GiftEval
Collection
3 items • Updated
idx stringlengths 16 54 | source stringclasses 2
values | skill stringclasses 3
values | frequency stringclasses 6
values | history_values stringlengths 119 5.11k | history_start stringclasses 47
values | history_end stringlengths 7 19 | future_values stringlengths 53 1.68k | future_start stringlengths 7 19 | future_end stringlengths 7 19 | entry_sep stringclasses 1
value | roi stringlengths 0 297 | pred_length int64 15 240 | system_prompt stringclasses 1
value | user_instruct stringlengths 182 237 | context_info stringlengths 70 1.49k | prompt stringlengths 505 5.76k |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
anomaly_explicit/template0/item0 | GIFT synthesize | anomaly | D | 45.104,79.022,108.380,130.293,349.321,558.734,758.739,950.305,1134.104,1311.993,1487.250,1661.686,1632.464,1607.898,1590.064,1344.266,1112.177,899.515,709.815,546.336,410.910,304.957,228.124,180.906,161.755,186.750,204.405,211.847,210.322,199.754,181.145,155.469,125.881,94.922,64.674,37.938,17.032,3.750,0.000,5.167,19.... | 2025-10-10 00:00:00 | 2026-03-14 00:00:00 | 402.873,436.584,467.769,493.818,512.793,523.240,524.807,517.773,501.788,479.120,451.661,420.819,390.106,361.369,337.711,320.128,311.013,310.448,318.996,336.027,360.150,390.035,422.790,456.281,488.259,516.004,537.864,551.647,556.554,552.514,539.779,519.717,493.478,463.748,432.535,402.473,375.970,355.366,342.552,337.904,... | 2026-03-15 00:00:00 | 2026-06-22 00:00:00 | , | 100 | You are a helpful assistant for time series forecasting. Think step by step to make a prediction. | Predict the sales of this store over the next 100 days, from 2026-03-15 00:00:00 to 2026-06-22 00:00:00.
The final result must be enclosed by '\boxed{' and '}', and where values are separated by ','. | This time series records the sales volume of a chain store. The data was recorded from 2025-10-10 00:00:00 to 2026-03-14 00:00:00 with the daily (D) frequency. Due to a software bug, the sales records from 2025-10-12 00:00:00 to 2025-11-03 00:00:00 and were significantly higher than normal. After updating the software ... | <history>45.104,79.022,108.380,130.293,349.321,558.734,758.739,950.305,1134.104,1311.993,1487.250,1661.686,1632.464,1607.898,1590.064,1344.266,1112.177,899.515,709.815,546.336,410.910,304.957,228.124,180.906,161.755,186.750,204.405,211.847,210.322,199.754,181.145,155.469,125.881,94.922,64.674,37.938,17.032,3.750,0.000,... | |
anomaly_explicit/template1/item0 | GIFT synthesize | anomaly | D | 46.772,99.481,164.879,238.480,316.446,395.768,470.917,538.994,596.387,639.657,664.900,674.772,663.466,636.520,593.470,536.605,467.878,392.910,314.391,238.260,165.277,103.903,52.939,18.215,0.000,0.491,19.617,54.671,104.645,168.635,241.853,320.678,400.364,477.662,548.539,608.676,655.085,685.787,461.035,460.935,530.947,62... | 2025-10-10 00:00:00 | 2026-03-14 00:00:00 | 1499.326,1442.447,1400.282,1372.734,1360.509,1367.624,1390.619,1431.066,1487.617,1555.376,1633.062,1714.711,1798.462,1879.529,1953.418,2017.664,2069.097,2103.539,2121.425,2120.529,2104.092,2070.618,2022.609,1964.755,1899.697,1828.783,1758.949,1694.447,1637.019,1592.951,1562.462,1548.716,1552.564,1574.543,1612.220,1666.... | 2026-03-15 00:00:00 | 2026-06-22 00:00:00 | , | 100 | You are a helpful assistant for time series forecasting. Think step by step to make a prediction. | Predict the sales of this store over the next 100 days, from 2026-03-15 00:00:00 to 2026-06-22 00:00:00.
The final result must be enclosed by '\boxed{' and '}', and where values are separated by ','. | This time series records the sales volume of a chain store. The data was recorded from 2025-10-10 00:00:00 to 2026-03-14 00:00:00 with the daily (D) frequency. Due to a software bug, the sales records from 2025-11-16 00:00:00 to 2025-12-19 00:00:00 and were significantly higher than normal. After updating the software ... | <history>46.772,99.481,164.879,238.480,316.446,395.768,470.917,538.994,596.387,639.657,664.900,674.772,663.466,636.520,593.470,536.605,467.878,392.910,314.391,238.260,165.277,103.903,52.939,18.215,0.000,0.491,19.617,54.671,104.645,168.635,241.853,320.678,400.364,477.662,548.539,608.676,655.085,685.787,461.035,460.935,5... | |
anomaly_explicit/template2/item0 | GIFT synthesize | anomaly | D | 0.000,4.764,9.816,347.463,352.129,357.037,361.771,366.596,371.127,42.951,47.887,52.604,57.297,61.968,66.791,71.599,76.038,81.062,85.932,90.727,95.615,100.096,104.990,109.651,114.286,119.451,124.063,128.492,133.504,471.437,476.085,480.917,485.670,490.408,495.209,166.946,171.702,176.499,181.160,186.039,190.568,195.340,20... | 2025-10-10 00:00:00 | 2026-08-29 00:00:00 | 567.330,563.851,560.510,557.218,553.930,550.381,547.282,544.045,540.575,537.263,533.990,530.680,527.374,523.913,520.628,517.192,513.912,510.718,840.694,836.994,833.730,830.488,827.130,823.966,487.302,483.954,480.738,477.371,473.923,470.542,467.508,464.291,460.522,457.616,453.955,450.759,447.493,443.993,440.871,437.349,... | 2026-08-30 00:00:00 | 2026-10-28 00:00:00 | , | 60 | You are a helpful assistant for time series forecasting. Think step by step to make a prediction. | Predict the sales of this store over the next 60 days, from 2026-08-30 00:00:00 to 2026-10-28 00:00:00.
The final result must be enclosed by '\boxed{' and '}', and where values are separated by ','. | This time series records the sales volume of a chain store. The data was recorded from 2025-10-10 00:00:00 to 2026-08-29 00:00:00 with the daily (D) frequency. Due to a software bug, the sales records from 2025-12-10 00:00:00 to 2026-01-21 00:00:00 and were significantly higher than normal. After updating the software ... | <history>0.000,4.764,9.816,347.463,352.129,357.037,361.771,366.596,371.127,42.951,47.887,52.604,57.297,61.968,66.791,71.599,76.038,81.062,85.932,90.727,95.615,100.096,104.990,109.651,114.286,119.451,124.063,128.492,133.504,471.437,476.085,480.917,485.670,490.408,495.209,166.946,171.702,176.499,181.160,186.039,190.568,1... | |
anomaly_explicit/template3/item0 | GIFT synthesize | anomaly | D | 6944.088,6945.398,6946.665,6947.786,8104.850,8105.924,8107.217,8108.393,8109.475,6954.818,6955.899,6957.059,6958.090,6959.145,6960.220,6961.318,6962.292,6963.399,6964.440,6965.396,6966.335,6967.329,6968.318,6969.262,6970.308,6971.189,6972.053,6973.114,6973.924,6974.849,6975.773,6976.581,6977.520,6978.297,6979.155,7115.... | 2025-10-10 00:00:00 | 2026-08-29 00:00:00 | 6466.536,6459.267,6451.852,6444.468,6437.123,6429.581,6422.198,6414.712,6407.187,6399.671,6392.087,6384.398,6376.877,6369.230,7517.373,7509.814,7502.162,7494.271,7486.602,6322.886,6315.160,6307.410,6299.605,6291.797,6283.850,6275.946,6268.076,6260.101,6252.187,6244.291,6236.252,6228.239,6220.270,6212.217,6204.159,6196.... | 2026-08-30 00:00:00 | 2026-10-28 00:00:00 | , | 60 | You are a helpful assistant for time series forecasting. Think step by step to make a prediction. | Predict the sales of this store over the next 60 days, from 2026-08-30 00:00:00 to 2026-10-28 00:00:00.
