{"url":"/dataset/autoformer","name":"Autoformer","full_name":null,"description_markdown":"This is dataset for A TIME SERIES IS WORTH 64 WORDS: LONG-TERM FORECASTING WITH TRANSFORMERS\r\nWe evaluate the performance of our proposed PatchTST on 8 popular datasets, including\r\nWeather, Traffic, Electricity, ILI and 4 ETT datasets (ETTh1, ETTh2, ETTm1, ETTm2). These\r\ndatasets have been extensively utilized for benchmarking and publicly available on (Wu et al., 2021).\r\nThe statistics of those datasets are summarized in Table 2. We would like to highlight several large\r\ndatasets: Weather, Traffic, and Electricity. They have many more number of time series, thus the\r\nresults would be more stable and less susceptible to overfitting than other smaller datasets.","description_withheld":null,"homepage":"","introduced_date":null,"introduced_date_note":null,"introduced_by":null,"license":null,"modalities":[],"tasks":[],"languages":[],"variants":["Autoformer"],"data_loaders":[{"repo":"https://github.com/yuqinie98/patchtst","url":"https://github.com/yuqinie98/patchtst","frameworks":[]}],"num_papers_in_archive":2,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[],"papers_with_a_benchmark_row":[],"syntology_totals":{"read_at":"2026-09-24T18:15:14+00:00","papers_with_samples":0,"samples_harvested":0,"samples_ran":0,"samples_unverified":0,"pointer_only_for_licence":0,"papers_with_no_sample_that_ran":0,"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."}