The Electricity Transformer Temperature (ETT) dataset is a resource for researchers and professionals working on time series forecasting, particularly in the context of energy demand and supply management. The ETT dataset specifically focuses on the temperature of electricity transformers, which is a critical parameter for the operational efficiency and longevity of these devices. Monitoring and predicting transformer temperatures can help in optimizing their performance, preventing failures, and ensuring a reliable electricity supply.
| OT | HUFL | HULL | LUFL | LULL | MUFL | MULL |
|---|---|---|---|---|---|---|
| 30.5310001373291 | 5.827000141143799 | 2.009000062942505 | 4.203000068664552 | 1.3400000333786009 | 1.5989999771118164 | 0.4620000123977661 |
| 27.78700065612793 | 5.692999839782715 | 2.075999975204468 | 4.142000198364259 | 1.371000051498413 | 1.4919999837875366 | 0.4259999990463257 |
| 27.78700065612793 | 5.1570000648498535 | 1.741000056266785 | 3.776999950408936 | 1.218000054359436 | 1.2790000438690186 | 0.35499998927116394 |
| 25.04400062561035 | 5.0900001525878915 | 1.9420000314712524 | 3.806999921798706 | 1.2790000438690186 | 1.2790000438690186 | 0.3910000026226044 |
| 21.947999954223643 | 5.357999801635742 | 1.9420000314712524 | 3.868000030517578 | 1.2790000438690186 | 1.4919999837875366 | 0.4620000123977661 |
| 21.173999786376953 | 5.625999927520752 | 2.1429998874664307 | 4.051000118255615 | 1.371000051498413 | 1.5279999971389768 | 0.5329999923706055 |
| 22.79199981689453 | 7.166999816894531 | 2.9470000267028813 | 5.026000022888184 | 1.8580000400543213 | 2.131999969482422 | 0.7820000052452087 |
| 23.143999099731445 | 7.434999942779541 | 3.282000064849853 | 5.086999893188477 | 2.2239999771118164 | 2.309999942779541 | 1.031000018119812 |
| 21.66699981689453 | 5.559000015258789 | 3.013999938964844 | 2.9549999237060547 | 1.4320000410079956 | 2.45199990272522 | 1.1729999780654907 |
| 17.445999145507812 | 4.554999828338623 | 2.5450000762939453 | 2.6800000667572017 | 1.371000051498413 | 1.919000029563904 | 0.8169999718666077 |
| 19.979000091552734 | 4.956999778747559 | 2.5450000762939453 | 2.9549999237060547 | 1.4919999837875366 | 1.9900000095367432 | 0.8529999852180481 |
| 20.11899948120117 | 5.760000228881836 | 2.5450000762939453 | 3.441999912261963 | 1.4919999837875366 | 2.203000068664551 | 0.8529999852180481 |
| 19.20499992370605 | 4.689000129699707 | 2.5450000762939453 | 2.8329999446868896 | 1.5230000019073486 | 1.812000036239624 | 0.8529999852180481 |
| 18.57200050354004 | 4.689000129699707 | 2.678999900817871 | 3.1070001125335693 | 1.6139999628067017 | 1.7769999504089355 | 1.24399995803833 |
| 19.55599975585937 | 5.0900001525878915 | 2.9470000267028813 | 2.559000015258789 | 1.4320000410079956 | 2.45199990272522 | 1.350000023841858 |
| 17.305000305175778 | 5.0900001525878915 | 3.1480000019073486 | 2.58899998664856 | 1.5230000019073486 | 2.486999988555908 | 1.350000023841858 |
| 19.48600006103516 | 4.2199997901916495 | 2.4110000133514404 | 2.61899995803833 | 1.4919999837875366 | 1.7059999704360962 | 0.7820000052452087 |
| 19.13400077819824 | 4.75600004196167 | 2.344000101089477 | 3.075999975204468 | 1.4919999837875366 | 1.6349999904632568 | 0.7110000252723694 |
| 20.68199920654297 | 5.625999927520752 | 2.880000114440918 | 3.075999975204468 | 1.4919999837875366 | 2.5230000019073486 | 1.2079999446868896 |
| 18.71199989318848 | 5.492000102996826 | 3.013999938964844 | 3.015000104904175 | 1.5529999732971191 | 2.45199990272522 | 1.2079999446868896 |
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Forecast generated by AutoTS with an increase RMSE metric weighting and a decreased sMAPE weighting.
Forecast generated by AutoTS with an increase MLE metric weighting.
Forecast generated by AutoTS with an increase MADE metric weighting.