The final result must be enclosed by '\boxed{' and '}', and where values are separated by ','. | This time series records the sales volume of a chain store. The data was recorded from 2025-10-10 00:00:00 to 2026-08-29 00:00:00 with the daily (D) frequency. Due to a software bug, the sales records from 2025-11-12 00:00:00 to 2025-12-27 00:00:00 and were significantly higher than normal. After updating the software ... | <history>6944.088,6945.398,6946.665,6947.786,8104.850,8105.924,8107.217,8108.393,8109.475,6954.818,6955.899,6957.059,6958.090,6959.145,6960.220,6961.318,6962.292,6963.399,6964.440,6965.396,6966.335,6967.329,6968.318,6969.262,6970.308,6971.189,6972.053,6973.114,6973.924,6974.849,6975.773,6976.581,6977.520,6978.297,6979.... | |
anomaly_explicit/template4/item0 | GIFT synthesize | anomaly | D | 0.230,0.000,0.366,0.137,0.345,0.323,0.349,0.397,75.318,181.330,286.902,393.023,498.804,604.807,710.371,816.593,922.629,1028.266,1134.330,1240.201,1346.289,1318.352,1212.853,1107.412,1001.988,896.562,791.123,685.764,580.448,475.020,369.519,264.109,158.908,53.371,7.201,192.690,378.194,563.654,749.032,934.683,1120.189,112... | 2025-10-10 00:00:00 | 2026-10-06 00:00:00 | 3220.355,3224.736,3229.461,3233.663,3238.274,3242.641,3247.277,3251.589,3255.986,3260.670,3265.206,3269.632,3274.239,3323.827,3434.091,3544.387,3654.920,3765.089,3875.486,3985.651,4096.160,4206.626,4317.014,4427.350,4537.837,4648.154,4684.358,4583.580,4482.299,4381.427,4280.275,4179.417,4078.499,3977.466,3876.758,3775.... | 2026-10-07 00:00:00 | 2027-03-05 00:00:00 | , | 150 | You are a helpful assistant for time series forecasting. Think step by step to make a prediction. | Predict the sales of this store over the next 150 days, from 2026-10-07 00:00:00 to 2027-03-05 00:00:00.
The final result must be enclosed by '\boxed{' and '}', and where values are separated by ','. | This time series records the sales volume of a chain store. The data was recorded from 2025-10-10 00:00:00 to 2026-10-06 00:00:00 with the daily (D) frequency. Due to a software bug, the sales records from 2025-11-12 00:00:00 to 2025-11-19 00:00:00 and were significantly higher than normal. After updating the software ... | <history>0.230,0.000,0.366,0.137,0.345,0.323,0.349,0.397,75.318,181.330,286.902,393.023,498.804,604.807,710.371,816.593,922.629,1028.266,1134.330,1240.201,1346.289,1318.352,1212.853,1107.412,1001.988,896.562,791.123,685.764,580.448,475.020,369.519,264.109,158.908,53.371,7.201,192.690,378.194,563.654,749.032,934.683,112... | |
anomaly_implicit/template0/item0 | GIFT synthesize | anomaly | D | 45.104,79.022,108.380,130.293,349.321,558.734,758.739,950.305,1134.104,1311.993,1487.250,1661.686,1632.464,1607.898,1590.064,1344.266,1112.177,899.515,709.815,546.336,410.910,304.957,228.124,180.906,161.755,186.750,204.405,211.847,210.322,199.754,181.145,155.469,125.881,94.922,64.674,37.938,17.032,3.750,0.000,5.167,19.... | 2025-10-10 00:00:00 | 2026-03-14 00:00:00 | 402.873,436.584,467.769,493.818,512.793,523.240,524.807,517.773,501.788,479.120,451.661,420.819,390.106,361.369,337.711,320.128,311.013,310.448,318.996,336.027,360.150,390.035,422.790,456.281,488.259,516.004,537.864,551.647,556.554,552.514,539.779,519.717,493.478,463.748,432.535,402.473,375.970,355.366,342.552,337.904,... | 2026-03-15 00:00:00 | 2026-06-22 00:00:00 | , | 100 | You are a helpful assistant for time series forecasting. Think step by step to make a prediction. | Predict the sales of this store over the next 100 days, from 2026-03-15 00:00:00 to 2026-06-22 00:00:00.
The final result must be enclosed by '\boxed{' and '}', and where values are separated by ','. | This time series records the sales volume of a chain store. The data was recorded from 2025-10-10 00:00:00 to 2026-03-14 00:00:00 with the daily (D) frequency. Due to a software bug, there was a period where the sales records were incorrect and were significantly higher than normal. After updating the software version,... | <history>45.104,79.022,108.380,130.293,349.321,558.734,758.739,950.305,1134.104,1311.993,1487.250,1661.686,1632.464,1607.898,1590.064,1344.266,1112.177,899.515,709.815,546.336,410.910,304.957,228.124,180.906,161.755,186.750,204.405,211.847,210.322,199.754,181.145,155.469,125.881,94.922,64.674,37.938,17.032,3.750,0.000,... | |
anomaly_implicit/template1/item0 | GIFT synthesize | anomaly | D | 46.772,99.481,164.879,238.480,316.446,395.768,470.917,538.994,596.387,639.657,664.900,674.772,663.466,636.520,593.470,536.605,467.878,392.910,314.391,238.260,165.277,103.903,52.939,18.215,0.000,0.491,19.617,54.671,104.645,168.635,241.853,320.678,400.364,477.662,548.539,608.676,655.085,685.787,461.035,460.935,530.947,62... | 2025-10-10 00:00:00 | 2026-03-14 00:00:00 | 1499.326,1442.447,1400.282,1372.734,1360.509,1367.624,1390.619,1431.066,1487.617,1555.376,1633.062,1714.711,1798.462,1879.529,1953.418,2017.664,2069.097,2103.539,2121.425,2120.529,2104.092,2070.618,2022.609,1964.755,1899.697,1828.783,1758.949,1694.447,1637.019,1592.951,1562.462,1548.716,1552.564,1574.543,1612.220,1666.... | 2026-03-15 00:00:00 | 2026-06-22 00:00:00 | , | 100 | You are a helpful assistant for time series forecasting. Think step by step to make a prediction. | Predict the sales of this store over the next 100 days, from 2026-03-15 00:00:00 to 2026-06-22 00:00:00.
The final result must be enclosed by '\boxed{' and '}', and where values are separated by ','. | This time series records the sales volume of a chain store. The data was recorded from 2025-10-10 00:00:00 to 2026-03-14 00:00:00 with the daily (D) frequency. Due to a software bug, there was a period where the sales records were incorrect and were significantly higher than normal. After updating the software version,... | <history>46.772,99.481,164.879,238.480,316.446,395.768,470.917,538.994,596.387,639.657,664.900,674.772,663.466,636.520,593.470,536.605,467.878,392.910,314.391,238.260,165.277,103.903,52.939,18.215,0.000,0.491,19.617,54.671,104.645,168.635,241.853,320.678,400.364,477.662,548.539,608.676,655.085,685.787,461.035,460.935,5... | |
anomaly_implicit/template2/item0 | GIFT synthesize | anomaly | D | 0.000,4.764,9.816,347.463,352.129,357.037,361.771,366.596,371.127,42.951,47.887,52.604,57.297,61.968,66.791,71.599,76.038,81.062,85.932,90.727,95.615,100.096,104.990,109.651,114.286,119.451,124.063,128.492,133.504,471.437,476.085,480.917,485.670,490.408,495.209,166.946,171.702,176.499,181.160,186.039,190.568,195.340,20... | 2025-10-10 00:00:00 | 2026-08-29 00:00:00 | 567.330,563.851,560.510,557.218,553.930,550.381,547.282,544.045,540.575,537.263,533.990,530.680,527.374,523.913,520.628,517.192,513.912,510.718,840.694,836.994,833.730,830.488,827.130,823.966,487.302,483.954,480.738,477.371,473.923,470.542,467.508,464.291,460.522,457.616,453.955,450.759,447.493,443.993,440.871,437.349,... | 2026-08-30 00:00:00 | 2026-10-28 00:00:00 | , | 60 | You are a helpful assistant for time series forecasting. Think step by step to make a prediction. | Predict the sales of this store over the next 60 days, from 2026-08-30 00:00:00 to 2026-10-28 00:00:00.
