutions per minute) and measure the gap between the internal pressure and the ambient pressure (in Pascals). The pressure gap can be estimated from the speed using the affinity laws. The task i... | The speed starts at 285.3. At 05:25:44, it rapidly and smoothly changes to 661.8. At 05:26:07, it rapidly and smoothly changes to 297.6. At 05:26:25, it rapidly and smoothly changes to 661.8. At 05:27:09, it rapidly and smoothly changes to 1593.0. | The maximal fan speed is 3000 rpm and the maximal pressure is 37.5 Pa. | -1 | {"pressure_gap":{"1970-01-01T05:25:27.000":-2.71875,"1970-01-01T05:25:28.000":-3.625,"1970-01-01T05:25:29.000":-3.2421875,"1970-01-01T05:25:30.000":-3.25,"1970-01-01T05:25:31.000":-3.3984375,"1970-01-01T05:25:32.000":-3.3125,"1970-01-01T05:25:33.000":-3.046875,"1970-01-01T05:25:34.000":-3.390625,"1970-01-01T05:25:35.00... | {"pressure_gap":{"1970-01-01T05:27:09.000":0.03125,"1970-01-01T05:27:10.000":2.421875,"1970-01-01T05:27:11.000":4.4296875,"1970-01-01T05:27:12.000":4.4296875,"1970-01-01T05:27:13.000":5.09375,"1970-01-01T05:27:14.000":5.484375,"1970-01-01T05:27:15.000":5.296875,"1970-01-01T05:27:16.000":5.5546875,"1970-01-01T05:27:17.0... | 0.146105 | [] | null | 37.5 | [] | [] |
ImplicitPressureFromSpeedTask | 5 | 1/2 | [
"c_cov",
"c_causal",
"c_i"
] | [
"forecasting",
"natural language processing",
"reasoning: causal",
"retrieval: memory",
"reasoning: math",
"instruction following"
] | The wind tunnel is a chamber with one controllable fan that pushes air through it. We can control the speed of the fan (in revolutions per minute) and measure the gap between the internal pressure and the ambient pressure (in Pascals). The pressure gap can be estimated from the speed using the affinity laws. The task i... | The speed starts at 285.5. At 05:31:26, it rapidly and smoothly changes to 285.9. At 05:32:07, it rapidly and smoothly changes to 1591.7. At 05:32:42, it rapidly and smoothly changes to 298.0. | The maximal fan speed is 3000 rpm and the maximal pressure is 37.5 Pa. | -1 | {"pressure_gap":{"1970-01-01T05:31:13.000":-1.984375,"1970-01-01T05:31:14.000":-2.1953125,"1970-01-01T05:31:15.000":-2.2265625,"1970-01-01T05:31:16.000":-2.3984375,"1970-01-01T05:31:17.000":-2.1640625,"1970-01-01T05:31:18.000":-2.3671875,"1970-01-01T05:31:19.000":-2.109375,"1970-01-01T05:31:20.000":-2.0546875,"1970-01-... | {"pressure_gap":{"1970-01-01T05:32:08.000":-0.890625,"1970-01-01T05:32:09.000":2.7109375,"1970-01-01T05:32:10.000":4.6875,"1970-01-01T05:32:11.000":5.4609375,"1970-01-01T05:32:12.000":5.59375,"1970-01-01T05:32:13.000":5.9921875,"1970-01-01T05:32:14.000":6.25,"1970-01-01T05:32:15.000":6.0078125,"1970-01-01T05:32:16.000"... | 0.146105 | [] | null | 37.5 | [] | [] |
MinimalCausalContextBivarLinSVAR | 1 | 1/3 | [
"c_cov",
"c_causal"
] | [
"forecasting",
"natural language processing",
"reasoning: math",
"reasoning: causal",
"retrieval: memory"
] | Given are variables X_0 and X_1, where X_0 is a covariate and X_1 is the variable to forecast. Variables are generated from a linear Structural Vector Autoregressive (SVAR) model with additive gauss noise and a noise scale of 5.533e-04, with lag = 3. | The task is to forecast the value of the variable X_1 at time t, given the values of the covariate X_0 and the variable X_1 itself at times t-1, ... t-3.
For the first 128 days, the covariate X_0 takes a value of 0.0443 from 2023-11-28 to 2024-01-13, 0.1107 from 2024-01-14 to 2024-02-29, 0.0443 from 2024-03-01 to 2024-... | 7 | {"1":{"2023-11-28T00:00:00.000":-0.8266298783,"2023-11-29T00:00:00.000":-0.7780156245,"2023-11-30T00:00:00.000":-0.7284346593,"2023-12-01T00:00:00.000":-0.70871434,"2023-12-02T00:00:00.000":-0.70752035,"2023-12-03T00:00:00.000":-0.6915582237,"2023-12-04T00:00:00.000":-0.6590863553,"2023-12-05T00:00:00.000":-0.637216648... | {"1":{"2024-04-04T00:00:00.000":-0.5720425533,"2024-04-05T00:00:00.000":-0.5734598296,"2024-04-06T00:00:00.000":-0.5981119966,"2024-04-07T00:00:00.000":-0.6304288878,"2024-04-08T00:00:00.000":-0.6409620289,"2024-04-09T00:00:00.000":-0.634275817,"2024-04-10T00:00:00.000":-0.6377524296,"2024-04-11T00:00:00.000":-0.658278... | 1.091876 | [] | null | null | [] | [] | |
MinimalCausalContextBivarLinSVAR | 2 | 1/3 | [
"c_cov",
"c_causal"
] | [
"forecasting",
"natural language processing",
"reasoning: math",
"reasoning: causal",
"retrieval: memory"
] | Given are variables X_0 and X_1, where X_0 is a covariate and X_1 is the variable to forecast. Variables are generated from a linear Structural Vector Autoregressive (SVAR) model with additive gauss noise and a noise scale of 5.127e-04, with lag = 3. | The task is to forecast the value of the variable X_1 at time t, given the values of the covariate X_0 and the variable X_1 itself at times t-1, ... t-3.
For the first 128 days, the covariate X_0 takes a value of 0.0103 from 2027-12-17 to 2027-12-18, 0.0615 from 2027-12-19 to 2028-02-07, 0.0103 from 2028-02-08 to 2028-... | 7 | {"1":{"2027-12-17T00:00:00.000":0.0256607228,"2027-12-18T00:00:00.000":0.0256697367,"2027-12-19T00:00:00.000":0.0254611533,"2027-12-20T00:00:00.000":0.0677652779,"2027-12-21T00:00:00.000":0.0665508378,"2027-12-22T00:00:00.000":0.1380973519,"2027-12-23T00:00:00.000":0.1545119509,"2027-12-24T00:00:00.000":0.2079950923,"2... | {"1":{"2028-04-23T00:00:00.000":0.1560266407,"2028-04-24T00:00:00.000":0.2765795662,"2028-04-25T00:00:00.000":0.2750721419,"2028-04-26T00:00:00.000":0.476744877,"2028-04-27T00:00:00.000":0.5228355513,"2028-04-28T00:00:00.000":0.6700140413,"2028-04-29T00:00:00.000":0.6514138956,"2028-04-30T00:00:00.000":0.6899620925,"20... | 1.091876 | [] | null | null | [] | [] | |
MinimalCausalContextBivarLinSVAR | 3 | 1/3 | [
"c_cov",
"c_causal"
] | [
"forecasting",
"natural language processing",
"reasoning: math",
"reasoning: causal",
"retrieval: memory"
] | Given are variables X_0 and X_1, where X_0 is a covariate and X_1 is the variable to forecast. Variables are generated from a linear Structural Vector Autoregressive (SVAR) model with additive gauss noise and a noise scale of 4.700e-04, with lag = 3. | The task is to forecast the value of the variable X_1 at time t, given the values of the covariate X_0 and the variable X_1 itself at times t-1, ... t-3.
For the first 128 days, the covariate X_0 takes a value of 0.0564 from 2026-03-14 to 2026-04-12, 0.0940 from 2026-04-13 to 2026-06-10, 0.0564 from 2026-06-11 to 2026-... | 7 | {"1":{"2026-03-14T00:00:00.000":-0.1610389586,"2026-03-15T00:00:00.000":-0.1598332335,"2026-03-16T00:00:00.000":-0.1569984096,"2026-03-17T00:00:00.000":-0.1581430119,"2026-03-18T00:00:00.000":-0.1564116088,"2026-03-19T00:00:00.000":-0.1578811809,"2026-03-20T00:00:00.000":-0.1575325158,"2026-03-21T00:00:00.000":-0.15920... | {"1":{"2026-07-20T00:00:00.000":-0.1650664323,"2026-07-21T00:00:00.000":-0.0485821053,"2026-07-22T00:00:00.000":-0.0905775908,"2026-07-23T00:00:00.000":-0.1017051505,"2026-07-24T00:00:00.000":-0.2296223354,"2026-07-25T00:00:00.000":-0.293578203,"2026-07-26T00:00:00.000":-0.436269551,"2026-07-27T00:00:00.000":-0.4917790... | 1.091876 | [] | null | null | [] | [] | |
MinimalCausalContextBivarLinSVAR | 4 | 1/3 | [
"c_cov",
"c_causal"
] | [
"forecasting",
"natural language processing",
"reasoning: math",
"reasoning: causal",
"retrieval: memory"
] | Given are variables X_0 and X_1, where X_0 is a covariate and X_1 is the variable to forecast. Variables are generated from a linear Structural Vector Autoregressive (SVAR) model with additive gauss noise and a noise scale of 1.487e-03, with lag = 3. | The task is to forecast the value of the variable X_1 at time t, given the values of the covariate X_0 and the variable X_1 itself at times t-1, ... t-3.
For the first 128 days, the covariate X_0 takes a value of 0.1190 from 2024-02-21 to 2024-03-11, 0.1784 from 2024-03-12 to 2024-05-06, 0.1784 from 2024-05-07 to 2024-... | 7 | {"1":{"2024-02-21T00:00:00.000":0.0312804743,"2024-02-22T00:00:00.000":0.0633427313,"2024-02-23T00:00:00.000":0.0321669061,"2024-02-24T00:00:00.000":0.0468362684,"2024-02-25T00:00:00.000":0.0290203037,"2024-02-26T00:00:00.000":0.0604014588,"2024-02-27T00:00:00.000":0.0341881522,"2024-02-28T00:00:00.000":0.0498739479,"2... | {"1":{"2024-06-28T00:00:00.000":0.0624522831,"2024-06-29T00:00:00.000":0.2048659975,"2024-06-30T00:00:00.000":0.4522524808,"2024-07-01T00:00:00.000":-0.0539993677,"2024-07-02T00:00:00.000":0.0976437477,"2024-07-03T00:00:00.000":0.1509613743,"2024-07-04T00:00:00.000":0.3902937999,"2024-07-05T00:00:00.000":0.0153031154,"... | 1.091876 | [] | null | null | [] | [] | |
MinimalCausalContextBivarLinSVAR | 5 | 1/3 | [
"c_cov",
"c_causal"
] | [
"forecasting",
"natural language processing",
"reasoning: math",
"reasoning: causal",
"retrieval: memory"
] | Given are variables X_0 and X_1, where X_0 is a covariate and X_1 is the variable to forecast. Variables are generated from a linear Structural Vector Autoregressive (SVAR) model with additive gauss noise and a noise scale of 2.782e-04, with lag = 3. | The task is to forecast the value of the variable X_1 at time t, given the values of the covariate X_0 and the variable X_1 itself at times t-1, ... t-3.
For the first 128 days, the covariate X_0 takes a value of 0.0223 from 2027-03-05 to 2027-03-20, 0.0056 from 2027-03-21 to 2027-04-29, 0.0056 from 2027-04-30 to 2027-... | 7 | {"1":{"2027-03-05T00:00:00.000":0.1424357757,"2027-03-06T00:00:00.000":0.1426731147,"2027-03-07T00:00:00.000":0.1425946699,"2027-03-08T00:00:00.000":0.1428674826,"2027-03-09T00:00:00.000":0.1417073386,"2027-03-10T00:00:00.000":0.1436316906,"2027-03-11T00:00:00.000":0.1409148238,"2027-03-12T00:00:00.000":0.1438636123,"2... | {"1":{"2027-07-11T00:00:00.000":0.0354272401,"2027-07-12T00:00:00.000":0.0647877393,"2027-07-13T00:00:00.000":0.0674095524,"2027-07-14T00:00:00.000":0.0701118108,"2027-07-15T00:00:00.000":0.0880784281,"2027-07-16T00:00:00.000":0.081323525,"2027-07-17T00:00:00.000":0.100049283,"2027-07-18T00:00:00.000":0.0956979392,"202... | 1.091876 | [] | null | null | [] | [] | |
FullCausalContextImplicitEquationBivarLinSVAR | 1 | 1/3 | [
"c_cov",
"c_causal"
] | [
"forecasting",
"natural language processing",
"reasoning: math",
"reasoning: causal",
"retrieval: memory"
] | Given are variables X_0 and X_1, where X_0 is a covariate and X_1 is the variable to forecast. Variables are generated from a linear Structural Vector Autoregressive (SVAR) model with additive gauss noise and a noise scale of 5.533e-04, with lag = 3. | The task is to forecast the value of the variable X_1 at time t, given the values of the covariate X_0 and the variable X_1 itself at times t-1, ... t-3.
For the first 128 days, the covariate X_0 takes a value of 0.0443 from 2023-11-28 to 2024-01-13, 0.1107 from 2024-01-14 to 2024-02-29, 0.0443 from 2024-03-01 to 2024-... | 7 | {"1":{"2023-11-28T00:00:00.000":-0.8266298783,"2023-11-29T00:00:00.000":-0.7780156245,"2023-11-30T00:00:00.000":-0.7284346593,"2023-12-01T00:00:00.000":-0.70871434,"2023-12-02T00:00:00.000":-0.70752035,"2023-12-03T00:00:00.000":-0.6915582237,"2023-12-04T00:00:00.000":-0.6590863553,"2023-12-05T00:00:00.000":-0.637216648... | {"1":{"2024-04-04T00:00:00.000":-0.5720425533,"2024-04-05T00:00:00.000":-0.5734598296,"2024-04-06T00:00:00.000":-0.5981119966,"2024-04-07T00:00:00.000":-0.6304288878,"2024-04-08T00:00:00.000":-0.6409620289,"2024-04-09T00:00:00.000":-0.634275817,"2024-04-10T00:00:00.000":-0.6377524296,"2024-04-11T00:00:00.000":-0.658278... | 1.091876 | [] | null | null | [] | [] | |
FullCausalContextImplicitEquationBivarLinSVAR | 2 | 1/3 | [
"c_cov",
"c_causal"
] | [
"forecasting",
"natural language processing",
"reasoning: math",
"reasoning: causal",
"retrieval: memory"
] | Given are variables X_0 and X_1, where X_0 is a covariate and X_1 is the variable to forecast. Variables are generated from a linear Structural Vector Autoregressive (SVAR) model with additive gauss noise and a noise scale of 5.127e-04, with lag = 3. | The task is to forecast the value of the variable X_1 at time t, given the values of the covariate X_0 and the variable X_1 itself at times t-1, ... t-3.
For the first 128 days, the covariate X_0 takes a value of 0.0103 from 2027-12-17 to 2027-12-18, 0.0615 from 2027-12-19 to 2028-02-07, 0.0103 from 2028-02-08 to 2028-... | 7 | {"1":{"2027-12-17T00:00:00.000":0.0256607228,"2027-12-18T00:00:00.000":0.0256697367,"2027-12-19T00:00:00.000":0.0254611533,"2027-12-20T00:00:00.000":0.0677652779,"2027-12-21T00:00:00.000":0.0665508378,"2027-12-22T00:00:00.000":0.1380973519,"2027-12-23T00:00:00.000":0.1545119509,"2027-12-24T00:00:00.000":0.2079950923,"2... | {"1":{"2028-04-23T00:00:00.000":0.1560266407,"2028-04-24T00:00:00.000":0.2765795662,"2028-04-25T00:00:00.000":0.2750721419,"2028-04-26T00:00:00.000":0.476744877,"2028-04-27T00:00:00.000":0.5228355513,"2028-04-28T00:00:00.000":0.6700140413,"2028-04-29T00:00:00.000":0.6514138956,"2028-04-30T00:00:00.000":0.6899620925,"20... | 1.091876 | [] | null | null | [] | [] | |
FullCausalContextImplicitEquationBivarLinSVAR | 3 | 1/3 | [
"c_cov",
"c_causal"
] | [
"forecasting",
"natural language processing",
"reasoning: math",
"reasoning: causal",
"retrieval: memory"
] | Given are variables X_0 and X_1, where X_0 is a covariate and X_1 is the variable to forecast. Variables are generated from a linear Structural Vector Autoregressive (SVAR) model with additive gauss noise and a noise scale of 4.700e-04, with lag = 3. | The task is to forecast the value of the variable X_1 at time t, given the values of the covariate X_0 and the variable X_1 itself at times t-1, ... t-3.
For the first 128 days, the covariate X_0 takes a value of 0.0564 from 2026-03-14 to 2026-04-12, 0.0940 from 2026-04-13 to 2026-06-10, 0.0564 from 2026-06-11 to 2026-... | 7 | {"1":{"2026-03-14T00:00:00.000":-0.1610389586,"2026-03-15T00:00:00.000":-0.1598332335,"2026-03-16T00:00:00.000":-0.1569984096,"2026-03-17T00:00:00.000":-0.1581430119,"2026-03-18T00:00:00.000":-0.1564116088,"2026-03-19T00:00:00.000":-0.1578811809,"2026-03-20T00:00:00.000":-0.1575325158,"2026-03-21T00:00:00.000":-0.15920... | {"1":{"2026-07-20T00:00:00.000":-0.1650664323,"2026-07-21T00:00:00.000":-0.0485821053,"2026-07-22T00:00:00.000":-0.0905775908,"2026-07-23T00:00:00.000":-0.1017051505,"2026-07-24T00:00:00.000":-0.2296223354,"2026-07-25T00:00:00.000":-0.293578203,"2026-07-26T00:00:00.000":-0.436269551,"2026-07-27T00:00:00.000":-0.4917790... | 1.091876 | [] | null | null | [] | [] | |
FullCausalContextImplicitEquationBivarLinSVAR | 4 | 1/3 | [
"c_cov",
"c_causal"
] | [
"forecasting",
"natural language processing",
"reasoning: math",
"reasoning: causal",
"retrieval: memory"
] | Given are variables X_0 and X_1, where X_0 is a covariate and X_1 is the variable to forecast. Variables are generated from a linear Structural Vector Autoregressive (SVAR) model with additive gauss noise and a noise scale of 1.487e-03, with lag = 3. | The task is to forecast the value of the variable X_1 at time t, given the values of the covariate X_0 and the variable X_1 itself at times t-1, ... t-3.
