Title: Enhancing Time Series Forecasting Through Reasoning: A Reinforcement Learning Approach for LLMs

Abstract:

To boost the precision of time series forecasting (TSF), researchers have developed numerous methodologies that have progressed from traditional statistical models to deep learning architectures driven by data. Although these approaches are effective, the majority continue to operate under a "fast-thinking" framework. This paradigm primarily focuses on identifying historical patterns and mapping them directly to future outcomes, thereby omitting an explicit reasoning process that utilizes intermediate time series analysis.

Conversely, recent "slow-thinking" Large Language Models (LLMs), such as OpenAI-o1, have demonstrated impressive multi-step reasoning abilities, providing a potential solution to these longstanding limitations. Nevertheless, relying solely on prompt engineering is insufficient due to constraints like high computational expenses and difficulties in optimization. To address these challenges, we introduce Time-R1, a novel approach that employs non-uniform sampling to incentivize and refine the model’s search for effective reasoning pathways. Empirical results indicate that Time-R1 delivers substantial improvements in forecasting accuracy across a wide range of datasets.

Source: arXiv Generated at: 2026-06-04 00:00:00 UTC