The final result must be enclosed by '\boxed{' and '}', and where values are separated by ','. | This time series records the sales volume of a chain store. The data was recorded from 2025-10-10 00:00:00 to 2026-08-29 00:00:00 with the daily (D) frequency. Due to a software bug, there was a period where the sales records were incorrect and were significantly higher than normal. After updating the software version,... | <history>0.000,4.764,9.816,347.463,352.129,357.037,361.771,366.596,371.127,42.951,47.887,52.604,57.297,61.968,66.791,71.599,76.038,81.062,85.932,90.727,95.615,100.096,104.990,109.651,114.286,119.451,124.063,128.492,133.504,471.437,476.085,480.917,485.670,490.408,495.209,166.946,171.702,176.499,181.160,186.039,190.568,1... | |
anomaly_implicit/template3/item0 | GIFT synthesize | anomaly | D | 6944.088,6945.398,6946.665,6947.786,8104.850,8105.924,8107.217,8108.393,8109.475,6954.818,6955.899,6957.059,6958.090,6959.145,6960.220,6961.318,6962.292,6963.399,6964.440,6965.396,6966.335,6967.329,6968.318,6969.262,6970.308,6971.189,6972.053,6973.114,6973.924,6974.849,6975.773,6976.581,6977.520,6978.297,6979.155,7115.... | 2025-10-10 00:00:00 | 2026-08-29 00:00:00 | 6466.536,6459.267,6451.852,6444.468,6437.123,6429.581,6422.198,6414.712,6407.187,6399.671,6392.087,6384.398,6376.877,6369.230,7517.373,7509.814,7502.162,7494.271,7486.602,6322.886,6315.160,6307.410,6299.605,6291.797,6283.850,6275.946,6268.076,6260.101,6252.187,6244.291,6236.252,6228.239,6220.270,6212.217,6204.159,6196.... | 2026-08-30 00:00:00 | 2026-10-28 00:00:00 | , | 60 | You are a helpful assistant for time series forecasting. Think step by step to make a prediction. | Predict the sales of this store over the next 60 days, from 2026-08-30 00:00:00 to 2026-10-28 00:00:00.
The final result must be enclosed by '\boxed{' and '}', and where values are separated by ','. | This time series records the sales volume of a chain store. The data was recorded from 2025-10-10 00:00:00 to 2026-08-29 00:00:00 with the daily (D) frequency. Due to a software bug, there was a period where the sales records were incorrect and were significantly higher than normal. After updating the software version,... | <history>6944.088,6945.398,6946.665,6947.786,8104.850,8105.924,8107.217,8108.393,8109.475,6954.818,6955.899,6957.059,6958.090,6959.145,6960.220,6961.318,6962.292,6963.399,6964.440,6965.396,6966.335,6967.329,6968.318,6969.262,6970.308,6971.189,6972.053,6973.114,6973.924,6974.849,6975.773,6976.581,6977.520,6978.297,6979.... | |
anomaly_implicit/template4/item0 | GIFT synthesize | anomaly | D | 0.230,0.000,0.366,0.137,0.345,0.323,0.349,0.397,75.318,181.330,286.902,393.023,498.804,604.807,710.371,816.593,922.629,1028.266,1134.330,1240.201,1346.289,1318.352,1212.853,1107.412,1001.988,896.562,791.123,685.764,580.448,475.020,369.519,264.109,158.908,53.371,7.201,192.690,378.194,563.654,749.032,934.683,1120.189,112... | 2025-10-10 00:00:00 | 2026-10-06 00:00:00 | 3220.355,3224.736,3229.461,3233.663,3238.274,3242.641,3247.277,3251.589,3255.986,3260.670,3265.206,3269.632,3274.239,3323.827,3434.091,3544.387,3654.920,3765.089,3875.486,3985.651,4096.160,4206.626,4317.014,4427.350,4537.837,4648.154,4684.358,4583.580,4482.299,4381.427,4280.275,4179.417,4078.499,3977.466,3876.758,3775.... | 2026-10-07 00:00:00 | 2027-03-05 00:00:00 | , | 150 | You are a helpful assistant for time series forecasting. Think step by step to make a prediction. | Predict the sales of this store over the next 150 days, from 2026-10-07 00:00:00 to 2027-03-05 00:00:00.
The final result must be enclosed by '\boxed{' and '}', and where values are separated by ','. | This time series records the sales volume of a chain store. The data was recorded from 2025-10-10 00:00:00 to 2026-10-06 00:00:00 with the daily (D) frequency. Due to a software bug, there was a period where the sales records were incorrect and were significantly higher than normal. After updating the software version,... | <history>0.230,0.000,0.366,0.137,0.345,0.323,0.349,0.397,75.318,181.330,286.902,393.023,498.804,604.807,710.371,816.593,922.629,1028.266,1134.330,1240.201,1346.289,1318.352,1212.853,1107.412,1001.988,896.562,791.123,685.764,580.448,475.020,369.519,264.109,158.908,53.371,7.201,192.690,378.194,563.654,749.032,934.683,112... | |
anomaly_explicit/template0/item1 | GIFT synthesize | anomaly | S | 9.803,9.726,9.650,9.574,9.499,9.424,9.349,9.274,9.200,9.126,9.052,8.979,8.905,8.833,8.760,8.688,8.616,8.544,8.473,8.402,8.331,8.261,8.190,8.121,8.051,7.982,7.913,7.844,7.776,7.708,7.640,7.573,7.505,7.439,7.372,7.306,7.240,7.174,7.108,7.044,6.979,6.914,6.850,6.900,7.177,7.683,8.415,9.376,10.564,11.980,13.623,15.495,17.5... | 2025-10-08 00:00:00 | 2025-10-08 00:03:25 | 0.374,0.359,0.344,0.330,0.316,0.303,0.290,0.276,0.264,0.251,0.239,0.227,0.216,0.205,0.194,0.183,0.173,0.163,0.153,0.144,0.135,0.126,0.117,0.109,0.101,0.093,0.086,0.079,0.072,0.066,0.060,0.054,0.048,0.043,0.038,0.033,0.029,0.025,0.021,0.018,0.015,0.012,0.009,0.007,0.005,0.004,0.002,0.001,0.000,0.000 | 2025-10-08 00:03:26 | 2025-10-08 00:04:15 | , | 50 | You are a helpful assistant for time series forecasting. Think step by step to make a prediction. | Predict the speed of the ship over the next 50 seconds, from 2025-10-08 00:03:26 to 2025-10-08 00:04:15.
The final result must be enclosed by '\boxed{' and '}', and where values are separated by ','. | This time series records the speed (knots) of a slowing down ship. The data was recorded from 2025-10-08 00:00:00 to 2025-10-08 00:03:25 and the frequency of recording is second (S). Due to sensors errors, readings from the periods of 2025-10-08 00:00:41 to 2025-10-08 00:01:20 and 2025-10-08 00:01:38 to 2025-10-08 00:0... | <history>9.803,9.726,9.650,9.574,9.499,9.424,9.349,9.274,9.200,9.126,9.052,8.979,8.905,8.833,8.760,8.688,8.616,8.544,8.473,8.402,8.331,8.261,8.190,8.121,8.051,7.982,7.913,7.844,7.776,7.708,7.640,7.573,7.505,7.439,7.372,7.306,7.240,7.174,7.108,7.044,6.979,6.914,6.850,6.900,7.177,7.683,8.415,9.376,10.564,11.980,13.623,15... | |
anomaly_explicit/template1/item1 | GIFT synthesize | anomaly | S | 14.855,14.855,14.852,14.854,14.850,14.849,14.846,14.843,14.839,14.835,14.830,14.827,14.821,7.059,22.881,26.085,9.680,2.817,6.463,11.880,11.110,27.974,22.310,17.379,20.249,2.389,1.111,9.652,14.676,14.660,14.648,14.636,14.620,14.605,14.591,14.576,14.558,14.542,14.521,14.508,14.489,14.469,14.451,14.434,14.411,14.391,14.37... | 2025-10-08 00:00:00 | 2025-10-08 00:03:25 | 5.160,5.067,4.971,4.878,4.780,4.683,4.590,4.491,4.394,4.294,4.197,4.097,4.000,3.900,3.797,3.698,3.598,3.494,3.393,3.289,3.187,3.084,2.980,2.876,2.770,2.665,2.561,2.453,2.346,2.242,2.132,2.026,1.916,1.806,1.699,1.587,1.478,1.365,1.255,1.142,1.032,0.920,0.806,0.692,0.577,0.464,0.348,0.234,0.115,0.000 | 2025-10-08 00:03:26 | 2025-10-08 00:04:15 | , | 50 | You are a helpful assistant for time series forecasting. Think step by step to make a prediction. | Predict the speed of the ship over the next 50 seconds, from 2025-10-08 00:03:26 to 2025-10-08 00:04:15.
