Tsururu库帮助快速组合时间序列预测策略，提升工业应用效率。
Tsururu: A Python-based Time Series Forecasting Strategies Library
- 支持全局与多变量方法灵活组合
- 可实现多步预测策略无缝切换
- 兼容多种预测模型，便于工业部署
当前时间序列研究主要集中在新模型开发，但训练模型时如何选择最优策略仍缺乏深入探讨。本文提出的Tsururu是一个Python库，连接前沿研究与工业应用，支持全局与多变量方法的灵活组合，以及多步预测策略的便捷配置。该库还实现了与多种预测模型的无缝集成，便于在实际场景中快速部署和实验。项目开源地址：https://github.com/sb-ai-lab/tsururu。
原文摘要 · Abstract (English)
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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