Tsururu: A Python-based Time Series Forecasting Strategies Library

Alina Kostromina, Kseniia Kuvshinova, Aleksandr Yugay, Andrey V. 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 .

External IDs:dblp:journals/corr/abs-2509-15843