The final result must be enclosed by '\boxed{' and '}', and where values are separated by ','. | This time series records the speed (knots) of a slowing down ship. The data was recorded from 2025-10-08 00:00:00 to 2025-10-08 00:03:25 and the frequency of recording is second (S). Due to sensors errors, readings from the periods of 2025-10-08 00:00:12 to 2025-10-08 00:00:27 and 2025-10-08 00:01:45 to 2025-10-08 00:0... | <history>14.855,14.855,14.852,14.854,14.850,14.849,14.846,14.843,14.839,14.835,14.830,14.827,14.821,7.059,22.881,26.085,9.680,2.817,6.463,11.880,11.110,27.974,22.310,17.379,20.249,2.389,1.111,9.652,14.676,14.660,14.648,14.636,14.620,14.605,14.591,14.576,14.558,14.542,14.521,14.508,14.489,14.469,14.451,14.434,14.411,14.... | |
anomaly_explicit/template2/item1 | GIFT synthesize | anomaly | S | 24.725,24.629,24.526,24.426,24.337,24.218,24.109,24.021,23.911,23.815,23.710,23.624,23.511,23.406,23.314,23.203,23.104,23.003,22.913,22.793,22.701,22.610,22.509,22.399,22.299,22.218,22.109,22.002,14.159,30.600,25.329,20.132,17.209,14.623,30.874,13.204,26.293,19.704,24.976,18.480,14.477,27.958,12.967,14.644,26.280,27.94... | 2025-10-08 00:00:00 | 2025-10-08 00:04:23 | 2.932,2.894,2.843,2.792,2.735,2.688,2.661,2.611,2.559,2.499,2.484,2.427,2.389,2.355,2.291,2.263,2.205,2.160,2.132,2.088,2.053,2.019,1.974,1.941,1.908,1.847,1.797,1.774,1.743,1.705,1.668,1.636,1.596,1.552,1.532,1.489,1.462,1.415,1.363,1.355,1.330,1.300,1.262,1.230,1.203,1.165,1.141,1.110,1.086,1.029,1.015,1.008,0.969,0.... | 2025-10-08 00:04:24 | 2025-10-08 00:06:23 | , | 120 | You are a helpful assistant for time series forecasting. Think step by step to make a prediction. | Predict the speed of the ship over the next 120 seconds, from 2025-10-08 00:04:24 to 2025-10-08 00:06:23.
The final result must be enclosed by '\boxed{' and '}', and where values are separated by ','. | This time series records the speed (knots) of a slowing down ship. The data was recorded from 2025-10-08 00:00:00 to 2025-10-08 00:04:23 and the frequency of recording is second (S). Due to sensors errors, readings from the periods of 2025-10-08 00:00:27 to 2025-10-08 00:01:24 and 2025-10-08 00:01:58 to 2025-10-08 00:0... | <history>24.725,24.629,24.526,24.426,24.337,24.218,24.109,24.021,23.911,23.815,23.710,23.624,23.511,23.406,23.314,23.203,23.104,23.003,22.913,22.793,22.701,22.610,22.509,22.399,22.299,22.218,22.109,22.002,14.159,30.600,25.329,20.132,17.209,14.623,30.874,13.204,26.293,19.704,24.976,18.480,14.477,27.958,12.967,14.644,26.... | |
anomaly_explicit/template3/item1 | GIFT synthesize | anomaly | S | 22.601,22.519,22.425,22.331,22.232,22.142,22.063,21.965,21.873,21.780,21.690,21.597,21.497,21.414,21.320,21.231,21.141,21.042,20.936,20.854,20.767,20.671,20.574,20.483,20.395,20.303,20.215,20.115,20.028,19.934,19.848,19.751,19.660,19.565,19.482,19.398,19.289,19.204,19.110,19.021,18.924,18.826,18.752,18.641,18.564,18.47... | 2025-10-08 00:00:00 | 2025-10-08 00:04:23 | 2.678,2.627,2.585,2.547,2.500,2.455,2.410,2.373,2.329,2.285,2.257,2.214,2.172,2.126,2.093,2.056,2.015,1.976,1.942,1.904,1.862,1.831,1.796,1.757,1.718,1.689,1.652,1.622,1.582,1.549,1.509,1.485,1.439,1.428,1.385,1.349,1.327,1.298,1.262,1.233,1.195,1.177,1.144,1.113,1.085,1.056,1.031,1.001,0.972,0.944,0.925,0.902,0.871,0.... | 2025-10-08 00:04:24 | 2025-10-08 00:06:23 | , | 120 | You are a helpful assistant for time series forecasting. Think step by step to make a prediction. | Predict the speed of the ship over the next 120 seconds, from 2025-10-08 00:04:24 to 2025-10-08 00:06:23.
The final result must be enclosed by '\boxed{' and '}', and where values are separated by ','. | This time series records the speed (knots) of a slowing down ship. The data was recorded from 2025-10-08 00:00:00 to 2025-10-08 00:04:23 and the frequency of recording is second (S). Due to sensors errors, readings from the periods of 2025-10-08 00:00:59 to 2025-10-08 00:01:36 and 2025-10-08 00:03:25 to 2025-10-08 00:0... | <history>22.601,22.519,22.425,22.331,22.232,22.142,22.063,21.965,21.873,21.780,21.690,21.597,21.497,21.414,21.320,21.231,21.141,21.042,20.936,20.854,20.767,20.671,20.574,20.483,20.395,20.303,20.215,20.115,20.028,19.934,19.848,19.751,19.660,19.565,19.482,19.398,19.289,19.204,19.110,19.021,18.924,18.826,18.752,18.641,18.... | |
anomaly_explicit/template4/item1 | GIFT synthesize | anomaly | S | 25.019,25.003,25.015,25.027,25.008,24.994,24.992,25.012,24.989,24.989,24.986,25.023,25.019,24.990,24.984,24.989,24.974,24.983,24.978,24.958,24.975,24.969,24.953,24.958,24.974,24.965,24.943,24.942,24.922,24.918,24.915,24.934,24.923,24.911,24.891,24.884,24.880,24.857,24.856,24.859,24.868,24.860,24.834,24.833,24.818,24.81... | 2025-10-08 00:00:00 | 2025-10-08 00:06:51 | 8.758,8.684,8.607,8.512,8.437,8.364,8.294,8.186,8.113,8.022,7.972,7.877,7.798,7.710,7.626,7.536,7.458,7.389,7.294,7.214,7.139,7.060,6.976,6.877,6.819,6.701,6.640,6.564,6.471,6.379,6.288,6.214,6.123,6.048,5.946,5.871,5.796,5.688,5.625,5.540,5.436,5.358,5.259,5.160,5.098,4.997,4.939,4.832,4.735,4.643,4.559,4.494,4.382,4.... | 2025-10-08 00:06:52 | 2025-10-08 00:08:31 | , | 100 | You are a helpful assistant for time series forecasting. Think step by step to make a prediction. | Predict the speed of the ship over the next 100 seconds, from 2025-10-08 00:06:52 to 2025-10-08 00:08:31.
