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.... | |
context_explicit/template0/item1 | GIFT synthesize | phase_change | H | 175.584,173.710,169.207,162.909,156.402,151.564,149.782,151.447,155.768,161.225,166.252,169.828,171.662,172.425,172.895,173.746,175.065,176.362,176.846,176.032,173.928,171.312,169.260,168.515,169.295,170.835,171.854,171.004,167.591,161.933,155.380,149.768,146.752,147.045,150.447,155.626,160.963,165.129,167.587,168.686,... | 2025-10-01 00:00:00 | 2025-10-09 07:00:00 | 90.741,92.351,93.725,94.101,93.085,90.896,88.329,86.481,86.055,87.044,88.488,89.083,87.567,83.492,77.371,70.812,65.658,63.391,64.613,68.726,74.326,79.816,83.923,86.402,87.576,88.377,89.478,91.064,92.748,93.654,93.254,91.456,88.915,86.627,85.582,86.035,87.394,88.470,87.927,84.875,79.431,72.883,66.987,63.500,63.395,66.46... | 2025-10-09 08:00:00 | 2025-10-11 15:00:00 | , | 56 | You are a helpful assistant for time series forecasting. Think step by step to make a prediction. | Forecast the future 56 readings from this sensor, from 2025-10-09 08:00:00 to 2025-10-11 15:00:00, given that the sensor has been fixed.
The final result must be enclosed by '\boxed{' and '}', and where values are separated by ','. | This time series records the temperature reading, in fahrenheit (F), from a sensor at city A. The data was recorded from 2025-10-01 00:00:00 to 2025-10-09 07:00:00 with the hour (H) frequency. Due to installation errors, the sensor was malfunctioned, which made 132 readings from 2025-10-01 00:00:00 to 2025-10-06 11:00:... | <history>175.584,173.710,169.207,162.909,156.402,151.564,149.782,151.447,155.768,161.225,166.252,169.828,171.662,172.425,172.895,173.746,175.065,176.362,176.846,176.032,173.928,171.312,169.260,168.515,169.295,170.835,171.854,171.004,167.591,161.933,155.380,149.768,146.752,147.045,150.447,155.626,160.963,165.129,167.587... | |
context_explicit/template1/item1 | GIFT synthesize | phase_change | H | 104.658,106.005,101.887,104.420,105.007,108.536,105.399,107.260,110.036,108.198,106.236,106.088,109.616,110.407,107.739,109.990,104.990,109.869,108.596,108.423,107.068,106.550,108.298,106.176,107.253,109.353,100.568,106.467,104.751,104.903,106.629,105.802,103.338,102.943,103.297,105.107,103.708,104.650,104.474,105.628,... | 2025-10-01 00:00:00 | 2025-10-07 11:00:00 | 89.590,92.075,92.144,92.265,87.380,91.216,89.566,90.758,92.548,89.584,90.155,83.913,88.643,88.853,88.944,85.802,88.890,86.720,87.140,86.910,85.636,85.342,86.572,88.185,86.906,85.284,84.730,86.493,84.400,82.237,84.861,83.249,86.156,87.616,85.608,89.374,89.933,89.092,85.833,86.261,86.438,88.064,82.599,85.664,85.078,85.26... | 2025-10-07 12:00:00 | 2025-10-11 15:00:00 | , | 100 | You are a helpful assistant for time series forecasting. Think step by step to make a prediction. | Forecast the future 100 readings from this sensor, from 2025-10-07 12:00:00 to 2025-10-11 15:00:00, given that the sensor has been fixed.
The final result must be enclosed by '\boxed{' and '}', and where values are separated by ','. | This time series records the temperature reading, in fahrenheit (F), from a sensor at city A. The data was recorded from 2025-10-01 00:00:00 to 2025-10-07 11:00:00 with the hour (H) frequency. Due to installation errors, the sensor was malfunctioned, which made 105 readings from 2025-10-01 00:00:00 to 2025-10-05 08:00:... | <history>104.658,106.005,101.887,104.420,105.007,108.536,105.399,107.260,110.036,108.198,106.236,106.088,109.616,110.407,107.739,109.990,104.990,109.869,108.596,108.423,107.068,106.550,108.298,106.176,107.253,109.353,100.568,106.467,104.751,104.903,106.629,105.802,103.338,102.943,103.297,105.107,103.708,104.650,104.474... | |
context_explicit/template2/item1 | GIFT synthesize | phase_change | H | 42.403,42.888,42.583,41.971,42.485,43.088,42.005,42.026,42.852,41.686,41.354,42.464,41.861,42.944,44.918,45.476,45.231,46.632,46.883,48.171,48.618,49.485,49.470,48.861,47.905,47.156,48.478,47.371,47.660,46.049,46.499,46.433,43.937,42.769,41.994,41.925,41.563,43.499,43.845,44.091,45.054,44.453,42.580,40.969,40.499,40.36... | 2025-08-01 00:00:00 | 2025-08-09 11:00:00 | 73.157,73.127,74.600,74.330,72.997,74.178,73.742,71.787,71.668,71.096,70.717,71.922,72.246,73.926,74.537,74.053,75.363,75.557,76.797,77.264,77.660,78.313,78.239,77.909,77.220,77.902,76.478,76.103,75.560,74.446,74.857,73.379,72.516,70.479,69.801,69.352,68.244,70.253,70.566,71.884,71.890,70.966,68.945,68.480,67.768,67.01... | 2025-08-09 12:00:00 | 2025-08-16 23:00:00 | , | 180 | You are a helpful assistant for time series forecasting. Think step by step to make a prediction. | Forecast the future 180 readings from this sensor, from 2025-08-09 12:00:00 to 2025-08-16 23:00:00, given that the sensor has been fixed.
The final result must be enclosed by '\boxed{' and '}', and where values are separated by ','. | This time series records the temperature reading, in fahrenheit (F), from a sensor at city A. The data was recorded from 2025-08-01 00:00:00 to 2025-08-09 11:00:00 with the hour (H) frequency. Due to installation errors, the sensor was malfunctioned, which made 202 readings from 2025-08-01 00:00:00 to 2025-08-09 09:00:... | <history>42.403,42.888,42.583,41.971,42.485,43.088,42.005,42.026,42.852,41.686,41.354,42.464,41.861,42.944,44.918,45.476,45.231,46.632,46.883,48.171,48.618,49.485,49.470,48.861,47.905,47.156,48.478,47.371,47.660,46.049,46.499,46.433,43.937,42.769,41.994,41.925,41.563,43.499,43.845,44.091,45.054,44.453,42.580,40.969,40.... | |
context_explicit/template3/item1 | GIFT synthesize | phase_change | H | 68.076,67.938,67.785,67.609,67.404,67.168,66.904,66.620,66.329,66.046,65.789,65.580,65.432,65.359,65.358,65.437,65.578,65.766,65.978,66.192,66.378,66.517,66.591,66.598,66.539,66.418,66.261,66.093,65.937,65.822,65.773,65.805,65.930,66.145,66.437,66.795,67.186,67.587,67.968,68.306,68.578,68.770,68.876,68.903,68.857,68.75... | 2025-08-01 00:00:00 | 2025-08-08 15:00:00 | 76.788,76.987,77.141,77.237,77.266,77.224,77.122,76.979,76.820,76.667,76.551,76.500,76.526,76.642,76.849,77.138,77.494,77.891,78.302,78.696,79.049,79.342,79.551,79.678,79.721,79.692,79.601,79.466,79.307,79.140,78.977,78.828,78.699,78.586,78.484,78.390,78.295,78.194,78.081,77.958,77.830,77.705,77.587,77.488,77.418,77.38... | 2025-08-08 16:00:00 | 2025-08-16 23:00:00 | , | 200 | You are a helpful assistant for time series forecasting. Think step by step to make a prediction. | Forecast the future 200 readings from this sensor, from 2025-08-08 16:00:00 to 2025-08-16 23:00:00, given that the sensor has been fixed.
The final result must be enclosed by '\boxed{' and '}', and where values are separated by ','. | This time series records the temperature reading, in fahrenheit (F), from a sensor at city A. The data was recorded from 2025-08-01 00:00:00 to 2025-08-08 15:00:00 with the hour (H) frequency. Due to installation errors, the sensor was malfunctioned, which made 90 readings from 2025-08-01 00:00:00 to 2025-08-04 17:00:0... | <history>68.076,67.938,67.785,67.609,67.404,67.168,66.904,66.620,66.329,66.046,65.789,65.580,65.432,65.359,65.358,65.437,65.578,65.766,65.978,66.192,66.378,66.517,66.591,66.598,66.539,66.418,66.261,66.093,65.937,65.822,65.773,65.805,65.930,66.145,66.437,66.795,67.186,67.587,67.968,68.306,68.578,68.770,68.876,68.903,68.... | |
context_explicit/template4/item1 | GIFT synthesize | phase_change | H | 62.004,61.553,61.109,60.691,60.294,59.910,59.566,59.249,58.971,58.729,58.529,58.366,58.249,58.178,58.153,58.169,58.229,58.333,58.489,58.684,58.913,59.192,59.493,59.836,60.207,60.599,61.020,61.454,61.905,62.370,62.838,63.309,63.771,64.234,64.679,65.113,65.526,65.910,66.276,66.612,66.905,67.168,67.393,67.576,67.716,67.81... | 2025-08-01 00:00:00 | 2025-08-20 05:00:00 | 75.936,75.691,75.490,75.323,75.195,75.118,75.078,75.086,75.134,75.233,75.371,75.552,75.784,76.043,76.343,76.678,77.048,77.442,77.869,78.311,78.777,79.251,79.735,80.232,80.730,81.210,81.695,82.163,82.620,83.052,83.464,83.848,84.198,84.518,84.801,85.051,85.253,85.414,85.534,85.607,85.628,85.618,85.557,85.451,85.310,85.11... | 2025-08-20 06:00:00 | 2025-08-22 07:00:00 | , | 50 | You are a helpful assistant for time series forecasting. Think step by step to make a prediction. | Forecast the future 50 readings from this sensor, from 2025-08-20 06:00:00 to 2025-08-22 07:00:00, given that the sensor has been fixed.
The final result must be enclosed by '\boxed{' and '}', and where values are separated by ','. | This time series records the temperature reading, in fahrenheit (F), from a sensor at city A. The data was recorded from 2025-08-01 00:00:00 to 2025-08-20 05:00:00 with the hour (H) frequency. Due to installation errors, the sensor was malfunctioned, which made 275 readings from 2025-08-01 00:00:00 to 2025-08-12 10:00:... | <history>62.004,61.553,61.109,60.691,60.294,59.910,59.566,59.249,58.971,58.729,58.529,58.366,58.249,58.178,58.153,58.169,58.229,58.333,58.489,58.684,58.913,59.192,59.493,59.836,60.207,60.599,61.020,61.454,61.905,62.370,62.838,63.309,63.771,64.234,64.679,65.113,65.526,65.910,66.276,66.612,66.905,67.168,67.393,67.576,67.... | |
context_implicit/template0/item1 | GIFT synthesize | phase_change | H | 175.584,173.710,169.207,162.909,156.402,151.564,149.782,151.447,155.768,161.225,166.252,169.828,171.662,172.425,172.895,173.746,175.065,176.362,176.846,176.032,173.928,171.312,169.260,168.515,169.295,170.835,171.854,171.004,167.591,161.933,155.380,149.768,146.752,147.045,150.447,155.626,160.963,165.129,167.587,168.686,... | 2025-10-01 00:00:00 | 2025-10-09 07:00:00 | 90.741,92.351,93.725,94.101,93.085,90.896,88.329,86.481,86.055,87.044,88.488,89.083,87.567,83.492,77.371,70.812,65.658,63.391,64.613,68.726,74.326,79.816,83.923,86.402,87.576,88.377,89.478,91.064,92.748,93.654,93.254,91.456,88.915,86.627,85.582,86.035,87.394,88.470,87.927,84.875,79.431,72.883,66.987,63.500,63.395,66.46... | 2025-10-09 08:00:00 | 2025-10-11 15:00:00 | , | 56 | You are a helpful assistant for time series forecasting. Think step by step to make a prediction. | Forecast the future 56 readings from this sensor, from 2025-10-09 08:00:00 to 2025-10-11 15:00:00, given that the sensor has been fixed.
The final result must be enclosed by '\boxed{' and '}', and where values are separated by ','. | This time series records the temperature reading, in fahrenheit (F), from a sensor at city A. The data was recorded from 2025-10-01 00:00:00 to 2025-10-09 07:00:00 with the hour (H) frequency. Due to installation errors, the sensor was malfunctioned, resulted in unrealiable readings. The technical team has replaced the... | <history>175.584,173.710,169.207,162.909,156.402,151.564,149.782,151.447,155.768,161.225,166.252,169.828,171.662,172.425,172.895,173.746,175.065,176.362,176.846,176.032,173.928,171.312,169.260,168.515,169.295,170.835,171.854,171.004,167.591,161.933,155.380,149.768,146.752,147.045,150.447,155.626,160.963,165.129,167.587... | |
context_implicit/template1/item1 | GIFT synthesize | phase_change | H | 104.658,106.005,101.887,104.420,105.007,108.536,105.399,107.260,110.036,108.198,106.236,106.088,109.616,110.407,107.739,109.990,104.990,109.869,108.596,108.423,107.068,106.550,108.298,106.176,107.253,109.353,100.568,106.467,104.751,104.903,106.629,105.802,103.338,102.943,103.297,105.107,103.708,104.650,104.474,105.628,... | 2025-10-01 00:00:00 | 2025-10-07 11:00:00 | 89.590,92.075,92.144,92.265,87.380,91.216,89.566,90.758,92.548,89.584,90.155,83.913,88.643,88.853,88.944,85.802,88.890,86.720,87.140,86.910,85.636,85.342,86.572,88.185,86.906,85.284,84.730,86.493,84.400,82.237,84.861,83.249,86.156,87.616,85.608,89.374,89.933,89.092,85.833,86.261,86.438,88.064,82.599,85.664,85.078,85.26... | 2025-10-07 12:00:00 | 2025-10-11 15:00:00 | , | 100 | You are a helpful assistant for time series forecasting. Think step by step to make a prediction. | Forecast the future 100 readings from this sensor, from 2025-10-07 12:00:00 to 2025-10-11 15:00:00, given that the sensor has been fixed.
The final result must be enclosed by '\boxed{' and '}', and where values are separated by ','. | This time series records the temperature reading, in fahrenheit (F), from a sensor at city A. The data was recorded from 2025-10-01 00:00:00 to 2025-10-07 11:00:00 with the hour (H) frequency. Due to installation errors, the sensor was malfunctioned, resulted in unrealiable readings. The technical team has replaced the... | <history>104.658,106.005,101.887,104.420,105.007,108.536,105.399,107.260,110.036,108.198,106.236,106.088,109.616,110.407,107.739,109.990,104.990,109.869,108.596,108.423,107.068,106.550,108.298,106.176,107.253,109.353,100.568,106.467,104.751,104.903,106.629,105.802,103.338,102.943,103.297,105.107,103.708,104.650,104.474... | |
context_implicit/template2/item1 | GIFT synthesize | phase_change | H | 42.403,42.888,42.583,41.971,42.485,43.088,42.005,42.026,42.852,41.686,41.354,42.464,41.861,42.944,44.918,45.476,45.231,46.632,46.883,48.171,48.618,49.485,49.470,48.861,47.905,47.156,48.478,47.371,47.660,46.049,46.499,46.433,43.937,42.769,41.994,41.925,41.563,43.499,43.845,44.091,45.054,44.453,42.580,40.969,40.499,40.36... | 2025-08-01 00:00:00 | 2025-08-09 11:00:00 | 73.157,73.127,74.600,74.330,72.997,74.178,73.742,71.787,71.668,71.096,70.717,71.922,72.246,73.926,74.537,74.053,75.363,75.557,76.797,77.264,77.660,78.313,78.239,77.909,77.220,77.902,76.478,76.103,75.560,74.446,74.857,73.379,72.516,70.479,69.801,69.352,68.244,70.253,70.566,71.884,71.890,70.966,68.945,68.480,67.768,67.01... | 2025-08-09 12:00:00 | 2025-08-16 23:00:00 | , | 180 | You are a helpful assistant for time series forecasting. Think step by step to make a prediction. | Forecast the future 180 readings from this sensor, from 2025-08-09 12:00:00 to 2025-08-16 23:00:00, given that the sensor has been fixed.
The final result must be enclosed by '\boxed{' and '}', and where values are separated by ','. | This time series records the temperature reading, in fahrenheit (F), from a sensor at city A. The data was recorded from 2025-08-01 00:00:00 to 2025-08-09 11:00:00 with the hour (H) frequency. Due to installation errors, the sensor was malfunctioned, resulted in unrealiable readings. The technical team has replaced the... | <history>42.403,42.888,42.583,41.971,42.485,43.088,42.005,42.026,42.852,41.686,41.354,42.464,41.861,42.944,44.918,45.476,45.231,46.632,46.883,48.171,48.618,49.485,49.470,48.861,47.905,47.156,48.478,47.371,47.660,46.049,46.499,46.433,43.937,42.769,41.994,41.925,41.563,43.499,43.845,44.091,45.054,44.453,42.580,40.969,40.... | |
context_implicit/template3/item1 | GIFT synthesize | phase_change | H | 68.076,67.938,67.785,67.609,67.404,67.168,66.904,66.620,66.329,66.046,65.789,65.580,65.432,65.359,65.358,65.437,65.578,65.766,65.978,66.192,66.378,66.517,66.591,66.598,66.539,66.418,66.261,66.093,65.937,65.822,65.773,65.805,65.930,66.145,66.437,66.795,67.186,67.587,67.968,68.306,68.578,68.770,68.876,68.903,68.857,68.75... | 2025-08-01 00:00:00 | 2025-08-08 15:00:00 | 76.788,76.987,77.141,77.237,77.266,77.224,77.122,76.979,76.820,76.667,76.551,76.500,76.526,76.642,76.849,77.138,77.494,77.891,78.302,78.696,79.049,79.342,79.551,79.678,79.721,79.692,79.601,79.466,79.307,79.140,78.977,78.828,78.699,78.586,78.484,78.390,78.295,78.194,78.081,77.958,77.830,77.705,77.587,77.488,77.418,77.38... | 2025-08-08 16:00:00 | 2025-08-16 23:00:00 | , | 200 | You are a helpful assistant for time series forecasting. Think step by step to make a prediction. | Forecast the future 200 readings from this sensor, from 2025-08-08 16:00:00 to 2025-08-16 23:00:00, given that the sensor has been fixed.
