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 .
@article{arxiv.2509.15843,
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},
journal= {arXiv preprint arXiv:2509.15843},
year = {2025}
}