Tsururu: A Python-based Time Series Forecasting Strategies Library

Alina Kostromina; Kseniia Kuvshinova; Aleksandr Yugay; Andrey Savchenko; Dmitry Simakov

IJCAI 20250 citations

Tsururu: A Python-based Time Series Forecasting Strategies Library

Alina Kostromina, Kseniia Kuvshinova, Aleksandr Yugay, Andrey Savchenko, Dmitry Simakov

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.

BibTeX

@inproceedings{ijcai2025_tsururuapythonba,
  title = {Tsururu: A Python-based Time Series Forecasting Strategies Library},
  author = {Alina Kostromina and Kseniia Kuvshinova and Aleksandr Yugay and Andrey Savchenko and Dmitry Simakov},
  booktitle = {IJCAI 2025},
  year = {2025}
}