The final result must be enclosed by '\boxed{' and '}', and where values are separated by ','. | This time series records the speed (knots) of a slowing down ship. The data was recorded from 2025-10-08 00:00:00 to 2025-10-08 00:06:51 and the frequency of recording is second (S). Due to sensors errors, readings from the periods of 2025-10-08 00:01:14 to 2025-10-08 00:02:18 and 2025-10-08 00:05:18 to 2025-10-08 00:0... | <history>25.019,25.003,25.015,25.027,25.008,24.994,24.992,25.012,24.989,24.989,24.986,25.023,25.019,24.990,24.984,24.989,24.974,24.983,24.978,24.958,24.975,24.969,24.953,24.958,24.974,24.965,24.943,24.942,24.922,24.918,24.915,24.934,24.923,24.911,24.891,24.884,24.880,24.857,24.856,24.859,24.868,24.860,24.834,24.833,24.... | |
anomaly_implicit/template0/item1 | GIFT synthesize | anomaly | S | 9.803,9.726,9.650,9.574,9.499,9.424,9.349,9.274,9.200,9.126,9.052,8.979,8.905,8.833,8.760,8.688,8.616,8.544,8.473,8.402,8.331,8.261,8.190,8.121,8.051,7.982,7.913,7.844,7.776,7.708,7.640,7.573,7.505,7.439,7.372,7.306,7.240,7.174,7.108,7.044,6.979,6.914,6.850,6.900,7.177,7.683,8.415,9.376,10.564,11.980,13.623,15.495,17.5... | 2025-10-08 00:00:00 | 2025-10-08 00:03:25 | 0.374,0.359,0.344,0.330,0.316,0.303,0.290,0.276,0.264,0.251,0.239,0.227,0.216,0.205,0.194,0.183,0.173,0.163,0.153,0.144,0.135,0.126,0.117,0.109,0.101,0.093,0.086,0.079,0.072,0.066,0.060,0.054,0.048,0.043,0.038,0.033,0.029,0.025,0.021,0.018,0.015,0.012,0.009,0.007,0.005,0.004,0.002,0.001,0.000,0.000 | 2025-10-08 00:03:26 | 2025-10-08 00:04:15 | , | 50 | You are a helpful assistant for time series forecasting. Think step by step to make a prediction. | Predict the speed of the ship over the next 50 seconds, from 2025-10-08 00:03:26 to 2025-10-08 00:04:15.
The final result must be enclosed by '\boxed{' and '}', and where values are separated by ','. | This time series records the speed (knots) of a slowing down ship. The data was recorded from 2025-10-08 00:00:00 to 2025-10-08 00:03:25 and the frequency of recording is second (S). Due to sensors errors, some readings were noisy and unreliable. However, the ship is slowing down steadily and is expected to reach a com... | <history>9.803,9.726,9.650,9.574,9.499,9.424,9.349,9.274,9.200,9.126,9.052,8.979,8.905,8.833,8.760,8.688,8.616,8.544,8.473,8.402,8.331,8.261,8.190,8.121,8.051,7.982,7.913,7.844,7.776,7.708,7.640,7.573,7.505,7.439,7.372,7.306,7.240,7.174,7.108,7.044,6.979,6.914,6.850,6.900,7.177,7.683,8.415,9.376,10.564,11.980,13.623,15... | |
anomaly_implicit/template1/item1 | GIFT synthesize | anomaly | S | 14.855,14.855,14.852,14.854,14.850,14.849,14.846,14.843,14.839,14.835,14.830,14.827,14.821,7.059,22.881,26.085,9.680,2.817,6.463,11.880,11.110,27.974,22.310,17.379,20.249,2.389,1.111,9.652,14.676,14.660,14.648,14.636,14.620,14.605,14.591,14.576,14.558,14.542,14.521,14.508,14.489,14.469,14.451,14.434,14.411,14.391,14.37... | 2025-10-08 00:00:00 | 2025-10-08 00:03:25 | 5.160,5.067,4.971,4.878,4.780,4.683,4.590,4.491,4.394,4.294,4.197,4.097,4.000,3.900,3.797,3.698,3.598,3.494,3.393,3.289,3.187,3.084,2.980,2.876,2.770,2.665,2.561,2.453,2.346,2.242,2.132,2.026,1.916,1.806,1.699,1.587,1.478,1.365,1.255,1.142,1.032,0.920,0.806,0.692,0.577,0.464,0.348,0.234,0.115,0.000 | 2025-10-08 00:03:26 | 2025-10-08 00:04:15 | , | 50 | You are a helpful assistant for time series forecasting. Think step by step to make a prediction. | Predict the speed of the ship over the next 50 seconds, from 2025-10-08 00:03:26 to 2025-10-08 00:04:15.
The final result must be enclosed by '\boxed{' and '}', and where values are separated by ','. | This time series records the speed (knots) of a slowing down ship. The data was recorded from 2025-10-08 00:00:00 to 2025-10-08 00:03:25 and the frequency of recording is second (S). Due to sensors errors, some readings were noisy and unreliable. However, the ship is slowing down steadily and is expected to reach a com... | <history>14.855,14.855,14.852,14.854,14.850,14.849,14.846,14.843,14.839,14.835,14.830,14.827,14.821,7.059,22.881,26.085,9.680,2.817,6.463,11.880,11.110,27.974,22.310,17.379,20.249,2.389,1.111,9.652,14.676,14.660,14.648,14.636,14.620,14.605,14.591,14.576,14.558,14.542,14.521,14.508,14.489,14.469,14.451,14.434,14.411,14.... | |
anomaly_implicit/template2/item1 | GIFT synthesize | anomaly | S | 24.725,24.629,24.526,24.426,24.337,24.218,24.109,24.021,23.911,23.815,23.710,23.624,23.511,23.406,23.314,23.203,23.104,23.003,22.913,22.793,22.701,22.610,22.509,22.399,22.299,22.218,22.109,22.002,14.159,30.600,25.329,20.132,17.209,14.623,30.874,13.204,26.293,19.704,24.976,18.480,14.477,27.958,12.967,14.644,26.280,27.94... | 2025-10-08 00:00:00 | 2025-10-08 00:04:23 | 2.932,2.894,2.843,2.792,2.735,2.688,2.661,2.611,2.559,2.499,2.484,2.427,2.389,2.355,2.291,2.263,2.205,2.160,2.132,2.088,2.053,2.019,1.974,1.941,1.908,1.847,1.797,1.774,1.743,1.705,1.668,1.636,1.596,1.552,1.532,1.489,1.462,1.415,1.363,1.355,1.330,1.300,1.262,1.230,1.203,1.165,1.141,1.110,1.086,1.029,1.015,1.008,0.969,0.... | 2025-10-08 00:04:24 | 2025-10-08 00:06:23 | , | 120 | You are a helpful assistant for time series forecasting. Think step by step to make a prediction. | Predict the speed of the ship over the next 120 seconds, from 2025-10-08 00:04:24 to 2025-10-08 00:06:23.
The final result must be enclosed by '\boxed{' and '}', and where values are separated by ','. | This time series records the speed (knots) of a slowing down ship. The data was recorded from 2025-10-08 00:00:00 to 2025-10-08 00:04:23 and the frequency of recording is second (S). Due to sensors errors, some readings were noisy and unreliable. However, the ship is slowing down steadily and is expected to reach a com... | <history>24.725,24.629,24.526,24.426,24.337,24.218,24.109,24.021,23.911,23.815,23.710,23.624,23.511,23.406,23.314,23.203,23.104,23.003,22.913,22.793,22.701,22.610,22.509,22.399,22.299,22.218,22.109,22.002,14.159,30.600,25.329,20.132,17.209,14.623,30.874,13.204,26.293,19.704,24.976,18.480,14.477,27.958,12.967,14.644,26.... | |
anomaly_implicit/template3/item1 | GIFT synthesize | anomaly | S | 22.601,22.519,22.425,22.331,22.232,22.142,22.063,21.965,21.873,21.780,21.690,21.597,21.497,21.414,21.320,21.231,21.141,21.042,20.936,20.854,20.767,20.671,20.574,20.483,20.395,20.303,20.215,20.115,20.028,19.934,19.848,19.751,19.660,19.565,19.482,19.398,19.289,19.204,19.110,19.021,18.924,18.826,18.752,18.641,18.564,18.47... | 2025-10-08 00:00:00 | 2025-10-08 00:04:23 | 2.678,2.627,2.585,2.547,2.500,2.455,2.410,2.373,2.329,2.285,2.257,2.214,2.172,2.126,2.093,2.056,2.015,1.976,1.942,1.904,1.862,1.831,1.796,1.757,1.718,1.689,1.652,1.622,1.582,1.549,1.509,1.485,1.439,1.428,1.385,1.349,1.327,1.298,1.262,1.233,1.195,1.177,1.144,1.113,1.085,1.056,1.031,1.001,0.972,0.944,0.925,0.902,0.871,0.... | 2025-10-08 00:04:24 | 2025-10-08 00:06:23 | , | 120 | You are a helpful assistant for time series forecasting. Think step by step to make a prediction. | Predict the speed of the ship over the next 120 seconds, from 2025-10-08 00:04:24 to 2025-10-08 00:06:23.
