Time Series Forecasting as Reasoning: A Slow-Thinking Approach with Reinforced LLMs

ArXi:2506.10630v2 Announce Type: replace To advance time series forecasting (TSF), various methods have been proposed to improve prediction accuracy, evolving from statistical techniques to data-driven deep learning architectures. Despite their effectiveness, most existing methods still adhere to a fast thinking paradigm-relying on extracting historical patterns and mapping them to future values as their core modeling philosophy, lacking an explicit thinking process that incorporates intermediate time series reasoning.