For the first 128 days, the covariate X_0 takes a value of 0.1190 from 2024-02-21 to 2024-03-11, 0.1784 from 2024-03-12 to 2024-05-06, 0.1784 from 2024-05-07 to 2024-... | 7 | {"1":{"2024-02-21T00:00:00.000":0.0312804743,"2024-02-22T00:00:00.000":0.0633427313,"2024-02-23T00:00:00.000":0.0321669061,"2024-02-24T00:00:00.000":0.0468362684,"2024-02-25T00:00:00.000":0.0290203037,"2024-02-26T00:00:00.000":0.0604014588,"2024-02-27T00:00:00.000":0.0341881522,"2024-02-28T00:00:00.000":0.0498739479,"2... | {"1":{"2024-06-28T00:00:00.000":0.0624522831,"2024-06-29T00:00:00.000":0.2048659975,"2024-06-30T00:00:00.000":0.4522524808,"2024-07-01T00:00:00.000":-0.0539993677,"2024-07-02T00:00:00.000":0.0976437477,"2024-07-03T00:00:00.000":0.1509613743,"2024-07-04T00:00:00.000":0.3902937999,"2024-07-05T00:00:00.000":0.0153031154,"... | 1.091876 | [] | null | null | [] | [] | |
FullCausalContextImplicitEquationBivarLinSVAR | 5 | 1/3 | [
"c_cov",
"c_causal"
] | [
"forecasting",
"natural language processing",
"reasoning: math",
"reasoning: causal",
"retrieval: memory"
] | Given are variables X_0 and X_1, where X_0 is a covariate and X_1 is the variable to forecast. Variables are generated from a linear Structural Vector Autoregressive (SVAR) model with additive gauss noise and a noise scale of 2.782e-04, with lag = 3. | The task is to forecast the value of the variable X_1 at time t, given the values of the covariate X_0 and the variable X_1 itself at times t-1, ... t-3.
For the first 128 days, the covariate X_0 takes a value of 0.0223 from 2027-03-05 to 2027-03-20, 0.0056 from 2027-03-21 to 2027-04-29, 0.0056 from 2027-04-30 to 2027-... | 7 | {"1":{"2027-03-05T00:00:00.000":0.1424357757,"2027-03-06T00:00:00.000":0.1426731147,"2027-03-07T00:00:00.000":0.1425946699,"2027-03-08T00:00:00.000":0.1428674826,"2027-03-09T00:00:00.000":0.1417073386,"2027-03-10T00:00:00.000":0.1436316906,"2027-03-11T00:00:00.000":0.1409148238,"2027-03-12T00:00:00.000":0.1438636123,"2... | {"1":{"2027-07-11T00:00:00.000":0.0354272401,"2027-07-12T00:00:00.000":0.0647877393,"2027-07-13T00:00:00.000":0.0674095524,"2027-07-14T00:00:00.000":0.0701118108,"2027-07-15T00:00:00.000":0.0880784281,"2027-07-16T00:00:00.000":0.081323525,"2027-07-17T00:00:00.000":0.100049283,"2027-07-18T00:00:00.000":0.0956979392,"202... | 1.091876 | [] | null | null | [] | [] | |
FullCausalContextExplicitEquationBivarLinSVAR | 1 | 1/3 | [
"c_cov",
"c_causal"
] | [
"forecasting",
"natural language processing",
"reasoning: math",
"reasoning: causal"
] | Given are variables X_0 and X_1, where X_0 is a covariate and X_1 is the variable to forecast. Variables are generated from a linear Structural Vector Autoregressive (SVAR) model with additive gauss noise and a noise scale of 5.533e-04, with lag = 3. | The task is to forecast the value of the variable X_1 at time t, given the values of the covariate X_0 and the variable X_1 itself at times t-1, ... t-3.
For the first 128 days, the covariate X_0 takes a value of 0.0443 from 2023-11-28 to 2024-01-13, 0.1107 from 2024-01-14 to 2024-02-29, 0.0443 from 2024-03-01 to 2024-... | 7 | {"1":{"2023-11-28T00:00:00.000":-0.8266298783,"2023-11-29T00:00:00.000":-0.7780156245,"2023-11-30T00:00:00.000":-0.7284346593,"2023-12-01T00:00:00.000":-0.70871434,"2023-12-02T00:00:00.000":-0.70752035,"2023-12-03T00:00:00.000":-0.6915582237,"2023-12-04T00:00:00.000":-0.6590863553,"2023-12-05T00:00:00.000":-0.637216648... | {"1":{"2024-04-04T00:00:00.000":-0.5720425533,"2024-04-05T00:00:00.000":-0.5734598296,"2024-04-06T00:00:00.000":-0.5981119966,"2024-04-07T00:00:00.000":-0.6304288878,"2024-04-08T00:00:00.000":-0.6409620289,"2024-04-09T00:00:00.000":-0.634275817,"2024-04-10T00:00:00.000":-0.6377524296,"2024-04-11T00:00:00.000":-0.658278... | 1.091876 | [] | null | null | [] | [] | |
FullCausalContextExplicitEquationBivarLinSVAR | 2 | 1/3 | [
"c_cov",
"c_causal"
] | [
"forecasting",
"natural language processing",
"reasoning: math",
"reasoning: causal"
] | Given are variables X_0 and X_1, where X_0 is a covariate and X_1 is the variable to forecast. Variables are generated from a linear Structural Vector Autoregressive (SVAR) model with additive gauss noise and a noise scale of 5.127e-04, with lag = 3. | The task is to forecast the value of the variable X_1 at time t, given the values of the covariate X_0 and the variable X_1 itself at times t-1, ... t-3.
For the first 128 days, the covariate X_0 takes a value of 0.0103 from 2027-12-17 to 2027-12-18, 0.0615 from 2027-12-19 to 2028-02-07, 0.0103 from 2028-02-08 to 2028-... | 7 | {"1":{"2027-12-17T00:00:00.000":0.0256607228,"2027-12-18T00:00:00.000":0.0256697367,"2027-12-19T00:00:00.000":0.0254611533,"2027-12-20T00:00:00.000":0.0677652779,"2027-12-21T00:00:00.000":0.0665508378,"2027-12-22T00:00:00.000":0.1380973519,"2027-12-23T00:00:00.000":0.1545119509,"2027-12-24T00:00:00.000":0.2079950923,"2... | {"1":{"2028-04-23T00:00:00.000":0.1560266407,"2028-04-24T00:00:00.000":0.2765795662,"2028-04-25T00:00:00.000":0.2750721419,"2028-04-26T00:00:00.000":0.476744877,"2028-04-27T00:00:00.000":0.5228355513,"2028-04-28T00:00:00.000":0.6700140413,"2028-04-29T00:00:00.000":0.6514138956,"2028-04-30T00:00:00.000":0.6899620925,"20... | 1.091876 | [] | null | null | [] | [] | |
FullCausalContextExplicitEquationBivarLinSVAR | 3 | 1/3 | [
"c_cov",
"c_causal"
] | [
"forecasting",
"natural language processing",
"reasoning: math",
"reasoning: causal"
] | Given are variables X_0 and X_1, where X_0 is a covariate and X_1 is the variable to forecast. Variables are generated from a linear Structural Vector Autoregressive (SVAR) model with additive gauss noise and a noise scale of 4.700e-04, with lag = 3. | The task is to forecast the value of the variable X_1 at time t, given the values of the covariate X_0 and the variable X_1 itself at times t-1, ... t-3.
For the first 128 days, the covariate X_0 takes a value of 0.0564 from 2026-03-14 to 2026-04-12, 0.0940 from 2026-04-13 to 2026-06-10, 0.0564 from 2026-06-11 to 2026-... | 7 | {"1":{"2026-03-14T00:00:00.000":-0.1610389586,"2026-03-15T00:00:00.000":-0.1598332335,"2026-03-16T00:00:00.000":-0.1569984096,"2026-03-17T00:00:00.000":-0.1581430119,"2026-03-18T00:00:00.000":-0.1564116088,"2026-03-19T00:00:00.000":-0.1578811809,"2026-03-20T00:00:00.000":-0.1575325158,"2026-03-21T00:00:00.000":-0.15920... | {"1":{"2026-07-20T00:00:00.000":-0.1650664323,"2026-07-21T00:00:00.000":-0.0485821053,"2026-07-22T00:00:00.000":-0.0905775908,"2026-07-23T00:00:00.000":-0.1017051505,"2026-07-24T00:00:00.000":-0.2296223354,"2026-07-25T00:00:00.000":-0.293578203,"2026-07-26T00:00:00.000":-0.436269551,"2026-07-27T00:00:00.000":-0.4917790... | 1.091876 | [] | null | null | [] | [] | |
FullCausalContextExplicitEquationBivarLinSVAR | 4 | 1/3 | [
"c_cov",
"c_causal"
] | [
"forecasting",
"natural language processing",
"reasoning: math",
"reasoning: causal"
] | Given are variables X_0 and X_1, where X_0 is a covariate and X_1 is the variable to forecast. Variables are generated from a linear Structural Vector Autoregressive (SVAR) model with additive gauss noise and a noise scale of 1.487e-03, with lag = 3. | The task is to forecast the value of the variable X_1 at time t, given the values of the covariate X_0 and the variable X_1 itself at times t-1, ... t-3.
For the first 128 days, the covariate X_0 takes a value of 0.1190 from 2024-02-21 to 2024-03-11, 0.1784 from 2024-03-12 to 2024-05-06, 0.1784 from 2024-05-07 to 2024-... | 7 | {"1":{"2024-02-21T00:00:00.000":0.0312804743,"2024-02-22T00:00:00.000":0.0633427313,"2024-02-23T00:00:00.000":0.0321669061,"2024-02-24T00:00:00.000":0.0468362684,"2024-02-25T00:00:00.000":0.0290203037,"2024-02-26T00:00:00.000":0.0604014588,"2024-02-27T00:00:00.000":0.0341881522,"2024-02-28T00:00:00.000":0.0498739479,"2... | {"1":{"2024-06-28T00:00:00.000":0.0624522831,"2024-06-29T00:00:00.000":0.2048659975,"2024-06-30T00:00:00.000":0.4522524808,"2024-07-01T00:00:00.000":-0.0539993677,"2024-07-02T00:00:00.000":0.0976437477,"2024-07-03T00:00:00.000":0.1509613743,"2024-07-04T00:00:00.000":0.3902937999,"2024-07-05T00:00:00.000":0.0153031154,"... | 1.091876 | [] | null | null | [] | [] | |
FullCausalContextExplicitEquationBivarLinSVAR | 5 | 1/3 | [
"c_cov",
"c_causal"
] | [
"forecasting",
"natural language processing",
"reasoning: math",
"reasoning: causal"
] | Given are variables X_0 and X_1, where X_0 is a covariate and X_1 is the variable to forecast. Variables are generated from a linear Structural Vector Autoregressive (SVAR) model with additive gauss noise and a noise scale of 2.782e-04, with lag = 3. | The task is to forecast the value of the variable X_1 at time t, given the values of the covariate X_0 and the variable X_1 itself at times t-1, ... t-3.
For the first 128 days, the covariate X_0 takes a value of 0.0223 from 2027-03-05 to 2027-03-20, 0.0056 from 2027-03-21 to 2027-04-29, 0.0056 from 2027-04-30 to 2027-... | 7 | {"1":{"2027-03-05T00:00:00.000":0.1424357757,"2027-03-06T00:00:00.000":0.1426731147,"2027-03-07T00:00:00.000":0.1425946699,"2027-03-08T00:00:00.000":0.1428674826,"2027-03-09T00:00:00.000":0.1417073386,"2027-03-10T00:00:00.000":0.1436316906,"2027-03-11T00:00:00.000":0.1409148238,"2027-03-12T00:00:00.000":0.1438636123,"2... | {"1":{"2027-07-11T00:00:00.000":0.0354272401,"2027-07-12T00:00:00.000":0.0647877393,"2027-07-13T00:00:00.000":0.0674095524,"2027-07-14T00:00:00.000":0.0701118108,"2027-07-15T00:00:00.000":0.0880784281,"2027-07-16T00:00:00.000":0.081323525,"2027-07-17T00:00:00.000":0.100049283,"2027-07-18T00:00:00.000":0.0956979392,"202... | 1.091876 | [] | null | null | [] | [] | |
MinimalInfoHalfDaySolarForecastTask | 1 | 1/3 | [
"c_i"
] | [
"forecasting",
"natural language processing",
"reasoning: deduction"
] | This series contains the amount of sunlight (in Watts per squared meter) arriving on a horizontal surface. | -1 | {"GHI":{"2022-11-19T00:00:00.000":0,"2022-11-19T00:10:00.000":0,"2022-11-19T00:20:00.000":0,"2022-11-19T00:30:00.000":0,"2022-11-19T00:40:00.000":0,"2022-11-19T00:50:00.000":0,"2022-11-19T01:00:00.000":0,"2022-11-19T01:10:00.000":0,"2022-11-19T01:20:00.000":0,"2022-11-19T01:30:00.000":0,"2022-11-19T01:40:00.000":0,"202... | {"GHI":{"2022-11-19T11:50:00.000":714,"2022-11-19T12:00:00.000":720,"2022-11-19T12:10:00.000":723,"2022-11-19T12:20:00.000":724,"2022-11-19T12:30:00.000":724,"2022-11-19T12:40:00.000":721,"2022-11-19T12:50:00.000":716,"2022-11-19T13:00:00.000":710,"2022-11-19T13:10:00.000":701,"2022-11-19T13:20:00.000":691,"2022-11-19T... | 0.001306 | [] | null | null | [] | [] | ||
MinimalInfoHalfDaySolarForecastTask | 2 | 1/3 | [
"c_i"
] | [
"forecasting",
"natural language processing",
"reasoning: deduction"
] | This series contains the amount of sunlight (in Watts per squared meter) arriving on a horizontal surface. | -1 | {"GHI":{"2022-07-17T00:00:00.000":0,"2022-07-17T00:10:00.000":0,"2022-07-17T00:20:00.000":0,"2022-07-17T00:30:00.000":0,"2022-07-17T00:40:00.000":0,"2022-07-17T00:50:00.000":0,"2022-07-17T01:00:00.000":0,"2022-07-17T01:10:00.000":0,"2022-07-17T01:20:00.000":0,"2022-07-17T01:30:00.000":0,"2022-07-17T01:40:00.000":0,"202... | {"GHI":{"2022-07-17T12:40:00.000":663,"2022-07-17T12:50:00.000":715,"2022-07-17T13:00:00.000":772,"2022-07-17T13:10:00.000":838,"2022-07-17T13:20:00.000":829,"2022-07-17T13:30:00.000":675,"2022-07-17T13:40:00.000":550,"2022-07-17T13:50:00.000":550,"2022-07-17T14:00:00.000":566,"2022-07-17T14:10:00.000":275,"2022-07-17T... | 0.001306 | [] | null | null | [] | [] | ||
MinimalInfoHalfDaySolarForecastTask | 3 | 1/3 | [
"c_i"
] | [
"forecasting",
"natural language processing",
"reasoning: deduction"