The final result must be enclosed by '\boxed{' and '}', and where values are separated by ','. | This time series records the speed (knots) of a slowing down ship. The data was recorded from 2025-10-08 00:00:00 to 2025-10-08 00:04:23 and the frequency of recording is second (S). Due to sensors errors, some readings were noisy and unreliable. However, the ship is slowing down steadily and is expected to reach a com... | <history>22.601,22.519,22.425,22.331,22.232,22.142,22.063,21.965,21.873,21.780,21.690,21.597,21.497,21.414,21.320,21.231,21.141,21.042,20.936,20.854,20.767,20.671,20.574,20.483,20.395,20.303,20.215,20.115,20.028,19.934,19.848,19.751,19.660,19.565,19.482,19.398,19.289,19.204,19.110,19.021,18.924,18.826,18.752,18.641,18.... | |
anomaly_implicit/template4/item1 | GIFT synthesize | anomaly | S | 25.019,25.003,25.015,25.027,25.008,24.994,24.992,25.012,24.989,24.989,24.986,25.023,25.019,24.990,24.984,24.989,24.974,24.983,24.978,24.958,24.975,24.969,24.953,24.958,24.974,24.965,24.943,24.942,24.922,24.918,24.915,24.934,24.923,24.911,24.891,24.884,24.880,24.857,24.856,24.859,24.868,24.860,24.834,24.833,24.818,24.81... | 2025-10-08 00:00:00 | 2025-10-08 00:06:51 | 8.758,8.684,8.607,8.512,8.437,8.364,8.294,8.186,8.113,8.022,7.972,7.877,7.798,7.710,7.626,7.536,7.458,7.389,7.294,7.214,7.139,7.060,6.976,6.877,6.819,6.701,6.640,6.564,6.471,6.379,6.288,6.214,6.123,6.048,5.946,5.871,5.796,5.688,5.625,5.540,5.436,5.358,5.259,5.160,5.098,4.997,4.939,4.832,4.735,4.643,4.559,4.494,4.382,4.... | 2025-10-08 00:06:52 | 2025-10-08 00:08:31 | , | 100 | You are a helpful assistant for time series forecasting. Think step by step to make a prediction. | Predict the speed of the ship over the next 100 seconds, from 2025-10-08 00:06:52 to 2025-10-08 00:08:31.
The final result must be enclosed by '\boxed{' and '}', and where values are separated by ','. | This time series records the speed (knots) of a slowing down ship. The data was recorded from 2025-10-08 00:00:00 to 2025-10-08 00:06:51 and the frequency of recording is second (S). Due to sensors errors, some readings were noisy and unreliable. However, the ship is slowing down steadily and is expected to reach a com... | <history>25.019,25.003,25.015,25.027,25.008,24.994,24.992,25.012,24.989,24.989,24.986,25.023,25.019,24.990,24.984,24.989,24.974,24.983,24.978,24.958,24.975,24.969,24.953,24.958,24.974,24.965,24.943,24.942,24.922,24.918,24.915,24.934,24.923,24.911,24.891,24.884,24.880,24.857,24.856,24.859,24.868,24.860,24.834,24.833,24.... | |
context_explicit/template0/item0 | GIFT synthesize | phase_change | T | 77.654,77.762,77.828,77.907,77.965,78.006,78.028,78.038,78.034,77.996,77.968,77.895,77.833,77.744,77.637,77.518,77.377,77.225,77.064,76.874,76.690,76.483,76.267,76.036,75.807,75.565,75.310,75.042,74.766,74.498,74.211,73.944,73.645,73.353,73.079,72.792,72.495,72.220,71.943,71.668,71.395,71.123,70.875,70.623,70.382,70.15... | 2025-10-08 00:00:00 | 2025-10-08 06:31:00 | 108.888,108.801,108.730,108.669,108.616,108.575,108.554,108.537,108.520,108.537,108.556,108.596,108.620,108.669,108.724,108.789,108.847,108.926,109.006,109.092,109.181,109.265,109.357,109.459,109.555,109.651,109.753,109.842,109.933,110.025,110.119,110.208,110.283,110.365,110.448,110.520,110.592,110.629,110.694,110.750,... | 2025-10-08 06:32:00 | 2025-10-08 08:31:00 | , | 120 | You are a helpful assistant for time series forecasting. Think step by step to make a prediction. | Forecast the traffic in the next 60 minutes, from 2025-10-08 06:32:00 to 2025-10-08 07:31:00, of this time series.
The final result must be enclosed by '\boxed{' and '}', and where values are separated by ','. | This time series records the traffic of a webiste. The traffic was recorded from 2025-10-08 00:00:00 to 2025-10-08 06:31:00 with the minutes (T) frequency. The webmaster ran an online event at 2025-10-08 02:21:00, leading to an increase of traffic. The traffic is expected to be high for the rest of the event. | <history>77.654,77.762,77.828,77.907,77.965,78.006,78.028,78.038,78.034,77.996,77.968,77.895,77.833,77.744,77.637,77.518,77.377,77.225,77.064,76.874,76.690,76.483,76.267,76.036,75.807,75.565,75.310,75.042,74.766,74.498,74.211,73.944,73.645,73.353,73.079,72.792,72.495,72.220,71.943,71.668,71.395,71.123,70.875,70.623,70.... | |
context_explicit/template1/item0 | GIFT synthesize | phase_change | T | 68.053,68.926,69.960,70.767,71.759,72.654,73.574,74.416,75.519,76.349,77.080,78.196,79.100,79.992,80.814,81.800,82.737,83.573,84.510,77.766,91.244,80.127,90.677,92.271,118.547,112.877,131.736,129.781,163.114,171.774,172.543,173.655,174.529,175.434,176.341,177.292,178.193,178.956,179.852,180.755,181.665,182.599,183.391,... | 2025-10-08 00:00:00 | 2025-10-08 02:35:00 | 267.538,268.021,268.490,269.075,269.740,269.942,270.606,271.150,271.551,272.099,272.531,273.050,273.462,274.058,274.527,274.877,275.350,275.774,276.286,276.668,277.149,277.609,277.904,278.414,278.745,279.066,279.567,279.940,280.474,280.820,281.170,281.553,281.848,282.329,282.696,282.947,283.555,283.693,284.212,284.419,... | 2025-10-08 02:36:00 | 2025-10-08 04:15:00 | , | 100 | You are a helpful assistant for time series forecasting. Think step by step to make a prediction. | Forecast the traffic in the next 60 minutes, from 2025-10-08 02:36:00 to 2025-10-08 03:35:00, of this time series.
The final result must be enclosed by '\boxed{' and '}', and where values are separated by ','. | This time series records the traffic of a webiste. The traffic was recorded from 2025-10-08 00:00:00 to 2025-10-08 02:35:00 with the minutes (T) frequency. The webmaster ran an online event at 2025-10-08 00:18:00, leading to an increase of traffic. The traffic is expected to be high for the rest of the event. | <history>68.053,68.926,69.960,70.767,71.759,72.654,73.574,74.416,75.519,76.349,77.080,78.196,79.100,79.992,80.814,81.800,82.737,83.573,84.510,77.766,91.244,80.127,90.677,92.271,118.547,112.877,131.736,129.781,163.114,171.774,172.543,173.655,174.529,175.434,176.341,177.292,178.193,178.956,179.852,180.755,181.665,182.599... | |
context_explicit/template2/item0 | GIFT synthesize | phase_change | T | 839.369,839.962,839.635,839.073,840.792,840.751,840.934,841.664,842.197,843.010,843.798,844.340,844.058,844.253,845.586,847.898,848.810,849.538,850.354,852.393,852.730,855.023,855.471,857.165,858.009,861.504,862.577,863.183,864.985,865.705,867.764,869.301,872.618,873.412,876.242,878.893,881.787,882.464,886.978,887.783,... | 2025-10-07 00:00:00 | 2025-10-07 02:35:00 | 3994.128,4003.133,4012.559,4022.494,4032.639,4041.298,4053.421,4063.813,4073.915,4085.908,4093.861,4105.931,4114.733,4125.447,4136.539,4147.525,4157.829,4168.897,4179.532,4191.485,4201.494,4213.780,4224.946,4236.837,4247.864,4259.014,4270.655,4282.067,4294.219,4304.726,4317.657,4327.532,4340.921,4350.448,4364.567,4375.... | 2025-10-07 02:36:00 | 2025-10-07 04:15:00 | , | 100 | You are a helpful assistant for time series forecasting. Think step by step to make a prediction. | Forecast the traffic in the next 60 minutes, from 2025-10-07 02:36:00 to 2025-10-07 03:35:00, of this time series.
