Tsururu: A Python-based Time Series Forecasting Strategies Library
This provides a tool for researchers and practitioners in time series forecasting to bridge research and industry applications, though it is incremental as it focuses on strategy selection rather than new models.
The paper tackles the problem of selecting optimal training approaches for time series forecasting models by introducing Tsururu, a Python library that enables flexible combinations of global and multivariate strategies and multi-step-ahead forecasting, with seamless integration into existing models.
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 .