The final result must be enclosed by '\boxed{' and '}', and where values are separated by ','. | This time series records the temperature reading, in fahrenheit (F), from a sensor at city A. The data was recorded from 2025-08-01 00:00:00 to 2025-08-08 15:00:00 with the hour (H) frequency. Due to installation errors, the sensor was malfunctioned, resulted in unrealiable readings. The technical team has replaced the... | <history>68.076,67.938,67.785,67.609,67.404,67.168,66.904,66.620,66.329,66.046,65.789,65.580,65.432,65.359,65.358,65.437,65.578,65.766,65.978,66.192,66.378,66.517,66.591,66.598,66.539,66.418,66.261,66.093,65.937,65.822,65.773,65.805,65.930,66.145,66.437,66.795,67.186,67.587,67.968,68.306,68.578,68.770,68.876,68.903,68.... | |
context_implicit/template4/item1 | GIFT synthesize | phase_change | H | 62.004,61.553,61.109,60.691,60.294,59.910,59.566,59.249,58.971,58.729,58.529,58.366,58.249,58.178,58.153,58.169,58.229,58.333,58.489,58.684,58.913,59.192,59.493,59.836,60.207,60.599,61.020,61.454,61.905,62.370,62.838,63.309,63.771,64.234,64.679,65.113,65.526,65.910,66.276,66.612,66.905,67.168,67.393,67.576,67.716,67.81... | 2025-08-01 00:00:00 | 2025-08-20 05:00:00 | 75.936,75.691,75.490,75.323,75.195,75.118,75.078,75.086,75.134,75.233,75.371,75.552,75.784,76.043,76.343,76.678,77.048,77.442,77.869,78.311,78.777,79.251,79.735,80.232,80.730,81.210,81.695,82.163,82.620,83.052,83.464,83.848,84.198,84.518,84.801,85.051,85.253,85.414,85.534,85.607,85.628,85.618,85.557,85.451,85.310,85.11... | 2025-08-20 06:00:00 | 2025-08-22 07:00:00 | , | 50 | You are a helpful assistant for time series forecasting. Think step by step to make a prediction. | Forecast the future 50 readings from this sensor, from 2025-08-20 06:00:00 to 2025-08-22 07:00:00, given that the sensor has been fixed.
The final result must be enclosed by '\boxed{' and '}', and where values are separated by ','. | This time series records the temperature reading, in fahrenheit (F), from a sensor at city A. The data was recorded from 2025-08-01 00:00:00 to 2025-08-20 05:00:00 with the hour (H) frequency. Due to installation errors, the sensor was malfunctioned, resulted in unrealiable readings. The technical team has replaced the... | <history>62.004,61.553,61.109,60.691,60.294,59.910,59.566,59.249,58.971,58.729,58.529,58.366,58.249,58.178,58.153,58.169,58.229,58.333,58.489,58.684,58.913,59.192,59.493,59.836,60.207,60.599,61.020,61.454,61.905,62.370,62.838,63.309,63.771,64.234,64.679,65.113,65.526,65.910,66.276,66.612,66.905,67.168,67.393,67.576,67.... | |
context_explicit/template0/item2 | GIFT synthesize | phase_change | D | 8929.508,10443.771,67981.323,125704.106,156345.302,206220.957,236573.433,264587.699,280818.577,303391.438,310741.613,295466.404,298356.436,294720.125,289977.006,289524.272,285677.974,286660.433,287499.064,275568.353,290152.988,287845.332,288125.522,293595.155,304947.917,309272.920,317241.779,323457.754,329403.385,33727... | 2025-10-06 | 2026-11-01 | 623352.188,624408.984,623449.479,629834.594,625842.862,619901.526,622809.908,633816.161,627558.157,632144.464,628463.566,631381.156,627763.639,624420.664,622564.709,618682.102,612198.099,627499.410,618496.452,627212.478,620929.816,616679.194,623299.196,615890.374,615056.956,610492.037,609604.964,606370.472,607220.847,6... | 2026-11-02 | 2027-03-01 | , | 120 | You are a helpful assistant for time series forecasting. Think step by step to make a prediction. | Forecast the total sales in the next 120 days of this global chain, from 2026-11-02 to 2027-03-01.
The final result must be enclosed by '\boxed{' and '}', and where values are separated by ','. | This time series records the sales of a global chain store. The data was recorded from 2025-10-06 to 2026-11-01, and the recording frequency is day (D). The owners implemented a rapid exanpansion strategy and opened two new stores on 2025-10-06 and 2026-01-01 respectively. After each new store opening, the sales volumn... | <history>8929.508,10443.771,67981.323,125704.106,156345.302,206220.957,236573.433,264587.699,280818.577,303391.438,310741.613,295466.404,298356.436,294720.125,289977.006,289524.272,285677.974,286660.433,287499.064,275568.353,290152.988,287845.332,288125.522,293595.155,304947.917,309272.920,317241.779,323457.754,329403.... | |
context_explicit/template1/item2 | GIFT synthesize | phase_change | D | 283.721,283.245,282.819,282.503,282.339,282.080,281.974,281.952,281.943,281.988,282.365,282.597,283.001,283.405,284.042,284.613,285.287,286.058,286.922,287.784,288.848,289.868,291.030,292.270,293.646,294.917,296.267,297.729,299.373,300.936,302.614,304.403,306.072,307.792,309.817,311.730,313.618,315.629,317.679,319.735,... | 2025-10-06 | 2026-11-01 | 834.774,833.183,831.392,829.731,827.866,826.199,824.378,822.613,820.723,819.012,817.100,815.319,813.508,811.761,809.987,808.011,806.384,804.654,803.007,801.309,799.825,798.091,796.621,795.236,793.812,792.439,790.985,789.683,788.603,787.428,786.277,785.284,784.306,783.537,782.641,781.894,781.296,780.809,780.356,779.817,... | 2026-11-02 | 2027-03-01 | , | 120 | You are a helpful assistant for time series forecasting. Think step by step to make a prediction. | Forecast the total sales in the next 120 days of this global chain, from 2026-11-02 to 2027-03-01.
The final result must be enclosed by '\boxed{' and '}', and where values are separated by ','. | This time series records the sales of a global chain store. The data was recorded from 2025-10-06 to 2026-11-01, and the recording frequency is day (D). The owners implemented a rapid exanpansion strategy and opened two new stores on 2026-02-23 and 2026-05-30 respectively. After each new store opening, the sales volumn... | <history>283.721,283.245,282.819,282.503,282.339,282.080,281.974,281.952,281.943,281.988,282.365,282.597,283.001,283.405,284.042,284.613,285.287,286.058,286.922,287.784,288.848,289.868,291.030,292.270,293.646,294.917,296.267,297.729,299.373,300.936,302.614,304.403,306.072,307.792,309.817,311.730,313.618,315.629,317.679... | |
context_explicit/template2/item2 | GIFT synthesize | phase_change | D | 479.431,476.140,473.786,472.449,471.995,472.048,472.347,472.387,471.799,470.357,467.704,463.918,459.103,453.543,447.722,442.220,437.570,434.268,432.820,433.475,436.400,441.415,448.266,456.482,465.410,474.455,482.878,490.137,495.654,499.219,500.702,500.174,497.947,494.419,490.048,485.526,481.209,477.493,474.743,472.992,... | 2025-10-08 | 2026-11-03 | 766.863,770.933,773.056,773.268,771.744,768.861,765.038,760.811,756.603,752.883,749.936,747.797,746.533,746.017,745.952,745.858,745.508,744.418,742.477,739.442,735.386,730.502,725.105,719.654,714.699,710.725,708.149,707.482,708.832,712.297,717.729,724.710,732.909,741.567,750.111,757.889,764.501,769.389,772.375,773.375,... | 2026-11-04 | 2027-03-03 | , | 120 | You are a helpful assistant for time series forecasting. Think step by step to make a prediction. | Forecast the total sales in the next 120 days of this global chain, from 2026-11-04 to 2027-03-03.
The final result must be enclosed by '\boxed{' and '}', and where values are separated by ','. | This time series records the sales of a global chain store. The data was recorded from 2025-10-08 to 2026-11-03, and the recording frequency is day (D). The owners implemented a rapid exanpansion strategy and opened two new stores on 2025-12-26 and 2026-07-11 respectively. After each new store opening, the sales volumn... | <history>479.431,476.140,473.786,472.449,471.995,472.048,472.347,472.387,471.799,470.357,467.704,463.918,459.103,453.543,447.722,442.220,437.570,434.268,432.820,433.475,436.400,441.415,448.266,456.482,465.410,474.455,482.878,490.137,495.654,499.219,500.702,500.174,497.947,494.419,490.048,485.526,481.209,477.493,474.743... | |
context_explicit/template3/item2 | GIFT synthesize | phase_change | D | 283.614,284.032,284.267,283.932,282.753,280.761,278.257,275.854,274.106,273.402,273.825,274.980,276.275,276.988,276.613,275.014,272.503,269.691,267.307,266.011,266.019,267.221,269.129,271.073,272.438,272.874,272.283,270.909,269.143,267.338,265.776,264.601,263.777,263.308,263.220,263.665,264.808,266.785,269.569,272.944,... | 2025-10-08 | 2026-11-03 | 365.958,365.208,365.571,366.737,368.058,368.824,368.525,366.997,364.506,361.661,359.230,357.835,357.754,358.913,360.779,362.744,364.195,364.699,364.182,362.869,361.095,359.264,357.666,356.419,355.554,355.051,354.982,355.416,356.574,358.553,361.315,364.673,368.178,371.329,373.732,375.142,375.661,375.558,375.291,375.197,... | 2026-11-04 | 2027-03-03 | , | 120 | You are a helpful assistant for time series forecasting. Think step by step to make a prediction. | Forecast the total sales in the next 120 days of this global chain, from 2026-11-04 to 2027-03-03.
The final result must be enclosed by '\boxed{' and '}', and where values are separated by ','. | This time series records the sales of a global chain store. The data was recorded from 2025-10-08 to 2026-11-03, and the recording frequency is day (D). The owners implemented a rapid exanpansion strategy and opened two new stores on 2025-11-30 and 2026-04-09 respectively. After each new store opening, the sales volumn... | <history>283.614,284.032,284.267,283.932,282.753,280.761,278.257,275.854,274.106,273.402,273.825,274.980,276.275,276.988,276.613,275.014,272.503,269.691,267.307,266.011,266.019,267.221,269.129,271.073,272.438,272.874,272.283,270.909,269.143,267.338,265.776,264.601,263.777,263.308,263.220,263.665,264.808,266.785,269.569... | |
context_explicit/template4/item2 | GIFT synthesize | phase_change | D | 611.558,612.137,612.767,613.546,614.575,615.832,617.370,619.054,620.760,622.321,623.521,624.145,624.018,622.973,620.969,617.990,614.084,609.484,604.389,599.077,593.899,589.113,585.069,581.937,579.906,578.984,680.064,580.251,582.487,585.077,585.544,589.350,591.294,596.740,602.014,598.720,598.171,602.458,603.265,600.638,... | 2025-10-08 | 2026-11-03 | 760.510,762.240,764.129,766.066,767.857,769.278,770.128,770.189,769.330,767.455,764.540,760.724,756.127,751.017,745.674,740.431,735.624,731.518,728.398,726.404,725.619,725.956,727.359,729.582,732.377,735.447,738.521,741.346,743.640,745.325,746.317,746.640,746.404,745.737,744.866,743.992,743.305,743.009,743.154,743.865,... | 2026-11-04 | 2027-03-03 | , | 120 | You are a helpful assistant for time series forecasting. Think step by step to make a prediction. | Forecast the total sales in the next 120 days of this global chain, from 2026-11-04 to 2027-03-03.
The final result must be enclosed by '\boxed{' and '}', and where values are separated by ','. | This time series records the sales of a global chain store. The data was recorded from 2025-10-08 to 2026-11-03, and the recording frequency is day (D). The owners implemented a rapid exanpansion strategy and opened two new stores on 2025-11-01 and 2026-06-21 respectively. After each new store opening, the sales volumn... | <history>611.558,612.137,612.767,613.546,614.575,615.832,617.370,619.054,620.760,622.321,623.521,624.145,624.018,622.973,620.969,617.990,614.084,609.484,604.389,599.077,593.899,589.113,585.069,581.937,579.906,578.984,680.064,580.251,582.487,585.077,585.544,589.350,591.294,596.740,602.014,598.720,598.171,602.458,603.265... | |
context_implicit/template0/item2 | GIFT synthesize | phase_change | D | 8929.508,10443.771,67981.323,125704.106,156345.302,206220.957,236573.433,264587.699,280818.577,303391.438,310741.613,295466.404,298356.436,294720.125,289977.006,289524.272,285677.974,286660.433,287499.064,275568.353,290152.988,287845.332,288125.522,293595.155,304947.917,309272.920,317241.779,323457.754,329403.385,33727... | 2025-10-06 | 2026-11-01 | 623352.188,624408.984,623449.479,629834.594,625842.862,619901.526,622809.908,633816.161,627558.157,632144.464,628463.566,631381.156,627763.639,624420.664,622564.709,618682.102,612198.099,627499.410,618496.452,627212.478,620929.816,616679.194,623299.196,615890.374,615056.956,610492.037,609604.964,606370.472,607220.847,6... | 2026-11-02 | 2027-03-01 | , | 120 | You are a helpful assistant for time series forecasting. Think step by step to make a prediction. | Forecast the total sales in the next 120 days of this global chain store, from 2026-11-02 to 2027-03-01.
The final result must be enclosed by '\boxed{' and '}', and where values are separated by ','. | This time series records the sales of a global chain store. The data was recorded from 2025-10-06 to 2026-11-01, and the recording frequency is day (D). The owners implemented a rapid exanpansion strategy and opened two new stores. After each new store opening, the sales volumne is expected to increase. Furthermore, on... | <history>8929.508,10443.771,67981.323,125704.106,156345.302,206220.957,236573.433,264587.699,280818.577,303391.438,310741.613,295466.404,298356.436,294720.125,289977.006,289524.272,285677.974,286660.433,287499.064,275568.353,290152.988,287845.332,288125.522,293595.155,304947.917,309272.920,317241.779,323457.754,329403.... | |
context_implicit/template1/item2 | GIFT synthesize | phase_change | D | 283.721,283.245,282.819,282.503,282.339,282.080,281.974,281.952,281.943,281.988,282.365,282.597,283.001,283.405,284.042,284.613,285.287,286.058,286.922,287.784,288.848,289.868,291.030,292.270,293.646,294.917,296.267,297.729,299.373,300.936,302.614,304.403,306.072,307.792,309.817,311.730,313.618,315.629,317.679,319.735,... | 2025-10-06 | 2026-11-01 | 834.774,833.183,831.392,829.731,827.866,826.199,824.378,822.613,820.723,819.012,817.100,815.319,813.508,811.761,809.987,808.011,806.384,804.654,803.007,801.309,799.825,798.091,796.621,795.236,793.812,792.439,790.985,789.683,788.603,787.428,786.277,785.284,784.306,783.537,782.641,781.894,781.296,780.809,780.356,779.817,... | 2026-11-02 | 2027-03-01 | , | 120 | You are a helpful assistant for time series forecasting. Think step by step to make a prediction. | Forecast the total sales in the next 120 days of this global chain store, from 2026-11-02 to 2027-03-01.
The final result must be enclosed by '\boxed{' and '}', and where values are separated by ','. | This time series records the sales of a global chain store. The data was recorded from 2025-10-06 to 2026-11-01, and the recording frequency is day (D). The owners implemented a rapid exanpansion strategy and opened two new stores. After each new store opening, the sales volumne is expected to increase. Furthermore, on... | <history>283.721,283.245,282.819,282.503,282.339,282.080,281.974,281.952,281.943,281.988,282.365,282.597,283.001,283.405,284.042,284.613,285.287,286.058,286.922,287.784,288.848,289.868,291.030,292.270,293.646,294.917,296.267,297.729,299.373,300.936,302.614,304.403,306.072,307.792,309.817,311.730,313.618,315.629,317.679... | |
context_implicit/template2/item2 | GIFT synthesize | phase_change | D | 479.431,476.140,473.786,472.449,471.995,472.048,472.347,472.387,471.799,470.357,467.704,463.918,459.103,453.543,447.722,442.220,437.570,434.268,432.820,433.475,436.400,441.415,448.266,456.482,465.410,474.455,482.878,490.137,495.654,499.219,500.702,500.174,497.947,494.419,490.048,485.526,481.209,477.493,474.743,472.992,... | 2025-10-08 | 2026-11-03 | 766.863,770.933,773.056,773.268,771.744,768.861,765.038,760.811,756.603,752.883,749.936,747.797,746.533,746.017,745.952,745.858,745.508,744.418,742.477,739.442,735.386,730.502,725.105,719.654,714.699,710.725,708.149,707.482,708.832,712.297,717.729,724.710,732.909,741.567,750.111,757.889,764.501,769.389,772.375,773.375,... | 2026-11-04 | 2027-03-03 | , | 120 | You are a helpful assistant for time series forecasting. Think step by step to make a prediction. | Forecast the total sales in the next 120 days of this global chain store, from 2026-11-04 to 2027-03-03.
The final result must be enclosed by '\boxed{' and '}', and where values are separated by ','. | This time series records the sales of a global chain store. The data was recorded from 2025-10-08 to 2026-11-03, and the recording frequency is day (D). The owners implemented a rapid exanpansion strategy and opened two new stores. After each new store opening, the sales volumne is expected to increase. Furthermore, on... | <history>479.431,476.140,473.786,472.449,471.995,472.048,472.347,472.387,471.799,470.357,467.704,463.918,459.103,453.543,447.722,442.220,437.570,434.268,432.820,433.475,436.400,441.415,448.266,456.482,465.410,474.455,482.878,490.137,495.654,499.219,500.702,500.174,497.947,494.419,490.048,485.526,481.209,477.493,474.743... | |
context_implicit/template3/item2 | GIFT synthesize | phase_change | D | 283.614,284.032,284.267,283.932,282.753,280.761,278.257,275.854,274.106,273.402,273.825,274.980,276.275,276.988,276.613,275.014,272.503,269.691,267.307,266.011,266.019,267.221,269.129,271.073,272.438,272.874,272.283,270.909,269.143,267.338,265.776,264.601,263.777,263.308,263.220,263.665,264.808,266.785,269.569,272.944,... | 2025-10-08 | 2026-11-03 | 365.958,365.208,365.571,366.737,368.058,368.824,368.525,366.997,364.506,361.661,359.230,357.835,357.754,358.913,360.779,362.744,364.195,364.699,364.182,362.869,361.095,359.264,357.666,356.419,355.554,355.051,354.982,355.416,356.574,358.553,361.315,364.673,368.178,371.329,373.732,375.142,375.661,375.558,375.291,375.197,... | 2026-11-04 | 2027-03-03 | , | 120 | You are a helpful assistant for time series forecasting. Think step by step to make a prediction. | Forecast the total sales in the next 120 days of this global chain store, from 2026-11-04 to 2027-03-03.
