---
title: "Tsururu: A Python-based Time Series Forecasting Strategies Library"
authors:
  - "Alina Kostromina"
  - "Kseniia Kuvshinova"
  - "Aleksandr Yugay"
  - "Andrey Savchenko"
  - "Dmitry Simakov"
doi: "10.24963/ijcai.2025/1266"
url: "https://ijcai.org/proceedings/2025/1266"
pdf: "https://ijcai.org/proceedings/2025/1266.pdf"
venue: "Proceedings of the Thirty-Fourth International Joint Conference on Artificial Intelligence"
year: 2025
pages: "11077-11081"
track: "Demo Track"
keywords:
  - "Data Mining: DM: Mining spatial and/or temporal data"
  - "Machine Learning: ML: Time series and data streams"
---

# Tsururu: A Python-based Time Series Forecasting Strategies Library

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

*Proceedings of the Thirty-Fourth International Joint Conference on Artificial Intelligence*, pages 11077-11081. DOI: [10.24963/ijcai.2025/1266](https://doi.org/10.24963/ijcai.2025/1266)

## 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.