{"ID":2868762,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2509.15843","arxiv_id":"2509.15843","title":"Tsururu: A Python-based Time Series Forecasting Strategies Library","abstract":"While current time series research focuses on developing new models, crucial questions of selecting an optimal approach for training such models are underexplored. Tsururu, a Python library introduced in this paper, bridges SoTA research and industry by enabling flexible combinations of global and multivariate approaches and multi-step-ahead forecasting strategies. It also enables seamless integration with various forecasting models. Available at https://github.com/sb-ai-lab/tsururu .","short_abstract":"While current time series research focuses on developing new models, crucial questions of selecting an optimal approach for training such models are underexplored. Tsururu, a Python library introduced in this paper, bridges SoTA research and industry by enabling flexible combinations of global and multivariate approach...","url_abs":"https://arxiv.org/abs/2509.15843","url_pdf":"https://arxiv.org/pdf/2509.15843v1","authors":"[\"Alina Kostromina\",\"Kseniia Kuvshinova\",\"Aleksandr Yugay\",\"Andrey Savchenko\",\"Dmitry Simakov\"]","published":"2025-09-19T10:26:00Z","proceeding":"cs.LG","tasks":"[\"cs.LG\"]","methods":"[]","has_code":false,"code_links":[{"ID":609618,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_id":2868762,"paper_url":"https://arxiv.org/abs/2509.15843","paper_title":"Tsururu: A Python-based Time Series Forecasting Strategies Library","repo_url":"https://github.com/sb-ai-lab/tsururu","is_official":false,"mentioned_in_paper":false,"mentioned_in_github":true,"github_stars":0}]}