The final result must be enclosed by '\boxed{' and '}', and where values are separated by ','. | This time series records the sales of a global chain store. The data was recorded from 2025-10-08 to 2026-11-03, and the recording frequency is day (D). The owners implemented a rapid exanpansion strategy and opened two new stores. After each new store opening, the sales volumne is expected to increase. Furthermore, on... | <history>283.614,284.032,284.267,283.932,282.753,280.761,278.257,275.854,274.106,273.402,273.825,274.980,276.275,276.988,276.613,275.014,272.503,269.691,267.307,266.011,266.019,267.221,269.129,271.073,272.438,272.874,272.283,270.909,269.143,267.338,265.776,264.601,263.777,263.308,263.220,263.665,264.808,266.785,269.569... | |
context_implicit/template4/item2 | GIFT synthesize | phase_change | D | 611.558,612.137,612.767,613.546,614.575,615.832,617.370,619.054,620.760,622.321,623.521,624.145,624.018,622.973,620.969,617.990,614.084,609.484,604.389,599.077,593.899,589.113,585.069,581.937,579.906,578.984,680.064,580.251,582.487,585.077,585.544,589.350,591.294,596.740,602.014,598.720,598.171,602.458,603.265,600.638,... | 2025-10-08 | 2026-11-03 | 760.510,762.240,764.129,766.066,767.857,769.278,770.128,770.189,769.330,767.455,764.540,760.724,756.127,751.017,745.674,740.431,735.624,731.518,728.398,726.404,725.619,725.956,727.359,729.582,732.377,735.447,738.521,741.346,743.640,745.325,746.317,746.640,746.404,745.737,744.866,743.992,743.305,743.009,743.154,743.865,... | 2026-11-04 | 2027-03-03 | , | 120 | You are a helpful assistant for time series forecasting. Think step by step to make a prediction. | Forecast the total sales in the next 120 days of this global chain store, from 2026-11-04 to 2027-03-03.
The final result must be enclosed by '\boxed{' and '}', and where values are separated by ','. | This time series records the sales of a global chain store. The data was recorded from 2025-10-08 to 2026-11-03, and the recording frequency is day (D). The owners implemented a rapid exanpansion strategy and opened two new stores. After each new store opening, the sales volumne is expected to increase. Furthermore, on... | <history>611.558,612.137,612.767,613.546,614.575,615.832,617.370,619.054,620.760,622.321,623.521,624.145,624.018,622.973,620.969,617.990,614.084,609.484,604.389,599.077,593.899,589.113,585.069,581.937,579.906,578.984,680.064,580.251,582.487,585.077,585.544,589.350,591.294,596.740,602.014,598.720,598.171,602.458,603.265... | |
anomaly_explicit/template0/item2 | GIFT synthesize | anomaly | D | 4046.000,4136.000,4306.000,4529.000,4752.000,4910.000,4945.000,4833.000,4591.000,4271.000,3951.000,3701.000,3572.000,3568.000,3664.000,3807.000,3938.000,4021.000,4044.000,4034.000,4035.000,4093.000,4230.000,4437.000,4671.000,4866.000,4960.000,4913.000,4721.000,4426.000,4096.000,3810.000,3627.000,3575.000,3639.000,3771.... | 2025-10-08 00:00:00 | 2026-03-12 00:00:00 | 4073.000,4171.000,4313.000,4446.000,4532.000,4566.000,4574.000,4596.000,4674.000,4828.000,5045.000,5282.000,5473.000,5559.000,5505.000,5311.000,5018.000,4695.000,4418.000,4242.000,4195.000,4260.000,4394.000,4538.000,4647.000,4704.000,4718.000,4729.000,4780.000,4903.000,5100.000,5338.000,5560.000,5699.000,5708.000,5569.... | 2026-03-13 | 2026-06-20 | , | 100 | You are a helpful assistant for time series forecasting. Think step by step to make a prediction. | Predict the future traffic of this attraction for the next 20 days period, from 2025-08-25 to 2025-09-13.
The final result must be enclosed by '\boxed{' and '}', and where values are separated by ','. | This time series records the traffic at a popular tourist attraction. The data was recorded from 2025-10-08 00:00:00 to 2026-03-12 00:00:00 with the daily (D) frequency. The experts predicted that the traffic should follow a standard seasonality pattern. However, due to system errors, the records from the periods of 20... | <history>4046.000,4136.000,4306.000,4529.000,4752.000,4910.000,4945.000,4833.000,4591.000,4271.000,3951.000,3701.000,3572.000,3568.000,3664.000,3807.000,3938.000,4021.000,4044.000,4034.000,4035.000,4093.000,4230.000,4437.000,4671.000,4866.000,4960.000,4913.000,4721.000,4426.000,4096.000,3810.000,3627.000,3575.000,3639.... | |
anomaly_explicit/template1/item2 | GIFT synthesize | anomaly | D | 6010.000,6078.000,6076.000,5993.000,5858.000,5719.000,5650.000,5713.000,5947.000,6359.000,6891.000,7460.000,7937.000,8199.000,8145.000,7726.000,6956.000,5904.000,4708.000,3515.000,2480.000,1719.000,1294.000,1200.000,1381.000,1744.000,2167.000,2560.000,2856.000,3030.000,3103.000,3127.000,3172.000,3288.000,3516.000,3853.... | 2025-10-08 00:00:00 | 2026-05-01 00:00:00 | 9924.000,9971.000,9980.000,10024.000,10155.000,10408.000,10769.000,11216.000,11688.000,12136.000,12513.000,12810.000,13039.000,13227.000,13417.000,13640.000,13919.000,14239.000,14571.000,14877.000,15102.000,15225.000,15236.000,15158.000,15030.000,14904.000,14830.000,14833.000,14903.000,15027.000,15152.000,15227.000,152... | 2026-05-02 | 2026-06-20 | , | 50 | You are a helpful assistant for time series forecasting. Think step by step to make a prediction. | Predict the future traffic of this attraction for the next 20 days period, from 2025-08-25 to 2025-09-13.
The final result must be enclosed by '\boxed{' and '}', and where values are separated by ','. | This time series records the traffic at a popular tourist attraction. The data was recorded from 2025-10-08 00:00:00 to 2026-05-01 00:00:00 with the daily (D) frequency. The experts predicted that the traffic should follow a standard seasonality pattern. However, due to system errors, the records from the periods of 20... | <history>6010.000,6078.000,6076.000,5993.000,5858.000,5719.000,5650.000,5713.000,5947.000,6359.000,6891.000,7460.000,7937.000,8199.000,8145.000,7726.000,6956.000,5904.000,4708.000,3515.000,2480.000,1719.000,1294.000,1200.000,1381.000,1744.000,2167.000,2560.000,2856.000,3030.000,3103.000,3127.000,3172.000,3288.000,3516.... | |
anomaly_explicit/template2/item2 | GIFT synthesize | anomaly | D | 58472.000,58748.000,59079.000,59273.000,120520.000,120748.000,121016.000,121274.000,121527.000,121754.000,122026.000,122334.000,122538.000,122868.000,123095.000,62376.000,62740.000,62966.000,63209.000,63503.000,63771.000,64018.000,64275.000,64537.000,64751.000,65058.000,65303.000,65571.000,65834.000,66113.000,66371.000... | 2025-10-08 00:00:00 | 2026-06-28 00:00:00 | 188629.000,188918.000,189171.000,189440.000,189687.000,189897.000,190165.000,190478.000,190743.000,190957.000,191299.000,130593.000,130922.000,131127.000,131334.000,131681.000,131907.000,132148.000,132435.000,132720.000,132944.000,133219.000,133465.000,133726.000,134028.000,134309.000,134530.000,134820.000,135112.000,1... | 2026-06-29 | 2026-10-26 | , | 120 | You are a helpful assistant for time series forecasting. Think step by step to make a prediction. | Predict the future traffic of this attraction for the next 20 days period, from 2025-08-25 to 2025-09-13.
The final result must be enclosed by '\boxed{' and '}', and where values are separated by ','. | This time series records the traffic at a popular tourist attraction. The data was recorded from 2025-10-08 00:00:00 to 2026-06-28 00:00:00 with the daily (D) frequency. The experts predicted that the traffic should follow a standard seasonality pattern. However, due to system errors, the records from the periods of 20... | <history>58472.000,58748.000,59079.000,59273.000,120520.000,120748.000,121016.000,121274.000,121527.000,121754.000,122026.000,122334.000,122538.000,122868.000,123095.000,62376.000,62740.000,62966.000,63209.000,63503.000,63771.000,64018.000,64275.000,64537.000,64751.000,65058.000,65303.000,65571.000,65834.000,66113.000,... | |
anomaly_explicit/template3/item2 | GIFT synthesize | anomaly | D | 188.000,188.000,188.000,188.000,188.000,188.000,189.000,264.000,264.000,264.000,189.000,189.000,189.000,190.000,190.000,190.000,190.000,190.000,190.000,190.000,190.000,191.000,191.000,191.000,191.000,191.000,191.000,191.000,192.000,192.000,192.000,192.000,192.000,267.000,268.000,268.000,193.000,193.000,193.000,193.000,... | 2025-10-08 00:00:00 | 2026-06-08 00:00:00 | 292.000,292.000,292.000,217.000,217.000,217.000,217.000,217.000,217.000,217.000,217.000,217.000,217.000,218.000,218.000,218.000,218.000,218.000,218.000,218.000,218.000,218.000,218.000,218.000,218.000,218.000,218.000,294.000,294.000,294.000,219.000,219.000,219.000,219.000,219.000,219.000,219.000,219.000,219.000,219.000,... | 2026-06-09 | 2026-10-26 | , | 140 | You are a helpful assistant for time series forecasting. Think step by step to make a prediction. | Predict the future traffic of this attraction for the next 20 days period, from 2025-08-25 to 2025-09-13.
The final result must be enclosed by '\boxed{' and '}', and where values are separated by ','. | This time series records the traffic at a popular tourist attraction. The data was recorded from 2025-10-08 00:00:00 to 2026-06-08 00:00:00 with the daily (D) frequency. The experts predicted that the traffic should follow a standard seasonality pattern. However, due to system errors, the records from the periods of 20... | <history>188.000,188.000,188.000,188.000,188.000,188.000,189.000,264.000,264.000,264.000,189.000,189.000,189.000,190.000,190.000,190.000,190.000,190.000,190.000,190.000,190.000,191.000,191.000,191.000,191.000,191.000,191.000,191.000,192.000,192.000,192.000,192.000,192.000,267.000,268.000,268.000,193.000,193.000,193.000... | |
anomaly_explicit/template4/item2 | GIFT synthesize | anomaly | D | 1934.000,1901.000,1871.000,1845.000,1822.000,1801.000,1781.000,1759.000,1737.000,1713.000,1687.000,1660.000,1633.000,1606.000,1582.000,1559.000,1536.000,1514.000,1493.000,1470.000,1447.000,1428.000,1410.000,1400.000,1400.000,1411.000,1435.000,1472.000,1519.000,1574.000,1631.000,1684.000,1728.000,1759.000,1775.000,1777.... | 2025-10-09 00:00:00 | 2026-11-24 00:00:00 | 3266.000,3230.000,3197.000,3170.000,3151.000,3138.000,3132.000,3132.000,3136.000,3145.000,3156.000,3173.000,3194.000,3220.000,3251.000,3287.000,3324.000,3361.000,3397.000,3426.000,3446.000,3455.000,3451.000,3436.000,3410.000,3377.000,3339.000,3298.000,3258.000,3222.000,3189.000,3161.000,3135.000,3111.000,3087.000,3061.... | 2026-11-25 | 2027-03-04 | , | 100 | You are a helpful assistant for time series forecasting. Think step by step to make a prediction. | Predict the future traffic of this attraction for the next 20 days period, from 2025-08-25 to 2025-09-13.
The final result must be enclosed by '\boxed{' and '}', and where values are separated by ','. | This time series records the traffic at a popular tourist attraction. The data was recorded from 2025-10-09 00:00:00 to 2026-11-24 00:00:00 with the daily (D) frequency. The experts predicted that the traffic should follow a standard seasonality pattern. However, due to system errors, the records from the periods of 20... | <history>1934.000,1901.000,1871.000,1845.000,1822.000,1801.000,1781.000,1759.000,1737.000,1713.000,1687.000,1660.000,1633.000,1606.000,1582.000,1559.000,1536.000,1514.000,1493.000,1470.000,1447.000,1428.000,1410.000,1400.000,1400.000,1411.000,1435.000,1472.000,1519.000,1574.000,1631.000,1684.000,1728.000,1759.000,1775.... | |
anomaly_implicit/template0/item2 | GIFT synthesize | anomaly | D | 4046.000,4136.000,4306.000,4529.000,4752.000,4910.000,4945.000,4833.000,4591.000,4271.000,3951.000,3701.000,3572.000,3568.000,3664.000,3807.000,3938.000,4021.000,4044.000,4034.000,4035.000,4093.000,4230.000,4437.000,4671.000,4866.000,4960.000,4913.000,4721.000,4426.000,4096.000,3810.000,3627.000,3575.000,3639.000,3771.... | 2025-10-08 00:00:00 | 2026-03-12 00:00:00 | 4073.000,4171.000,4313.000,4446.000,4532.000,4566.000,4574.000,4596.000,4674.000,4828.000,5045.000,5282.000,5473.000,5559.000,5505.000,5311.000,5018.000,4695.000,4418.000,4242.000,4195.000,4260.000,4394.000,4538.000,4647.000,4704.000,4718.000,4729.000,4780.000,4903.000,5100.000,5338.000,5560.000,5699.000,5708.000,5569.... | 2026-03-13 00:00:00 | 2026-06-20 00:00:00 | , | 100 | You are a helpful assistant for time series forecasting. Think step by step to make a prediction. | Predict the future traffic of this attraction for the next 100 days period, from 2026-03-13 00:00:00 to 2026-06-20 00:00:00.
The final result must be enclosed by '\boxed{' and '}', and where values are separated by ','. | This time series records the traffic at a popular tourist attraction. The data was recorded from 2025-10-08 00:00:00 to 2026-03-12 00:00:00 with the daily (D) frequency. The experts predicted that the traffic should follow a standard seasonality pattern. However, due to system errors, there were periods where the readi... | <history>4046.000,4136.000,4306.000,4529.000,4752.000,4910.000,4945.000,4833.000,4591.000,4271.000,3951.000,3701.000,3572.000,3568.000,3664.000,3807.000,3938.000,4021.000,4044.000,4034.000,4035.000,4093.000,4230.000,4437.000,4671.000,4866.000,4960.000,4913.000,4721.000,4426.000,4096.000,3810.000,3627.000,3575.000,3639.... | |
anomaly_implicit/template1/item2 | GIFT synthesize | anomaly | D | 6010.000,6078.000,6076.000,5993.000,5858.000,5719.000,5650.000,5713.000,5947.000,6359.000,6891.000,7460.000,7937.000,8199.000,8145.000,7726.000,6956.000,5904.000,4708.000,3515.000,2480.000,1719.000,1294.000,1200.000,1381.000,1744.000,2167.000,2560.000,2856.000,3030.000,3103.000,3127.000,3172.000,3288.000,3516.000,3853.... | 2025-10-08 00:00:00 | 2026-05-01 00:00:00 | 9924.000,9971.000,9980.000,10024.000,10155.000,10408.000,10769.000,11216.000,11688.000,12136.000,12513.000,12810.000,13039.000,13227.000,13417.000,13640.000,13919.000,14239.000,14571.000,14877.000,15102.000,15225.000,15236.000,15158.000,15030.000,14904.000,14830.000,14833.000,14903.000,15027.000,15152.000,15227.000,152... | 2026-05-02 00:00:00 | 2026-06-20 00:00:00 | , | 50 | You are a helpful assistant for time series forecasting. Think step by step to make a prediction. | Predict the future traffic of this attraction for the next 50 days period, from 2026-05-02 00:00:00 to 2026-06-20 00:00:00.
The final result must be enclosed by '\boxed{' and '}', and where values are separated by ','. | This time series records the traffic at a popular tourist attraction. The data was recorded from 2025-10-08 00:00:00 to 2026-05-01 00:00:00 with the daily (D) frequency. The experts predicted that the traffic should follow a standard seasonality pattern. However, due to system errors, there were periods where the readi... | <history>6010.000,6078.000,6076.000,5993.000,5858.000,5719.000,5650.000,5713.000,5947.000,6359.000,6891.000,7460.000,7937.000,8199.000,8145.000,7726.000,6956.000,5904.000,4708.000,3515.000,2480.000,1719.000,1294.000,1200.000,1381.000,1744.000,2167.000,2560.000,2856.000,3030.000,3103.000,3127.000,3172.000,3288.000,3516.... | |
anomaly_implicit/template2/item2 | GIFT synthesize | anomaly | D | 58472.000,58748.000,59079.000,59273.000,120520.000,120748.000,121016.000,121274.000,121527.000,121754.000,122026.000,122334.000,122538.000,122868.000,123095.000,62376.000,62740.000,62966.000,63209.000,63503.000,63771.000,64018.000,64275.000,64537.000,64751.000,65058.000,65303.000,65571.000,65834.000,66113.000,66371.000... | 2025-10-08 00:00:00 | 2026-06-28 00:00:00 | 188629.000,188918.000,189171.000,189440.000,189687.000,189897.000,190165.000,190478.000,190743.000,190957.000,191299.000,130593.000,130922.000,131127.000,131334.000,131681.000,131907.000,132148.000,132435.000,132720.000,132944.000,133219.000,133465.000,133726.000,134028.000,134309.000,134530.000,134820.000,135112.000,1... | 2026-06-29 00:00:00 | 2026-10-26 00:00:00 | , | 120 | You are a helpful assistant for time series forecasting. Think step by step to make a prediction. | Predict the future traffic of this attraction for the next 120 days period, from 2026-06-29 00:00:00 to 2026-10-26 00:00:00.
