Eliciting Chain-of-Thought Reasoning for Time Series Analysis using Reinforcement Learning

ArXi:2510.01116v2 Announce Type: replace Complex numerical time series analysis often demands multi-step reasoning capabilities beyond current models' reach. Tasks like medical diagnosis and weather forecasting require sequential reasoning processes - including counterfactual analysis, logical deduction, knowledge application, and multi-modal contextual integration - that existing time series models cannot explicitly perform.