{"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/sepformer-based-models-more-efficient-models","title":"Sepformer-based Models: More Efficient Models for Long Sequence Time-Series Forecasting","arxiv_id":null,"date":"2022-12-23","proceeding":"IEEE Transactions on Emerging Topics in Computing 2022 12","authors":["Jin Fan","Zehao Wang","Danfeng Sun","Huifeng Wu"],"abstract":"Forecasting long sequence time series plays a crucial role in many applications such as anomaly detection and financial predictions. Achieving consistently good results requires a model that can precisely capture the long-range dependencies in input sequences. And very few current models can meet the requirements. Informer has recently demonstrated state-of-the-art accuracy in LSTF. Yet several other aspects of its performance leave much room for improvement. These include: 1) complexity - Informer has a relatively high computational complexity and a high memory overhead; 2) nuance - there is limited ability to capture the subtle features in a data stream; 3) interpretability - the inference procedure of Informer is not explainable; 4) extensibility - accuracy is poor with extra-long multivariate time series. To address these issues, we propose a suite of models under the banner Sepformer. The set comprises Sepformer and two variants SWformer and Mini-SWformer. Sepformer uses separate networks to extract data stream features in parallel. SWformer and Mini-SWformer dramatically separate high-frequency and low-frequency components to process the data stream and reduce the requirement for GPU memory by adopting a discrete wavelet transform. Extensive experiments show that the Sepformer models substantially outperform state-of-the-art methods in terms of accuracy, computational complexity and usage of GPU memory use.","url_abs":"https://ieeexplore.ieee.org/abstract/document/9998510","url_pdf":"https://ieeexplore.ieee.org/abstract/document/9998510","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":"sepformer-based-models-more-efficient-models","repo_url":"https://github.com/wzhSteve/Sepformer","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":null,"task_name":"GPU"},{"task_slug":"time-series-1","task_name":"Time Series"},{"task_slug":"time-series-forecasting","task_name":"Time Series Forecasting"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}