{"url":"/dataset/etth1-96","name":"ETTh1 (96)","full_name":"ETT (Electricity Transformer Temperature)","description_markdown":"The **Electricity Transformer Temperature** (**ETT**) is a crucial indicator in the electric power long-term deployment. This dataset consists of 2 years data from two separated counties in China. To explore the granularity on the Long sequence time-series forecasting (LSTF) problem, different subsets are created, {ETTh1, ETTh2} for 1-hour-level and ETTm1 for 15-minutes-level. Each data point consists of the target value ”oil temperature” and 6 power load features. The train/val/test is 12/4/4 months.\r\n\r\nSource: [https://arxiv.org/pdf/2012.07436.pdf](https://arxiv.org/pdf/2012.07436.pdf)\r\nImage Source: [https://github.com/zhouhaoyi/ETDataset](https://github.com/zhouhaoyi/ETDataset)","description_withheld":null,"homepage":"https://github.com/zhouhaoyi/ETDataset","introduced_date":"2020-12-14","introduced_date_note":null,"introduced_by":{"paper":"/paper/informer-beyond-efficient-transformer-for","title":"Informer: Beyond Efficient Transformer for Long Sequence Time-Series Forecasting","first_author":"Haoyi Zhou","url":null},"license":null,"modalities":[],"tasks":[{"name":"Time Series Forecasting","url":"/task/time-series-forecasting","datasets_with_task":"/datasets/task/time-series-forecasting"},{"name":"GLinear","url":"/task/glinear","datasets_with_task":"/datasets/task/glinear"}],"languages":[],"variants":["ETTh1 (96)"],"data_loaders":[],"num_papers_in_archive":22,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[{"leaderboard":"/sota/time-series-forecasting-on-etth1-96-4","task":"Time Series Forecasting","dataset_variant":"ETTh1 (96)","rows":2,"metrics":["MAE","MSE"],"first_row_in_archive_order":{"model":"Ada-MSHyper","paper":"/paper/ada-mshyper-adaptive-multi-scale-hypergraph","metrics":{"MAE":"0.393"},"code_links":[{"title":"shangzongjiang/Ada-MSHyper","url":"https://github.com/shangzongjiang/Ada-MSHyper"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"},{"leaderboard":"/sota/glinear-on-etth1-96","task":"GLinear","dataset_variant":"ETTh1 (96)","rows":1,"metrics":["MSE"],"first_row_in_archive_order":{"model":"GLinear","paper":"/paper/bridging-simplicity-and-sophistication-using-1","metrics":{"MSE":"0.3820"},"code_links":[{"title":"t-rizvi/GLinear","url":"https://github.com/t-rizvi/GLinear"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"}],"papers_with_a_benchmark_row":[{"paper":"/paper/bridging-simplicity-and-sophistication-using-1","title":"Bridging Simplicity and Sophistication using GLinear: A Novel Architecture for Enhanced Time Series Prediction","date":"2025-01-02","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/ada-mshyper-adaptive-multi-scale-hypergraph","title":"Ada-MSHyper: Adaptive Multi-Scale Hypergraph Transformer for Time Series Forecasting","date":"2024-10-31","rows_on_this_dataset":1,"code_links":1,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":4,"samples_ran":0,"samples_unverified":4,"pointer_only_for_licence":4,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/tsmixer-lightweight-mlp-mixer-model-for","title":"TSMixer: Lightweight MLP-Mixer Model for Multivariate Time Series Forecasting","date":"2023-06-14","rows_on_this_dataset":1,"code_links":1,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":8,"samples_ran":0,"samples_unverified":8,"pointer_only_for_licence":0,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}}],"syntology_totals":{"read_at":"2026-09-24T18:15:14+00:00","papers_with_samples":2,"samples_harvested":12,"samples_ran":0,"samples_unverified":12,"pointer_only_for_licence":4,"papers_with_no_sample_that_ran":2,"note":"the per-paper counts above, summed; not a rate"},"papers_note":"The archive never published its papers-using-dataset list; these are papers with a leaderboard row on this dataset's benchmarks."}