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.
Cite
Text
Kostromina et al. "Tsururu: A Python-Based Time Series Forecasting Strategies Library." International Joint Conference on Artificial Intelligence, 2025. doi:10.24963/IJCAI.2025/1266Markdown
[Kostromina et al. "Tsururu: A Python-Based Time Series Forecasting Strategies Library." International Joint Conference on Artificial Intelligence, 2025.](https://mlanthology.org/ijcai/2025/kostromina2025ijcai-tsururu/) doi:10.24963/IJCAI.2025/1266BibTeX
@inproceedings{kostromina2025ijcai-tsururu,
title = {{Tsururu: A Python-Based Time Series Forecasting Strategies Library}},
author = {Kostromina, Alina and Kuvshinova, Kseniia and Yugay, Aleksandr and Savchenko, Andrey V. and Simakov, Dmitry},
booktitle = {International Joint Conference on Artificial Intelligence},
year = {2025},
pages = {11077-11081},
doi = {10.24963/IJCAI.2025/1266},
url = {https://mlanthology.org/ijcai/2025/kostromina2025ijcai-tsururu/}
}