{"url":"/sota/time-series-forecasting-on-etth2-168-1","task":{"name":"Time Series Forecasting","url":"/task/time-series-forecasting","note":null},"dataset":{"name":"ETTh2 (168) Multivariate","url":null},"category":"Time Series","categories":["Time Series"],"category_note":null,"description":"**Time Series Forecasting** is the task of fitting a model to historical, time-stamped data in order to predict future values. Traditional approaches include moving average, exponential smoothing, and ARIMA, though models as various as RNNs, Transformers, or XGBoost can also be applied. The most popular benchmark is the ETTh1 dataset. Models are typically evaluated using the Mean Square Error (MSE) or Root Mean Square Error (RMSE). \r\n\r\n<span style=\"color:grey; opacity: 0.6\">( Image credit: [ThaiBinh Nguyen](https://github.com/tn16jv/Stock-Price-Prediction) )</span>","description_from":"task","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","rank":"the archive's row order at snapshot; not re-ranked","rows_end_at":"2025-07-28","rows_withheld_as_spam":0,"metric_values":"the archive's strings, untouched"},"metrics":["MSE","MAE"],"metric_direction":{"note":"inferred from the metric name only (the archive records no direction); null = not inferred, chart draws points only","by_metric":{"MSE":"lower","MAE":"lower"}},"counts":{"rows":4,"rows_with_code":4,"rows_with_paper_page":4,"rows_dated":4,"rows_using_additional_data":0},"rows":[{"rank_in_archive_order":1,"model":"SCINet","metrics":{"MAE":"0.38","MSE":"0.342"},"uses_additional_data":false,"paper_date":"2021-06-17","paper":"/paper/time-series-is-a-special-sequence-forecasting","paper_url":"https://arxiv.org/abs/2106.09305v3","paper_title":"SCINet: Time Series Modeling and Forecasting with Sample Convolution and Interaction","code":"https://github.com/WenjieDu/PyPOTS","n_code_links":6,"syntology":{"n_ran":2,"n_unverified":4,"n_samples":6,"n_pointer_only_licence":0}},{"rank_in_archive_order":2,"model":"Informer","metrics":{"MAE":"0.996","MSE":"1.512"},"uses_additional_data":false,"paper_date":"2020-12-14","paper":"/paper/informer-beyond-efficient-transformer-for","paper_url":"https://arxiv.org/abs/2012.07436v3","paper_title":"Informer: Beyond Efficient Transformer for Long Sequence Time-Series Forecasting","code":"https://github.com/zhouhaoyi/Informer2020","n_code_links":14,"syntology":{"n_ran":62,"n_unverified":14,"n_samples":76,"n_pointer_only_licence":13}},{"rank_in_archive_order":3,"model":"Transformer","metrics":{"MAE":"0.9726","MSE":"1.6225"},"uses_additional_data":false,"paper_date":"2021-07-19","paper":"/paper/long-term-series-forecasting-with-query","paper_url":"https://arxiv.org/abs/2107.08687v2","paper_title":"Long-term series forecasting with Query Selector -- efficient model of sparse attention","code":"https://github.com/moraieu/query-selector","n_code_links":2,"syntology":null},{"rank_in_archive_order":4,"model":"QuerySelector","metrics":{"MAE":"1.0125","MSE":"1.7385"},"uses_additional_data":false,"paper_date":"2021-07-19","paper":"/paper/long-term-series-forecasting-with-query","paper_url":"https://arxiv.org/abs/2107.08687v2","paper_title":"Long-term series forecasting with Query Selector -- efficient model of sparse attention","code":"https://github.com/moraieu/query-selector","n_code_links":2,"syntology":null}],"since_archive":{"claim":"Results that newer papers report for their own method, placed here by Syntology. A model pointed at the cell in the paper's own table; the number was read from that cell and checked against this leaderboard's metric, dataset, split and scale; an independent check that saw this leaderboard's other rows and every other leaderboard on the same dataset accepted it. Not reviewed by the paper's authors or by the archive's editors, and not ranked against the archive rows.","extraction_file_present":true,"measurement":{"test_papers":883,"papers_with_output":881,"judged_true":108,"judged":110,"wilson95_lower":0.9361,"measured_on":"2026-09-24","frozen_commit":"0e3de0df94"},"measurement_note":"blind adjudication of accepted entries on a held-out split of archive papers, rules frozen before the test","coverage":{"sentence":"Syntology has checked 6,821 of the 9,623 papers on this site that are newer than the archive; results from the others appear after they are checked.","complete":false,"papers_newer_than_archive":9623,"papers_checked":6821,"papers_extracted_not_yet_verified":65,"boards_without_verdict":29,"papers_not_yet_extracted":2737},"order":"newest first by month (arXiv date, else the arXiv-id month), then arXiv id descending","columns":[],"entries":[]},"syntology":{"read_at":"2026-09-25T09:33:49+00:00","claim":"Per row: N of M harvested code samples from that row's paper executed on a synthesized fixture; the other M-N are unverified. Not a reproduction of the row's number; not a correctness claim. n_pointer_only_licence counts samples the site points at rather than redistributes (a licence axis, independent of ran/unverified).","rows_with_graph_line":2,"rows_with_any_sample_ran":2,"distinct_papers_with_graph_line":2,"distinct_papers_with_any_sample_ran":2,"samples_over_distinct_papers":{"n_ran":64,"n_unverified":18,"n_samples":82,"n_pointer_only_licence":13,"note":"each paper (arXiv id) counted once, however many rows it is behind; this is the page-level figure"},"samples_row_weighted":{"n_ran":64,"n_unverified":18,"n_samples":82,"n_pointer_only_licence":13,"note":"row-weighted: a paper behind several rows is counted once per row; inflated relative to samples_over_distinct_papers by design, kept for readers summing the per-row syntology blocks"}}}