The final result must be enclosed by '\boxed{' and '}', and where values are separated by ','. | This time series records the traffic at a popular tourist attraction. The data was recorded from 2025-10-08 00:00:00 to 2026-06-28 00:00:00 with the daily (D) frequency. The experts predicted that the traffic should follow a standard seasonality pattern. However, due to system errors, there were periods where the readi... | <history>58472.000,58748.000,59079.000,59273.000,120520.000,120748.000,121016.000,121274.000,121527.000,121754.000,122026.000,122334.000,122538.000,122868.000,123095.000,62376.000,62740.000,62966.000,63209.000,63503.000,63771.000,64018.000,64275.000,64537.000,64751.000,65058.000,65303.000,65571.000,65834.000,66113.000,... | |
anomaly_implicit/template3/item2 | GIFT synthesize | anomaly | D | 188.000,188.000,188.000,188.000,188.000,188.000,189.000,264.000,264.000,264.000,189.000,189.000,189.000,190.000,190.000,190.000,190.000,190.000,190.000,190.000,190.000,191.000,191.000,191.000,191.000,191.000,191.000,191.000,192.000,192.000,192.000,192.000,192.000,267.000,268.000,268.000,193.000,193.000,193.000,193.000,... | 2025-10-08 00:00:00 | 2026-06-08 00:00:00 | 292.000,292.000,292.000,217.000,217.000,217.000,217.000,217.000,217.000,217.000,217.000,217.000,217.000,218.000,218.000,218.000,218.000,218.000,218.000,218.000,218.000,218.000,218.000,218.000,218.000,218.000,218.000,294.000,294.000,294.000,219.000,219.000,219.000,219.000,219.000,219.000,219.000,219.000,219.000,219.000,... | 2026-06-09 00:00:00 | 2026-10-26 00:00:00 | , | 140 | You are a helpful assistant for time series forecasting. Think step by step to make a prediction. | Predict the future traffic of this attraction for the next 140 days period, from 2026-06-09 00:00:00 to 2026-10-26 00:00:00.
The final result must be enclosed by '\boxed{' and '}', and where values are separated by ','. | This time series records the traffic at a popular tourist attraction. The data was recorded from 2025-10-08 00:00:00 to 2026-06-08 00:00:00 with the daily (D) frequency. The experts predicted that the traffic should follow a standard seasonality pattern. However, due to system errors, there were periods where the readi... | <history>188.000,188.000,188.000,188.000,188.000,188.000,189.000,264.000,264.000,264.000,189.000,189.000,189.000,190.000,190.000,190.000,190.000,190.000,190.000,190.000,190.000,191.000,191.000,191.000,191.000,191.000,191.000,191.000,192.000,192.000,192.000,192.000,192.000,267.000,268.000,268.000,193.000,193.000,193.000... | |
anomaly_implicit/template4/item2 | GIFT synthesize | anomaly | D | 1934.000,1901.000,1871.000,1845.000,1822.000,1801.000,1781.000,1759.000,1737.000,1713.000,1687.000,1660.000,1633.000,1606.000,1582.000,1559.000,1536.000,1514.000,1493.000,1470.000,1447.000,1428.000,1410.000,1400.000,1400.000,1411.000,1435.000,1472.000,1519.000,1574.000,1631.000,1684.000,1728.000,1759.000,1775.000,1777.... | 2025-10-09 00:00:00 | 2026-11-24 00:00:00 | 3266.000,3230.000,3197.000,3170.000,3151.000,3138.000,3132.000,3132.000,3136.000,3145.000,3156.000,3173.000,3194.000,3220.000,3251.000,3287.000,3324.000,3361.000,3397.000,3426.000,3446.000,3455.000,3451.000,3436.000,3410.000,3377.000,3339.000,3298.000,3258.000,3222.000,3189.000,3161.000,3135.000,3111.000,3087.000,3061.... | 2026-11-25 00:00:00 | 2027-03-04 00:00:00 | , | 100 | You are a helpful assistant for time series forecasting. Think step by step to make a prediction. | Predict the future traffic of this attraction for the next 100 days period, from 2026-11-25 00:00:00 to 2027-03-04 00:00:00.
The final result must be enclosed by '\boxed{' and '}', and where values are separated by ','. | This time series records the traffic at a popular tourist attraction. The data was recorded from 2025-10-09 00:00:00 to 2026-11-24 00:00:00 with the daily (D) frequency. The experts predicted that the traffic should follow a standard seasonality pattern. However, due to system errors, there were periods where the readi... | <history>1934.000,1901.000,1871.000,1845.000,1822.000,1801.000,1781.000,1759.000,1737.000,1713.000,1687.000,1660.000,1633.000,1606.000,1582.000,1559.000,1536.000,1514.000,1493.000,1470.000,1447.000,1428.000,1410.000,1400.000,1400.000,1411.000,1435.000,1472.000,1519.000,1574.000,1631.000,1684.000,1728.000,1759.000,1775.... | |
context_explicit/template0/item3 | GIFT synthesize | phase_change | 5T | 0.791,0.795,0.798,0.800,0.803,0.804,0.805,0.806,0.806,0.806,0.806,0.805,0.804,0.803,0.802,0.802,0.801,0.800,0.800,0.800,0.800,0.800,0.801,0.802,0.803,0.805,0.806,0.808,0.810,0.812,0.814,0.815,0.817,0.818,0.819,0.820,0.820,0.819,0.818,0.817,0.815,0.812,0.808,0.804,0.800,0.795,0.789,0.783,0.777,0.771,0.764,0.758,0.751,0.... | 2025-10-07 00:00:00 | 2025-10-07 13:45:00 | 0.619,0.621,0.622,0.623,0.624,0.624,0.624,0.624,0.623,0.622,0.622,0.621,0.620,0.619,0.619,0.618,0.618,0.618,0.619,0.619,0.620,0.621,0.623,0.624,0.626,0.628,0.630,0.632,0.633,0.635,0.636,0.637,0.637,0.638,0.637,0.636,0.635,0.632,0.630,0.626,0.622,0.618,0.613,0.607,0.601,0.595,0.589,0.582,0.576,0.569,0.563,0.557,0.552,0.... | 2025-10-07 13:50:00 | 2025-10-07 21:15:00 | , | 90 | You are a helpful assistant for time series forecasting. Think step by step to make a prediction. | Forecast the future 90 readings from this sensor, from 2025-10-07 13:50:00 to 2025-10-07 21:15:00, given that the firmware has been updated.
The final result must be enclosed by '\boxed{' and '}', and where values are separated by ','. | This time series records the CPU usage of a newly installed data center. The data was recorded from 2025-10-07 00:00:00 to 2025-10-07 13:45:00, and the recording frequency is 5 minutes (5T). Due to an outdated firmware, the first 100 CPU load reading, from 2025-10-07 07:45:00 to 2025-10-07 08:15:00, were significantly ... | <history>0.791,0.795,0.798,0.800,0.803,0.804,0.805,0.806,0.806,0.806,0.806,0.805,0.804,0.803,0.802,0.802,0.801,0.800,0.800,0.800,0.800,0.800,0.801,0.802,0.803,0.805,0.806,0.808,0.810,0.812,0.814,0.815,0.817,0.818,0.819,0.820,0.820,0.819,0.818,0.817,0.815,0.812,0.808,0.804,0.800,0.795,0.789,0.783,0.777,0.771,0.764,0.758... | |
context_explicit/template1/item3 | GIFT synthesize | phase_change | 5T | 48.538,48.623,48.706,48.795,48.874,48.960,49.047,49.128,49.213,49.294,49.378,49.464,49.546,49.630,49.716,49.799,49.876,49.964,50.048,50.129,50.214,50.295,50.382,50.466,50.548,50.632,50.717,50.795,50.879,50.963,51.041,51.126,51.212,51.294,51.373,51.456,51.541,51.618,51.702,51.782,51.863,51.945,52.028,52.107,52.191,52.27... | 2025-10-07 00:00:00 | 2025-10-07 18:45:00 | 52.049,52.066,52.081,52.096,52.110,52.124,52.138,52.150,52.157,52.167,52.181,52.192,52.199,52.208,52.217,52.226,52.234,52.242,52.245,52.252,52.259,52.262,52.265,52.271,52.276,52.278,52.279,52.280,52.283,52.286 | 2025-10-07 18:50:00 | 2025-10-07 21:15:00 | , | 30 | You are a helpful assistant for time series forecasting. Think step by step to make a prediction. | Forecast the future 30 readings from this sensor, from 2025-10-07 18:50:00 to 2025-10-07 21:15:00, given that the firmware has been updated.
The final result must be enclosed by '\boxed{' and '}', and where values are separated by ','. | This time series records the CPU usage of a newly installed data center. The data was recorded from 2025-10-07 00:00:00 to 2025-10-07 18:45:00, and the recording frequency is 5 minutes (5T). Due to an outdated firmware, the first 184 CPU load reading, from 2025-10-07 15:00:00 to 2025-10-07 15:15:00, were significantly ... | <history>48.538,48.623,48.706,48.795,48.874,48.960,49.047,49.128,49.213,49.294,49.378,49.464,49.546,49.630,49.716,49.799,49.876,49.964,50.048,50.129,50.214,50.295,50.382,50.466,50.548,50.632,50.717,50.795,50.879,50.963,51.041,51.126,51.212,51.294,51.373,51.456,51.541,51.618,51.702,51.782,51.863,51.945,52.028,52.107,52.... | |
context_explicit/template2/item3 | GIFT synthesize | phase_change | 5T | 0.542,0.541,0.539,0.537,0.533,0.529,0.525,0.522,0.519,0.518,0.518,0.520,0.525,0.531,0.539,0.548,0.558,0.569,0.579,0.589,0.596,0.603,0.607,0.610,0.611,0.610,0.608,0.605,0.601,0.598,0.595,0.593,0.591,0.590,0.590,0.590,0.591,0.591,0.590,0.589,0.588,0.585,0.582,0.578,0.574,0.570,0.567,0.563,0.561,0.559,0.558,0.558,0.558,0.... | 2025-10-07 00:00:00 | 2025-10-07 19:25:00 | 0.519,0.517,0.515,0.513,0.511,0.509,0.507,0.506,0.505,0.505,0.505,0.505,0.505,0.505,0.505,0.505,0.505,0.505,0.505,0.504,0.504,0.504,0.505,0.506,0.507,0.508,0.509,0.510,0.510,0.510,0.508,0.506,0.504,0.500,0.496,0.493,0.490,0.488,0.487,0.488,0.491,0.496,0.503,0.512,0.522,0.533,0.544,0.554,0.563,0.572,0.578,0.582,0.584,0.... | 2025-10-07 19:30:00 | 2025-10-08 07:55:00 | , | 150 | You are a helpful assistant for time series forecasting. Think step by step to make a prediction. | Forecast the future 150 readings from this sensor, from 2025-10-07 19:30:00 to 2025-10-08 07:55:00, given that the firmware has been updated.
The final result must be enclosed by '\boxed{' and '}', and where values are separated by ','. | This time series records the CPU usage of a newly installed data center. The data was recorded from 2025-10-07 00:00:00 to 2025-10-07 19:25:00, and the recording frequency is 5 minutes (5T). Due to an outdated firmware, the first 141 CPU load reading, from 2025-10-07 10:50:00 to 2025-10-07 11:40:00, were significantly ... | <history>0.542,0.541,0.539,0.537,0.533,0.529,0.525,0.522,0.519,0.518,0.518,0.520,0.525,0.531,0.539,0.548,0.558,0.569,0.579,0.589,0.596,0.603,0.607,0.610,0.611,0.610,0.608,0.605,0.601,0.598,0.595,0.593,0.591,0.590,0.590,0.590,0.591,0.591,0.590,0.589,0.588,0.585,0.582,0.578,0.574,0.570,0.567,0.563,0.561,0.559,0.558,0.558... | |
context_explicit/template3/item3 | GIFT synthesize | phase_change | 5T | 79.630,79.587,79.548,79.511,81.308,86.100,90.906,89.415,84.535,79.669,79.250,79.211,79.180,79.134,79.106,79.062,79.029,78.986,78.950,78.916,78.879,78.839,78.799,78.760,78.731,78.689,78.648,78.619,78.576,78.535,78.501,78.469,78.429,78.396,78.356,78.324,78.574,83.379,88.172,89.751,84.870,80.000,78.054,78.027,77.989,77.94... | 2025-10-06 00:00:00 | 2025-10-06 19:25:00 | 67.111,62.248,60.842,60.818,60.794,60.773,60.758,60.736,60.712,60.693,60.674,60.647,60.635,60.616,60.590,60.572,60.556,60.534,60.508,60.490,60.474,60.456,60.439,60.418,60.399,60.385,60.361,60.346,60.322,64.428,69.252,72.866,68.005,63.156,60.222,60.205,60.188,60.166,60.151,60.141,60.122,60.105,60.095,60.074,60.063,60.04... | 2025-10-06 19:30:00 | 2025-10-07 07:55:00 | , | 150 | You are a helpful assistant for time series forecasting. Think step by step to make a prediction. | Forecast the future 150 readings from this sensor, from 2025-10-06 19:30:00 to 2025-10-07 07:55:00, given that the firmware has been updated.
The final result must be enclosed by '\boxed{' and '}', and where values are separated by ','. | This time series records the CPU usage of a newly installed data center. The data was recorded from 2025-10-06 00:00:00 to 2025-10-06 19:25:00, and the recording frequency is 5 minutes (5T). Due to an outdated firmware, the first 203 CPU load reading, from 2025-10-06 16:40:00 to 2025-10-06 16:50:00, were significantly ... | <history>79.630,79.587,79.548,79.511,81.308,86.100,90.906,89.415,84.535,79.669,79.250,79.211,79.180,79.134,79.106,79.062,79.029,78.986,78.950,78.916,78.879,78.839,78.799,78.760,78.731,78.689,78.648,78.619,78.576,78.535,78.501,78.469,78.429,78.396,78.356,78.324,78.574,83.379,88.172,89.751,84.870,80.000,78.054,78.027,77.... | |
context_explicit/template4/item3 | GIFT synthesize | phase_change | 5T | 91.097,89.218,87.351,85.623,84.147,82.980,82.194,81.790,81.761,82.049,82.576,83.253,83.974,84.649,85.194,85.575,85.751,85.738,85.568,85.295,84.993,84.722,84.539,84.494,84.603,84.849,85.200,85.600,85.955,86.179,86.185,85.910,85.310,84.365,83.085,81.543,79.810,77.986,76.196,74.560,73.158,72.093,71.408,71.111,71.184,71.57... | 2025-10-06 00:00:00 | 2025-10-06 22:35:00 | 62.516,62.530,62.663,62.940,63.383,63.956,64.609,65.276,65.858,66.284,66.466,66.339,65.890,65.095,64.009,62.688,61.221,59.730,58.316,57.096,56.160,55.564,55.338,55.479,55.951,56.685,57.594,58.584,59.548,60.410,61.118,61.618,61.924,62.046,62.044,61.979,61.911,61.924,62.053,62.337,62.778,63.342,63.984,64.630,65.194,65.59... | 2025-10-06 22:40:00 | 2025-10-07 18:35:00 | , | 240 | You are a helpful assistant for time series forecasting. Think step by step to make a prediction. | Forecast the future 240 readings from this sensor, from 2025-10-06 22:40:00 to 2025-10-07 18:35:00, given that the firmware has been updated.
The final result must be enclosed by '\boxed{' and '}', and where values are separated by ','. | This time series records the CPU usage of a newly installed data center. The data was recorded from 2025-10-06 00:00:00 to 2025-10-06 22:35:00, and the recording frequency is 5 minutes (5T). Due to an outdated firmware, the first 55 CPU load reading, from 2025-10-06 03:45:00 to 2025-10-06 04:30:00, were significantly h... | <history>91.097,89.218,87.351,85.623,84.147,82.980,82.194,81.790,81.761,82.049,82.576,83.253,83.974,84.649,85.194,85.575,85.751,85.738,85.568,85.295,84.993,84.722,84.539,84.494,84.603,84.849,85.200,85.600,85.955,86.179,86.185,85.910,85.310,84.365,83.085,81.543,79.810,77.986,76.196,74.560,73.158,72.093,71.408,71.111,71.... | |
context_implicit/template0/item3 | GIFT synthesize | phase_change | 5T | 0.791,0.795,0.798,0.800,0.803,0.804,0.805,0.806,0.806,0.806,0.806,0.805,0.804,0.803,0.802,0.802,0.801,0.800,0.800,0.800,0.800,0.800,0.801,0.802,0.803,0.805,0.806,0.808,0.810,0.812,0.814,0.815,0.817,0.818,0.819,0.820,0.820,0.819,0.818,0.817,0.815,0.812,0.808,0.804,0.800,0.795,0.789,0.783,0.777,0.771,0.764,0.758,0.751,0.... | 2025-10-07 00:00:00 | 2025-10-07 13:45:00 | 0.619,0.621,0.622,0.623,0.624,0.624,0.624,0.624,0.623,0.622,0.622,0.621,0.620,0.619,0.619,0.618,0.618,0.618,0.619,0.619,0.620,0.621,0.623,0.624,0.626,0.628,0.630,0.632,0.633,0.635,0.636,0.637,0.637,0.638,0.637,0.636,0.635,0.632,0.630,0.626,0.622,0.618,0.613,0.607,0.601,0.595,0.589,0.582,0.576,0.569,0.563,0.557,0.552,0.... | 2025-10-07 13:50:00 | 2025-10-07 21:15:00 | , | 90 | You are a helpful assistant for time series forecasting. Think step by step to make a prediction. | Forecast the future 90 readings from this sensor, from 2025-10-07 13:50:00 to 2025-10-07 21:15:00, given that the firmware has been updated.
