{"ID":2860269,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2510.04134","arxiv_id":"2510.04134","title":"PhaseFormer: From Patches to Phases for Efficient and Effective Time Series Forecasting","abstract":"Periodicity is a fundamental characteristic of time series data and has long played a central role in forecasting. Recent deep learning methods strengthen the exploitation of periodicity by treating patches as basic tokens, thereby improving predictive effectiveness. However, their efficiency remains a bottleneck due to large parameter counts and heavy computational costs. This paper provides, for the first time, a clear explanation of why patch-level processing is inherently inefficient, supported by strong evidence from real-world data. To address these limitations, we introduce a phase perspective for modeling periodicity and present an efficient yet effective solution, PhaseFormer. PhaseFormer features phase-wise prediction through compact phase embeddings and efficient cross-phase interaction enabled by a lightweight routing mechanism. Extensive experiments demonstrate that PhaseFormer achieves state-of-the-art performance with around 1k parameters, consistently across benchmark datasets. Notably, it excels on large-scale and complex datasets, where models with comparable efficiency often struggle. This work marks a significant step toward truly efficient and effective time series forecasting. Code is available at this repository: https://github.com/neumyor/PhaseFormer_TSL","short_abstract":"Periodicity is a fundamental characteristic of time series data and has long played a central role in forecasting. Recent deep learning methods strengthen the exploitation of periodicity by treating patches as basic tokens, thereby improving predictive effectiveness. However, their efficiency remains a bottleneck due t...","url_abs":"https://arxiv.org/abs/2510.04134","url_pdf":"https://arxiv.org/pdf/2510.04134v1","authors":"[\"Yiming Niu\",\"Jinliang Deng\",\"Yongxin Tong\"]","published":"2025-10-05T10:34:19Z","proceeding":"cs.LG","tasks":"[\"cs.LG\",\"cs.AI\"]","methods":"[]","has_code":false,"code_links":[{"ID":608710,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_id":2860269,"paper_url":"https://arxiv.org/abs/2510.04134","paper_title":"PhaseFormer: From Patches to Phases for Efficient and Effective Time Series Forecasting","repo_url":"https://github.com/neumyor/PhaseFormer_TSL","is_official":false,"mentioned_in_paper":false,"mentioned_in_github":true,"github_stars":0}]}