] | This series contains the amount of sunlight (in Watts per squared meter) arriving on a horizontal surface. | -1 | {"GHI":{"2022-12-01T00:00:00.000":0,"2022-12-01T00:10:00.000":0,"2022-12-01T00:20:00.000":0,"2022-12-01T00:30:00.000":0,"2022-12-01T00:40:00.000":0,"2022-12-01T00:50:00.000":0,"2022-12-01T01:00:00.000":0,"2022-12-01T01:10:00.000":0,"2022-12-01T01:20:00.000":0,"2022-12-01T01:30:00.000":0,"2022-12-01T01:40:00.000":0,"202... | {"GHI":{"2022-12-01T11:00:00.000":331,"2022-12-01T11:10:00.000":287,"2022-12-01T11:20:00.000":477,"2022-12-01T11:30:00.000":767,"2022-12-01T11:40:00.000":777,"2022-12-01T11:50:00.000":785,"2022-12-01T12:00:00.000":575,"2022-12-01T12:10:00.000":402,"2022-12-01T12:20:00.000":451,"2022-12-01T12:30:00.000":611,"2022-12-01T... | 0.001306 | [] | null | null | [] | [] | ||
MinimalInfoHalfDaySolarForecastTask | 4 | 1/3 | [
"c_i"
] | [
"forecasting",
"natural language processing",
"reasoning: deduction"
] | This series contains the amount of sunlight (in Watts per squared meter) arriving on a horizontal surface. | -1 | {"GHI":{"2022-12-23T00:00:00.000":0,"2022-12-23T00:10:00.000":0,"2022-12-23T00:20:00.000":0,"2022-12-23T00:30:00.000":0,"2022-12-23T00:40:00.000":0,"2022-12-23T00:50:00.000":0,"2022-12-23T01:00:00.000":0,"2022-12-23T01:10:00.000":0,"2022-12-23T01:20:00.000":0,"2022-12-23T01:30:00.000":0,"2022-12-23T01:40:00.000":0,"202... | {"GHI":{"2022-12-23T11:20:00.000":729,"2022-12-23T11:30:00.000":742,"2022-12-23T11:40:00.000":754,"2022-12-23T11:50:00.000":764,"2022-12-23T12:00:00.000":772,"2022-12-23T12:10:00.000":777,"2022-12-23T12:20:00.000":781,"2022-12-23T12:30:00.000":783,"2022-12-23T12:40:00.000":783,"2022-12-23T12:50:00.000":781,"2022-12-23T... | 0.001306 | [] | null | null | [] | [] | ||
MinimalInfoHalfDaySolarForecastTask | 5 | 1/3 | [
"c_i"
] | [
"forecasting",
"natural language processing",
"reasoning: deduction"
] | This series contains the amount of sunlight (in Watts per squared meter) arriving on a horizontal surface. | -1 | {"GHI":{"2022-10-28T00:00:00.000":0,"2022-10-28T00:10:00.000":0,"2022-10-28T00:20:00.000":0,"2022-10-28T00:30:00.000":0,"2022-10-28T00:40:00.000":0,"2022-10-28T00:50:00.000":0,"2022-10-28T01:00:00.000":0,"2022-10-28T01:10:00.000":0,"2022-10-28T01:20:00.000":0,"2022-10-28T01:30:00.000":0,"2022-10-28T01:40:00.000":0,"202... | {"GHI":{"2022-10-28T13:10:00.000":14,"2022-10-28T13:20:00.000":14,"2022-10-28T13:30:00.000":38,"2022-10-28T13:40:00.000":4,"2022-10-28T13:50:00.000":6,"2022-10-28T14:00:00.000":5,"2022-10-28T14:10:00.000":4,"2022-10-28T14:20:00.000":4,"2022-10-28T14:30:00.000":86,"2022-10-28T14:40:00.000":54,"2022-10-28T14:50:00.000":3... | 0.001306 | [] | null | null | [] | [] | ||
LocaleInfoHalfDaySolarForecastTask | 1 | 1/3 | [
"c_i"
] | [
"forecasting",
"natural language processing",
"reasoning: deduction"
] | This series contains the amount of sunlight (in Watts per squared meter) arriving on a horizontal surface, for a location in Florida, United States. | -1 | {"GHI":{"2022-11-19T00:00:00.000":0,"2022-11-19T00:10:00.000":0,"2022-11-19T00:20:00.000":0,"2022-11-19T00:30:00.000":0,"2022-11-19T00:40:00.000":0,"2022-11-19T00:50:00.000":0,"2022-11-19T01:00:00.000":0,"2022-11-19T01:10:00.000":0,"2022-11-19T01:20:00.000":0,"2022-11-19T01:30:00.000":0,"2022-11-19T01:40:00.000":0,"202... | {"GHI":{"2022-11-19T11:50:00.000":714,"2022-11-19T12:00:00.000":720,"2022-11-19T12:10:00.000":723,"2022-11-19T12:20:00.000":724,"2022-11-19T12:30:00.000":724,"2022-11-19T12:40:00.000":721,"2022-11-19T12:50:00.000":716,"2022-11-19T13:00:00.000":710,"2022-11-19T13:10:00.000":701,"2022-11-19T13:20:00.000":691,"2022-11-19T... | 0.001306 | [] | null | null | [] | [] | ||
LocaleInfoHalfDaySolarForecastTask | 2 | 1/3 | [
"c_i"
] | [
"forecasting",
"natural language processing",
"reasoning: deduction"
] | This series contains the amount of sunlight (in Watts per squared meter) arriving on a horizontal surface, for a location in Québec, Canada. | -1 | {"GHI":{"2022-07-17T00:00:00.000":0,"2022-07-17T00:10:00.000":0,"2022-07-17T00:20:00.000":0,"2022-07-17T00:30:00.000":0,"2022-07-17T00:40:00.000":0,"2022-07-17T00:50:00.000":0,"2022-07-17T01:00:00.000":0,"2022-07-17T01:10:00.000":0,"2022-07-17T01:20:00.000":0,"2022-07-17T01:30:00.000":0,"2022-07-17T01:40:00.000":0,"202... | {"GHI":{"2022-07-17T12:40:00.000":663,"2022-07-17T12:50:00.000":715,"2022-07-17T13:00:00.000":772,"2022-07-17T13:10:00.000":838,"2022-07-17T13:20:00.000":829,"2022-07-17T13:30:00.000":675,"2022-07-17T13:40:00.000":550,"2022-07-17T13:50:00.000":550,"2022-07-17T14:00:00.000":566,"2022-07-17T14:10:00.000":275,"2022-07-17T... | 0.001306 | [] | null | null | [] | [] | ||
LocaleInfoHalfDaySolarForecastTask | 3 | 1/3 | [
"c_i"
] | [
"forecasting",
"natural language processing",
"reasoning: deduction"
] | This series contains the amount of sunlight (in Watts per squared meter) arriving on a horizontal surface, for a location in La Altagracia, Dominican Republic. | -1 | {"GHI":{"2022-12-01T00:00:00.000":0,"2022-12-01T00:10:00.000":0,"2022-12-01T00:20:00.000":0,"2022-12-01T00:30:00.000":0,"2022-12-01T00:40:00.000":0,"2022-12-01T00:50:00.000":0,"2022-12-01T01:00:00.000":0,"2022-12-01T01:10:00.000":0,"2022-12-01T01:20:00.000":0,"2022-12-01T01:30:00.000":0,"2022-12-01T01:40:00.000":0,"202... | {"GHI":{"2022-12-01T11:00:00.000":331,"2022-12-01T11:10:00.000":287,"2022-12-01T11:20:00.000":477,"2022-12-01T11:30:00.000":767,"2022-12-01T11:40:00.000":777,"2022-12-01T11:50:00.000":785,"2022-12-01T12:00:00.000":575,"2022-12-01T12:10:00.000":402,"2022-12-01T12:20:00.000":451,"2022-12-01T12:30:00.000":611,"2022-12-01T... | 0.001306 | [] | null | null | [] | [] | ||
LocaleInfoHalfDaySolarForecastTask | 4 | 1/3 | [
"c_i"
] | [
"forecasting",
"natural language processing",
"reasoning: deduction"
] | This series contains the amount of sunlight (in Watts per squared meter) arriving on a horizontal surface, for a location in La Altagracia, Dominican Republic. | -1 | {"GHI":{"2022-12-23T00:00:00.000":0,"2022-12-23T00:10:00.000":0,"2022-12-23T00:20:00.000":0,"2022-12-23T00:30:00.000":0,"2022-12-23T00:40:00.000":0,"2022-12-23T00:50:00.000":0,"2022-12-23T01:00:00.000":0,"2022-12-23T01:10:00.000":0,"2022-12-23T01:20:00.000":0,"2022-12-23T01:30:00.000":0,"2022-12-23T01:40:00.000":0,"202... | {"GHI":{"2022-12-23T11:20:00.000":729,"2022-12-23T11:30:00.000":742,"2022-12-23T11:40:00.000":754,"2022-12-23T11:50:00.000":764,"2022-12-23T12:00:00.000":772,"2022-12-23T12:10:00.000":777,"2022-12-23T12:20:00.000":781,"2022-12-23T12:30:00.000":783,"2022-12-23T12:40:00.000":783,"2022-12-23T12:50:00.000":781,"2022-12-23T... | 0.001306 | [] | null | null | [] | [] | ||
LocaleInfoHalfDaySolarForecastTask | 5 | 1/3 | [
"c_i"
] | [
"forecasting",
"natural language processing",
"reasoning: deduction"
] | This series contains the amount of sunlight (in Watts per squared meter) arriving on a horizontal surface, for a location in Alaska, United States. | -1 | {"GHI":{"2022-10-28T00:00:00.000":0,"2022-10-28T00:10:00.000":0,"2022-10-28T00:20:00.000":0,"2022-10-28T00:30:00.000":0,"2022-10-28T00:40:00.000":0,"2022-10-28T00:50:00.000":0,"2022-10-28T01:00:00.000":0,"2022-10-28T01:10:00.000":0,"2022-10-28T01:20:00.000":0,"2022-10-28T01:30:00.000":0,"2022-10-28T01:40:00.000":0,"202... | {"GHI":{"2022-10-28T13:10:00.000":14,"2022-10-28T13:20:00.000":14,"2022-10-28T13:30:00.000":38,"2022-10-28T13:40:00.000":4,"2022-10-28T13:50:00.000":6,"2022-10-28T14:00:00.000":5,"2022-10-28T14:10:00.000":4,"2022-10-28T14:20:00.000":4,"2022-10-28T14:30:00.000":86,"2022-10-28T14:40:00.000":54,"2022-10-28T14:50:00.000":3... | 0.001306 | [] | null | null | [] | [] | ||
ZenithInfoHalfDaySolarForecastTask | 1 | 1/3 | [
"c_i",
"c_h"
] | [
"forecasting",
"natural language processing",
"reasoning: deduction"
] | This series contains the amount of sunlight (in Watts per squared meter) arriving on a horizontal surface, for a location in Florida, United States.
Over the previous 90 days, the maximum sunlight happened on average at 12:25:33. | -1 | {"GHI":{"2022-11-19T00:00:00.000":0,"2022-11-19T00:10:00.000":0,"2022-11-19T00:20:00.000":0,"2022-11-19T00:30:00.000":0,"2022-11-19T00:40:00.000":0,"2022-11-19T00:50:00.000":0,"2022-11-19T01:00:00.000":0,"2022-11-19T01:10:00.000":0,"2022-11-19T01:20:00.000":0,"2022-11-19T01:30:00.000":0,"2022-11-19T01:40:00.000":0,"202... | {"GHI":{"2022-11-19T11:50:00.000":714,"2022-11-19T12:00:00.000":720,"2022-11-19T12:10:00.000":723,"2022-11-19T12:20:00.000":724,"2022-11-19T12:30:00.000":724,"2022-11-19T12:40:00.000":721,"2022-11-19T12:50:00.000":716,"2022-11-19T13:00:00.000":710,"2022-11-19T13:10:00.000":701,"2022-11-19T13:20:00.000":691,"2022-11-19T... | 0.001306 | [] | null | null | [] | [] | ||
ZenithInfoHalfDaySolarForecastTask | 2 | 1/3 | [
"c_i",
"c_h"
] | [
"forecasting",
"natural language processing",
"reasoning: deduction"
] | This series contains the amount of sunlight (in Watts per squared meter) arriving on a horizontal surface, for a location in Québec, Canada.
Over the previous 90 days, the maximum sunlight happened on average at 11:54:40. | -1 | {"GHI":{"2022-07-17T00:00:00.000":0,"2022-07-17T00:10:00.000":0,"2022-07-17T00:20:00.000":0,"2022-07-17T00:30:00.000":0,"2022-07-17T00:40:00.000":0,"2022-07-17T00:50:00.000":0,"2022-07-17T01:00:00.000":0,"2022-07-17T01:10:00.000":0,"2022-07-17T01:20:00.000":0,"2022-07-17T01:30:00.000":0,"2022-07-17T01:40:00.000":0,"202... | {"GHI":{"2022-07-17T12:40:00.000":663,"2022-07-17T12:50:00.000":715,"2022-07-17T13:00:00.000":772,"2022-07-17T13:10:00.000":838,"2022-07-17T13:20:00.000":829,"2022-07-17T13:30:00.000":675,"2022-07-17T13:40:00.000":550,"2022-07-17T13:50:00.000":550,"2022-07-17T14:00:00.000":566,"2022-07-17T14:10:00.000":275,"2022-07-17T... | 0.001306 | [] | null | null | [] | [] | ||
ZenithInfoHalfDaySolarForecastTask | 3 | 1/3 | [
"c_i",
"c_h"
] | [
"forecasting",
"natural language processing",
"reasoning: deduction"
] | This series contains the amount of sunlight (in Watts per squared meter) arriving on a horizontal surface, for a location in La Altagracia, Dominican Republic.
Over the previous 90 days, the maximum sunlight happened on average at 12:20:13. | -1 | {"GHI":{"2022-12-01T00:00:00.000":0,"2022-12-01T00:10:00.000":0,"2022-12-01T00:20:00.000":0,"2022-12-01T00:30:00.000":0,"2022-12-01T00:40:00.000":0,"2022-12-01T00:50:00.000":0,"2022-12-01T01:00:00.000":0,"2022-12-01T01:10:00.000":0,"2022-12-01T01:20:00.000":0,"2022-12-01T01:30:00.000":0,"2022-12-01T01:40:00.000":0,"202... | {"GHI":{"2022-12-01T11:00:00.000":331,"2022-12-01T11:10:00.000":287,"2022-12-01T11:20:00.000":477,"2022-12-01T11:30:00.000":767,"2022-12-01T11:40:00.000":777,"2022-12-01T11:50:00.000":785,"2022-12-01T12:00:00.000":575,"2022-12-01T12:10:00.000":402,"2022-12-01T12:20:00.000":451,"2022-12-01T12:30:00.000":611,"2022-12-01T... | 0.001306 | [] | null | null | [] | [] | ||
ZenithInfoHalfDaySolarForecastTask | 4 | 1/3 | [
"c_i",
"c_h"
] | [
"forecasting",
"natural language processing",
"reasoning: deduction"
] | This series contains the amount of sunlight (in Watts per squared meter) arriving on a horizontal surface, for a location in La Altagracia, Dominican Republic.
Over the previous 90 days, the maximum sunlight happened on average at 12:19:27. | -1 | {"GHI":{"2022-12-23T00:00:00.000":0,"2022-12-23T00:10:00.000":0,"2022-12-23T00:20:00.000":0,"2022-12-23T00:30:00.000":0,"2022-12-23T00:40:00.000":0,"2022-12-23T00:50:00.000":0,"2022-12-23T01:00:00.000":0,"2022-12-23T01:10:00.000":0,"2022-12-23T01:20:00.000":0,"2022-12-23T01:30:00.000":0,"2022-12-23T01:40:00.000":0,"202... | {"GHI":{"2022-12-23T11:20:00.000":729,"2022-12-23T11:30:00.000":742,"2022-12-23T11:40:00.000":754,"2022-12-23T11:50:00.000":764,"2022-12-23T12:00:00.000":772,"2022-12-23T12:10:00.000":777,"2022-12-23T12:20:00.000":781,"2022-12-23T12:30:00.000":783,"2022-12-23T12:40:00.000":783,"2022-12-23T12:50:00.000":781,"2022-12-23T... | 0.001306 | [] | null | null | [] | [] | ||
ZenithInfoHalfDaySolarForecastTask | 5 | 1/3 | [
"c_i",
"c_h"
] | [
"forecasting",
"natural language processing",
"reasoning: deduction"
] | This series contains the amount of sunlight (in Watts per squared meter) arriving on a horizontal surface, for a location in Alaska, United States.