The final result must be enclosed by '\boxed{' and '}', and where values are separated by ','. | This time series records the CPU usage of a newly installed data center. The data was recorded from 2025-10-07 00:00:00 to 2025-10-07 13:45:00, and the recording frequency is 5 minutes (5T). Due to an outdated firmware, the CPU load was significantly higher than normal. After the firmware update, the CPU runs normally ... | <history>0.791,0.795,0.798,0.800,0.803,0.804,0.805,0.806,0.806,0.806,0.806,0.805,0.804,0.803,0.802,0.802,0.801,0.800,0.800,0.800,0.800,0.800,0.801,0.802,0.803,0.805,0.806,0.808,0.810,0.812,0.814,0.815,0.817,0.818,0.819,0.820,0.820,0.819,0.818,0.817,0.815,0.812,0.808,0.804,0.800,0.795,0.789,0.783,0.777,0.771,0.764,0.758... | |
context_implicit/template1/item3 | GIFT synthesize | phase_change | 5T | 48.538,48.623,48.706,48.795,48.874,48.960,49.047,49.128,49.213,49.294,49.378,49.464,49.546,49.630,49.716,49.799,49.876,49.964,50.048,50.129,50.214,50.295,50.382,50.466,50.548,50.632,50.717,50.795,50.879,50.963,51.041,51.126,51.212,51.294,51.373,51.456,51.541,51.618,51.702,51.782,51.863,51.945,52.028,52.107,52.191,52.27... | 2025-10-07 00:00:00 | 2025-10-07 18:45:00 | 52.049,52.066,52.081,52.096,52.110,52.124,52.138,52.150,52.157,52.167,52.181,52.192,52.199,52.208,52.217,52.226,52.234,52.242,52.245,52.252,52.259,52.262,52.265,52.271,52.276,52.278,52.279,52.280,52.283,52.286 | 2025-10-07 18:50:00 | 2025-10-07 21:15:00 | , | 30 | You are a helpful assistant for time series forecasting. Think step by step to make a prediction. | Forecast the future 30 readings from this sensor, from 2025-10-07 18:50:00 to 2025-10-07 21:15:00, given that the firmware has been updated.
The final result must be enclosed by '\boxed{' and '}', and where values are separated by ','. | This time series records the CPU usage of a newly installed data center. The data was recorded from 2025-10-07 00:00:00 to 2025-10-07 18:45:00, and the recording frequency is 5 minutes (5T). Due to an outdated firmware, the CPU load was significantly higher than normal. After the firmware update, the CPU runs normally ... | <history>48.538,48.623,48.706,48.795,48.874,48.960,49.047,49.128,49.213,49.294,49.378,49.464,49.546,49.630,49.716,49.799,49.876,49.964,50.048,50.129,50.214,50.295,50.382,50.466,50.548,50.632,50.717,50.795,50.879,50.963,51.041,51.126,51.212,51.294,51.373,51.456,51.541,51.618,51.702,51.782,51.863,51.945,52.028,52.107,52.... | |
context_implicit/template2/item3 | GIFT synthesize | phase_change | 5T | 0.542,0.541,0.539,0.537,0.533,0.529,0.525,0.522,0.519,0.518,0.518,0.520,0.525,0.531,0.539,0.548,0.558,0.569,0.579,0.589,0.596,0.603,0.607,0.610,0.611,0.610,0.608,0.605,0.601,0.598,0.595,0.593,0.591,0.590,0.590,0.590,0.591,0.591,0.590,0.589,0.588,0.585,0.582,0.578,0.574,0.570,0.567,0.563,0.561,0.559,0.558,0.558,0.558,0.... | 2025-10-07 00:00:00 | 2025-10-07 19:25:00 | 0.519,0.517,0.515,0.513,0.511,0.509,0.507,0.506,0.505,0.505,0.505,0.505,0.505,0.505,0.505,0.505,0.505,0.505,0.505,0.504,0.504,0.504,0.505,0.506,0.507,0.508,0.509,0.510,0.510,0.510,0.508,0.506,0.504,0.500,0.496,0.493,0.490,0.488,0.487,0.488,0.491,0.496,0.503,0.512,0.522,0.533,0.544,0.554,0.563,0.572,0.578,0.582,0.584,0.... | 2025-10-07 19:30:00 | 2025-10-08 07:55:00 | , | 150 | You are a helpful assistant for time series forecasting. Think step by step to make a prediction. | Forecast the future 150 readings from this sensor, from 2025-10-07 19:30:00 to 2025-10-08 07:55:00, given that the firmware has been updated.
The final result must be enclosed by '\boxed{' and '}', and where values are separated by ','. | This time series records the CPU usage of a newly installed data center. The data was recorded from 2025-10-07 00:00:00 to 2025-10-07 19:25:00, and the recording frequency is 5 minutes (5T). Due to an outdated firmware, the CPU load was significantly higher than normal. After the firmware update, the CPU runs normally ... | <history>0.542,0.541,0.539,0.537,0.533,0.529,0.525,0.522,0.519,0.518,0.518,0.520,0.525,0.531,0.539,0.548,0.558,0.569,0.579,0.589,0.596,0.603,0.607,0.610,0.611,0.610,0.608,0.605,0.601,0.598,0.595,0.593,0.591,0.590,0.590,0.590,0.591,0.591,0.590,0.589,0.588,0.585,0.582,0.578,0.574,0.570,0.567,0.563,0.561,0.559,0.558,0.558... | |
context_implicit/template3/item3 | GIFT synthesize | phase_change | 5T | 79.630,79.587,79.548,79.511,81.308,86.100,90.906,89.415,84.535,79.669,79.250,79.211,79.180,79.134,79.106,79.062,79.029,78.986,78.950,78.916,78.879,78.839,78.799,78.760,78.731,78.689,78.648,78.619,78.576,78.535,78.501,78.469,78.429,78.396,78.356,78.324,78.574,83.379,88.172,89.751,84.870,80.000,78.054,78.027,77.989,77.94... | 2025-10-06 00:00:00 | 2025-10-06 19:25:00 | 67.111,62.248,60.842,60.818,60.794,60.773,60.758,60.736,60.712,60.693,60.674,60.647,60.635,60.616,60.590,60.572,60.556,60.534,60.508,60.490,60.474,60.456,60.439,60.418,60.399,60.385,60.361,60.346,60.322,64.428,69.252,72.866,68.005,63.156,60.222,60.205,60.188,60.166,60.151,60.141,60.122,60.105,60.095,60.074,60.063,60.04... | 2025-10-06 19:30:00 | 2025-10-07 07:55:00 | , | 150 | You are a helpful assistant for time series forecasting. Think step by step to make a prediction. | Forecast the future 150 readings from this sensor, from 2025-10-06 19:30:00 to 2025-10-07 07:55:00, given that the firmware has been updated.
The final result must be enclosed by '\boxed{' and '}', and where values are separated by ','. | This time series records the CPU usage of a newly installed data center. The data was recorded from 2025-10-06 00:00:00 to 2025-10-06 19:25:00, and the recording frequency is 5 minutes (5T). Due to an outdated firmware, the CPU load was significantly higher than normal. After the firmware update, the CPU runs normally ... | <history>79.630,79.587,79.548,79.511,81.308,86.100,90.906,89.415,84.535,79.669,79.250,79.211,79.180,79.134,79.106,79.062,79.029,78.986,78.950,78.916,78.879,78.839,78.799,78.760,78.731,78.689,78.648,78.619,78.576,78.535,78.501,78.469,78.429,78.396,78.356,78.324,78.574,83.379,88.172,89.751,84.870,80.000,78.054,78.027,77.... | |
context_implicit/template4/item3 | GIFT synthesize | phase_change | 5T | 91.097,89.218,87.351,85.623,84.147,82.980,82.194,81.790,81.761,82.049,82.576,83.253,83.974,84.649,85.194,85.575,85.751,85.738,85.568,85.295,84.993,84.722,84.539,84.494,84.603,84.849,85.200,85.600,85.955,86.179,86.185,85.910,85.310,84.365,83.085,81.543,79.810,77.986,76.196,74.560,73.158,72.093,71.408,71.111,71.184,71.57... | 2025-10-06 00:00:00 | 2025-10-06 22:35:00 | 62.516,62.530,62.663,62.940,63.383,63.956,64.609,65.276,65.858,66.284,66.466,66.339,65.890,65.095,64.009,62.688,61.221,59.730,58.316,57.096,56.160,55.564,55.338,55.479,55.951,56.685,57.594,58.584,59.548,60.410,61.118,61.618,61.924,62.046,62.044,61.979,61.911,61.924,62.053,62.337,62.778,63.342,63.984,64.630,65.194,65.59... | 2025-10-06 22:40:00 | 2025-10-07 18:35:00 | , | 240 | You are a helpful assistant for time series forecasting. Think step by step to make a prediction. | Forecast the future 240 readings from this sensor, from 2025-10-06 22:40:00 to 2025-10-07 18:35:00, given that the firmware has been updated.
The final result must be enclosed by '\boxed{' and '}', and where values are separated by ','. | This time series records the CPU usage of a newly installed data center. The data was recorded from 2025-10-06 00:00:00 to 2025-10-06 22:35:00, and the recording frequency is 5 minutes (5T). Due to an outdated firmware, the CPU load was significantly higher than normal. After the firmware update, the CPU runs normally ... | <history>91.097,89.218,87.351,85.623,84.147,82.980,82.194,81.790,81.761,82.049,82.576,83.253,83.974,84.649,85.194,85.575,85.751,85.738,85.568,85.295,84.993,84.722,84.539,84.494,84.603,84.849,85.200,85.600,85.955,86.179,86.185,85.910,85.310,84.365,83.085,81.543,79.810,77.986,76.196,74.560,73.158,72.093,71.408,71.111,71.... | |
anomaly_explicit/template0/item3 | GIFT synthesize | anomaly | T | 9.781,9.765,9.776,9.768,9.773,30.031,30.039,30.030,30.017,30.055,30.051,9.818,9.791,9.798,9.844,9.847,9.845,9.871,9.874,9.897,9.921,9.904,9.930,9.926,30.219,30.246,30.247,30.277,30.276,10.036,10.063,10.068,10.121,10.113,10.130,10.144,10.191,10.207,10.237,10.255,10.291,10.318,10.363,30.611,30.662,30.684,30.716,30.774,10... | 2025-10-10 00:00:00 | 2025-10-10 03:15:00 | 42.589,42.700,22.569,22.703,22.846,22.965,23.125,23.245,23.362,23.510,23.635,23.787,23.918,24.031,24.176,44.565,44.707,44.882,45.002,45.113,25.021,25.154,25.286,25.444,25.578,25.740,25.880,26.043,26.160,26.316,26.472,26.602,26.737,26.887,47.313,47.456,47.612,47.759,47.929,27.795,27.980,28.120,28.250,28.428,28.589,28.75... | 2025-10-10 03:16:00 | 2025-10-10 04:15:00 | , | 60 | You are a helpful assistant for time series forecasting. Think step by step to make a prediction. | Forecast the CPU usage over the next 60 minutes, from 2025-10-10 03:16:00 to 2025-10-10 04:15:00.
The final result must be enclosed by '\boxed{' and '}', and where values are separated by ','. | This time series records the CPU usage of a data center. The data was recorded from 2025-10-10 00:00:00 to 2025-10-10 03:15:00 with the minute (T) frequency. Due to a firmware's bug, the readings from 2025-10-10 00:50:00 to 2025-10-10 01:22:00 and 2025-10-10 01:58:00 to 2025-10-10 02:29:00 were noisy, and even had unre... | <history>9.781,9.765,9.776,9.768,9.773,30.031,30.039,30.030,30.017,30.055,30.051,9.818,9.791,9.798,9.844,9.847,9.845,9.871,9.874,9.897,9.921,9.904,9.930,9.926,30.219,30.246,30.247,30.277,30.276,10.036,10.063,10.068,10.121,10.113,10.130,10.144,10.191,10.207,10.237,10.255,10.291,10.318,10.363,30.611,30.662,30.684,30.716,... | |
anomaly_explicit/template1/item3 | GIFT synthesize | anomaly | T | 100.000,98.332,96.588,94.913,93.262,91.586,89.941,88.311,100.000,100.000,100.000,100.000,80.338,78.817,77.311,75.748,74.284,72.798,71.297,69.853,68.441,66.987,65.606,64.234,62.850,61.556,60.211,58.900,57.666,56.400,55.113,53.917,52.693,51.549,50.342,49.214,48.102,96.517,95.461,94.415,67.549,65.419,22.123,18.820,42.444,... | 2025-10-10 00:00:00 | 2025-10-10 03:25:00 | 78.396,78.080,77.816,77.553,77.263,100.000,100.000,100.000,100.000,100.000,75.325,75.014,74.617,74.205,73.889,73.494,73.066,72.659,72.244,71.804,71.330,70.869,70.429,69.935,69.469,68.939,68.460,67.951,67.422,66.880,66.352,65.734,65.206,64.590,100.000,100.000,100.000,100.000,100.000,60.808,60.134,59.457,58.762,58.065,57... | 2025-10-10 03:26:00 | 2025-10-10 04:15:00 | , | 50 | You are a helpful assistant for time series forecasting. Think step by step to make a prediction. | Forecast the CPU usage over the next 50 minutes, from 2025-10-10 03:26:00 to 2025-10-10 04:15:00.
The final result must be enclosed by '\boxed{' and '}', and where values are separated by ','. | This time series records the CPU usage of a data center. The data was recorded from 2025-10-10 00:00:00 to 2025-10-10 03:25:00 with the minute (T) frequency. Due to a firmware's bug, the readings from 2025-10-10 00:39:00 to 2025-10-10 01:17:00 and 2025-10-10 02:44:00 to 2025-10-10 03:09:00 were noisy, and even had unre... | <history>100.000,98.332,96.588,94.913,93.262,91.586,89.941,88.311,100.000,100.000,100.000,100.000,80.338,78.817,77.311,75.748,74.284,72.798,71.297,69.853,68.441,66.987,65.606,64.234,62.850,61.556,60.211,58.900,57.666,56.400,55.113,53.917,52.693,51.549,50.342,49.214,48.102,96.517,95.461,94.415,67.549,65.419,22.123,18.82... | |
anomaly_explicit/template2/item3 | GIFT synthesize | anomaly | T | 66.345,69.288,72.639,75.996,79.358,82.712,81.034,77.268,73.470,69.675,65.886,63.957,63.767,63.553,63.333,63.132,62.928,62.716,62.507,62.291,62.101,61.889,61.697,61.462,61.275,61.060,60.872,60.668,60.461,60.254,60.053,59.856,59.656,59.460,59.277,60.764,64.154,67.523,70.895,74.292,75.563,71.804,68.016,64.250,60.473,57.11... | 2025-10-10 00:00:00 | 2025-10-10 04:25:00 | 17.117,18.914,20.301,24.675,26.943,21.736,25.221,22.581,27.862,27.799,29.910,33.415,36.927,40.439,43.965,44.484,40.820,37.219,33.575,29.915,27.142,27.110,27.044,26.980,26.920,26.878,26.809,26.785,26.723,26.679,26.613,26.586,26.520,26.460,26.414,26.364,26.323,26.271,26.219,26.185,26.133,26.099,26.051,26.014,26.646,30.17... | 2025-10-10 04:26:00 | 2025-10-10 06:25:00 | , | 120 | You are a helpful assistant for time series forecasting. Think step by step to make a prediction. | Forecast the CPU usage over the next 120 minutes, from 2025-10-10 04:26:00 to 2025-10-10 06:25:00.
The final result must be enclosed by '\boxed{' and '}', and where values are separated by ','. | This time series records the CPU usage of a data center. The data was recorded from 2025-10-10 00:00:00 to 2025-10-10 04:25:00 with the minute (T) frequency. Due to a firmware's bug, the readings from 2025-10-10 02:01:00 to 2025-10-10 02:15:00 and 2025-10-10 03:58:00 to 2025-10-10 04:33:00 were noisy, and even had unre... | <history>66.345,69.288,72.639,75.996,79.358,82.712,81.034,77.268,73.470,69.675,65.886,63.957,63.767,63.553,63.333,63.132,62.928,62.716,62.507,62.291,62.101,61.889,61.697,61.462,61.275,61.060,60.872,60.668,60.461,60.254,60.053,59.856,59.656,59.460,59.277,60.764,64.154,67.523,70.895,74.292,75.563,71.804,68.016,64.250,60.... | |
anomaly_explicit/template3/item3 | GIFT synthesize | anomaly | T | 73.687,73.595,73.526,73.449,73.375,73.303,73.214,73.149,73.088,73.003,72.921,72.857,72.770,72.700,72.621,72.547,72.477,72.413,72.326,72.268,72.172,72.106,72.045,71.976,71.892,71.827,71.755,71.680,71.613,71.544,71.465,71.395,71.338,71.259,71.192,71.124,71.054,70.978,70.912,70.857,70.787,70.697,70.636,70.567,70.499,70.43... | 2025-10-10 00:00:00 | 2025-10-10 04:45:00 | 59.888,59.858,59.846,59.830,59.813,59.786,59.775,59.754,59.712,59.711,59.694,59.685,59.664,59.646,59.626,59.619,59.596,59.585,59.571,59.543,59.536,59.518,59.498,59.483,59.468,59.465,59.440,59.413,59.404,59.390,59.394,59.372,59.351,59.342,59.336,59.314,59.293,59.289,59.286,59.276,59.238,59.245,59.228,59.206,59.206,59.19... | 2025-10-10 04:46:00 | 2025-10-10 06:25:00 | , | 100 | You are a helpful assistant for time series forecasting. Think step by step to make a prediction. | Forecast the CPU usage over the next 100 minutes, from 2025-10-10 04:46:00 to 2025-10-10 06:25:00.
