Researchers propose a new approach to time series forecasting called Time-R1. This method uses reinforcement learning to fine-tune large language models (LLMs) so they can reason about time series data in a more deliberate and step-by-step way, rather than just relying on pattern recognition. The goal is to improve the accuracy of forecasts by allowing the model to think more slowly and deliberately about the data. Experiments show that Time-R1 outperforms other methods on a range of datasets. --- Why it matters: This matters because current time series forecasting methods often rely on quick, shallow processing of historical patterns, which can lead to inaccurate predictions. By using LLMs with slow thinking capabilities, researchers hope to improve the accuracy and reliability of forecasts in fields like finance, energy, and climate modeling. Source: https://arxiv.org/abs/2506.10630