Over the previous 90 days, the maximum sunlight happened on average at 11:54:13. | -1 | {"GHI":{"2022-10-28T00:00:00.000":0,"2022-10-28T00:10:00.000":0,"2022-10-28T00:20:00.000":0,"2022-10-28T00:30:00.000":0,"2022-10-28T00:40:00.000":0,"2022-10-28T00:50:00.000":0,"2022-10-28T01:00:00.000":0,"2022-10-28T01:10:00.000":0,"2022-10-28T01:20:00.000":0,"2022-10-28T01:30:00.000":0,"2022-10-28T01:40:00.000":0,"202... | {"GHI":{"2022-10-28T13:10:00.000":14,"2022-10-28T13:20:00.000":14,"2022-10-28T13:30:00.000":38,"2022-10-28T13:40:00.000":4,"2022-10-28T13:50:00.000":6,"2022-10-28T14:00:00.000":5,"2022-10-28T14:10:00.000":4,"2022-10-28T14:20:00.000":4,"2022-10-28T14:30:00.000":86,"2022-10-28T14:40:00.000":54,"2022-10-28T14:50:00.000":3... | 0.001306 | [] | null | null | [] | [] | ||
SimilarLocationDaySolarForecastTask | 1 | 1/3 | [
"c_i"
] | [
"forecasting",
"natural language processing",
"reasoning: analogy",
"retrieval: memory"
] | This series estimates the power production for a given day of a new solar power plant located in the state of Georgia, which has a humid subtropical climate. | -1 | {"0":{"2006-08-07 00:00":0.0,"2006-08-07 00:10":0.0,"2006-08-07 00:20":0.0,"2006-08-07 00:30":0.0,"2006-08-07 00:40":0.0,"2006-08-07 00:50":0.0,"2006-08-07 01:00":0.0,"2006-08-07 01:10":0.0,"2006-08-07 01:20":0.0,"2006-08-07 01:30":0.0,"2006-08-07 01:40":0.0,"2006-08-07 01:50":0.0,"2006-08-07 02:00":0.0,"2006-08-07 02:... | {"0":{"2006-08-07 08:20":17.5179299253,"2006-08-07 08:30":18.7098870754,"2006-08-07 08:40":19.4589840311,"2006-08-07 08:50":19.9882580655,"2006-08-07 09:00":20.7888352916,"2006-08-07 09:10":21.3955295471,"2006-08-07 09:20":21.9402937551,"2006-08-07 09:30":22.3580268463,"2006-08-07 09:40":22.6194989639,"2006-08-07 09:50... | 0.047322 | [] | 0 | null | [] | [] | ||
SimilarLocationDaySolarForecastTask | 2 | 1/3 | [
"c_i"
] | [
"forecasting",
"natural language processing",
"reasoning: analogy",
"retrieval: memory"
] | This series estimates the power production for a given day of a new solar power plant located in the state of Georgia, which has a humid subtropical climate. | -1 | {"0":{"2006-12-16 00:00":0.0,"2006-12-16 00:10":0.0,"2006-12-16 00:20":0.0,"2006-12-16 00:30":0.0,"2006-12-16 00:40":0.0,"2006-12-16 00:50":0.0,"2006-12-16 01:00":0.0,"2006-12-16 01:10":0.0,"2006-12-16 01:20":0.0,"2006-12-16 01:30":0.0,"2006-12-16 01:40":0.0,"2006-12-16 01:50":0.0,"2006-12-16 02:00":0.0,"2006-12-16 02:... | {"0":{"2006-12-16 07:50":8.0293008208,"2006-12-16 08:00":9.4872760785,"2006-12-16 08:10":10.6606506373,"2006-12-16 08:20":11.7867018059,"2006-12-16 08:30":12.9065971326,"2006-12-16 08:40":13.9635470067,"2006-12-16 08:50":15.1477196694,"2006-12-16 09:00":15.7305725476,"2006-12-16 09:10":16.6569862298,"2006-12-16 09:20":... | 0.047322 | [] | 0 | null | [] | [] | ||
SimilarLocationDaySolarForecastTask | 3 | 1/3 | [
"c_i"
] | [
"forecasting",
"natural language processing",
"reasoning: analogy",
"retrieval: memory"
] | This series estimates the power production for a given day of a new solar power plant located in the state of Georgia, which has a humid subtropical climate. | -1 | {"0":{"2006-10-15 00:00":0.0,"2006-10-15 00:10":0.0,"2006-10-15 00:20":0.0,"2006-10-15 00:30":0.0,"2006-10-15 00:40":0.0,"2006-10-15 00:50":0.0,"2006-10-15 01:00":0.0,"2006-10-15 01:10":0.0,"2006-10-15 01:20":0.0,"2006-10-15 01:30":0.0,"2006-10-15 01:40":0.0,"2006-10-15 01:50":0.0,"2006-10-15 02:00":0.0,"2006-10-15 02:... | {"0":{"2006-10-15 07:50":12.8869962515,"2006-10-15 08:00":14.3252339023,"2006-10-15 08:10":15.1174236199,"2006-10-15 08:20":15.9294419533,"2006-10-15 08:30":16.7945758396,"2006-10-15 08:40":17.4001153941,"2006-10-15 08:50":18.4261426181,"2006-10-15 09:00":19.1668244976,"2006-10-15 09:10":20.2016320779,"2006-10-15 09:20... | 0.047322 | [] | 0 | null | [] | [] | ||
SimilarLocationDaySolarForecastTask | 4 | 1/3 | [
"c_i"
] | [
"forecasting",
"natural language processing",
"reasoning: analogy",
"retrieval: memory"
] | This series estimates the power production for a given day of a new solar power plant located in the state of Georgia, which has a humid subtropical climate. | -1 | {"0":{"2006-10-31 00:00":0.0,"2006-10-31 00:10":0.0,"2006-10-31 00:20":0.0,"2006-10-31 00:30":0.0,"2006-10-31 00:40":0.0,"2006-10-31 00:50":0.0,"2006-10-31 01:00":0.0,"2006-10-31 01:10":0.0,"2006-10-31 01:20":0.0,"2006-10-31 01:30":0.0,"2006-10-31 01:40":0.0,"2006-10-31 01:50":0.0,"2006-10-31 02:00":0.0,"2006-10-31 02:... | {"0":{"2006-10-31 07:40":5.9972337279,"2006-10-31 07:50":6.9690267096,"2006-10-31 08:00":7.708186453,"2006-10-31 08:10":8.6308985074,"2006-10-31 08:20":9.6693360351,"2006-10-31 08:30":10.5311173991,"2006-10-31 08:40":11.5033612976,"2006-10-31 08:50":12.5313185,"2006-10-31 09:00":13.1131902,"2006-10-31 09:10":13.7764265... | 0.047322 | [] | 0 | null | [] | [] | ||
SimilarLocationDaySolarForecastTask | 5 | 1/3 | [
"c_i"
] | [
"forecasting",
"natural language processing",
"reasoning: analogy",
"retrieval: memory"
] | This series estimates the power production for a given day of a new solar power plant located in the state of Georgia, which has a humid subtropical climate. | -1 | {"0":{"2006-10-08 00:00":0.0,"2006-10-08 00:10":0.0,"2006-10-08 00:20":0.0,"2006-10-08 00:30":0.0,"2006-10-08 00:40":0.0,"2006-10-08 00:50":0.0,"2006-10-08 01:00":0.0,"2006-10-08 01:10":0.0,"2006-10-08 01:20":0.0,"2006-10-08 01:30":0.0,"2006-10-08 01:40":0.0,"2006-10-08 01:50":0.0,"2006-10-08 02:00":0.0,"2006-10-08 02:... | {"0":{"2006-10-08 07:30":11.9958865159,"2006-10-08 07:40":13.480032965,"2006-10-08 07:50":14.8045539937,"2006-10-08 08:00":16.1457243968,"2006-10-08 08:10":16.9255699817,"2006-10-08 08:20":18.1957384976,"2006-10-08 08:30":19.1876321639,"2006-10-08 08:40":20.273607985,"2006-10-08 08:50":21.1377693294,"2006-10-08 09:00":... | 0.047322 | [] | 0 | null | [] | [] | ||
ExplicitSimilarLocationDaySolarForecastTask | 1 | 1/3 | [
"c_i"
] | [
"forecasting",
"natural language processing",
"reasoning: analogy",
"retrieval: memory",
"retrieval: memory"
] | This series estimates the power production for a given day of a new solar power plant located in the state of Georgia, which has a climate similar to Alabama's. | -1 | {"0":{"2006-08-07 00:00":0.0,"2006-08-07 00:10":0.0,"2006-08-07 00:20":0.0,"2006-08-07 00:30":0.0,"2006-08-07 00:40":0.0,"2006-08-07 00:50":0.0,"2006-08-07 01:00":0.0,"2006-08-07 01:10":0.0,"2006-08-07 01:20":0.0,"2006-08-07 01:30":0.0,"2006-08-07 01:40":0.0,"2006-08-07 01:50":0.0,"2006-08-07 02:00":0.0,"2006-08-07 02:... | {"0":{"2006-08-07 08:20":17.5179299253,"2006-08-07 08:30":18.7098870754,"2006-08-07 08:40":19.4589840311,"2006-08-07 08:50":19.9882580655,"2006-08-07 09:00":20.7888352916,"2006-08-07 09:10":21.3955295471,"2006-08-07 09:20":21.9402937551,"2006-08-07 09:30":22.3580268463,"2006-08-07 09:40":22.6194989639,"2006-08-07 09:50... | 0.047322 | [] | 0 | null | [] | [] | ||
ExplicitSimilarLocationDaySolarForecastTask | 2 | 1/3 | [
"c_i"
] | [
"forecasting",
"natural language processing",
"reasoning: analogy",
"retrieval: memory",
"retrieval: memory"
] | This series estimates the power production for a given day of a new solar power plant located in the state of Georgia, which has a climate similar to Alabama's. | -1 | {"0":{"2006-12-16 00:00":0.0,"2006-12-16 00:10":0.0,"2006-12-16 00:20":0.0,"2006-12-16 00:30":0.0,"2006-12-16 00:40":0.0,"2006-12-16 00:50":0.0,"2006-12-16 01:00":0.0,"2006-12-16 01:10":0.0,"2006-12-16 01:20":0.0,"2006-12-16 01:30":0.0,"2006-12-16 01:40":0.0,"2006-12-16 01:50":0.0,"2006-12-16 02:00":0.0,"2006-12-16 02:... | {"0":{"2006-12-16 07:50":8.0293008208,"2006-12-16 08:00":9.4872760785,"2006-12-16 08:10":10.6606506373,"2006-12-16 08:20":11.7867018059,"2006-12-16 08:30":12.9065971326,"2006-12-16 08:40":13.9635470067,"2006-12-16 08:50":15.1477196694,"2006-12-16 09:00":15.7305725476,"2006-12-16 09:10":16.6569862298,"2006-12-16 09:20":... | 0.047322 | [] | 0 | null | [] | [] | ||
ExplicitSimilarLocationDaySolarForecastTask | 3 | 1/3 | [
"c_i"
] | [
"forecasting",
"natural language processing",
"reasoning: analogy",
"retrieval: memory",
"retrieval: memory"
] | This series estimates the power production for a given day of a new solar power plant located in the state of Georgia, which has a climate similar to Alabama's. | -1 | {"0":{"2006-10-15 00:00":0.0,"2006-10-15 00:10":0.0,"2006-10-15 00:20":0.0,"2006-10-15 00:30":0.0,"2006-10-15 00:40":0.0,"2006-10-15 00:50":0.0,"2006-10-15 01:00":0.0,"2006-10-15 01:10":0.0,"2006-10-15 01:20":0.0,"2006-10-15 01:30":0.0,"2006-10-15 01:40":0.0,"2006-10-15 01:50":0.0,"2006-10-15 02:00":0.0,"2006-10-15 02:... | {"0":{"2006-10-15 07:50":12.8869962515,"2006-10-15 08:00":14.3252339023,"2006-10-15 08:10":15.1174236199,"2006-10-15 08:20":15.9294419533,"2006-10-15 08:30":16.7945758396,"2006-10-15 08:40":17.4001153941,"2006-10-15 08:50":18.4261426181,"2006-10-15 09:00":19.1668244976,"2006-10-15 09:10":20.2016320779,"2006-10-15 09:20... | 0.047322 | [] | 0 | null | [] | [] | ||
ExplicitSimilarLocationDaySolarForecastTask | 4 | 1/3 | [
"c_i"
] | [
"forecasting",
"natural language processing",
"reasoning: analogy",
"retrieval: memory",
"retrieval: memory"
] | This series estimates the power production for a given day of a new solar power plant located in the state of Georgia, which has a climate similar to Alabama's. | -1 | {"0":{"2006-10-31 00:00":0.0,"2006-10-31 00:10":0.0,"2006-10-31 00:20":0.0,"2006-10-31 00:30":0.0,"2006-10-31 00:40":0.0,"2006-10-31 00:50":0.0,"2006-10-31 01:00":0.0,"2006-10-31 01:10":0.0,"2006-10-31 01:20":0.0,"2006-10-31 01:30":0.0,"2006-10-31 01:40":0.0,"2006-10-31 01:50":0.0,"2006-10-31 02:00":0.0,"2006-10-31 02:... | {"0":{"2006-10-31 07:40":5.9972337279,"2006-10-31 07:50":6.9690267096,"2006-10-31 08:00":7.708186453,"2006-10-31 08:10":8.6308985074,"2006-10-31 08:20":9.6693360351,"2006-10-31 08:30":10.5311173991,"2006-10-31 08:40":11.5033612976,"2006-10-31 08:50":12.5313185,"2006-10-31 09:00":13.1131902,"2006-10-31 09:10":13.7764265... | 0.047322 | [] | 0 | null | [] | [] | ||
ExplicitSimilarLocationDaySolarForecastTask | 5 | 1/3 | [
"c_i"
] | [
"forecasting",
"natural language processing",
"reasoning: analogy",
"retrieval: memory",
"retrieval: memory"
] | This series estimates the power production for a given day of a new solar power plant located in the state of Georgia, which has a climate similar to Alabama's. | -1 | {"0":{"2006-10-08 00:00":0.0,"2006-10-08 00:10":0.0,"2006-10-08 00:20":0.0,"2006-10-08 00:30":0.0,"2006-10-08 00:40":0.0,"2006-10-08 00:50":0.0,"2006-10-08 01:00":0.0,"2006-10-08 01:10":0.0,"2006-10-08 01:20":0.0,"2006-10-08 01:30":0.0,"2006-10-08 01:40":0.0,"2006-10-08 01:50":0.0,"2006-10-08 02:00":0.0,"2006-10-08 02:... | {"0":{"2006-10-08 07:30":11.9958865159,"2006-10-08 07:40":13.480032965,"2006-10-08 07:50":14.8045539937,"2006-10-08 08:00":16.1457243968,"2006-10-08 08:10":16.9255699817,"2006-10-08 08:20":18.1957384976,"2006-10-08 08:30":19.1876321639,"2006-10-08 08:40":20.273607985,"2006-10-08 08:50":21.1377693294,"2006-10-08 09:00":... | 0.047322 | [] | 0 | null | [] | [] | ||
SimilarLocationWithReferenceDaySolarForecastTask | 1 | 1/3 | [
"c_i"
] | [
"forecasting",
"natural language processing",
"reasoning: deduction",
"retrieval: memory"
] | This series estimates the power production for a given day of a new solar power plant located in the state of Georgia, which has a humid subtropical climate. As reference, the maximal power production in similar states on June 20th was of 25.83 at 11:10:00. | -1 | {"0":{"2006-08-07 00:00":0.0,"2006-08-07 00:10":0.0,"2006-08-07 00:20":0.0,"2006-08-07 00:30":0.0,"2006-08-07 00:40":0.0,"2006-08-07 00:50":0.0,"2006-08-07 01:00":0.0,"2006-08-07 01:10":0.0,"2006-08-07 01:20":0.0,"2006-08-07 01:30":0.0,"2006-08-07 01:40":0.0,"2006-08-07 01:50":0.0,"2006-08-07 02:00":0.0,"2006-08-07 02:... | {"0":{"2006-08-07 08:20":17.5179299253,"2006-08-07 08:30":18.7098870754,"2006-08-07 08:40":19.4589840311,"2006-08-07 08:50":19.9882580655,"2006-08-07 09:00":20.7888352916,"2006-08-07 09:10":21.3955295471,"2006-08-07 09:20":21.9402937551,"2006-08-07 09:30":22.3580268463,"2006-08-07 09:40":22.6194989639,"2006-08-07 09:50... | 0.047322 | [] | 0 | null | [] | [] | ||
SimilarLocationWithReferenceDaySolarForecastTask | 2 | 1/3 | [
"c_i"
] | [
"forecasting",
"natural language processing",
"reasoning: deduction",
"retrieval: memory"
] | This series estimates the power production for a given day of a new solar power plant located in the state of Georgia, which has a humid subtropical climate. As reference, the maximal power production in similar states on June 20th was of 25.83 at 11:10:00. | -1 | {"0":{"2006-12-16 00:00":0.0,"2006-12-16 00:10":0.0,"2006-12-16 00:20":0.0,"2006-12-16 00:30":0.0,"2006-12-16 00:40":0.0,"2006-12-16 00:50":0.0,"2006-12-16 01:00":0.0,"2006-12-16 01:10":0.0,"2006-12-16 01:20":0.0,"2006-12-16 01:30":0.0,"2006-12-16 01:40":0.0,"2006-12-16 01:50":0.0,"2006-12-16 02:00":0.0,"2006-12-16 02:... | {"0":{"2006-12-16 07:50":8.0293008208,"2006-12-16 08:00":9.4872760785,"2006-12-16 08:10":10.6606506373,"2006-12-16 08:20":11.7867018059,"2006-12-16 08:30":12.9065971326,"2006-12-16 08:40":13.9635470067,"2006-12-16 08:50":15.1477196694,"2006-12-16 09:00":15.7305725476,"2006-12-16 09:10":16.6569862298,"2006-12-16 09:20":... | 0.047322 | [] | 0 | null | [] | [] | ||
SimilarLocationWithReferenceDaySolarForecastTask | 3 | 1/3 | [
"c_i"
] | [
"forecasting",
"natural language processing",
"reasoning: deduction",
"retrieval: memory"