The final result must be enclosed by '\boxed{' and '}', and where values are separated by ','. | This time series records the CPU usage of a data center. The data was recorded from 2025-10-10 00:00:00 to 2025-10-10 04:45:00 with the minute (T) frequency. Due to a firmware's bug, the readings from 2025-10-10 01:47:00 to 2025-10-10 02:38:00 and 2025-10-10 03:54:00 to 2025-10-10 04:13:00 were noisy, and even had unre... | <history>73.687,73.595,73.526,73.449,73.375,73.303,73.214,73.149,73.088,73.003,72.921,72.857,72.770,72.700,72.621,72.547,72.477,72.413,72.326,72.268,72.172,72.106,72.045,71.976,71.892,71.827,71.755,71.680,71.613,71.544,71.465,71.395,71.338,71.259,71.192,71.124,71.054,70.978,70.912,70.857,70.787,70.697,70.636,70.567,70.... | |
anomaly_explicit/template4/item3 | GIFT synthesize | anomaly | T | 49.374,49.321,49.817,50.010,49.558,48.544,47.327,46.170,45.153,44.135,43.112,42.349,42.368,43.664,46.279,49.582,52.394,53.483,52.196,48.893,44.901,42.007,41.609,44.130,48.848,54.272,58.893,61.908,63.463,64.414,65.718,67.755,70.071,71.589,71.220,68.488,63.841,58.486,53.814,50.808,49.636,49.739,50.211,50.302,49.728,48.66... | 2025-10-10 00:00:00 | 2025-10-10 06:51:00 | 77.849,81.273,84.538,86.317,85.758,82.952,79.060,75.878,75.018,77.164,81.793,87.476,92.594,96.101,97.982,99.019,100.000,100.000,100.000,100.000,100.000,100.000,100.000,95.791,90.948,87.652,86.272,86.326,86.934,87.251,86.905,85.997,84.924,83.967,83.174,82.401,81.579,80.942,81.033,82.348,85.034,88.475,91.525,92.917,91.94... | 2025-10-10 06:52:00 | 2025-10-10 08:31:00 | , | 100 | You are a helpful assistant for time series forecasting. Think step by step to make a prediction. | Forecast the CPU usage over the next 100 minutes, from 2025-10-10 06:52:00 to 2025-10-10 08:31:00.
The final result must be enclosed by '\boxed{' and '}', and where values are separated by ','. | This time series records the CPU usage of a data center. The data was recorded from 2025-10-10 00:00:00 to 2025-10-10 06:51:00 with the minute (T) frequency. Due to a firmware's bug, the readings from 2025-10-10 01:09:00 to 2025-10-10 02:05:00 and 2025-10-10 03:27:00 to 2025-10-10 04:17:00 were noisy, and even had unre... | <history>49.374,49.321,49.817,50.010,49.558,48.544,47.327,46.170,45.153,44.135,43.112,42.349,42.368,43.664,46.279,49.582,52.394,53.483,52.196,48.893,44.901,42.007,41.609,44.130,48.848,54.272,58.893,61.908,63.463,64.414,65.718,67.755,70.071,71.589,71.220,68.488,63.841,58.486,53.814,50.808,49.636,49.739,50.211,50.302,49.... | |
anomaly_implicit/template0/item3 | GIFT synthesize | anomaly | T | 9.781,9.765,9.776,9.768,9.773,30.031,30.039,30.030,30.017,30.055,30.051,9.818,9.791,9.798,9.844,9.847,9.845,9.871,9.874,9.897,9.921,9.904,9.930,9.926,30.219,30.246,30.247,30.277,30.276,10.036,10.063,10.068,10.121,10.113,10.130,10.144,10.191,10.207,10.237,10.255,10.291,10.318,10.363,30.611,30.662,30.684,30.716,30.774,10... | 2025-10-10 00:00:00 | 2025-10-10 03:15:00 | 42.589,42.700,22.569,22.703,22.846,22.965,23.125,23.245,23.362,23.510,23.635,23.787,23.918,24.031,24.176,44.565,44.707,44.882,45.002,45.113,25.021,25.154,25.286,25.444,25.578,25.740,25.880,26.043,26.160,26.316,26.472,26.602,26.737,26.887,47.313,47.456,47.612,47.759,47.929,27.795,27.980,28.120,28.250,28.428,28.589,28.75... | 2025-10-10 03:16:00 | 2025-10-10 04:15:00 | , | 60 | You are a helpful assistant for time series forecasting. Think step by step to make a prediction. | Forecast the CPU usage over the next 60 minutes, from 2025-10-10 03:16:00 to 2025-10-10 04:15:00.
The final result must be enclosed by '\boxed{' and '}', and where values are separated by ','. | This time series records the CPU usage of a data center. The data was recorded from 2025-10-10 00:00:00 to 2025-10-10 03:15:00 with the minute (T) frequency. Due to a firmware's bug, there are periods where the readings were noisy, and even had unreasonable values. The adminitstrator quickly installed a patch fix and t... | <history>9.781,9.765,9.776,9.768,9.773,30.031,30.039,30.030,30.017,30.055,30.051,9.818,9.791,9.798,9.844,9.847,9.845,9.871,9.874,9.897,9.921,9.904,9.930,9.926,30.219,30.246,30.247,30.277,30.276,10.036,10.063,10.068,10.121,10.113,10.130,10.144,10.191,10.207,10.237,10.255,10.291,10.318,10.363,30.611,30.662,30.684,30.716,... | |
anomaly_implicit/template1/item3 | GIFT synthesize | anomaly | T | 100.000,98.332,96.588,94.913,93.262,91.586,89.941,88.311,100.000,100.000,100.000,100.000,80.338,78.817,77.311,75.748,74.284,72.798,71.297,69.853,68.441,66.987,65.606,64.234,62.850,61.556,60.211,58.900,57.666,56.400,55.113,53.917,52.693,51.549,50.342,49.214,48.102,96.517,95.461,94.415,67.549,65.419,22.123,18.820,42.444,... | 2025-10-10 00:00:00 | 2025-10-10 03:25:00 | 78.396,78.080,77.816,77.553,77.263,100.000,100.000,100.000,100.000,100.000,75.325,75.014,74.617,74.205,73.889,73.494,73.066,72.659,72.244,71.804,71.330,70.869,70.429,69.935,69.469,68.939,68.460,67.951,67.422,66.880,66.352,65.734,65.206,64.590,100.000,100.000,100.000,100.000,100.000,60.808,60.134,59.457,58.762,58.065,57... | 2025-10-10 03:26:00 | 2025-10-10 04:15:00 | , | 50 | You are a helpful assistant for time series forecasting. Think step by step to make a prediction. | Forecast the CPU usage over the next 50 minutes, from 2025-10-10 03:26:00 to 2025-10-10 04:15:00.
The final result must be enclosed by '\boxed{' and '}', and where values are separated by ','. | This time series records the CPU usage of a data center. The data was recorded from 2025-10-10 00:00:00 to 2025-10-10 03:25:00 with the minute (T) frequency. Due to a firmware's bug, there are periods where the readings were noisy, and even had unreasonable values. The adminitstrator quickly installed a patch fix and t... | <history>100.000,98.332,96.588,94.913,93.262,91.586,89.941,88.311,100.000,100.000,100.000,100.000,80.338,78.817,77.311,75.748,74.284,72.798,71.297,69.853,68.441,66.987,65.606,64.234,62.850,61.556,60.211,58.900,57.666,56.400,55.113,53.917,52.693,51.549,50.342,49.214,48.102,96.517,95.461,94.415,67.549,65.419,22.123,18.82... | |
anomaly_implicit/template2/item3 | GIFT synthesize | anomaly | T | 66.345,69.288,72.639,75.996,79.358,82.712,81.034,77.268,73.470,69.675,65.886,63.957,63.767,63.553,63.333,63.132,62.928,62.716,62.507,62.291,62.101,61.889,61.697,61.462,61.275,61.060,60.872,60.668,60.461,60.254,60.053,59.856,59.656,59.460,59.277,60.764,64.154,67.523,70.895,74.292,75.563,71.804,68.016,64.250,60.473,57.11... | 2025-10-10 00:00:00 | 2025-10-10 04:25:00 | 17.117,18.914,20.301,24.675,26.943,21.736,25.221,22.581,27.862,27.799,29.910,33.415,36.927,40.439,43.965,44.484,40.820,37.219,33.575,29.915,27.142,27.110,27.044,26.980,26.920,26.878,26.809,26.785,26.723,26.679,26.613,26.586,26.520,26.460,26.414,26.364,26.323,26.271,26.219,26.185,26.133,26.099,26.051,26.014,26.646,30.17... | 2025-10-10 04:26:00 | 2025-10-10 06:25:00 | , | 120 | You are a helpful assistant for time series forecasting. Think step by step to make a prediction. | Forecast the CPU usage over the next 120 minutes, from 2025-10-10 04:26:00 to 2025-10-10 06:25:00.
The final result must be enclosed by '\boxed{' and '}', and where values are separated by ','. | This time series records the CPU usage of a data center. The data was recorded from 2025-10-10 00:00:00 to 2025-10-10 04:25:00 with the minute (T) frequency. Due to a firmware's bug, there are periods where the readings were noisy, and even had unreasonable values. The adminitstrator quickly installed a patch fix and t... | <history>66.345,69.288,72.639,75.996,79.358,82.712,81.034,77.268,73.470,69.675,65.886,63.957,63.767,63.553,63.333,63.132,62.928,62.716,62.507,62.291,62.101,61.889,61.697,61.462,61.275,61.060,60.872,60.668,60.461,60.254,60.053,59.856,59.656,59.460,59.277,60.764,64.154,67.523,70.895,74.292,75.563,71.804,68.016,64.250,60.... | |
anomaly_implicit/template3/item3 | GIFT synthesize | anomaly | T | 73.687,73.595,73.526,73.449,73.375,73.303,73.214,73.149,73.088,73.003,72.921,72.857,72.770,72.700,72.621,72.547,72.477,72.413,72.326,72.268,72.172,72.106,72.045,71.976,71.892,71.827,71.755,71.680,71.613,71.544,71.465,71.395,71.338,71.259,71.192,71.124,71.054,70.978,70.912,70.857,70.787,70.697,70.636,70.567,70.499,70.43... | 2025-10-10 00:00:00 | 2025-10-10 04:45:00 | 59.888,59.858,59.846,59.830,59.813,59.786,59.775,59.754,59.712,59.711,59.694,59.685,59.664,59.646,59.626,59.619,59.596,59.585,59.571,59.543,59.536,59.518,59.498,59.483,59.468,59.465,59.440,59.413,59.404,59.390,59.394,59.372,59.351,59.342,59.336,59.314,59.293,59.289,59.286,59.276,59.238,59.245,59.228,59.206,59.206,59.19... | 2025-10-10 04:46:00 | 2025-10-10 06:25:00 | , | 100 | You are a helpful assistant for time series forecasting. Think step by step to make a prediction. | Forecast the CPU usage over the next 100 minutes, from 2025-10-10 04:46:00 to 2025-10-10 06:25:00.
The final result must be enclosed by '\boxed{' and '}', and where values are separated by ','. | This time series records the CPU usage of a data center. The data was recorded from 2025-10-10 00:00:00 to 2025-10-10 04:45:00 with the minute (T) frequency. Due to a firmware's bug, there are periods where the readings were noisy, and even had unreasonable values. The adminitstrator quickly installed a patch fix and t... | <history>73.687,73.595,73.526,73.449,73.375,73.303,73.214,73.149,73.088,73.003,72.921,72.857,72.770,72.700,72.621,72.547,72.477,72.413,72.326,72.268,72.172,72.106,72.045,71.976,71.892,71.827,71.755,71.680,71.613,71.544,71.465,71.395,71.338,71.259,71.192,71.124,71.054,70.978,70.912,70.857,70.787,70.697,70.636,70.567,70.... | |
anomaly_implicit/template4/item3 | GIFT synthesize | anomaly | T | 49.374,49.321,49.817,50.010,49.558,48.544,47.327,46.170,45.153,44.135,43.112,42.349,42.368,43.664,46.279,49.582,52.394,53.483,52.196,48.893,44.901,42.007,41.609,44.130,48.848,54.272,58.893,61.908,63.463,64.414,65.718,67.755,70.071,71.589,71.220,68.488,63.841,58.486,53.814,50.808,49.636,49.739,50.211,50.302,49.728,48.66... | 2025-10-10 00:00:00 | 2025-10-10 06:51:00 | 77.849,81.273,84.538,86.317,85.758,82.952,79.060,75.878,75.018,77.164,81.793,87.476,92.594,96.101,97.982,99.019,100.000,100.000,100.000,100.000,100.000,100.000,100.000,95.791,90.948,87.652,86.272,86.326,86.934,87.251,86.905,85.997,84.924,83.967,83.174,82.401,81.579,80.942,81.033,82.348,85.034,88.475,91.525,92.917,91.94... | 2025-10-10 06:52:00 | 2025-10-10 08:31:00 | , | 100 | You are a helpful assistant for time series forecasting. Think step by step to make a prediction. | Forecast the CPU usage over the next 100 minutes, from 2025-10-10 06:52:00 to 2025-10-10 08:31:00.
The final result must be enclosed by '\boxed{' and '}', and where values are separated by ','. | This time series records the CPU usage of a data center. The data was recorded from 2025-10-10 00:00:00 to 2025-10-10 06:51:00 with the minute (T) frequency. Due to a firmware's bug, there are periods where the readings were noisy, and even had unreasonable values. The adminitstrator quickly installed a patch fix and t... | <history>49.374,49.321,49.817,50.010,49.558,48.544,47.327,46.170,45.153,44.135,43.112,42.349,42.368,43.664,46.279,49.582,52.394,53.483,52.196,48.893,44.901,42.007,41.609,44.130,48.848,54.272,58.893,61.908,63.463,64.414,65.718,67.755,70.071,71.589,71.220,68.488,63.841,58.486,53.814,50.808,49.636,49.739,50.211,50.302,49.... | |
anomaly_explicit/template0/item4 | GIFT synthesize | anomaly | H | 164.000,183.000,168.000,148.000,142.000,172.000,134.000,163.000,191.000,218.000,159.000,164.000,132.000,218.000,178.000,147.000,141.000,174.000,190.000,197.000,212.000,181.000,228.000,239.000,208.000,167.000,160.000,154.000,150.000,168.000,187.000,2.000,400.000,783.000,951.000,1072.000,1342.000,1208.000,1095.000,1521.0... | 2025-10-10 00:00:00 | 2025-10-16 11:00:00 | 182.000,187.000,161.000,182.000,144.000,171.000,146.000,155.000,154.000,150.000,125.000,215.000,161.000,116.000,142.000,159.000,184.000,197.000,161.000,173.000,126.000,150.000,186.000,137.000,190.000,187.000,145.000,196.000,157.000,195.000,174.000,185.000,135.000,137.000,217.000,166.000,174.000,193.000,179.000,188.000,... | 2025-10-16 12:00:00 | 2025-10-20 15:00:00 | , | 100 | You are a helpful assistant for time series forecasting. Think step by step to make a prediction. | Forecast the electricty consumption in the next 100 hours, from 2025-10-16 12:00:00 to 2025-10-20 15:00:00.
The final result must be enclosed by '\boxed{' and '}', and where values are separated by ','. | This time series records the elecitricy consumption (in Watts) of a home office. The data was recorded from 2025-10-10 00:00:00 to 2025-10-16 11:00:00 with the hour (H) frequency. During the periods of 2025-10-11 06:00:00 to 2025-10-12 12:00:00 and 2025-10-14 02:00:00 to 2025-10-15 08:00:00, the team was in a deadline ... | <history>164.000,183.000,168.000,148.000,142.000,172.000,134.000,163.000,191.000,218.000,159.000,164.000,132.000,218.000,178.000,147.000,141.000,174.000,190.000,197.000,212.000,181.000,228.000,239.000,208.000,167.000,160.000,154.000,150.000,168.000,187.000,2.000,400.000,783.000,951.000,1072.000,1342.000,1208.000,1095.0... | |
anomaly_explicit/template1/item4 | GIFT synthesize | anomaly | H | 91.000,74.000,59.000,90.000,94.000,48.000,104.000,91.000,51.000,68.000,68.000,70.000,69.000,97.000,47.000,80.000,109.000,76.000,97.000,78.000,108.000,63.000,80.000,83.000,56.000,73.000,76.000,115.000,78.000,67.000,55.000,85.000,90.000,95.000,95.000,149.000,152.000,200.000,235.000,262.000,256.000,317.000,367.000,384.000... | 2025-10-10 00:00:00 | 2025-10-16 11:00:00 | 326.000,312.000,282.000,288.000,305.000,308.000,307.000,284.000,270.000,284.000,291.000,273.000,289.000,273.000,302.000,287.000,311.000,272.000,294.000,302.000,292.000,300.000,279.000,261.000,267.000,256.000,273.000,273.000,295.000,275.000,285.000,282.000,259.000,285.000,313.000,297.000,266.000,323.000,284.000,272.000,... | 2025-10-16 12:00:00 | 2025-10-20 15:00:00 | , | 100 | You are a helpful assistant for time series forecasting. Think step by step to make a prediction. | Forecast the electricty consumption in the next 100 hours, from 2025-10-16 12:00:00 to 2025-10-20 15:00:00.
The final result must be enclosed by '\boxed{' and '}', and where values are separated by ','. | This time series records the elecitricy consumption (in Watts) of a home office. The data was recorded from 2025-10-10 00:00:00 to 2025-10-16 11:00:00 with the hour (H) frequency. During the periods of 2025-10-11 08:00:00 to 2025-10-12 17:00:00 and 2025-10-14 12:00:00 to 2025-10-14 16:00:00, the team was in a deadline ... | <history>91.000,74.000,59.000,90.000,94.000,48.000,104.000,91.000,51.000,68.000,68.000,70.000,69.000,97.000,47.000,80.000,109.000,76.000,97.000,78.000,108.000,63.000,80.000,83.000,56.000,73.000,76.000,115.000,78.000,67.000,55.000,85.000,90.000,95.000,95.000,149.000,152.000,200.000,235.000,262.000,256.000,317.000,367.00... | |
anomaly_explicit/template2/item4 | GIFT synthesize | anomaly | H | 93.000,89.000,112.000,125.000,154.000,123.000,135.000,114.000,108.000,140.000,78.000,103.000,89.000,106.000,130.000,61.000,78.000,76.000,72.000,60.000,69.000,56.000,105.000,60.000,132.000,86.000,130.000,110.000,82.000,84.000,126.000,119.000,81.000,116.000,131.000,117.000,123.000,151.000,111.000,83.000,122.000,78.000,67... | 2025-10-10 00:00:00 | 2025-10-22 05:00:00 | 302.000,326.000,328.000,326.000,328.000,281.000,283.000,291.000,241.000,221.000,221.000,214.000,189.000,193.000,193.000,158.000,136.000,160.000,142.000,147.000,94.000,109.000,140.000,61.000,106.000,57.000,111.000,99.000,82.000,10.000,58.000,48.000,88.000,103.000,97.000,77.000,116.000,98.000,101.000,115.000,111.000,116.... | 2025-10-22 06:00:00 | 2025-10-26 23:00:00 | , | 90 | You are a helpful assistant for time series forecasting. Think step by step to make a prediction. | Forecast the electricty consumption in the next 90 hours, from 2025-10-22 06:00:00 to 2025-10-25 23:00:00.
