{"ID":2834981,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2512.01038","arxiv_id":"2512.01038","title":"FMTK: A Modular Toolkit for Composable Time Series Foundation Model Pipelines","abstract":"Foundation models (FMs) have opened new avenues for machine learning applications due to their ability to adapt to new and unseen tasks with minimal or no further training. Time-series foundation models (TSFMs) -- FMs trained on time-series data -- have shown strong performance on classification, regression, and imputation tasks. Recent pipelines combine TSFMs with task-specific encoders, decoders, and adapters to improve performance; however, assembling such pipelines typically requires ad hoc, model-specific implementations that hinder modularity and reproducibility. We introduce FMTK, an open-source, lightweight and extensible toolkit for constructing and fine-tuning TSFM pipelines via standardized backbone and component abstractions. FMTK enables flexible composition across models and tasks, achieving correctness and performance with an average of seven lines of code. https://github.com/umassos/FMTK","short_abstract":"Foundation models (FMs) have opened new avenues for machine learning applications due to their ability to adapt to new and unseen tasks with minimal or no further training. Time-series foundation models (TSFMs) -- FMs trained on time-series data -- have shown strong performance on classification, regression, and imputa...","url_abs":"https://arxiv.org/abs/2512.01038","url_pdf":"https://arxiv.org/pdf/2512.01038v1","authors":"[\"Hetvi Shastri\",\"Pragya Sharma\",\"Walid A. Hanafy\",\"Mani Srivastava\",\"Prashant Shenoy\"]","published":"2025-11-30T19:14:04Z","proceeding":"cs.LG","tasks":"[\"cs.LG\",\"cs.AI\"]","methods":"[]","has_code":false,"code_links":[{"ID":606467,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_id":2834981,"paper_url":"https://arxiv.org/abs/2512.01038","paper_title":"FMTK: A Modular Toolkit for Composable Time Series Foundation Model Pipelines","repo_url":"https://github.com/umassos/FMTK","is_official":false,"mentioned_in_paper":false,"mentioned_in_github":true,"github_stars":0}]}