] | This series estimates the power production for a given day of a new solar power plant located in the state of Georgia, which has a humid subtropical climate. As reference, the maximal power production in similar states on June 20th was of 25.83 at 11:10:00. | -1 | {"0":{"2006-10-15 00:00":0.0,"2006-10-15 00:10":0.0,"2006-10-15 00:20":0.0,"2006-10-15 00:30":0.0,"2006-10-15 00:40":0.0,"2006-10-15 00:50":0.0,"2006-10-15 01:00":0.0,"2006-10-15 01:10":0.0,"2006-10-15 01:20":0.0,"2006-10-15 01:30":0.0,"2006-10-15 01:40":0.0,"2006-10-15 01:50":0.0,"2006-10-15 02:00":0.0,"2006-10-15 02:... | {"0":{"2006-10-15 07:50":12.8869962515,"2006-10-15 08:00":14.3252339023,"2006-10-15 08:10":15.1174236199,"2006-10-15 08:20":15.9294419533,"2006-10-15 08:30":16.7945758396,"2006-10-15 08:40":17.4001153941,"2006-10-15 08:50":18.4261426181,"2006-10-15 09:00":19.1668244976,"2006-10-15 09:10":20.2016320779,"2006-10-15 09:20... | 0.047322 | [] | 0 | null | [] | [] | ||
SimilarLocationWithReferenceDaySolarForecastTask | 4 | 1/3 | [
"c_i"
] | [
"forecasting",
"natural language processing",
"reasoning: deduction",
"retrieval: memory"
] | This series estimates the power production for a given day of a new solar power plant located in the state of Georgia, which has a humid subtropical climate. As reference, the maximal power production in similar states on June 20th was of 25.83 at 11:10:00. | -1 | {"0":{"2006-10-31 00:00":0.0,"2006-10-31 00:10":0.0,"2006-10-31 00:20":0.0,"2006-10-31 00:30":0.0,"2006-10-31 00:40":0.0,"2006-10-31 00:50":0.0,"2006-10-31 01:00":0.0,"2006-10-31 01:10":0.0,"2006-10-31 01:20":0.0,"2006-10-31 01:30":0.0,"2006-10-31 01:40":0.0,"2006-10-31 01:50":0.0,"2006-10-31 02:00":0.0,"2006-10-31 02:... | {"0":{"2006-10-31 07:40":5.9972337279,"2006-10-31 07:50":6.9690267096,"2006-10-31 08:00":7.708186453,"2006-10-31 08:10":8.6308985074,"2006-10-31 08:20":9.6693360351,"2006-10-31 08:30":10.5311173991,"2006-10-31 08:40":11.5033612976,"2006-10-31 08:50":12.5313185,"2006-10-31 09:00":13.1131902,"2006-10-31 09:10":13.7764265... | 0.047322 | [] | 0 | null | [] | [] | ||
SimilarLocationWithReferenceDaySolarForecastTask | 5 | 1/3 | [
"c_i"
] | [
"forecasting",
"natural language processing",
"reasoning: deduction",
"retrieval: memory"
] | This series estimates the power production for a given day of a new solar power plant located in the state of Georgia, which has a humid subtropical climate. As reference, the maximal power production in similar states on June 20th was of 25.83 at 11:10:00. | -1 | {"0":{"2006-10-08 00:00":0.0,"2006-10-08 00:10":0.0,"2006-10-08 00:20":0.0,"2006-10-08 00:30":0.0,"2006-10-08 00:40":0.0,"2006-10-08 00:50":0.0,"2006-10-08 01:00":0.0,"2006-10-08 01:10":0.0,"2006-10-08 01:20":0.0,"2006-10-08 01:30":0.0,"2006-10-08 01:40":0.0,"2006-10-08 01:50":0.0,"2006-10-08 02:00":0.0,"2006-10-08 02:... | {"0":{"2006-10-08 07:30":11.9958865159,"2006-10-08 07:40":13.480032965,"2006-10-08 07:50":14.8045539937,"2006-10-08 08:00":16.1457243968,"2006-10-08 08:10":16.9255699817,"2006-10-08 08:20":18.1957384976,"2006-10-08 08:30":19.1876321639,"2006-10-08 08:40":20.273607985,"2006-10-08 08:50":21.1377693294,"2006-10-08 09:00":... | 0.047322 | [] | 0 | null | [] | [] | ||
ImplicitTrafficForecastTaskwithHolidaysInPredictionWindow | 1 | 1/4 | [
"c_i"
] | [
"forecasting",
"natural language processing",
"retrieval: memory",
"reasoning: deduction"
] | This series contains the road occupancy rates on a freeway in the San Francisco Bay area. Note that traffic on this freeway typically reduces on holidays. | 24 | {"Occupancy (%)":{"2024-06-27T00:00:00.000":1.2,"2024-06-27T01:00:00.000":1.3,"2024-06-27T02:00:00.000":2.4,"2024-06-27T03:00:00.000":6.2,"2024-06-27T04:00:00.000":19.9,"2024-06-27T05:00:00.000":38.0,"2024-06-27T06:00:00.000":31.4,"2024-06-27T07:00:00.000":37.6,"2024-06-27T08:00:00.000":32.0,"2024-06-27T09:00:00.000":8... | {"Occupancy (%)":{"2024-07-04T00:00:00.000":1.3,"2024-07-04T01:00:00.000":1.1,"2024-07-04T02:00:00.000":1.5,"2024-07-04T03:00:00.000":1.9,"2024-07-04T04:00:00.000":2.3,"2024-07-04T05:00:00.000":2.3,"2024-07-04T06:00:00.000":2.6,"2024-07-04T07:00:00.000":2.6,"2024-07-04T08:00:00.000":3.3,"2024-07-04T09:00:00.000":4.4,"2... | 0.102417 | [
0,
1,
2,
3,
4,
5,
6,
7,
8,
9,
10,
11,
12,
13,
14,
15,
16,
17,
18,
19,
20,
21,
22,
23
] | null | null | [] | [] | ||
ImplicitTrafficForecastTaskwithHolidaysInPredictionWindow | 2 | 1/4 | [
"c_i"
] | [
"forecasting",
"natural language processing",
"retrieval: memory",
"reasoning: deduction"
] | This series contains the road occupancy rates on a freeway in the San Francisco Bay area. Note that traffic on this freeway typically reduces on holidays. | 24 | {"Occupancy (%)":{"2024-05-20T00:00:00.000":0.2,"2024-05-20T01:00:00.000":0.1,"2024-05-20T02:00:00.000":0.0,"2024-05-20T03:00:00.000":0.0,"2024-05-20T04:00:00.000":0.1,"2024-05-20T05:00:00.000":0.2,"2024-05-20T06:00:00.000":0.5,"2024-05-20T07:00:00.000":1.0,"2024-05-20T08:00:00.000":1.2,"2024-05-20T09:00:00.000":2.0,"2... | {"Occupancy (%)":{"2024-05-27T00:00:00.000":0.3,"2024-05-27T01:00:00.000":0.1,"2024-05-27T02:00:00.000":0.1,"2024-05-27T03:00:00.000":0.1,"2024-05-27T04:00:00.000":0.1,"2024-05-27T05:00:00.000":0.2,"2024-05-27T06:00:00.000":0.6,"2024-05-27T07:00:00.000":0.8,"2024-05-27T08:00:00.000":1.3,"2024-05-27T09:00:00.000":2.6,"2... | 0.102417 | [
0,
1,
2,
3,
4,
5,
6,
7,
8,
9,
10,
11,
12,
13,
14,
15,
16,
17,
18,
19,
20,
21,
22,
23
] | null | null | [] | [] | ||
ImplicitTrafficForecastTaskwithHolidaysInPredictionWindow | 3 | 1/4 | [
"c_i"
] | [
"forecasting",
"natural language processing",
"retrieval: memory",
"reasoning: deduction"
] | This series contains the road occupancy rates on a freeway in the San Francisco Bay area. Note that traffic on this freeway typically reduces on holidays. | 24 | {"Occupancy (%)":{"2024-06-27T00:00:00.000":0.5,"2024-06-27T01:00:00.000":0.5,"2024-06-27T02:00:00.000":0.6,"2024-06-27T03:00:00.000":1.2,"2024-06-27T04:00:00.000":3.4,"2024-06-27T05:00:00.000":5.4,"2024-06-27T06:00:00.000":5.3,"2024-06-27T07:00:00.000":6.9,"2024-06-27T08:00:00.000":4.7,"2024-06-27T09:00:00.000":4.8,"2... | {"Occupancy (%)":{"2024-07-04T00:00:00.000":1.1,"2024-07-04T01:00:00.000":0.6,"2024-07-04T02:00:00.000":0.6,"2024-07-04T03:00:00.000":0.8,"2024-07-04T04:00:00.000":1.9,"2024-07-04T05:00:00.000":2.5,"2024-07-04T06:00:00.000":2.4,"2024-07-04T07:00:00.000":2.3,"2024-07-04T08:00:00.000":2.3,"2024-07-04T09:00:00.000":2.6,"2... | 0.102417 | [
0,
1,
2,
3,
4,
5,
6,
7,
8,
9,
10,
11,
12,
13,
14,
15,
16,
17,
18,
19,
20,
21,
22,
23
] | null | null | [] | [] | ||
ImplicitTrafficForecastTaskwithHolidaysInPredictionWindow | 4 | 1/4 | [
"c_i"
] | [
"forecasting",
"natural language processing",
"retrieval: memory",
"reasoning: deduction"
] | This series contains the road occupancy rates on a freeway in the San Francisco Bay area. Note that traffic on this freeway typically reduces on holidays. | 24 | {"Occupancy (%)":{"2024-06-27T00:00:00.000":6.7,"2024-06-27T01:00:00.000":3.8,"2024-06-27T02:00:00.000":3.2,"2024-06-27T03:00:00.000":3.0,"2024-06-27T04:00:00.000":6.4,"2024-06-27T05:00:00.000":8.5,"2024-06-27T06:00:00.000":11.1,"2024-06-27T07:00:00.000":11.4,"2024-06-27T08:00:00.000":12.0,"2024-06-27T09:00:00.000":12.... | {"Occupancy (%)":{"2024-07-04T00:00:00.000":4.8,"2024-07-04T01:00:00.000":3.8,"2024-07-04T02:00:00.000":3.0,"2024-07-04T03:00:00.000":2.3,"2024-07-04T04:00:00.000":2.4,"2024-07-04T05:00:00.000":3.5,"2024-07-04T06:00:00.000":4.7,"2024-07-04T07:00:00.000":5.5,"2024-07-04T08:00:00.000":6.1,"2024-07-04T09:00:00.000":6.9,"2... | 0.102417 | [
0,
1,
2,
3,
4,
5,
6,
7,
8,
9,
10,
11,
12,
13,
14,
15,
16,
17,
18,
19,
20,
21,
22,
23
] | null | null | [] | [] | ||
ImplicitTrafficForecastTaskwithHolidaysInPredictionWindow | 5 | 1/4 | [
"c_i"
] | [
"forecasting",
"natural language processing",
"retrieval: memory",
"reasoning: deduction"
] | This series contains the road occupancy rates on a freeway in the San Francisco Bay area. Note that traffic on this freeway typically reduces on holidays. | 24 | {"Occupancy (%)":{"2024-05-20T00:00:00.000":0.5,"2024-05-20T01:00:00.000":1.0,"2024-05-20T02:00:00.000":0.9,"2024-05-20T03:00:00.000":0.5,"2024-05-20T04:00:00.000":0.2,"2024-05-20T05:00:00.000":0.8,"2024-05-20T06:00:00.000":2.2,"2024-05-20T07:00:00.000":5.2,"2024-05-20T08:00:00.000":4.5,"2024-05-20T09:00:00.000":5.3,"2... | {"Occupancy (%)":{"2024-05-27T00:00:00.000":0.3,"2024-05-27T01:00:00.000":0.1,"2024-05-27T02:00:00.000":0.1,"2024-05-27T03:00:00.000":0.0,"2024-05-27T04:00:00.000":0.1,"2024-05-27T05:00:00.000":0.1,"2024-05-27T06:00:00.000":0.3,"2024-05-27T07:00:00.000":0.8,"2024-05-27T08:00:00.000":1.4,"2024-05-27T09:00:00.000":1.9,"2... | 0.102417 | [
0,
1,
2,
3,
4,
5,
6,
7,
8,
9,
10,
11,
12,
13,
14,
15,
16,
17,
18,
19,
20,
21,
22,
23
] | null | null | [] | [] | ||
ExplicitTrafficForecastTaskwithHolidaysInPredictionWindow | 1 | 1/4 | [
"c_i",
"c_cov"
] | [
"forecasting",
"natural language processing",
"reasoning: deduction"
] | This series contains the road occupancy rates on a freeway in the San Francisco Bay area. Note that 2024-07-04 is a holiday due to Independence Day. Note that traffic on this freeway typically reduces on holidays. | 24 | {"Occupancy (%)":{"2024-06-27T00:00:00.000":1.2,"2024-06-27T01:00:00.000":1.3,"2024-06-27T02:00:00.000":2.4,"2024-06-27T03:00:00.000":6.2,"2024-06-27T04:00:00.000":19.9,"2024-06-27T05:00:00.000":38.0,"2024-06-27T06:00:00.000":31.4,"2024-06-27T07:00:00.000":37.6,"2024-06-27T08:00:00.000":32.0,"2024-06-27T09:00:00.000":8... | {"Occupancy (%)":{"2024-07-04T00:00:00.000":1.3,"2024-07-04T01:00:00.000":1.1,"2024-07-04T02:00:00.000":1.5,"2024-07-04T03:00:00.000":1.9,"2024-07-04T04:00:00.000":2.3,"2024-07-04T05:00:00.000":2.3,"2024-07-04T06:00:00.000":2.6,"2024-07-04T07:00:00.000":2.6,"2024-07-04T08:00:00.000":3.3,"2024-07-04T09:00:00.000":4.4,"2... | 0.102417 | [
0,
1,
2,
3,
4,
5,
6,
7,
8,
9,
10,
11,
12,
13,
14,
15,
16,
17,
18,
19,
20,
21,
22,
23
] | null | null | [] | [] | ||
ExplicitTrafficForecastTaskwithHolidaysInPredictionWindow | 2 | 1/4 | [
"c_i",
"c_cov"
] | [
"forecasting",
"natural language processing",
"reasoning: deduction"
] | This series contains the road occupancy rates on a freeway in the San Francisco Bay area. Note that 2024-05-27 is a holiday due to Memorial Day. Note that traffic on this freeway typically reduces on holidays. | 24 | {"Occupancy (%)":{"2024-05-20T00:00:00.000":0.2,"2024-05-20T01:00:00.000":0.1,"2024-05-20T02:00:00.000":0.0,"2024-05-20T03:00:00.000":0.0,"2024-05-20T04:00:00.000":0.1,"2024-05-20T05:00:00.000":0.2,"2024-05-20T06:00:00.000":0.5,"2024-05-20T07:00:00.000":1.0,"2024-05-20T08:00:00.000":1.2,"2024-05-20T09:00:00.000":2.0,"2... | {"Occupancy (%)":{"2024-05-27T00:00:00.000":0.3,"2024-05-27T01:00:00.000":0.1,"2024-05-27T02:00:00.000":0.1,"2024-05-27T03:00:00.000":0.1,"2024-05-27T04:00:00.000":0.1,"2024-05-27T05:00:00.000":0.2,"2024-05-27T06:00:00.000":0.6,"2024-05-27T07:00:00.000":0.8,"2024-05-27T08:00:00.000":1.3,"2024-05-27T09:00:00.000":2.6,"2... | 0.102417 | [
0,
1,
2,
3,
4,
5,
6,
7,
8,
9,
10,
11,
12,
13,
14,
15,
16,
17,
18,
19,
20,
21,
22,
23
] | null | null | [] | [] | ||
ExplicitTrafficForecastTaskwithHolidaysInPredictionWindow | 3 | 1/4 | [
"c_i",
"c_cov"
] | [
"forecasting",
"natural language processing",
"reasoning: deduction"
] | This series contains the road occupancy rates on a freeway in the San Francisco Bay area. Note that 2024-07-04 is a holiday due to Independence Day. Note that traffic on this freeway typically reduces on holidays. | 24 | {"Occupancy (%)":{"2024-06-27T00:00:00.000":0.5,"2024-06-27T01:00:00.000":0.5,"2024-06-27T02:00:00.000":0.6,"2024-06-27T03:00:00.000":1.2,"2024-06-27T04:00:00.000":3.4,"2024-06-27T05:00:00.000":5.4,"2024-06-27T06:00:00.000":5.3,"2024-06-27T07:00:00.000":6.9,"2024-06-27T08:00:00.000":4.7,"2024-06-27T09:00:00.000":4.8,"2... | {"Occupancy (%)":{"2024-07-04T00:00:00.000":1.1,"2024-07-04T01:00:00.000":0.6,"2024-07-04T02:00:00.000":0.6,"2024-07-04T03:00:00.000":0.8,"2024-07-04T04:00:00.000":1.9,"2024-07-04T05:00:00.000":2.5,"2024-07-04T06:00:00.000":2.4,"2024-07-04T07:00:00.000":2.3,"2024-07-04T08:00:00.000":2.3,"2024-07-04T09:00:00.000":2.6,"2... | 0.102417 | [
0,
1,
2,
3,
4,
5,
6,
7,
8,
9,
10,
11,
12,
13,
14,
15,
16,
17,
18,
19,
20,
21,
22,
23
] | null | null | [] | [] | ||
ExplicitTrafficForecastTaskwithHolidaysInPredictionWindow | 4 | 1/4 | [
"c_i",
"c_cov"
] | [
"forecasting",
"natural language processing",
"reasoning: deduction"
] | This series contains the road occupancy rates on a freeway in the San Francisco Bay area. Note that 2024-07-04 is a holiday due to Independence Day. Note that traffic on this freeway typically reduces on holidays. | 24 | {"Occupancy (%)":{"2024-06-27T00:00:00.000":6.7,"2024-06-27T01:00:00.000":3.8,"2024-06-27T02:00:00.000":3.2,"2024-06-27T03:00:00.000":3.0,"2024-06-27T04:00:00.000":6.4,"2024-06-27T05:00:00.000":8.5,"2024-06-27T06:00:00.000":11.1,"2024-06-27T07:00:00.000":11.4,"2024-06-27T08:00:00.000":12.0,"2024-06-27T09:00:00.000":12.... | {"Occupancy (%)":{"2024-07-04T00:00:00.000":4.8,"2024-07-04T01:00:00.000":3.8,"2024-07-04T02:00:00.000":3.0,"2024-07-04T03:00:00.000":2.3,"2024-07-04T04:00:00.000":2.4,"2024-07-04T05:00:00.000":3.5,"2024-07-04T06:00:00.000":4.7,"2024-07-04T07:00:00.000":5.5,"2024-07-04T08:00:00.000":6.1,"2024-07-04T09:00:00.000":6.9,"2... | 0.102417 | [
0,
1,
2,
3,
4,
5,
6,
7,
8,
9,
10,
11,
12,
13,
14,
15,
16,
17,
18,
19,
20,
21,
22,
23
] | null | null | [] | [] | ||
ExplicitTrafficForecastTaskwithHolidaysInPredictionWindow | 5 | 1/4 | [
"c_i",
"c_cov"
] | [
"forecasting",
"natural language processing",
"reasoning: deduction"
] | This series contains the road occupancy rates on a freeway in the San Francisco Bay area. Note that 2024-05-27 is a holiday due to Memorial Day. Note that traffic on this freeway typically reduces on holidays. | 24 | {"Occupancy (%)":{"2024-05-20T00:00:00.000":0.5,"2024-05-20T01:00:00.000":1.0,"2024-05-20T02:00:00.000":0.9,"2024-05-20T03:00:00.000":0.5,"2024-05-20T04:00:00.000":0.2,"2024-05-20T05:00:00.000":0.8,"2024-05-20T06:00:00.000":2.2,"2024-05-20T07:00:00.000":5.2,"2024-05-20T08:00:00.000":4.5,"2024-05-20T09:00:00.000":5.3,"2... | {"Occupancy (%)":{"2024-05-27T00:00:00.000":0.3,"2024-05-27T01:00:00.000":0.1,"2024-05-27T02:00:00.000":0.1,"2024-05-27T03:00:00.000":0.0,"2024-05-27T04:00:00.000":0.1,"2024-05-27T05:00:00.000":0.1,"2024-05-27T06:00:00.000":0.3,"2024-05-27T07:00:00.000":0.8,"2024-05-27T08:00:00.000":1.4,"2024-05-27T09:00:00.000":1.9,"2... | 0.102417 | [