The final result must be enclosed by '\boxed{' and '}', and where values are separated by ','. | This time series records the elecitricy consumption (in Watts) of a home office. The data was recorded from 2025-10-10 00:00:00 to 2025-10-22 05:00:00 with the hour (H) frequency. During the periods of 2025-10-17 02:00:00 to 2025-10-18 02:00:00 and 2025-10-20 23:00:00 to 2025-10-23 09:00:00, the team was in a deadline ... | <history>93.000,89.000,112.000,125.000,154.000,123.000,135.000,114.000,108.000,140.000,78.000,103.000,89.000,106.000,130.000,61.000,78.000,76.000,72.000,60.000,69.000,56.000,105.000,60.000,132.000,86.000,130.000,110.000,82.000,84.000,126.000,119.000,81.000,116.000,131.000,117.000,123.000,151.000,111.000,83.000,122.000,... | |
anomaly_explicit/template3/item4 | GIFT synthesize | anomaly | H | 6.000,6.000,6.000,6.000,6.000,6.000,6.000,342.000,342.000,342.000,342.000,342.000,342.000,342.000,342.000,342.000,6.000,6.000,6.000,6.000,6.000,6.000,6.000,6.000,6.000,6.000,6.000,6.000,6.000,6.000,6.000,6.000,6.000,6.000,6.000,57.000,108.000,159.000,211.000,262.000,313.000,649.000,649.000,649.000,655.000,658.000,595.0... | 2025-10-10 00:00:00 | 2025-10-20 23:00:00 | 6.000,6.000,6.000,6.000,6.000,6.000,6.000,6.000,6.000,6.000,6.000,6.000,6.000,6.000,6.000,6.000,6.000,342.000,342.000,342.000,342.000,342.000,342.000,342.000,342.000,342.000,6.000,6.000,6.000,6.000,6.000,6.000,6.000,6.000,6.000,6.000,6.000,6.000,6.000,6.000,6.000,6.000,6.000,6.000,6.000,6.000,6.000,6.000,6.000,6.000,6.... | 2025-10-21 00:00:00 | 2025-10-25 23:00:00 | , | 120 | You are a helpful assistant for time series forecasting. Think step by step to make a prediction. | Forecast the electricty consumption in the next 120 hours, from 2025-10-21 00:00:00 to 2025-10-25 23:00:00.
The final result must be enclosed by '\boxed{' and '}', and where values are separated by ','. | This time series records the elecitricy consumption (in Watts) of a home office. The data was recorded from 2025-10-10 00:00:00 to 2025-10-20 23:00:00 with the hour (H) frequency. During the periods of 2025-10-11 09:00:00 to 2025-10-12 18:00:00 and 2025-10-14 08:00:00 to 2025-10-15 04:00:00, the team was in a deadline ... | <history>6.000,6.000,6.000,6.000,6.000,6.000,6.000,342.000,342.000,342.000,342.000,342.000,342.000,342.000,342.000,342.000,6.000,6.000,6.000,6.000,6.000,6.000,6.000,6.000,6.000,6.000,6.000,6.000,6.000,6.000,6.000,6.000,6.000,6.000,6.000,57.000,108.000,159.000,211.000,262.000,313.000,649.000,649.000,649.000,655.000,658.... | |
anomaly_explicit/template4/item4 | GIFT synthesize | anomaly | H | 36.000,36.000,37.000,38.000,39.000,40.000,126.000,220.000,313.000,274.000,182.000,91.000,48.000,49.000,50.000,50.000,52.000,53.000,53.000,54.000,56.000,57.000,57.000,58.000,60.000,61.000,62.000,63.000,64.000,65.000,66.000,67.000,68.000,69.000,70.000,71.000,72.000,73.000,74.000,75.000,76.000,77.000,78.000,79.000,80.000,... | 2025-10-10 00:00:00 | 2025-10-23 19:00:00 | 318.000,319.000,319.000,320.000,320.000,321.000,321.000,322.000,322.000,376.000,469.000,561.000,586.000,493.000,402.000,326.000,326.000,326.000,327.000,328.000,328.000,329.000,329.000,330.000,330.000,331.000,331.000,332.000,332.000,332.000,333.000,333.000,334.000,334.000,335.000,335.000,336.000,336.000,336.000,337.000,... | 2025-10-23 20:00:00 | 2025-10-31 07:00:00 | , | 180 | You are a helpful assistant for time series forecasting. Think step by step to make a prediction. | Forecast the electricty consumption in the next 180 hours, from 2025-10-23 20:00:00 to 2025-10-31 07:00:00.
The final result must be enclosed by '\boxed{' and '}', and where values are separated by ','. | This time series records the elecitricy consumption (in Watts) of a home office. The data was recorded from 2025-10-10 00:00:00 to 2025-10-23 19:00:00 with the hour (H) frequency. During the periods of 2025-10-11 21:00:00 to 2025-10-13 01:00:00 and 2025-10-18 06:00:00 to 2025-10-20 00:00:00, the team was in a deadline ... | <history>36.000,36.000,37.000,38.000,39.000,40.000,126.000,220.000,313.000,274.000,182.000,91.000,48.000,49.000,50.000,50.000,52.000,53.000,53.000,54.000,56.000,57.000,57.000,58.000,60.000,61.000,62.000,63.000,64.000,65.000,66.000,67.000,68.000,69.000,70.000,71.000,72.000,73.000,74.000,75.000,76.000,77.000,78.000,79.00... | |
anomaly_implicit/template0/item4 | GIFT synthesize | anomaly | H | 164.000,183.000,168.000,148.000,142.000,172.000,134.000,163.000,191.000,218.000,159.000,164.000,132.000,218.000,178.000,147.000,141.000,174.000,190.000,197.000,212.000,181.000,228.000,239.000,208.000,167.000,160.000,154.000,150.000,168.000,187.000,2.000,400.000,783.000,951.000,1072.000,1342.000,1208.000,1095.000,1521.0... | 2025-10-10 00:00:00 | 2025-10-16 11:00:00 | 182.000,187.000,161.000,182.000,144.000,171.000,146.000,155.000,154.000,150.000,125.000,215.000,161.000,116.000,142.000,159.000,184.000,197.000,161.000,173.000,126.000,150.000,186.000,137.000,190.000,187.000,145.000,196.000,157.000,195.000,174.000,185.000,135.000,137.000,217.000,166.000,174.000,193.000,179.000,188.000,... | 2025-10-16 12:00:00 | 2025-10-20 15:00:00 | , | 100 | You are a helpful assistant for time series forecasting. Think step by step to make a prediction. | Forecast the electricty consumption in the next 100 hours, from 2025-10-16 12:00:00 to 2025-10-20 15:00:00.
The final result must be enclosed by '\boxed{' and '}', and where values are separated by ','. | This time series records the elecitricy consumption (in Watts) of a home office. The data was recorded from 2025-10-10 00:00:00 to 2025-10-16 11:00:00 with the hour (H) frequency. There were some deadline periods that required more extensive machinery usage, leadning to higher power consumptions. However, the deadlines... | <history>164.000,183.000,168.000,148.000,142.000,172.000,134.000,163.000,191.000,218.000,159.000,164.000,132.000,218.000,178.000,147.000,141.000,174.000,190.000,197.000,212.000,181.000,228.000,239.000,208.000,167.000,160.000,154.000,150.000,168.000,187.000,2.000,400.000,783.000,951.000,1072.000,1342.000,1208.000,1095.0... | |
anomaly_implicit/template1/item4 | GIFT synthesize | anomaly | H | 91.000,74.000,59.000,90.000,94.000,48.000,104.000,91.000,51.000,68.000,68.000,70.000,69.000,97.000,47.000,80.000,109.000,76.000,97.000,78.000,108.000,63.000,80.000,83.000,56.000,73.000,76.000,115.000,78.000,67.000,55.000,85.000,90.000,95.000,95.000,149.000,152.000,200.000,235.000,262.000,256.000,317.000,367.000,384.000... | 2025-10-10 00:00:00 | 2025-10-16 11:00:00 | 326.000,312.000,282.000,288.000,305.000,308.000,307.000,284.000,270.000,284.000,291.000,273.000,289.000,273.000,302.000,287.000,311.000,272.000,294.000,302.000,292.000,300.000,279.000,261.000,267.000,256.000,273.000,273.000,295.000,275.000,285.000,282.000,259.000,285.000,313.000,297.000,266.000,323.000,284.000,272.000,... | 2025-10-16 12:00:00 | 2025-10-20 15:00:00 | , | 100 | You are a helpful assistant for time series forecasting. Think step by step to make a prediction. | Forecast the electricty consumption in the next 100 hours, from 2025-10-16 12:00:00 to 2025-10-20 15:00:00.
The final result must be enclosed by '\boxed{' and '}', and where values are separated by ','. | This time series records the elecitricy consumption (in Watts) of a home office. The data was recorded from 2025-10-10 00:00:00 to 2025-10-16 11:00:00 with the hour (H) frequency. There were some deadline periods that required more extensive machinery usage, leadning to higher power consumptions. However, the deadlines... | <history>91.000,74.000,59.000,90.000,94.000,48.000,104.000,91.000,51.000,68.000,68.000,70.000,69.000,97.000,47.000,80.000,109.000,76.000,97.000,78.000,108.000,63.000,80.000,83.000,56.000,73.000,76.000,115.000,78.000,67.000,55.000,85.000,90.000,95.000,95.000,149.000,152.000,200.000,235.000,262.000,256.000,317.000,367.00... | |
anomaly_implicit/template2/item4 | GIFT synthesize | anomaly | H | 93.000,89.000,112.000,125.000,154.000,123.000,135.000,114.000,108.000,140.000,78.000,103.000,89.000,106.000,130.000,61.000,78.000,76.000,72.000,60.000,69.000,56.000,105.000,60.000,132.000,86.000,130.000,110.000,82.000,84.000,126.000,119.000,81.000,116.000,131.000,117.000,123.000,151.000,111.000,83.000,122.000,78.000,67... | 2025-10-10 00:00:00 | 2025-10-22 05:00:00 | 302.000,326.000,328.000,326.000,328.000,281.000,283.000,291.000,241.000,221.000,221.000,214.000,189.000,193.000,193.000,158.000,136.000,160.000,142.000,147.000,94.000,109.000,140.000,61.000,106.000,57.000,111.000,99.000,82.000,10.000,58.000,48.000,88.000,103.000,97.000,77.000,116.000,98.000,101.000,115.000,111.000,116.... | 2025-10-22 06:00:00 | 2025-10-25 23:00:00 | , | 90 | You are a helpful assistant for time series forecasting. Think step by step to make a prediction. | Forecast the electricty consumption in the next 90 hours, from 2025-10-22 06:00:00 to 2025-10-25 23:00:00.
The final result must be enclosed by '\boxed{' and '}', and where values are separated by ','. | This time series records the elecitricy consumption (in Watts) of a home office. The data was recorded from 2025-10-10 00:00:00 to 2025-10-22 05:00:00 with the hour (H) frequency. There were some deadline periods that required more extensive machinery usage, leadning to higher power consumptions. However, the deadlines... | <history>93.000,89.000,112.000,125.000,154.000,123.000,135.000,114.000,108.000,140.000,78.000,103.000,89.000,106.000,130.000,61.000,78.000,76.000,72.000,60.000,69.000,56.000,105.000,60.000,132.000,86.000,130.000,110.000,82.000,84.000,126.000,119.000,81.000,116.000,131.000,117.000,123.000,151.000,111.000,83.000,122.000,... | |
anomaly_implicit/template3/item4 | GIFT synthesize | anomaly | H | 6.000,6.000,6.000,6.000,6.000,6.000,6.000,342.000,342.000,342.000,342.000,342.000,342.000,342.000,342.000,342.000,6.000,6.000,6.000,6.000,6.000,6.000,6.000,6.000,6.000,6.000,6.000,6.000,6.000,6.000,6.000,6.000,6.000,6.000,6.000,57.000,108.000,159.000,211.000,262.000,313.000,649.000,649.000,649.000,655.000,658.000,595.0... | 2025-10-10 00:00:00 | 2025-10-20 23:00:00 | 6.000,6.000,6.000,6.000,6.000,6.000,6.000,6.000,6.000,6.000,6.000,6.000,6.000,6.000,6.000,6.000,6.000,342.000,342.000,342.000,342.000,342.000,342.000,342.000,342.000,342.000,6.000,6.000,6.000,6.000,6.000,6.000,6.000,6.000,6.000,6.000,6.000,6.000,6.000,6.000,6.000,6.000,6.000,6.000,6.000,6.000,6.000,6.000,6.000,6.000,6.... | 2025-10-21 00:00:00 | 2025-10-25 23:00:00 | , | 120 | You are a helpful assistant for time series forecasting. Think step by step to make a prediction. | Forecast the electricty consumption in the next 120 hours, from 2025-10-21 00:00:00 to 2025-10-25 23:00:00.
The final result must be enclosed by '\boxed{' and '}', and where values are separated by ','. | This time series records the elecitricy consumption (in Watts) of a home office. The data was recorded from 2025-10-10 00:00:00 to 2025-10-20 23:00:00 with the hour (H) frequency. There were some deadline periods that required more extensive machinery usage, leadning to higher power consumptions. However, the deadlines... | <history>6.000,6.000,6.000,6.000,6.000,6.000,6.000,342.000,342.000,342.000,342.000,342.000,342.000,342.000,342.000,342.000,6.000,6.000,6.000,6.000,6.000,6.000,6.000,6.000,6.000,6.000,6.000,6.000,6.000,6.000,6.000,6.000,6.000,6.000,6.000,57.000,108.000,159.000,211.000,262.000,313.000,649.000,649.000,649.000,655.000,658.... | |
anomaly_implicit/template4/item4 | GIFT synthesize | anomaly | H | 36.000,36.000,37.000,38.000,39.000,40.000,126.000,220.000,313.000,274.000,182.000,91.000,48.000,49.000,50.000,50.000,52.000,53.000,53.000,54.000,56.000,57.000,57.000,58.000,60.000,61.000,62.000,63.000,64.000,65.000,66.000,67.000,68.000,69.000,70.000,71.000,72.000,73.000,74.000,75.000,76.000,77.000,78.000,79.000,80.000,... | 2025-10-10 00:00:00 | 2025-10-23 19:00:00 | 318.000,319.000,319.000,320.000,320.000,321.000,321.000,322.000,322.000,376.000,469.000,561.000,586.000,493.000,402.000,326.000,326.000,326.000,327.000,328.000,328.000,329.000,329.000,330.000,330.000,331.000,331.000,332.000,332.000,332.000,333.000,333.000,334.000,334.000,335.000,335.000,336.000,336.000,336.000,337.000,... | 2025-10-23 20:00:00 | 2025-10-31 07:00:00 | , | 180 | You are a helpful assistant for time series forecasting. Think step by step to make a prediction. | Forecast the electricty consumption in the next 180 hours, from 2025-10-23 20:00:00 to 2025-10-31 07:00:00.
The final result must be enclosed by '\boxed{' and '}', and where values are separated by ','. | This time series records the elecitricy consumption (in Watts) of a home office. The data was recorded from 2025-10-10 00:00:00 to 2025-10-23 19:00:00 with the hour (H) frequency. There were some deadline periods that required more extensive machinery usage, leadning to higher power consumptions. However, the deadlines... | <history>36.000,36.000,37.000,38.000,39.000,40.000,126.000,220.000,313.000,274.000,182.000,91.000,48.000,49.000,50.000,50.000,52.000,53.000,53.000,54.000,56.000,57.000,57.000,58.000,60.000,61.000,62.000,63.000,64.000,65.000,66.000,67.000,68.000,69.000,70.000,71.000,72.000,73.000,74.000,75.000,76.000,77.000,78.000,79.00... | |
context_explicit/template0/item4 | GIFT synthesize | phase_change | H | 132.709,143.681,153.423,161.263,167.841,173.403,178.252,183.377,188.803,195.039,201.054,206.577,210.432,212.148,210.885,206.295,198.386,188.100,176.189,164.487,153.658,145.727,141.099,140.027,142.538,147.559,153.515,159.152,162.701,163.212,159.448,151.423,140.069,126.048,111.038,97.029,85.449,77.524,74.272,75.735,81.35... | 2025-10-06 00:00:00 | 2025-10-14 13:00:00 | 177.730,164.293,149.906,135.979,124.222,116.026,112.068,112.764,117.560,126.000,136.853,148.610,160.553,171.403,180.778,188.711,195.300,201.144,206.475,212.171,218.006,223.978,229.651,234.467,237.277,237.859,235.067,229.198,220.487,209.781,198.023,186.699,177.095,170.172,167.010,166.927,169.876,175.096,180.669,185.217,... | 2025-10-14 14:00:00 | 2025-10-16 15:00:00 | , | 50 | You are a helpful assistant for time series forecasting. Think step by step to make a prediction. | Forecast the future electricity usage of this household in the next 50 hours, from 2025-10-14 14:00:00 to 2025-10-16 15:00:00.
The final result must be enclosed by '\boxed{' and '}', and where values are separated by ','. | This time series records the electricity usage (in Watt, W) of a household. The readings were recorded from 2025-10-06 00:00:00 to 2025-10-14 13:00:00, and the recording frequency is hour (H). Initially, the owner started rennovation from 2025-10-12 06:00:00 to 2025-10-12 12:00:00 and did not stay at the unit, leading ... | <history>132.709,143.681,153.423,161.263,167.841,173.403,178.252,183.377,188.803,195.039,201.054,206.577,210.432,212.148,210.885,206.295,198.386,188.100,176.189,164.487,153.658,145.727,141.099,140.027,142.538,147.559,153.515,159.152,162.701,163.212,159.448,151.423,140.069,126.048,111.038,97.029,85.449,77.524,74.272,75.... | |
context_explicit/template1/item4 | GIFT synthesize | phase_change | H | 165.057,169.811,169.712,164.879,156.188,146.097,137.006,130.171,125.972,123.336,120.636,117.313,112.221,106.740,102.167,99.314,98.602,98.738,98.039,94.948,88.956,80.992,73.681,70.336,73.104,82.259,95.815,109.662,118.650,119.470,110.546,94.137,74.559,57.892,49.511,51.684,64.656,85.001,108.136,129.805,147.488,160.283,169... | 2025-10-06 00:00:00 | 2025-10-11 15:00:00 | 261.954,270.255,279.504,288.482,295.503,298.472,296.501,289.569,280.219,269.999,261.482,255.292,251.375,248.124,244.570,239.849,237.619,242.410,255.612,277.115,307.423,344.263,386.124,431.748,424.146,415.706,409.717,408.687,414.708,426.570,441.197,453.616,459.486,455.902,442.652,423.769,404.366,390.506,386.597,394.137,... | 2025-10-11 16:00:00 | 2025-10-16 15:00:00 | , | 120 | You are a helpful assistant for time series forecasting. Think step by step to make a prediction. | Forecast the future electricity usage of this household in the next 120 hours, from 2025-10-11 16:00:00 to 2025-10-16 15:00:00.