The final result must be enclosed by '\boxed{' and '}', and where values are separated by ','. | This time series records the traffic of a webiste. The traffic was recorded from 2025-10-07 00:00:00 to 2025-10-07 02:35:00 with the minutes (T) frequency. The webmaster ran an online event at 2025-10-07 01:10:00, leading to an increase of traffic. The traffic is expected to be high for the rest of the event. | <history>839.369,839.962,839.635,839.073,840.792,840.751,840.934,841.664,842.197,843.010,843.798,844.340,844.058,844.253,845.586,847.898,848.810,849.538,850.354,852.393,852.730,855.023,855.471,857.165,858.009,861.504,862.577,863.183,864.985,865.705,867.764,869.301,872.618,873.412,876.242,878.893,881.787,882.464,886.978... | |
context_explicit/template3/item0 | GIFT synthesize | phase_change | T | 37.204,41.912,39.814,41.331,42.088,39.806,39.569,39.479,38.919,39.672,41.326,37.546,39.696,40.254,37.513,38.159,38.008,39.111,37.615,42.954,36.072,40.517,38.212,38.164,39.842,37.556,38.825,37.838,35.959,38.227,38.894,36.296,36.495,38.780,39.853,37.793,41.394,37.853,40.613,36.270,41.814,38.814,40.785,38.964,41.513,40.59... | 2025-10-07 00:00:00 | 2025-10-07 03:53:00 | 67.449,65.518,65.362,67.953,65.799,69.032,65.316,66.867,65.960,63.276,65.160,65.845,65.289,67.250,66.616,62.850,63.838,65.608,63.881,63.926,66.464,68.056,63.506,63.544,67.801,62.989,63.676,67.576,68.191,65.886,65.060,68.352,70.082,65.625,65.607,66.160,67.628,68.129,69.362,68.148,70.945,67.924,66.895,68.869,68.228,69.58... | 2025-10-07 03:54:00 | 2025-10-07 06:23:00 | , | 150 | You are a helpful assistant for time series forecasting. Think step by step to make a prediction. | Forecast the traffic in the next 60 minutes, from 2025-10-07 03:54:00 to 2025-10-07 04:53:00, of this time series.
The final result must be enclosed by '\boxed{' and '}', and where values are separated by ','. | This time series records the traffic of a webiste. The traffic was recorded from 2025-10-07 00:00:00 to 2025-10-07 03:53:00 with the minutes (T) frequency. The webmaster ran an online event at 2025-10-07 01:46:00, leading to an increase of traffic. The traffic is expected to be high for the rest of the event. | <history>37.204,41.912,39.814,41.331,42.088,39.806,39.569,39.479,38.919,39.672,41.326,37.546,39.696,40.254,37.513,38.159,38.008,39.111,37.615,42.954,36.072,40.517,38.212,38.164,39.842,37.556,38.825,37.838,35.959,38.227,38.894,36.296,36.495,38.780,39.853,37.793,41.394,37.853,40.613,36.270,41.814,38.814,40.785,38.964,41.... | |
context_explicit/template4/item0 | GIFT synthesize | phase_change | T | 7200.378,4592.884,6222.893,7077.956,6839.441,6452.646,8114.307,8619.239,9369.519,7023.154,9057.831,7664.191,8683.972,8554.980,7787.065,8361.943,8110.618,7815.972,6504.497,8409.822,7445.998,8013.396,7767.750,6884.176,5946.087,6373.278,5146.195,5302.175,6665.887,7962.915,6863.586,7372.069,7724.296,8854.848,7226.928,8265.... | 2025-10-07 00:00:00 | 2025-10-07 04:23:00 | 44274.600,46180.358,45372.796,45200.444,47909.012,45739.829,46900.637,46271.819,48341.060,45930.607,45164.682,47631.788,46440.244,47387.428,47019.544,47740.154,45885.525,46337.318,46477.850,47590.830,45501.922,45668.269,46580.702,47614.048,45089.559,46205.832,46884.223,47932.492,46483.092,45503.013,46543.293,45508.449,... | 2025-10-07 04:24:00 | 2025-10-07 06:23:00 | , | 120 | You are a helpful assistant for time series forecasting. Think step by step to make a prediction. | Forecast the traffic in the next 60 minutes, from 2025-10-07 04:24:00 to 2025-10-07 05:23:00, of this time series.
The final result must be enclosed by '\boxed{' and '}', and where values are separated by ','. | This time series records the traffic of a webiste. The traffic was recorded from 2025-10-07 00:00:00 to 2025-10-07 04:23:00 with the minutes (T) frequency. The webmaster ran an online event at 2025-10-07 03:27:00, leading to an increase of traffic. The traffic is expected to be high for the rest of the event. | <history>7200.378,4592.884,6222.893,7077.956,6839.441,6452.646,8114.307,8619.239,9369.519,7023.154,9057.831,7664.191,8683.972,8554.980,7787.065,8361.943,8110.618,7815.972,6504.497,8409.822,7445.998,8013.396,7767.750,6884.176,5946.087,6373.278,5146.195,5302.175,6665.887,7962.915,6863.586,7372.069,7724.296,8854.848,7226.... | |
context_implicit/template0/item0 | GIFT synthesize | phase_change | T | 77.654,77.762,77.828,77.907,77.965,78.006,78.028,78.038,78.034,77.996,77.968,77.895,77.833,77.744,77.637,77.518,77.377,77.225,77.064,76.874,76.690,76.483,76.267,76.036,75.807,75.565,75.310,75.042,74.766,74.498,74.211,73.944,73.645,73.353,73.079,72.792,72.495,72.220,71.943,71.668,71.395,71.123,70.875,70.623,70.382,70.15... | 2025-10-08 00:00:00 | 2025-10-08 06:31:00 | 108.888,108.801,108.730,108.669,108.616,108.575,108.554,108.537,108.520,108.537,108.556,108.596,108.620,108.669,108.724,108.789,108.847,108.926,109.006,109.092,109.181,109.265,109.357,109.459,109.555,109.651,109.753,109.842,109.933,110.025,110.119,110.208,110.283,110.365,110.448,110.520,110.592,110.629,110.694,110.750,... | 2025-10-08 06:32:00 | 2025-10-08 08:31:00 | , | 120 | You are a helpful assistant for time series forecasting. Think step by step to make a prediction. | Forecast the traffic in the next 120 minutes, from 2025-10-08 06:32:00 to 2025-10-08 08:31:00, of this time series.
The final result must be enclosed by '\boxed{' and '}', and where values are separated by ','. | This time series records the traffic of a webiste. The traffic was recorded from 2025-10-08 00:00:00 to 2025-10-08 06:31:00 with the minutes (T) frequency. The webmaster ran an online event, leading to an increase of traffic. The traffic is expected to be high to the rest of the event. | <history>77.654,77.762,77.828,77.907,77.965,78.006,78.028,78.038,78.034,77.996,77.968,77.895,77.833,77.744,77.637,77.518,77.377,77.225,77.064,76.874,76.690,76.483,76.267,76.036,75.807,75.565,75.310,75.042,74.766,74.498,74.211,73.944,73.645,73.353,73.079,72.792,72.495,72.220,71.943,71.668,71.395,71.123,70.875,70.623,70.... | |
context_implicit/template1/item0 | GIFT synthesize | phase_change | T | 68.053,68.926,69.960,70.767,71.759,72.654,73.574,74.416,75.519,76.349,77.080,78.196,79.100,79.992,80.814,81.800,82.737,83.573,84.510,77.766,91.244,80.127,90.677,92.271,118.547,112.877,131.736,129.781,163.114,171.774,172.543,173.655,174.529,175.434,176.341,177.292,178.193,178.956,179.852,180.755,181.665,182.599,183.391,... | 2025-10-08 00:00:00 | 2025-10-08 02:35:00 | 267.538,268.021,268.490,269.075,269.740,269.942,270.606,271.150,271.551,272.099,272.531,273.050,273.462,274.058,274.527,274.877,275.350,275.774,276.286,276.668,277.149,277.609,277.904,278.414,278.745,279.066,279.567,279.940,280.474,280.820,281.170,281.553,281.848,282.329,282.696,282.947,283.555,283.693,284.212,284.419,... | 2025-10-08 02:36:00 | 2025-10-08 04:15:00 | , | 100 | You are a helpful assistant for time series forecasting. Think step by step to make a prediction. | Forecast the traffic in the next 100 minutes, from 2025-10-08 02:36:00 to 2025-10-08 04:15:00, of this time series.