0,
1,
2,
3,
4,
5,
6,
7,
8,
9,
10,
11,
12,
13,
14,
15,
16,
17,
18,
19,
20,
21,
22,
23
] | null | null | [] | [] | ||
ExplicitWithDaysTrafficForecastTaskwithHolidaysInPredictionWindow | 1 | 1/4 | [
"c_i",
"c_cov"
] | [
"forecasting",
"natural language processing",
"reasoning: deduction"
] | This series contains the road occupancy rates on a freeway in the San Francisco Bay area. The days for which the forecast is required are Thursday, Friday, Saturday. Note that 2024-07-04 is a holiday due to Independence Day. Note that traffic on this freeway typically reduces on holidays. | 24 | {"Occupancy (%)":{"2024-06-27T00:00:00.000":1.2,"2024-06-27T01:00:00.000":1.3,"2024-06-27T02:00:00.000":2.4,"2024-06-27T03:00:00.000":6.2,"2024-06-27T04:00:00.000":19.9,"2024-06-27T05:00:00.000":38.0,"2024-06-27T06:00:00.000":31.4,"2024-06-27T07:00:00.000":37.6,"2024-06-27T08:00:00.000":32.0,"2024-06-27T09:00:00.000":8... | {"Occupancy (%)":{"2024-07-04T00:00:00.000":1.3,"2024-07-04T01:00:00.000":1.1,"2024-07-04T02:00:00.000":1.5,"2024-07-04T03:00:00.000":1.9,"2024-07-04T04:00:00.000":2.3,"2024-07-04T05:00:00.000":2.3,"2024-07-04T06:00:00.000":2.6,"2024-07-04T07:00:00.000":2.6,"2024-07-04T08:00:00.000":3.3,"2024-07-04T09:00:00.000":4.4,"2... | 0.102417 | [
0,
1,
2,
3,
4,
5,
6,
7,
8,
9,
10,
11,
12,
13,
14,
15,
16,
17,
18,
19,
20,
21,
22,
23
] | null | null | [] | [] | ||
ExplicitWithDaysTrafficForecastTaskwithHolidaysInPredictionWindow | 2 | 1/4 | [
"c_i",
"c_cov"
] | [
"forecasting",
"natural language processing",
"reasoning: deduction"
] | This series contains the road occupancy rates on a freeway in the San Francisco Bay area. The days for which the forecast is required are Monday, Tuesday, Wednesday. Note that 2024-05-27 is a holiday due to Memorial Day. Note that traffic on this freeway typically reduces on holidays. | 24 | {"Occupancy (%)":{"2024-05-20T00:00:00.000":0.2,"2024-05-20T01:00:00.000":0.1,"2024-05-20T02:00:00.000":0.0,"2024-05-20T03:00:00.000":0.0,"2024-05-20T04:00:00.000":0.1,"2024-05-20T05:00:00.000":0.2,"2024-05-20T06:00:00.000":0.5,"2024-05-20T07:00:00.000":1.0,"2024-05-20T08:00:00.000":1.2,"2024-05-20T09:00:00.000":2.0,"2... | {"Occupancy (%)":{"2024-05-27T00:00:00.000":0.3,"2024-05-27T01:00:00.000":0.1,"2024-05-27T02:00:00.000":0.1,"2024-05-27T03:00:00.000":0.1,"2024-05-27T04:00:00.000":0.1,"2024-05-27T05:00:00.000":0.2,"2024-05-27T06:00:00.000":0.6,"2024-05-27T07:00:00.000":0.8,"2024-05-27T08:00:00.000":1.3,"2024-05-27T09:00:00.000":2.6,"2... | 0.102417 | [
0,
1,
2,
3,
4,
5,
6,
7,
8,
9,
10,
11,
12,
13,
14,
15,
16,
17,
18,
19,
20,
21,
22,
23
] | null | null | [] | [] | ||
ExplicitWithDaysTrafficForecastTaskwithHolidaysInPredictionWindow | 3 | 1/4 | [
"c_i",
"c_cov"
] | [
"forecasting",
"natural language processing",
"reasoning: deduction"
] | This series contains the road occupancy rates on a freeway in the San Francisco Bay area. The days for which the forecast is required are Thursday, Friday, Saturday. Note that 2024-07-04 is a holiday due to Independence Day. Note that traffic on this freeway typically reduces on holidays. | 24 | {"Occupancy (%)":{"2024-06-27T00:00:00.000":0.5,"2024-06-27T01:00:00.000":0.5,"2024-06-27T02:00:00.000":0.6,"2024-06-27T03:00:00.000":1.2,"2024-06-27T04:00:00.000":3.4,"2024-06-27T05:00:00.000":5.4,"2024-06-27T06:00:00.000":5.3,"2024-06-27T07:00:00.000":6.9,"2024-06-27T08:00:00.000":4.7,"2024-06-27T09:00:00.000":4.8,"2... | {"Occupancy (%)":{"2024-07-04T00:00:00.000":1.1,"2024-07-04T01:00:00.000":0.6,"2024-07-04T02:00:00.000":0.6,"2024-07-04T03:00:00.000":0.8,"2024-07-04T04:00:00.000":1.9,"2024-07-04T05:00:00.000":2.5,"2024-07-04T06:00:00.000":2.4,"2024-07-04T07:00:00.000":2.3,"2024-07-04T08:00:00.000":2.3,"2024-07-04T09:00:00.000":2.6,"2... | 0.102417 | [
0,
1,
2,
3,
4,
5,
6,
7,
8,
9,
10,
11,
12,
13,
14,
15,
16,
17,
18,
19,
20,
21,
22,
23
] | null | null | [] | [] | ||
ExplicitWithDaysTrafficForecastTaskwithHolidaysInPredictionWindow | 4 | 1/4 | [
"c_i",
"c_cov"
] | [
"forecasting",
"natural language processing",
"reasoning: deduction"
] | This series contains the road occupancy rates on a freeway in the San Francisco Bay area. The days for which the forecast is required are Thursday, Friday, Saturday. Note that 2024-07-04 is a holiday due to Independence Day. Note that traffic on this freeway typically reduces on holidays. | 24 | {"Occupancy (%)":{"2024-06-27T00:00:00.000":6.7,"2024-06-27T01:00:00.000":3.8,"2024-06-27T02:00:00.000":3.2,"2024-06-27T03:00:00.000":3.0,"2024-06-27T04:00:00.000":6.4,"2024-06-27T05:00:00.000":8.5,"2024-06-27T06:00:00.000":11.1,"2024-06-27T07:00:00.000":11.4,"2024-06-27T08:00:00.000":12.0,"2024-06-27T09:00:00.000":12.... | {"Occupancy (%)":{"2024-07-04T00:00:00.000":4.8,"2024-07-04T01:00:00.000":3.8,"2024-07-04T02:00:00.000":3.0,"2024-07-04T03:00:00.000":2.3,"2024-07-04T04:00:00.000":2.4,"2024-07-04T05:00:00.000":3.5,"2024-07-04T06:00:00.000":4.7,"2024-07-04T07:00:00.000":5.5,"2024-07-04T08:00:00.000":6.1,"2024-07-04T09:00:00.000":6.9,"2... | 0.102417 | [
0,
1,
2,
3,
4,
5,
6,
7,
8,
9,
10,
11,
12,
13,
14,
15,
16,
17,
18,
19,
20,
21,
22,
23
] | null | null | [] | [] | ||
ExplicitWithDaysTrafficForecastTaskwithHolidaysInPredictionWindow | 5 | 1/4 | [
"c_i",
"c_cov"
] | [
"forecasting",
"natural language processing",
"reasoning: deduction"
] | This series contains the road occupancy rates on a freeway in the San Francisco Bay area. The days for which the forecast is required are Monday, Tuesday, Wednesday. Note that 2024-05-27 is a holiday due to Memorial Day. Note that traffic on this freeway typically reduces on holidays. | 24 | {"Occupancy (%)":{"2024-05-20T00:00:00.000":0.5,"2024-05-20T01:00:00.000":1.0,"2024-05-20T02:00:00.000":0.9,"2024-05-20T03:00:00.000":0.5,"2024-05-20T04:00:00.000":0.2,"2024-05-20T05:00:00.000":0.8,"2024-05-20T06:00:00.000":2.2,"2024-05-20T07:00:00.000":5.2,"2024-05-20T08:00:00.000":4.5,"2024-05-20T09:00:00.000":5.3,"2... | {"Occupancy (%)":{"2024-05-27T00:00:00.000":0.3,"2024-05-27T01:00:00.000":0.1,"2024-05-27T02:00:00.000":0.1,"2024-05-27T03:00:00.000":0.0,"2024-05-27T04:00:00.000":0.1,"2024-05-27T05:00:00.000":0.1,"2024-05-27T06:00:00.000":0.3,"2024-05-27T07:00:00.000":0.8,"2024-05-27T08:00:00.000":1.4,"2024-05-27T09:00:00.000":1.9,"2... | 0.102417 | [
0,
1,
2,
3,
4,
5,
6,
7,
8,
9,
10,
11,
12,
13,
14,
15,
16,
17,
18,
19,
20,
21,
22,
23
] | null | null | [] | [] | ||
ExplicitWithDatesAndDaysTrafficForecastTaskwithHolidaysInPredictionWindow | 1 | 1/4 | [
"c_i",
"c_cov"
] | [
"forecasting",
"natural language processing",
"reasoning: deduction"
] | This series contains the road occupancy rates on a freeway in the San Francisco Bay area. The days for which the forecast is required are Thursday 2024-07-04, Friday 2024-07-05, Saturday 2024-07-06. Note that 2024-07-04 is a holiday due to Independence Day. Note that traffic on this freeway typically reduces on holiday... | 24 | {"Occupancy (%)":{"2024-06-27T00:00:00.000":1.2,"2024-06-27T01:00:00.000":1.3,"2024-06-27T02:00:00.000":2.4,"2024-06-27T03:00:00.000":6.2,"2024-06-27T04:00:00.000":19.9,"2024-06-27T05:00:00.000":38.0,"2024-06-27T06:00:00.000":31.4,"2024-06-27T07:00:00.000":37.6,"2024-06-27T08:00:00.000":32.0,"2024-06-27T09:00:00.000":8... | {"Occupancy (%)":{"2024-07-04T00:00:00.000":1.3,"2024-07-04T01:00:00.000":1.1,"2024-07-04T02:00:00.000":1.5,"2024-07-04T03:00:00.000":1.9,"2024-07-04T04:00:00.000":2.3,"2024-07-04T05:00:00.000":2.3,"2024-07-04T06:00:00.000":2.6,"2024-07-04T07:00:00.000":2.6,"2024-07-04T08:00:00.000":3.3,"2024-07-04T09:00:00.000":4.4,"2... | 0.102417 | [
0,
1,
2,
3,
4,
5,
6,
7,
8,
9,
10,
11,
12,
13,
14,
15,
16,
17,
18,
19,
20,
21,
22,
23
] | null | null | [] | [] | ||
ExplicitWithDatesAndDaysTrafficForecastTaskwithHolidaysInPredictionWindow | 2 | 1/4 | [
"c_i",
"c_cov"
] | [
"forecasting",
"natural language processing",
"reasoning: deduction"
] | This series contains the road occupancy rates on a freeway in the San Francisco Bay area. The days for which the forecast is required are Monday 2024-05-27, Tuesday 2024-05-28, Wednesday 2024-05-29. Note that 2024-05-27 is a holiday due to Memorial Day. Note that traffic on this freeway typically reduces on holidays. | 24 | {"Occupancy (%)":{"2024-05-20T00:00:00.000":0.2,"2024-05-20T01:00:00.000":0.1,"2024-05-20T02:00:00.000":0.0,"2024-05-20T03:00:00.000":0.0,"2024-05-20T04:00:00.000":0.1,"2024-05-20T05:00:00.000":0.2,"2024-05-20T06:00:00.000":0.5,"2024-05-20T07:00:00.000":1.0,"2024-05-20T08:00:00.000":1.2,"2024-05-20T09:00:00.000":2.0,"2... | {"Occupancy (%)":{"2024-05-27T00:00:00.000":0.3,"2024-05-27T01:00:00.000":0.1,"2024-05-27T02:00:00.000":0.1,"2024-05-27T03:00:00.000":0.1,"2024-05-27T04:00:00.000":0.1,"2024-05-27T05:00:00.000":0.2,"2024-05-27T06:00:00.000":0.6,"2024-05-27T07:00:00.000":0.8,"2024-05-27T08:00:00.000":1.3,"2024-05-27T09:00:00.000":2.6,"2... | 0.102417 | [
0,
1,
2,
3,
4,
5,
6,
7,
8,
9,
10,
11,
12,
13,
14,
15,
16,
17,
18,
19,
20,
21,
22,
23
] | null | null | [] | [] | ||
ExplicitWithDatesAndDaysTrafficForecastTaskwithHolidaysInPredictionWindow | 3 | 1/4 | [
"c_i",
"c_cov"
] | [
"forecasting",
"natural language processing",
"reasoning: deduction"
] | This series contains the road occupancy rates on a freeway in the San Francisco Bay area. The days for which the forecast is required are Thursday 2024-07-04, Friday 2024-07-05, Saturday 2024-07-06. Note that 2024-07-04 is a holiday due to Independence Day. Note that traffic on this freeway typically reduces on holiday... | 24 | {"Occupancy (%)":{"2024-06-27T00:00:00.000":0.5,"2024-06-27T01:00:00.000":0.5,"2024-06-27T02:00:00.000":0.6,"2024-06-27T03:00:00.000":1.2,"2024-06-27T04:00:00.000":3.4,"2024-06-27T05:00:00.000":5.4,"2024-06-27T06:00:00.000":5.3,"2024-06-27T07:00:00.000":6.9,"2024-06-27T08:00:00.000":4.7,"2024-06-27T09:00:00.000":4.8,"2... | {"Occupancy (%)":{"2024-07-04T00:00:00.000":1.1,"2024-07-04T01:00:00.000":0.6,"2024-07-04T02:00:00.000":0.6,"2024-07-04T03:00:00.000":0.8,"2024-07-04T04:00:00.000":1.9,"2024-07-04T05:00:00.000":2.5,"2024-07-04T06:00:00.000":2.4,"2024-07-04T07:00:00.000":2.3,"2024-07-04T08:00:00.000":2.3,"2024-07-04T09:00:00.000":2.6,"2... | 0.102417 | [
0,
1,
2,
3,
4,
5,
6,
7,
8,
9,
10,
11,
12,
13,
14,
15,
16,
17,
18,
19,
20,
21,
22,
23
] | null | null | [] | [] | ||
ExplicitWithDatesAndDaysTrafficForecastTaskwithHolidaysInPredictionWindow | 4 | 1/4 | [
"c_i",
"c_cov"
] | [
"forecasting",
"natural language processing",
"reasoning: deduction"
] | This series contains the road occupancy rates on a freeway in the San Francisco Bay area. The days for which the forecast is required are Thursday 2024-07-04, Friday 2024-07-05, Saturday 2024-07-06. Note that 2024-07-04 is a holiday due to Independence Day. Note that traffic on this freeway typically reduces on holiday... | 24 | {"Occupancy (%)":{"2024-06-27T00:00:00.000":6.7,"2024-06-27T01:00:00.000":3.8,"2024-06-27T02:00:00.000":3.2,"2024-06-27T03:00:00.000":3.0,"2024-06-27T04:00:00.000":6.4,"2024-06-27T05:00:00.000":8.5,"2024-06-27T06:00:00.000":11.1,"2024-06-27T07:00:00.000":11.4,"2024-06-27T08:00:00.000":12.0,"2024-06-27T09:00:00.000":12.... | {"Occupancy (%)":{"2024-07-04T00:00:00.000":4.8,"2024-07-04T01:00:00.000":3.8,"2024-07-04T02:00:00.000":3.0,"2024-07-04T03:00:00.000":2.3,"2024-07-04T04:00:00.000":2.4,"2024-07-04T05:00:00.000":3.5,"2024-07-04T06:00:00.000":4.7,"2024-07-04T07:00:00.000":5.5,"2024-07-04T08:00:00.000":6.1,"2024-07-04T09:00:00.000":6.9,"2... | 0.102417 | [
0,
1,
2,
3,
4,
5,
6,
7,
8,
9,
10,
11,
12,
13,
14,
15,
16,
17,
18,
19,
20,
21,
22,
23
] | null | null | [] | [] | ||
ExplicitWithDatesAndDaysTrafficForecastTaskwithHolidaysInPredictionWindow | 5 | 1/4 | [
"c_i",
"c_cov"
] | [
"forecasting",
"natural language processing",
"reasoning: deduction"
] | This series contains the road occupancy rates on a freeway in the San Francisco Bay area. The days for which the forecast is required are Monday 2024-05-27, Tuesday 2024-05-28, Wednesday 2024-05-29. Note that 2024-05-27 is a holiday due to Memorial Day. Note that traffic on this freeway typically reduces on holidays. | 24 | {"Occupancy (%)":{"2024-05-20T00:00:00.000":0.5,"2024-05-20T01:00:00.000":1.0,"2024-05-20T02:00:00.000":0.9,"2024-05-20T03:00:00.000":0.5,"2024-05-20T04:00:00.000":0.2,"2024-05-20T05:00:00.000":0.8,"2024-05-20T06:00:00.000":2.2,"2024-05-20T07:00:00.000":5.2,"2024-05-20T08:00:00.000":4.5,"2024-05-20T09:00:00.000":5.3,"2... | {"Occupancy (%)":{"2024-05-27T00:00:00.000":0.3,"2024-05-27T01:00:00.000":0.1,"2024-05-27T02:00:00.000":0.1,"2024-05-27T03:00:00.000":0.0,"2024-05-27T04:00:00.000":0.1,"2024-05-27T05:00:00.000":0.1,"2024-05-27T06:00:00.000":0.3,"2024-05-27T07:00:00.000":0.8,"2024-05-27T08:00:00.000":1.4,"2024-05-27T09:00:00.000":1.9,"2... | 0.102417 | [
0,
1,
2,
3,
4,
5,
6,
7,
8,
9,
10,
11,
12,
13,
14,
15,
16,
17,
18,
19,
20,
21,
22,
23
] | null | null | [] | [] | ||
DirectNormalIrradianceFromCloudStatus | 1 | 1/4 | [
"c_i",
"c_cov"
] | [
"forecasting",
"natural language processing",
"reasoning: deduction"
] | This series contains Direct Normal Irradiance for a location in Sinaloa, Mexico.
The Direct Normal Irradiance is the total amount of sun energy (in Watts per squared meter) arriving directly from the sun on a surface perpendicular to the sunlight direction. | At the beginning of the series, the weather was cloudy.
At 2022-07-13 11:00:00, the weather became clear.
At 2022-07-13 19:00:00, the weather became cloudy.
At 2022-07-14 12:00:00, the weather became clear.
At 2022-07-14 13:00:00, the weather became cloudy.