The final result must be enclosed by '\boxed{' and '}', and where values are separated by ','. | This time series records the electricity usage (in Watt, W) of a household. The readings were recorded from 2025-10-06 00:00:00 to 2025-10-11 15:00:00, and the recording frequency is hour (H). Initially, the owner started rennovation from 2025-10-12 06:00:00 to 2025-10-12 15:00:00 and did not stay at the unit, leading ... | <history>165.057,169.811,169.712,164.879,156.188,146.097,137.006,130.171,125.972,123.336,120.636,117.313,112.221,106.740,102.167,99.314,98.602,98.738,98.039,94.948,88.956,80.992,73.681,70.336,73.104,82.259,95.815,109.662,118.650,119.470,110.546,94.137,74.559,57.892,49.511,51.684,64.656,85.001,108.136,129.805,147.488,16... | |
context_explicit/template2/item4 | GIFT synthesize | phase_change | H | 38.740,38.741,38.733,38.737,38.737,38.735,49.734,49.726,49.736,49.730,49.728,49.730,49.727,49.732,49.734,49.734,38.732,38.735,38.727,38.735,38.732,38.732,38.735,38.729,38.729,38.734,38.727,38.735,38.739,38.733,38.739,38.736,38.731,38.731,38.738,38.732,38.733,38.734,38.740,38.739,38.740,38.739,38.740,38.734,38.733,38.73... | 2025-10-06 00:00:00 | 2025-10-14 11:00:00 | 48.772,48.765,48.766,48.767,48.772,48.769,48.766,48.763,48.769,48.768,48.766,48.764,48.768,48.766,48.764,59.762,59.761,59.762,59.763,59.758,59.764,59.767,59.759,59.762,59.762,48.770,48.766,48.763,48.771,48.760,48.764,48.767,48.768,48.763,48.769,48.771,48.769,48.769,48.768,48.767,48.766,48.774,48.776,48.765,48.770,48.76... | 2025-10-14 12:00:00 | 2025-10-21 11:00:00 | , | 180 | You are a helpful assistant for time series forecasting. Think step by step to make a prediction. | Forecast the future electricity usage of this household in the next 180 hours, from 2025-10-14 12:00:00 to 2025-10-21 23:00:00.
The final result must be enclosed by '\boxed{' and '}', and where values are separated by ','. | This time series records the electricity usage (in Watt, W) of a household. The readings were recorded from 2025-10-06 00:00:00 to 2025-10-14 11:00:00, and the recording frequency is hour (H). Initially, the owner started rennovation from 2025-10-09 10:00:00 to 2025-10-09 17:00:00 and did not stay at the unit, leading ... | <history>38.740,38.741,38.733,38.737,38.737,38.735,49.734,49.726,49.736,49.730,49.728,49.730,49.727,49.732,49.734,49.734,38.732,38.735,38.727,38.735,38.732,38.732,38.735,38.729,38.729,38.734,38.727,38.735,38.739,38.733,38.739,38.736,38.731,38.731,38.738,38.732,38.733,38.734,38.740,38.739,38.740,38.739,38.740,38.734,38.... | |
context_explicit/template3/item4 | GIFT synthesize | phase_change | H | 79.742,79.063,86.222,76.930,84.691,79.866,87.759,81.304,84.841,80.333,83.924,80.176,81.411,75.950,80.680,77.341,77.195,78.938,82.396,76.844,82.374,79.167,87.615,79.174,83.193,78.850,83.360,74.784,85.056,80.058,87.639,76.094,75.190,78.901,80.077,82.486,78.898,74.105,78.360,81.768,76.696,81.486,81.798,80.041,81.032,75.21... | 2025-10-06 00:00:00 | 2025-10-16 23:00:00 | 88.898,82.918,86.021,92.559,87.151,85.991,83.629,91.887,85.893,96.535,88.453,84.282,90.163,90.264,84.888,85.919,86.031,83.832,87.644,87.422,88.927,87.573,92.675,83.083,86.834,83.967,83.720,85.167,89.120,85.145,85.258,87.397,87.010,88.283,89.725,83.795,85.880,83.311,87.421,84.490,82.227,86.920,83.536,82.076,86.535,87.04... | 2025-10-17 00:00:00 | 2025-10-21 23:00:00 | , | 120 | You are a helpful assistant for time series forecasting. Think step by step to make a prediction. | Forecast the future electricity usage of this household in the next 120 hours, from 2025-10-17 00:00:00 to 2025-10-21 23:00:00.
The final result must be enclosed by '\boxed{' and '}', and where values are separated by ','. | This time series records the electricity usage (in Watt, W) of a household. The readings were recorded from 2025-10-06 00:00:00 to 2025-10-16 23:00:00, and the recording frequency is hour (H). Initially, the owner started rennovation from 2025-10-14 07:00:00 to 2025-10-14 08:00:00 and did not stay at the unit, leading ... | <history>79.742,79.063,86.222,76.930,84.691,79.866,87.759,81.304,84.841,80.333,83.924,80.176,81.411,75.950,80.680,77.341,77.195,78.938,82.396,76.844,82.374,79.167,87.615,79.174,83.193,78.850,83.360,74.784,85.056,80.058,87.639,76.094,75.190,78.901,80.077,82.486,78.898,74.105,78.360,81.768,76.696,81.486,81.798,80.041,81.... | |
context_explicit/template4/item4 | GIFT synthesize | phase_change | H | 50.191,50.381,51.211,51.715,52.023,51.889,52.479,51.565,52.422,52.319,52.907,53.439,51.290,51.519,51.671,53.014,52.401,53.820,54.598,54.667,54.008,53.710,53.791,53.038,53.753,54.060,55.180,53.996,54.301,52.806,51.644,51.504,51.685,50.712,50.384,50.456,49.482,50.258,50.259,50.949,52.511,51.593,51.579,51.852,53.142,52.44... | 2025-10-06 00:00:00 | 2025-10-19 19:00:00 | 59.438,59.810,57.749,57.063,56.611,56.396,56.530,56.209,55.515,56.406,57.065,56.921,57.175,57.301,56.807,57.488,57.792,58.862,58.406,58.257,58.282,58.011,58.548,57.858,58.839,59.078,60.128,60.108,59.974,59.724,59.264,59.459,59.072,58.661,60.054,60.157,60.578,60.055,59.524,57.996,58.219,57.501,57.506,55.241,55.421,56.03... | 2025-10-19 20:00:00 | 2025-10-27 07:00:00 | , | 180 | You are a helpful assistant for time series forecasting. Think step by step to make a prediction. | Forecast the future electricity usage of this household in the next 180 hours, from 2025-10-19 20:00:00 to 2025-10-27 07:00:00.
The final result must be enclosed by '\boxed{' and '}', and where values are separated by ','. | This time series records the electricity usage (in Watt, W) of a household. The readings were recorded from 2025-10-06 00:00:00 to 2025-10-19 19:00:00, and the recording frequency is hour (H). Initially, the owner started rennovation from 2025-10-12 18:00:00 to 2025-10-13 00:00:00 and did not stay at the unit, leading ... | <history>50.191,50.381,51.211,51.715,52.023,51.889,52.479,51.565,52.422,52.319,52.907,53.439,51.290,51.519,51.671,53.014,52.401,53.820,54.598,54.667,54.008,53.710,53.791,53.038,53.753,54.060,55.180,53.996,54.301,52.806,51.644,51.504,51.685,50.712,50.384,50.456,49.482,50.258,50.259,50.949,52.511,51.593,51.579,51.852,53.... | |
context_implicit/template0/item4 | GIFT synthesize | phase_change | H | 132.709,143.681,153.423,161.263,167.841,173.403,178.252,183.377,188.803,195.039,201.054,206.577,210.432,212.148,210.885,206.295,198.386,188.100,176.189,164.487,153.658,145.727,141.099,140.027,142.538,147.559,153.515,159.152,162.701,163.212,159.448,151.423,140.069,126.048,111.038,97.029,85.449,77.524,74.272,75.735,81.35... | 2025-10-06 00:00:00 | 2025-10-14 13:00:00 | 177.730,164.293,149.906,135.979,124.222,116.026,112.068,112.764,117.560,126.000,136.853,148.610,160.553,171.403,180.778,188.711,195.300,201.144,206.475,212.171,218.006,223.978,229.651,234.467,237.277,237.859,235.067,229.198,220.487,209.781,198.023,186.699,177.095,170.172,167.010,166.927,169.876,175.096,180.669,185.217,... | 2025-10-14 14:00:00 | 2025-10-16 15:00:00 | , | 50 | You are a helpful assistant for time series forecasting. Think step by step to make a prediction. | Forecast the future electricity usage of this household in the next 50 hours, from 2025-10-14 14:00:00 to 2025-10-16 15:00:00.
The final result must be enclosed by '\boxed{' and '}', and where values are separated by ','. | This time series records the electricity usage (in Watts, W) of a household. The readings were recorded from 2025-10-06 00:00:00 to 2025-10-14 13:00:00, and the recording frequency is hour (H). Initially, the house underwent rennovation and no one stayed at the unit, leading to low power usage. After the rennovation fi... | <history>132.709,143.681,153.423,161.263,167.841,173.403,178.252,183.377,188.803,195.039,201.054,206.577,210.432,212.148,210.885,206.295,198.386,188.100,176.189,164.487,153.658,145.727,141.099,140.027,142.538,147.559,153.515,159.152,162.701,163.212,159.448,151.423,140.069,126.048,111.038,97.029,85.449,77.524,74.272,75.... | |
context_implicit/template1/item4 | GIFT synthesize | phase_change | H | 165.057,169.811,169.712,164.879,156.188,146.097,137.006,130.171,125.972,123.336,120.636,117.313,112.221,106.740,102.167,99.314,98.602,98.738,98.039,94.948,88.956,80.992,73.681,70.336,73.104,82.259,95.815,109.662,118.650,119.470,110.546,94.137,74.559,57.892,49.511,51.684,64.656,85.001,108.136,129.805,147.488,160.283,169... | 2025-10-06 00:00:00 | 2025-10-11 15:00:00 | 261.954,270.255,279.504,288.482,295.503,298.472,296.501,289.569,280.219,269.999,261.482,255.292,251.375,248.124,244.570,239.849,237.619,242.410,255.612,277.115,307.423,344.263,386.124,431.748,424.146,415.706,409.717,408.687,414.708,426.570,441.197,453.616,459.486,455.902,442.652,423.769,404.366,390.506,386.597,394.137,... | 2025-10-11 16:00:00 | 2025-10-16 15:00:00 | , | 120 | You are a helpful assistant for time series forecasting. Think step by step to make a prediction. | Forecast the future electricity usage of this household in the next 120 hours, from 2025-10-11 16:00:00 to 2025-10-16 15:00:00.
The final result must be enclosed by '\boxed{' and '}', and where values are separated by ','. | This time series records the electricity usage (in Watts, W) of a household. The readings were recorded from 2025-10-06 00:00:00 to 2025-10-11 15:00:00, and the recording frequency is hour (H). Initially, the house underwent rennovation and no one stayed at the unit, leading to low power usage. After the rennovation fi... | <history>165.057,169.811,169.712,164.879,156.188,146.097,137.006,130.171,125.972,123.336,120.636,117.313,112.221,106.740,102.167,99.314,98.602,98.738,98.039,94.948,88.956,80.992,73.681,70.336,73.104,82.259,95.815,109.662,118.650,119.470,110.546,94.137,74.559,57.892,49.511,51.684,64.656,85.001,108.136,129.805,147.488,16... | |
context_implicit/template2/item4 | GIFT synthesize | phase_change | H | 38.740,38.741,38.733,38.737,38.737,38.735,49.734,49.726,49.736,49.730,49.728,49.730,49.727,49.732,49.734,49.734,38.732,38.735,38.727,38.735,38.732,38.732,38.735,38.729,38.729,38.734,38.727,38.735,38.739,38.733,38.739,38.736,38.731,38.731,38.738,38.732,38.733,38.734,38.740,38.739,38.740,38.739,38.740,38.734,38.733,38.73... | 2025-10-06 00:00:00 | 2025-10-14 11:00:00 | 48.772,48.765,48.766,48.767,48.772,48.769,48.766,48.763,48.769,48.768,48.766,48.764,48.768,48.766,48.764,59.762,59.761,59.762,59.763,59.758,59.764,59.767,59.759,59.762,59.762,48.770,48.766,48.763,48.771,48.760,48.764,48.767,48.768,48.763,48.769,48.771,48.769,48.769,48.768,48.767,48.766,48.774,48.776,48.765,48.770,48.76... | 2025-10-14 12:00:00 | 2025-10-21 23:00:00 | , | 180 | You are a helpful assistant for time series forecasting. Think step by step to make a prediction. | Forecast the future electricity usage of this household in the next 180 hours, from 2025-10-14 12:00:00 to 2025-10-21 23:00:00.
The final result must be enclosed by '\boxed{' and '}', and where values are separated by ','. | This time series records the electricity usage (in Watts, W) of a household. The readings were recorded from 2025-10-06 00:00:00 to 2025-10-14 11:00:00, and the recording frequency is hour (H). Initially, the house underwent rennovation and no one stayed at the unit, leading to low power usage. After the rennovation fi... | <history>38.740,38.741,38.733,38.737,38.737,38.735,49.734,49.726,49.736,49.730,49.728,49.730,49.727,49.732,49.734,49.734,38.732,38.735,38.727,38.735,38.732,38.732,38.735,38.729,38.729,38.734,38.727,38.735,38.739,38.733,38.739,38.736,38.731,38.731,38.738,38.732,38.733,38.734,38.740,38.739,38.740,38.739,38.740,38.734,38.... | |
context_implicit/template3/item4 | GIFT synthesize | phase_change | H | 79.742,79.063,86.222,76.930,84.691,79.866,87.759,81.304,84.841,80.333,83.924,80.176,81.411,75.950,80.680,77.341,77.195,78.938,82.396,76.844,82.374,79.167,87.615,79.174,83.193,78.850,83.360,74.784,85.056,80.058,87.639,76.094,75.190,78.901,80.077,82.486,78.898,74.105,78.360,81.768,76.696,81.486,81.798,80.041,81.032,75.21... | 2025-10-06 00:00:00 | 2025-10-16 23:00:00 | 88.898,82.918,86.021,92.559,87.151,85.991,83.629,91.887,85.893,96.535,88.453,84.282,90.163,90.264,84.888,85.919,86.031,83.832,87.644,87.422,88.927,87.573,92.675,83.083,86.834,83.967,83.720,85.167,89.120,85.145,85.258,87.397,87.010,88.283,89.725,83.795,85.880,83.311,87.421,84.490,82.227,86.920,83.536,82.076,86.535,87.04... | 2025-10-17 00:00:00 | 2025-10-21 23:00:00 | , | 120 | You are a helpful assistant for time series forecasting. Think step by step to make a prediction. | Forecast the future electricity usage of this household in the next 120 hours, from 2025-10-17 00:00:00 to 2025-10-21 23:00:00.
The final result must be enclosed by '\boxed{' and '}', and where values are separated by ','. | This time series records the electricity usage (in Watts, W) of a household. The readings were recorded from 2025-10-06 00:00:00 to 2025-10-16 23:00:00, and the recording frequency is hour (H). Initially, the house underwent rennovation and no one stayed at the unit, leading to low power usage. After the rennovation fi... | <history>79.742,79.063,86.222,76.930,84.691,79.866,87.759,81.304,84.841,80.333,83.924,80.176,81.411,75.950,80.680,77.341,77.195,78.938,82.396,76.844,82.374,79.167,87.615,79.174,83.193,78.850,83.360,74.784,85.056,80.058,87.639,76.094,75.190,78.901,80.077,82.486,78.898,74.105,78.360,81.768,76.696,81.486,81.798,80.041,81.... | |
context_implicit/template4/item4 | GIFT synthesize | phase_change | H | 50.191,50.381,51.211,51.715,52.023,51.889,52.479,51.565,52.422,52.319,52.907,53.439,51.290,51.519,51.671,53.014,52.401,53.820,54.598,54.667,54.008,53.710,53.791,53.038,53.753,54.060,55.180,53.996,54.301,52.806,51.644,51.504,51.685,50.712,50.384,50.456,49.482,50.258,50.259,50.949,52.511,51.593,51.579,51.852,53.142,52.44... | 2025-10-06 00:00:00 | 2025-10-19 19:00:00 | 59.438,59.810,57.749,57.063,56.611,56.396,56.530,56.209,55.515,56.406,57.065,56.921,57.175,57.301,56.807,57.488,57.792,58.862,58.406,58.257,58.282,58.011,58.548,57.858,58.839,59.078,60.128,60.108,59.974,59.724,59.264,59.459,59.072,58.661,60.054,60.157,60.578,60.055,59.524,57.996,58.219,57.501,57.506,55.241,55.421,56.03... | 2025-10-19 20:00:00 | 2025-10-27 07:00:00 | , | 180 | You are a helpful assistant for time series forecasting. Think step by step to make a prediction. | Forecast the future electricity usage of this household in the next 180 hours, from 2025-10-19 20:00:00 to 2025-10-27 07:00:00.
The final result must be enclosed by '\boxed{' and '}', and where values are separated by ','. | This time series records the electricity usage (in Watts, W) of a household. The readings were recorded from 2025-10-06 00:00:00 to 2025-10-19 19:00:00, and the recording frequency is hour (H). Initially, the house underwent rennovation and no one stayed at the unit, leading to low power usage. After the rennovation fi... | <history>50.191,50.381,51.211,51.715,52.023,51.889,52.479,51.565,52.422,52.319,52.907,53.439,51.290,51.519,51.671,53.014,52.401,53.820,54.598,54.667,54.008,53.710,53.791,53.038,53.753,54.060,55.180,53.996,54.301,52.806,51.644,51.504,51.685,50.712,50.384,50.456,49.482,50.258,50.259,50.949,52.511,51.593,51.579,51.852,53.... |
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