Tsururu: A Python-based Time Series Forecasting Strategies Library
Fuente:
arXiv
Guardado en:
| Autores principales: | , , , , |
|---|---|
| Formato: | Preprint |
| Publicado: |
2025
|
| Materias: | |
| Acceso en línea: | |
| Etiquetas: |
Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
| _version_ | 1866911163490500608 |
|---|---|
| author | Kostromina, Alina Kuvshinova, Kseniia Yugay, Aleksandr Savchenko, Andrey Simakov, Dmitry |
| author_facet | Kostromina, Alina Kuvshinova, Kseniia Yugay, Aleksandr Savchenko, Andrey Simakov, Dmitry |
| contents | 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 . |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_15843 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Tsururu: A Python-based Time Series Forecasting Strategies Library Kostromina, Alina Kuvshinova, Kseniia Yugay, Aleksandr Savchenko, Andrey Simakov, Dmitry Machine Learning 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 . |
| title | Tsururu: A Python-based Time Series Forecasting Strategies Library |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2509.15843 |