At 2022-07-15 06:00:00, we expect that the weather will become... | 24 | {"DNI":{"2022-07-13T00:00:00.000":0,"2022-07-13T01:00:00.000":0,"2022-07-13T02:00:00.000":0,"2022-07-13T03:00:00.000":0,"2022-07-13T04:00:00.000":0,"2022-07-13T05:00:00.000":0,"2022-07-13T06:00:00.000":0,"2022-07-13T07:00:00.000":32,"2022-07-13T08:00:00.000":48,"2022-07-13T09:00:00.000":321,"2022-07-13T10:00:00.000":53... | {"DNI":{"2022-07-15T00:00:00.000":0,"2022-07-15T01:00:00.000":0,"2022-07-15T02:00:00.000":0,"2022-07-15T03:00:00.000":0,"2022-07-15T04:00:00.000":0,"2022-07-15T05:00:00.000":0,"2022-07-15T06:00:00.000":74,"2022-07-15T07:00:00.000":5,"2022-07-15T08:00:00.000":42,"2022-07-15T09:00:00.000":6,"2022-07-15T10:00:00.000":729,... | 0.001389 | [] | 0 | null | [] | [] | |
DirectNormalIrradianceFromCloudStatus | 2 | 1/4 | [
"c_i",
"c_cov"
] | [
"forecasting",
"natural language processing",
"reasoning: deduction"
] | This series contains Direct Normal Irradiance for a location in Ontario, Canada.
The Direct Normal Irradiance is the total amount of sun energy (in Watts per squared meter) arriving directly from the sun on a surface perpendicular to the sunlight direction. | At the beginning of the series, the weather was cloudy.
At 2022-08-09 01:00:00, the weather became clear.
At 2022-08-09 02:00:00, the weather became cloudy.
At 2022-08-09 04:00:00, the weather became clear.
At 2022-08-09 05:00:00, the weather became cloudy.
At 2022-08-09 18:00:00, the weather became clear.
At 2022-08-1... | 24 | {"DNI":{"2022-08-09T00:00:00.000":0,"2022-08-09T01:00:00.000":0,"2022-08-09T02:00:00.000":0,"2022-08-09T03:00:00.000":0,"2022-08-09T04:00:00.000":0,"2022-08-09T05:00:00.000":0,"2022-08-09T06:00:00.000":0,"2022-08-09T07:00:00.000":0,"2022-08-09T08:00:00.000":0,"2022-08-09T09:00:00.000":0,"2022-08-09T10:00:00.000":0,"202... | {"DNI":{"2022-08-11T00:00:00.000":0,"2022-08-11T01:00:00.000":0,"2022-08-11T02:00:00.000":0,"2022-08-11T03:00:00.000":0,"2022-08-11T04:00:00.000":0,"2022-08-11T05:00:00.000":0,"2022-08-11T06:00:00.000":302,"2022-08-11T07:00:00.000":587,"2022-08-11T08:00:00.000":726,"2022-08-11T09:00:00.000":801,"2022-08-11T10:00:00.000... | 0.001389 | [] | 0 | null | [] | [] | |
DirectNormalIrradianceFromCloudStatus | 3 | 1/4 | [
"c_i",
"c_cov"
] | [
"forecasting",
"natural language processing",
"reasoning: deduction"
] | This series contains Direct Normal Irradiance for a location in Amazonas, Brazil.
The Direct Normal Irradiance is the total amount of sun energy (in Watts per squared meter) arriving directly from the sun on a surface perpendicular to the sunlight direction. | At the beginning of the series, the weather was cloudy.
At 2022-10-14 06:00:00, the weather became clear.
At 2022-10-14 08:00:00, the weather became cloudy.
At 2022-10-14 12:00:00, the weather became clear.
At 2022-10-14 14:00:00, the weather became cloudy.
At 2022-10-15 12:00:00, the weather became clear.
At 2022-10-1... | 24 | {"DNI":{"2022-10-14T00:00:00.000":0,"2022-10-14T01:00:00.000":0,"2022-10-14T02:00:00.000":0,"2022-10-14T03:00:00.000":0,"2022-10-14T04:00:00.000":0,"2022-10-14T05:00:00.000":0,"2022-10-14T06:00:00.000":29,"2022-10-14T07:00:00.000":261,"2022-10-14T08:00:00.000":313,"2022-10-14T09:00:00.000":440,"2022-10-14T10:00:00.000"... | {"DNI":{"2022-10-16T00:00:00.000":0,"2022-10-16T01:00:00.000":0,"2022-10-16T02:00:00.000":0,"2022-10-16T03:00:00.000":0,"2022-10-16T04:00:00.000":0,"2022-10-16T05:00:00.000":0,"2022-10-16T06:00:00.000":45,"2022-10-16T07:00:00.000":4,"2022-10-16T08:00:00.000":0,"2022-10-16T09:00:00.000":132,"2022-10-16T10:00:00.000":61,... | 0.001389 | [] | 0 | null | [] | [] | |
DirectNormalIrradianceFromCloudStatus | 4 | 1/4 | [
"c_i",
"c_cov"
] | [
"forecasting",
"natural language processing",
"reasoning: deduction"
] | This series contains Direct Normal Irradiance for a location in Florida, United States.
The Direct Normal Irradiance is the total amount of sun energy (in Watts per squared meter) arriving directly from the sun on a surface perpendicular to the sunlight direction. | At the beginning of the series, the weather was cloudy.
At 2022-01-23 20:00:00, the weather became clear.
At 2022-01-23 21:00:00, the weather became cloudy.
At 2022-01-23 23:00:00, the weather became clear.
At 2022-01-24 00:00:00, the weather became cloudy.
At 2022-01-24 01:00:00, the weather became clear.
At 2022-01-2... | 24 | {"DNI":{"2022-01-23T00:00:00.000":0,"2022-01-23T01:00:00.000":0,"2022-01-23T02:00:00.000":0,"2022-01-23T03:00:00.000":0,"2022-01-23T04:00:00.000":0,"2022-01-23T05:00:00.000":0,"2022-01-23T06:00:00.000":0,"2022-01-23T07:00:00.000":0,"2022-01-23T08:00:00.000":0,"2022-01-23T09:00:00.000":7,"2022-01-23T10:00:00.000":40,"20... | {"DNI":{"2022-01-25T00:00:00.000":0,"2022-01-25T01:00:00.000":0,"2022-01-25T02:00:00.000":0,"2022-01-25T03:00:00.000":0,"2022-01-25T04:00:00.000":0,"2022-01-25T05:00:00.000":0,"2022-01-25T06:00:00.000":0,"2022-01-25T07:00:00.000":0,"2022-01-25T08:00:00.000":447,"2022-01-25T09:00:00.000":776,"2022-01-25T10:00:00.000":89... | 0.001389 | [] | 0 | null | [] | [] | |
DirectNormalIrradianceFromCloudStatus | 5 | 1/4 | [
"c_i",
"c_cov"
] | [
"forecasting",
"natural language processing",
"reasoning: deduction"
] | This series contains Direct Normal Irradiance for a location in Amazonas, Brazil.
The Direct Normal Irradiance is the total amount of sun energy (in Watts per squared meter) arriving directly from the sun on a surface perpendicular to the sunlight direction. | At the beginning of the series, the weather was clear.
At 2022-06-04 02:00:00, the weather became cloudy.
At 2022-06-04 10:00:00, the weather became clear.
At 2022-06-04 14:00:00, the weather became cloudy.
At 2022-06-05 20:00:00, the weather became clear.
At 2022-06-05 21:00:00, the weather became cloudy.
At 2022-06-0... | 24 | {"DNI":{"2022-06-04T00:00:00.000":0,"2022-06-04T01:00:00.000":0,"2022-06-04T02:00:00.000":0,"2022-06-04T03:00:00.000":0,"2022-06-04T04:00:00.000":0,"2022-06-04T05:00:00.000":0,"2022-06-04T06:00:00.000":0,"2022-06-04T07:00:00.000":8,"2022-06-04T08:00:00.000":33,"2022-06-04T09:00:00.000":169,"2022-06-04T10:00:00.000":817... | {"DNI":{"2022-06-06T00:00:00.000":0,"2022-06-06T01:00:00.000":0,"2022-06-06T02:00:00.000":0,"2022-06-06T03:00:00.000":0,"2022-06-06T04:00:00.000":0,"2022-06-06T05:00:00.000":0,"2022-06-06T06:00:00.000":0,"2022-06-06T07:00:00.000":430,"2022-06-06T08:00:00.000":654,"2022-06-06T09:00:00.000":272,"2022-06-06T10:00:00.000":... | 0.001389 | [] | 0 | null | [] | [] | |
ExplicitDirectNormalIrradianceFromCloudStatus | 1 | 1/4 | [
"c_i",
"c_cov"
] | [
"forecasting",
"natural language processing",
"instruction following"
] | This series contains Direct Normal Irradiance for a location in Sinaloa, Mexico.
The Direct Normal Irradiance is the total amount of sun energy (in Watts per squared meter) arriving directly from the sun on a surface perpendicular to the sunlight direction. When there are no clouds to block the sun, the Direct Normal I... | At the beginning of the series, the weather was cloudy.
At 2022-07-13 11:00:00, the weather became clear.
At 2022-07-13 19:00:00, the weather became cloudy.
At 2022-07-14 12:00:00, the weather became clear.
At 2022-07-14 13:00:00, the weather became cloudy.
At 2022-07-15 06:00:00, we expect that the weather will become... | 24 | {"DNI":{"2022-07-13T00:00:00.000":0,"2022-07-13T01:00:00.000":0,"2022-07-13T02:00:00.000":0,"2022-07-13T03:00:00.000":0,"2022-07-13T04:00:00.000":0,"2022-07-13T05:00:00.000":0,"2022-07-13T06:00:00.000":0,"2022-07-13T07:00:00.000":32,"2022-07-13T08:00:00.000":48,"2022-07-13T09:00:00.000":321,"2022-07-13T10:00:00.000":53... | {"DNI":{"2022-07-15T00:00:00.000":0,"2022-07-15T01:00:00.000":0,"2022-07-15T02:00:00.000":0,"2022-07-15T03:00:00.000":0,"2022-07-15T04:00:00.000":0,"2022-07-15T05:00:00.000":0,"2022-07-15T06:00:00.000":74,"2022-07-15T07:00:00.000":5,"2022-07-15T08:00:00.000":42,"2022-07-15T09:00:00.000":6,"2022-07-15T10:00:00.000":729,... | 0.001389 | [] | 0 | null | [] | [] | |
ExplicitDirectNormalIrradianceFromCloudStatus | 2 | 1/4 | [
"c_i",
"c_cov"
] | [
"forecasting",
"natural language processing",
"instruction following"
] | This series contains Direct Normal Irradiance for a location in Ontario, Canada.
The Direct Normal Irradiance is the total amount of sun energy (in Watts per squared meter) arriving directly from the sun on a surface perpendicular to the sunlight direction. When there are no clouds to block the sun, the Direct Normal I... | At the beginning of the series, the weather was cloudy.
At 2022-08-09 01:00:00, the weather became clear.
At 2022-08-09 02:00:00, the weather became cloudy.
At 2022-08-09 04:00:00, the weather became clear.
At 2022-08-09 05:00:00, the weather became cloudy.
At 2022-08-09 18:00:00, the weather became clear.
At 2022-08-1... | 24 | {"DNI":{"2022-08-09T00:00:00.000":0,"2022-08-09T01:00:00.000":0,"2022-08-09T02:00:00.000":0,"2022-08-09T03:00:00.000":0,"2022-08-09T04:00:00.000":0,"2022-08-09T05:00:00.000":0,"2022-08-09T06:00:00.000":0,"2022-08-09T07:00:00.000":0,"2022-08-09T08:00:00.000":0,"2022-08-09T09:00:00.000":0,"2022-08-09T10:00:00.000":0,"202... | {"DNI":{"2022-08-11T00:00:00.000":0,"2022-08-11T01:00:00.000":0,"2022-08-11T02:00:00.000":0,"2022-08-11T03:00:00.000":0,"2022-08-11T04:00:00.000":0,"2022-08-11T05:00:00.000":0,"2022-08-11T06:00:00.000":302,"2022-08-11T07:00:00.000":587,"2022-08-11T08:00:00.000":726,"2022-08-11T09:00:00.000":801,"2022-08-11T10:00:00.000... | 0.001389 | [] | 0 | null | [] | [] | |
ExplicitDirectNormalIrradianceFromCloudStatus | 3 | 1/4 | [
"c_i",
"c_cov"
] | [
"forecasting",
"natural language processing",
"instruction following"
] | This series contains Direct Normal Irradiance for a location in Amazonas, Brazil.
The Direct Normal Irradiance is the total amount of sun energy (in Watts per squared meter) arriving directly from the sun on a surface perpendicular to the sunlight direction. When there are no clouds to block the sun, the Direct Normal ... | At the beginning of the series, the weather was cloudy.
At 2022-10-14 06:00:00, the weather became clear.
At 2022-10-14 08:00:00, the weather became cloudy.
At 2022-10-14 12:00:00, the weather became clear.
At 2022-10-14 14:00:00, the weather became cloudy.
At 2022-10-15 12:00:00, the weather became clear.
At 2022-10-1... | 24 | {"DNI":{"2022-10-14T00:00:00.000":0,"2022-10-14T01:00:00.000":0,"2022-10-14T02:00:00.000":0,"2022-10-14T03:00:00.000":0,"2022-10-14T04:00:00.000":0,"2022-10-14T05:00:00.000":0,"2022-10-14T06:00:00.000":29,"2022-10-14T07:00:00.000":261,"2022-10-14T08:00:00.000":313,"2022-10-14T09:00:00.000":440,"2022-10-14T10:00:00.000"... | {"DNI":{"2022-10-16T00:00:00.000":0,"2022-10-16T01:00:00.000":0,"2022-10-16T02:00:00.000":0,"2022-10-16T03:00:00.000":0,"2022-10-16T04:00:00.000":0,"2022-10-16T05:00:00.000":0,"2022-10-16T06:00:00.000":45,"2022-10-16T07:00:00.000":4,"2022-10-16T08:00:00.000":0,"2022-10-16T09:00:00.000":132,"2022-10-16T10:00:00.000":61,... | 0.001389 | [] | 0 | null | [] | [] | |
ExplicitDirectNormalIrradianceFromCloudStatus | 4 | 1/4 | [
"c_i",
"c_cov"
] | [
"forecasting",
"natural language processing",
"instruction following"
] | This series contains Direct Normal Irradiance for a location in Florida, United States.
The Direct Normal Irradiance is the total amount of sun energy (in Watts per squared meter) arriving directly from the sun on a surface perpendicular to the sunlight direction. When there are no clouds to block the sun, the Direct N... | At the beginning of the series, the weather was cloudy.
At 2022-01-23 20:00:00, the weather became clear.
At 2022-01-23 21:00:00, the weather became cloudy.
At 2022-01-23 23:00:00, the weather became clear.
At 2022-01-24 00:00:00, the weather became cloudy.
At 2022-01-24 01:00:00, the weather became clear.
At 2022-01-2... | 24 | {"DNI":{"2022-01-23T00:00:00.000":0,"2022-01-23T01:00:00.000":0,"2022-01-23T02:00:00.000":0,"2022-01-23T03:00:00.000":0,"2022-01-23T04:00:00.000":0,"2022-01-23T05:00:00.000":0,"2022-01-23T06:00:00.000":0,"2022-01-23T07:00:00.000":0,"2022-01-23T08:00:00.000":0,"2022-01-23T09:00:00.000":7,"2022-01-23T10:00:00.000":40,"20... | {"DNI":{"2022-01-25T00:00:00.000":0,"2022-01-25T01:00:00.000":0,"2022-01-25T02:00:00.000":0,"2022-01-25T03:00:00.000":0,"2022-01-25T04:00:00.000":0,"2022-01-25T05:00:00.000":0,"2022-01-25T06:00:00.000":0,"2022-01-25T07:00:00.000":0,"2022-01-25T08:00:00.000":447,"2022-01-25T09:00:00.000":776,"2022-01-25T10:00:00.000":89... | 0.001389 | [] | 0 | null | [] | [] | |
ExplicitDirectNormalIrradianceFromCloudStatus | 5 | 1/4 | [
"c_i",
"c_cov"
] | [
"forecasting",
"natural language processing",
"instruction following"
] | This series contains Direct Normal Irradiance for a location in Amazonas, Brazil.
The Direct Normal Irradiance is the total amount of sun energy (in Watts per squared meter) arriving directly from the sun on a surface perpendicular to the sunlight direction. When there are no clouds to block the sun, the Direct Normal ... | At the beginning of the series, the weather was clear.
At 2022-06-04 02:00:00, the weather became cloudy.
At 2022-06-04 10:00:00, the weather became clear.
At 2022-06-04 14:00:00, the weather became cloudy.
At 2022-06-05 20:00:00, the weather became clear.
At 2022-06-05 21:00:00, the weather became cloudy.
At 2022-06-0... | 24 | {"DNI":{"2022-06-04T00:00:00.000":0,"2022-06-04T01:00:00.000":0,"2022-06-04T02:00:00.000":0,"2022-06-04T03:00:00.000":0,"2022-06-04T04:00:00.000":0,"2022-06-04T05:00:00.000":0,"2022-06-04T06:00:00.000":0,"2022-06-04T07:00:00.000":8,"2022-06-04T08:00:00.000":33,"2022-06-04T09:00:00.000":169,"2022-06-04T10:00:00.000":817... | {"DNI":{"2022-06-06T00:00:00.000":0,"2022-06-06T01:00:00.000":0,"2022-06-06T02:00:00.000":0,"2022-06-06T03:00:00.000":0,"2022-06-06T04:00:00.000":0,"2022-06-06T05:00:00.000":0,"2022-06-06T06:00:00.000":0,"2022-06-06T07:00:00.000":430,"2022-06-06T08:00:00.000":654,"2022-06-06T09:00:00.000":272,"2022-06-06T10:00:00.000":... | 0.001389 | [] | 0 | null | [] | [] | |
DiffuseHorizontalIrradianceFromCloudStatus | 1 | 1/4 | [
"c_i",
"c_cov"
] | [
"forecasting",
"natural language processing",
"reasoning: deduction"
] | This series contains Diffuse Horizontal Irradiance for a location in Sinaloa, Mexico.
The Diffuse Horizontal Irradiance is the total amount of sun energy (in Watts per squared meter) arriving indirectly on a horizontal surface, ignoring the direct sunlight. | At the beginning of the series, the weather was cloudy.
At 2022-07-13 11:00:00, the weather became clear.
At 2022-07-13 19:00:00, the weather became cloudy.
At 2022-07-14 12:00:00, the weather became clear.
At 2022-07-14 13:00:00, the weather became cloudy.