The final result must be enclosed by '\boxed{' and '}', and where values are separated by ','. | This time series records the traffic of a webiste. The traffic was recorded from 2025-10-08 00:00:00 to 2025-10-08 02:35:00 with the minutes (T) frequency. The webmaster ran an online event, leading to an increase of traffic. The traffic is expected to be high to the rest of the event. | <history>68.053,68.926,69.960,70.767,71.759,72.654,73.574,74.416,75.519,76.349,77.080,78.196,79.100,79.992,80.814,81.800,82.737,83.573,84.510,77.766,91.244,80.127,90.677,92.271,118.547,112.877,131.736,129.781,163.114,171.774,172.543,173.655,174.529,175.434,176.341,177.292,178.193,178.956,179.852,180.755,181.665,182.599... | |
context_implicit/template2/item0 | GIFT synthesize | phase_change | T | 839.369,839.962,839.635,839.073,840.792,840.751,840.934,841.664,842.197,843.010,843.798,844.340,844.058,844.253,845.586,847.898,848.810,849.538,850.354,852.393,852.730,855.023,855.471,857.165,858.009,861.504,862.577,863.183,864.985,865.705,867.764,869.301,872.618,873.412,876.242,878.893,881.787,882.464,886.978,887.783,... | 2025-10-07 00:00:00 | 2025-10-07 02:35:00 | 3994.128,4003.133,4012.559,4022.494,4032.639,4041.298,4053.421,4063.813,4073.915,4085.908,4093.861,4105.931,4114.733,4125.447,4136.539,4147.525,4157.829,4168.897,4179.532,4191.485,4201.494,4213.780,4224.946,4236.837,4247.864,4259.014,4270.655,4282.067,4294.219,4304.726,4317.657,4327.532,4340.921,4350.448,4364.567,4375.... | 2025-10-07 02:36:00 | 2025-10-07 04:15:00 | , | 100 | You are a helpful assistant for time series forecasting. Think step by step to make a prediction. | Forecast the traffic in the next 100 minutes, from 2025-10-07 02:36:00 to 2025-10-07 04:15:00, of this time series.
The final result must be enclosed by '\boxed{' and '}', and where values are separated by ','. | This time series records the traffic of a webiste. The traffic was recorded from 2025-10-07 00:00:00 to 2025-10-07 02:35:00 with the minutes (T) frequency. The webmaster ran an online event, leading to an increase of traffic. The traffic is expected to be high to the rest of the event. | <history>839.369,839.962,839.635,839.073,840.792,840.751,840.934,841.664,842.197,843.010,843.798,844.340,844.058,844.253,845.586,847.898,848.810,849.538,850.354,852.393,852.730,855.023,855.471,857.165,858.009,861.504,862.577,863.183,864.985,865.705,867.764,869.301,872.618,873.412,876.242,878.893,881.787,882.464,886.978... | |
context_implicit/template3/item0 | GIFT synthesize | phase_change | T | 37.204,41.912,39.814,41.331,42.088,39.806,39.569,39.479,38.919,39.672,41.326,37.546,39.696,40.254,37.513,38.159,38.008,39.111,37.615,42.954,36.072,40.517,38.212,38.164,39.842,37.556,38.825,37.838,35.959,38.227,38.894,36.296,36.495,38.780,39.853,37.793,41.394,37.853,40.613,36.270,41.814,38.814,40.785,38.964,41.513,40.59... | 2025-10-07 00:00:00 | 2025-10-07 03:53:00 | 67.449,65.518,65.362,67.953,65.799,69.032,65.316,66.867,65.960,63.276,65.160,65.845,65.289,67.250,66.616,62.850,63.838,65.608,63.881,63.926,66.464,68.056,63.506,63.544,67.801,62.989,63.676,67.576,68.191,65.886,65.060,68.352,70.082,65.625,65.607,66.160,67.628,68.129,69.362,68.148,70.945,67.924,66.895,68.869,68.228,69.58... | 2025-10-07 03:54:00 | 2025-10-07 06:23:00 | , | 150 | You are a helpful assistant for time series forecasting. Think step by step to make a prediction. | Forecast the traffic in the next 150 minutes, from 2025-10-07 03:54:00 to 2025-10-07 06:23:00, of this time series.
The final result must be enclosed by '\boxed{' and '}', and where values are separated by ','. | This time series records the traffic of a webiste. The traffic was recorded from 2025-10-07 00:00:00 to 2025-10-07 03:53:00 with the minutes (T) frequency. The webmaster ran an online event, leading to an increase of traffic. The traffic is expected to be high to the rest of the event. | <history>37.204,41.912,39.814,41.331,42.088,39.806,39.569,39.479,38.919,39.672,41.326,37.546,39.696,40.254,37.513,38.159,38.008,39.111,37.615,42.954,36.072,40.517,38.212,38.164,39.842,37.556,38.825,37.838,35.959,38.227,38.894,36.296,36.495,38.780,39.853,37.793,41.394,37.853,40.613,36.270,41.814,38.814,40.785,38.964,41.... | |
context_implicit/template4/item0 | GIFT synthesize | phase_change | T | 7200.378,4592.884,6222.893,7077.956,6839.441,6452.646,8114.307,8619.239,9369.519,7023.154,9057.831,7664.191,8683.972,8554.980,7787.065,8361.943,8110.618,7815.972,6504.497,8409.822,7445.998,8013.396,7767.750,6884.176,5946.087,6373.278,5146.195,5302.175,6665.887,7962.915,6863.586,7372.069,7724.296,8854.848,7226.928,8265.... | 2025-10-07 00:00:00 | 2025-10-07 04:23:00 | 44274.600,46180.358,45372.796,45200.444,47909.012,45739.829,46900.637,46271.819,48341.060,45930.607,45164.682,47631.788,46440.244,47387.428,47019.544,47740.154,45885.525,46337.318,46477.850,47590.830,45501.922,45668.269,46580.702,47614.048,45089.559,46205.832,46884.223,47932.492,46483.092,45503.013,46543.293,45508.449,... | 2025-10-07 04:24:00 | 2025-10-07 06:23:00 | , | 120 | You are a helpful assistant for time series forecasting. Think step by step to make a prediction. | Forecast the traffic in the next 120 minutes, from 2025-10-07 04:24:00 to 2025-10-07 06:23:00, of this time series.
The final result must be enclosed by '\boxed{' and '}', and where values are separated by ','. | This time series records the traffic of a webiste. The traffic was recorded from 2025-10-07 00:00:00 to 2025-10-07 04:23:00 with the minutes (T) frequency. The webmaster ran an online event, leading to an increase of traffic. The traffic is expected to be high to the rest of the event. | <history>7200.378,4592.884,6222.893,7077.956,6839.441,6452.646,8114.307,8619.239,9369.519,7023.154,9057.831,7664.191,8683.972,8554.980,7787.065,8361.943,8110.618,7815.972,6504.497,8409.822,7445.998,8013.396,7767.750,6884.176,5946.087,6373.278,5146.195,5302.175,6665.887,7962.915,6863.586,7372.069,7724.296,8854.848,7226.... |
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Gift-EvalCTX Parquet
This repository hosts the GIFT-EvalCTX dataset in parquet format, highly compatible with LLMs. Each row is a sample of the dataset and contain the following fields:
- idx: unique id of the sample
- source: source of the same
- skill: the types of context
- frequency: frequency of the sample
- history_values: a string containing values of the history
- history_start: starting timestamp of the history
- history_end: ending timestamp of the history
- future_values: a string containing values of the forecasting future
- future_start: starting timestamp of the future
- future_end: ending timestamp of the future
- entry_sep: separator used to split the string in history_values, future_values, and roi into array of floats
- roi: region of interest, indicating the indices of the timestamp that are affected by the context and used in evaluation
- pred_length: number of timestamps to forecast
- system_prompt: default system prompt
- user_instruct: query to forecast
- context_info: context for the current sample
- prompt: a complete prompt to query LLM
Note that all fields contain string only, you need to convert them into the appropriate format (array of floats, or datetime).
Example usage
from datasets import load_dataset
ds = load_dataset(
"Salesforce/GiftEvalCTX",
"gift_ctx",
split="train"
)
print(len(ds))
print(ds[0].keys())
This repository is made public for research purposes only.
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