{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/sparsetsf-modeling-long-term-time-series","title":"SparseTSF: Modeling Long-term Time Series Forecasting with 1k Parameters","arxiv_id":"2405.00946","date":"2024-05-02","proceeding":null,"authors":["Shengsheng Lin","Weiwei Lin","Wentai Wu","Haojun Chen","Junjie Yang"],"abstract":"This paper introduces SparseTSF, a novel, extremely lightweight model for Long-term Time Series Forecasting (LTSF), designed to address the challenges of modeling complex temporal dependencies over extended horizons with minimal computational resources. At the heart of SparseTSF lies the Cross-Period Sparse Forecasting technique, which simplifies the forecasting task by decoupling the periodicity and trend in time series data. This technique involves downsampling the original sequences to focus on cross-period trend prediction, effectively extracting periodic features while minimizing the model's complexity and parameter count. Based on this technique, the SparseTSF model uses fewer than *1k* parameters to achieve competitive or superior performance compared to state-of-the-art models. Furthermore, SparseTSF showcases remarkable generalization capabilities, making it well-suited for scenarios with limited computational resources, small samples, or low-quality data. The code is publicly available at this repository: https://github.com/lss-1138/SparseTSF.","url_abs":"https://arxiv.org/abs/2405.00946v2","url_pdf":"https://arxiv.org/pdf/2405.00946v2.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"sparsetsf-modeling-long-term-time-series","repo_url":"https://github.com/lss-1138/SparseTSF","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok","spdx":"Apache-2.0"}}],"tasks":[{"task_slug":"time-series-1","task_name":"Time Series"},{"task_slug":"time-series-forecasting","task_name":"Time Series Forecasting"}],"methods":[{"method_slug":"focus","method_name":"Focus"}],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/time-series-forecasting-on-etth1-336-1","task":"Time Series Forecasting","dataset":"ETTh1 (336) Multivariate","model":"SparseTSF","rank_in_archive_order":33,"of":72,"metrics":{"MSE":"0.434"},"uses_additional_data":false},{"leaderboard":"/sota/time-series-forecasting-on-etth1-720-1","task":"Time Series Forecasting","dataset":"ETTh1 (720) Multivariate","model":"SparseTSF","rank_in_archive_order":2,"of":22,"metrics":{"MSE":"0.426"},"uses_additional_data":false}],"syntology":{"syntology_url":"https://syntology.ai/paper/2405.00946","atlas_url":"https://app.syntology.ai/?focus=2405.00946","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}