At 2022-07-15 06:00:00, we expect that the weather will become... | 24 | {"DHI":{"2022-07-13T00:00:00.000":0,"2022-07-13T01:00:00.000":0,"2022-07-13T02:00:00.000":0,"2022-07-13T03:00:00.000":0,"2022-07-13T04:00:00.000":0,"2022-07-13T05:00:00.000":0,"2022-07-13T06:00:00.000":19,"2022-07-13T07:00:00.000":138,"2022-07-13T08:00:00.000":219,"2022-07-13T09:00:00.000":287,"2022-07-13T10:00:00.000"... | {"DHI":{"2022-07-15T00:00:00.000":0,"2022-07-15T01:00:00.000":0,"2022-07-15T02:00:00.000":0,"2022-07-15T03:00:00.000":0,"2022-07-15T04:00:00.000":0,"2022-07-15T05:00:00.000":0,"2022-07-15T06:00:00.000":40,"2022-07-15T07:00:00.000":138,"2022-07-15T08:00:00.000":276,"2022-07-15T09:00:00.000":291,"2022-07-15T10:00:00.000"... | 0.003154 | [] | 0 | null | [] | [] | |
DiffuseHorizontalIrradianceFromCloudStatus | 2 | 1/4 | [
"c_i",
"c_cov"
] | [
"forecasting",
"natural language processing",
"reasoning: deduction"
] | This series contains Diffuse Horizontal Irradiance for a location in Ontario, Canada.
The Diffuse Horizontal Irradiance is the total amount of sun energy (in Watts per squared meter) arriving indirectly on a horizontal surface, ignoring the direct sunlight. | At the beginning of the series, the weather was cloudy.
At 2022-08-09 01:00:00, the weather became clear.
At 2022-08-09 02:00:00, the weather became cloudy.
At 2022-08-09 04:00:00, the weather became clear.
At 2022-08-09 05:00:00, the weather became cloudy.
At 2022-08-09 18:00:00, the weather became clear.
At 2022-08-1... | 24 | {"DHI":{"2022-08-09T00:00:00.000":0,"2022-08-09T01:00:00.000":0,"2022-08-09T02:00:00.000":0,"2022-08-09T03:00:00.000":0,"2022-08-09T04:00:00.000":0,"2022-08-09T05:00:00.000":0,"2022-08-09T06:00:00.000":15,"2022-08-09T07:00:00.000":22,"2022-08-09T08:00:00.000":24,"2022-08-09T09:00:00.000":50,"2022-08-09T10:00:00.000":64... | {"DHI":{"2022-08-11T00:00:00.000":0,"2022-08-11T01:00:00.000":0,"2022-08-11T02:00:00.000":0,"2022-08-11T03:00:00.000":0,"2022-08-11T04:00:00.000":0,"2022-08-11T05:00:00.000":0,"2022-08-11T06:00:00.000":27,"2022-08-11T07:00:00.000":55,"2022-08-11T08:00:00.000":73,"2022-08-11T09:00:00.000":86,"2022-08-11T10:00:00.000":95... | 0.003154 | [] | 0 | null | [] | [] | |
DiffuseHorizontalIrradianceFromCloudStatus | 3 | 1/4 | [
"c_i",
"c_cov"
] | [
"forecasting",
"natural language processing",
"reasoning: deduction"
] | This series contains Diffuse Horizontal Irradiance for a location in Amazonas, Brazil.
The Diffuse Horizontal Irradiance is the total amount of sun energy (in Watts per squared meter) arriving indirectly on a horizontal surface, ignoring the direct sunlight. | At the beginning of the series, the weather was cloudy.
At 2022-10-14 06:00:00, the weather became clear.
At 2022-10-14 08:00:00, the weather became cloudy.
At 2022-10-14 12:00:00, the weather became clear.
At 2022-10-14 14:00:00, the weather became cloudy.
At 2022-10-15 12:00:00, the weather became clear.
At 2022-10-1... | 24 | {"DHI":{"2022-10-14T00:00:00.000":0,"2022-10-14T01:00:00.000":0,"2022-10-14T02:00:00.000":0,"2022-10-14T03:00:00.000":0,"2022-10-14T04:00:00.000":0,"2022-10-14T05:00:00.000":0,"2022-10-14T06:00:00.000":17,"2022-10-14T07:00:00.000":138,"2022-10-14T08:00:00.000":284,"2022-10-14T09:00:00.000":338,"2022-10-14T10:00:00.000"... | {"DHI":{"2022-10-16T00:00:00.000":0,"2022-10-16T01:00:00.000":0,"2022-10-16T02:00:00.000":0,"2022-10-16T03:00:00.000":0,"2022-10-16T04:00:00.000":0,"2022-10-16T05:00:00.000":0,"2022-10-16T06:00:00.000":21,"2022-10-16T07:00:00.000":143,"2022-10-16T08:00:00.000":59,"2022-10-16T09:00:00.000":447,"2022-10-16T10:00:00.000":... | 0.003154 | [] | 0 | null | [] | [] | |
DiffuseHorizontalIrradianceFromCloudStatus | 4 | 1/4 | [
"c_i",
"c_cov"
] | [
"forecasting",
"natural language processing",
"reasoning: deduction"
] | This series contains Diffuse Horizontal Irradiance for a location in Florida, United States.
The Diffuse Horizontal Irradiance is the total amount of sun energy (in Watts per squared meter) arriving indirectly on a horizontal surface, ignoring the direct sunlight. | At the beginning of the series, the weather was cloudy.
At 2022-01-23 20:00:00, the weather became clear.
At 2022-01-23 21:00:00, the weather became cloudy.
At 2022-01-23 23:00:00, the weather became clear.
At 2022-01-24 00:00:00, the weather became cloudy.
At 2022-01-24 01:00:00, the weather became clear.
At 2022-01-2... | 24 | {"DHI":{"2022-01-23T00:00:00.000":0,"2022-01-23T01:00:00.000":0,"2022-01-23T02:00:00.000":0,"2022-01-23T03:00:00.000":0,"2022-01-23T04:00:00.000":0,"2022-01-23T05:00:00.000":0,"2022-01-23T06:00:00.000":0,"2022-01-23T07:00:00.000":0,"2022-01-23T08:00:00.000":18,"2022-01-23T09:00:00.000":90,"2022-01-23T10:00:00.000":198,... | {"DHI":{"2022-01-25T00:00:00.000":0,"2022-01-25T01:00:00.000":0,"2022-01-25T02:00:00.000":0,"2022-01-25T03:00:00.000":0,"2022-01-25T04:00:00.000":0,"2022-01-25T05:00:00.000":0,"2022-01-25T06:00:00.000":0,"2022-01-25T07:00:00.000":0,"2022-01-25T08:00:00.000":26,"2022-01-25T09:00:00.000":52,"2022-01-25T10:00:00.000":64,"... | 0.003154 | [] | 0 | null | [] | [] | |
DiffuseHorizontalIrradianceFromCloudStatus | 5 | 1/4 | [
"c_i",
"c_cov"
] | [
"forecasting",
"natural language processing",
"reasoning: deduction"
] | This series contains Diffuse Horizontal Irradiance for a location in Amazonas, Brazil.
The Diffuse Horizontal Irradiance is the total amount of sun energy (in Watts per squared meter) arriving indirectly on a horizontal surface, ignoring the direct sunlight. | At the beginning of the series, the weather was clear.
At 2022-06-04 02:00:00, the weather became cloudy.
At 2022-06-04 10:00:00, the weather became clear.
At 2022-06-04 14:00:00, the weather became cloudy.
At 2022-06-05 20:00:00, the weather became clear.
At 2022-06-05 21:00:00, the weather became cloudy.
At 2022-06-0... | 24 | {"DHI":{"2022-06-04T00:00:00.000":0,"2022-06-04T01:00:00.000":0,"2022-06-04T02:00:00.000":0,"2022-06-04T03:00:00.000":0,"2022-06-04T04:00:00.000":0,"2022-06-04T05:00:00.000":0,"2022-06-04T06:00:00.000":0,"2022-06-04T07:00:00.000":104,"2022-06-04T08:00:00.000":220,"2022-06-04T09:00:00.000":333,"2022-06-04T10:00:00.000":... | {"DHI":{"2022-06-06T00:00:00.000":0,"2022-06-06T01:00:00.000":0,"2022-06-06T02:00:00.000":0,"2022-06-06T03:00:00.000":0,"2022-06-06T04:00:00.000":0,"2022-06-06T05:00:00.000":0,"2022-06-06T06:00:00.000":0,"2022-06-06T07:00:00.000":62,"2022-06-06T08:00:00.000":89,"2022-06-06T09:00:00.000":252,"2022-06-06T10:00:00.000":32... | 0.003154 | [] | 0 | null | [] | [] | |
ExplicitDiffuseHorizontalIrradianceFromCloudStatus | 1 | 1/4 | [
"c_i",
"c_cov"
] | [
"forecasting",
"natural language processing",
"instruction following"
] | This series contains Diffuse Horizontal Irradiance for a location in Sinaloa, Mexico.
The Diffuse Horizontal Irradiance is the total amount of sun energy (in Watts per squared meter) arriving indirectly on a horizontal surface, ignoring the direct sunlight. Even when there are no clouds to scatter the sun light, there ... | At the beginning of the series, the weather was cloudy.
At 2022-07-13 11:00:00, the weather became clear.
At 2022-07-13 19:00:00, the weather became cloudy.
At 2022-07-14 12:00:00, the weather became clear.
At 2022-07-14 13:00:00, the weather became cloudy.
At 2022-07-15 06:00:00, we expect that the weather will become... | 24 | {"DHI":{"2022-07-13T00:00:00.000":0,"2022-07-13T01:00:00.000":0,"2022-07-13T02:00:00.000":0,"2022-07-13T03:00:00.000":0,"2022-07-13T04:00:00.000":0,"2022-07-13T05:00:00.000":0,"2022-07-13T06:00:00.000":19,"2022-07-13T07:00:00.000":138,"2022-07-13T08:00:00.000":219,"2022-07-13T09:00:00.000":287,"2022-07-13T10:00:00.000"... | {"DHI":{"2022-07-15T00:00:00.000":0,"2022-07-15T01:00:00.000":0,"2022-07-15T02:00:00.000":0,"2022-07-15T03:00:00.000":0,"2022-07-15T04:00:00.000":0,"2022-07-15T05:00:00.000":0,"2022-07-15T06:00:00.000":40,"2022-07-15T07:00:00.000":138,"2022-07-15T08:00:00.000":276,"2022-07-15T09:00:00.000":291,"2022-07-15T10:00:00.000"... | 0.003154 | [] | 0 | null | [] | [] | |
ExplicitDiffuseHorizontalIrradianceFromCloudStatus | 2 | 1/4 | [
"c_i",
"c_cov"
] | [
"forecasting",
"natural language processing",
"instruction following"
] | This series contains Diffuse Horizontal Irradiance for a location in Ontario, Canada.
The Diffuse Horizontal Irradiance is the total amount of sun energy (in Watts per squared meter) arriving indirectly on a horizontal surface, ignoring the direct sunlight. Even when there are no clouds to scatter the sun light, there ... | At the beginning of the series, the weather was cloudy.
At 2022-08-09 01:00:00, the weather became clear.
At 2022-08-09 02:00:00, the weather became cloudy.
At 2022-08-09 04:00:00, the weather became clear.
At 2022-08-09 05:00:00, the weather became cloudy.
At 2022-08-09 18:00:00, the weather became clear.
At 2022-08-1... | 24 | {"DHI":{"2022-08-09T00:00:00.000":0,"2022-08-09T01:00:00.000":0,"2022-08-09T02:00:00.000":0,"2022-08-09T03:00:00.000":0,"2022-08-09T04:00:00.000":0,"2022-08-09T05:00:00.000":0,"2022-08-09T06:00:00.000":15,"2022-08-09T07:00:00.000":22,"2022-08-09T08:00:00.000":24,"2022-08-09T09:00:00.000":50,"2022-08-09T10:00:00.000":64... | {"DHI":{"2022-08-11T00:00:00.000":0,"2022-08-11T01:00:00.000":0,"2022-08-11T02:00:00.000":0,"2022-08-11T03:00:00.000":0,"2022-08-11T04:00:00.000":0,"2022-08-11T05:00:00.000":0,"2022-08-11T06:00:00.000":27,"2022-08-11T07:00:00.000":55,"2022-08-11T08:00:00.000":73,"2022-08-11T09:00:00.000":86,"2022-08-11T10:00:00.000":95... | 0.003154 | [] | 0 | null | [] | [] | |
ExplicitDiffuseHorizontalIrradianceFromCloudStatus | 3 | 1/4 | [
"c_i",
"c_cov"
] | [
"forecasting",
"natural language processing",
"instruction following"
] | This series contains Diffuse Horizontal Irradiance for a location in Amazonas, Brazil.
The Diffuse Horizontal Irradiance is the total amount of sun energy (in Watts per squared meter) arriving indirectly on a horizontal surface, ignoring the direct sunlight. Even when there are no clouds to scatter the sun light, there... | At the beginning of the series, the weather was cloudy.
At 2022-10-14 06:00:00, the weather became clear.
At 2022-10-14 08:00:00, the weather became cloudy.
At 2022-10-14 12:00:00, the weather became clear.
At 2022-10-14 14:00:00, the weather became cloudy.
At 2022-10-15 12:00:00, the weather became clear.
At 2022-10-1... | 24 | {"DHI":{"2022-10-14T00:00:00.000":0,"2022-10-14T01:00:00.000":0,"2022-10-14T02:00:00.000":0,"2022-10-14T03:00:00.000":0,"2022-10-14T04:00:00.000":0,"2022-10-14T05:00:00.000":0,"2022-10-14T06:00:00.000":17,"2022-10-14T07:00:00.000":138,"2022-10-14T08:00:00.000":284,"2022-10-14T09:00:00.000":338,"2022-10-14T10:00:00.000"... | {"DHI":{"2022-10-16T00:00:00.000":0,"2022-10-16T01:00:00.000":0,"2022-10-16T02:00:00.000":0,"2022-10-16T03:00:00.000":0,"2022-10-16T04:00:00.000":0,"2022-10-16T05:00:00.000":0,"2022-10-16T06:00:00.000":21,"2022-10-16T07:00:00.000":143,"2022-10-16T08:00:00.000":59,"2022-10-16T09:00:00.000":447,"2022-10-16T10:00:00.000":... | 0.003154 | [] | 0 | null | [] | [] | |
ExplicitDiffuseHorizontalIrradianceFromCloudStatus | 4 | 1/4 | [
"c_i",
"c_cov"
] | [
"forecasting",
"natural language processing",
"instruction following"
] | This series contains Diffuse Horizontal Irradiance for a location in Florida, United States.
The Diffuse Horizontal Irradiance is the total amount of sun energy (in Watts per squared meter) arriving indirectly on a horizontal surface, ignoring the direct sunlight. Even when there are no clouds to scatter the sun light,... | At the beginning of the series, the weather was cloudy.
At 2022-01-23 20:00:00, the weather became clear.
At 2022-01-23 21:00:00, the weather became cloudy.
At 2022-01-23 23:00:00, the weather became clear.
At 2022-01-24 00:00:00, the weather became cloudy.
At 2022-01-24 01:00:00, the weather became clear.
At 2022-01-2... | 24 | {"DHI":{"2022-01-23T00:00:00.000":0,"2022-01-23T01:00:00.000":0,"2022-01-23T02:00:00.000":0,"2022-01-23T03:00:00.000":0,"2022-01-23T04:00:00.000":0,"2022-01-23T05:00:00.000":0,"2022-01-23T06:00:00.000":0,"2022-01-23T07:00:00.000":0,"2022-01-23T08:00:00.000":18,"2022-01-23T09:00:00.000":90,"2022-01-23T10:00:00.000":198,... | {"DHI":{"2022-01-25T00:00:00.000":0,"2022-01-25T01:00:00.000":0,"2022-01-25T02:00:00.000":0,"2022-01-25T03:00:00.000":0,"2022-01-25T04:00:00.000":0,"2022-01-25T05:00:00.000":0,"2022-01-25T06:00:00.000":0,"2022-01-25T07:00:00.000":0,"2022-01-25T08:00:00.000":26,"2022-01-25T09:00:00.000":52,"2022-01-25T10:00:00.000":64,"... | 0.003154 | [] | 0 | null | [] | [] | |
ExplicitDiffuseHorizontalIrradianceFromCloudStatus | 5 | 1/4 | [
"c_i",
"c_cov"
] | [
"forecasting",
"natural language processing",
"instruction following"
] | This series contains Diffuse Horizontal Irradiance for a location in Amazonas, Brazil.
The Diffuse Horizontal Irradiance is the total amount of sun energy (in Watts per squared meter) arriving indirectly on a horizontal surface, ignoring the direct sunlight. Even when there are no clouds to scatter the sun light, there... | At the beginning of the series, the weather was clear.
At 2022-06-04 02:00:00, the weather became cloudy.
At 2022-06-04 10:00:00, the weather became clear.
At 2022-06-04 14:00:00, the weather became cloudy.
At 2022-06-05 20:00:00, the weather became clear.
At 2022-06-05 21:00:00, the weather became cloudy.
At 2022-06-0... | 24 | {"DHI":{"2022-06-04T00:00:00.000":0,"2022-06-04T01:00:00.000":0,"2022-06-04T02:00:00.000":0,"2022-06-04T03:00:00.000":0,"2022-06-04T04:00:00.000":0,"2022-06-04T05:00:00.000":0,"2022-06-04T06:00:00.000":0,"2022-06-04T07:00:00.000":104,"2022-06-04T08:00:00.000":220,"2022-06-04T09:00:00.000":333,"2022-06-04T10:00:00.000":... | {"DHI":{"2022-06-06T00:00:00.000":0,"2022-06-06T01:00:00.000":0,"2022-06-06T02:00:00.000":0,"2022-06-06T03:00:00.000":0,"2022-06-06T04:00:00.000":0,"2022-06-06T05:00:00.000":0,"2022-06-06T06:00:00.000":0,"2022-06-06T07:00:00.000":62,"2022-06-06T08:00:00.000":89,"2022-06-06T09:00:00.000":252,"2022-06-06T10:00:00.000":32... | 0.003154 | [] | 0 | null | [] | [] |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.