Tsururu: A Python-based Time Series Forecasting Strategies Library

Alina Kostromina, Kseniia Kuvshinova, Aleksandr Yugay, Andrey Savchenko, Dmitry Simakov
9/19/2025
cs.LG

Official code: sb-ai-lab/tsururu (231 stars, Apache-2.0, last commit 2026-04-27).

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 .

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Code Implementations(1)

sb-ai-lab/tsururuOfficial100%
Apache-2.0

Tsururu is a Python-based library that provides a wide range of multi-series and multi-point-ahead prediction strategies, compatible with any underlying model, including neural networks.

23119PythonOct 31, 20235 months agoApache-2.0

Cite this paper

@article{kostromina2025tsururu,
  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},
  year   = {2025},
  eprint = {2509.15843},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2509.15843v1}
}

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