Time Series Reasoning

Time series reasoning asks models to find trends, events, and relationships in sequential data, then use them to answer questions or make forecasts in settings such as health, finance, and incident response. Approaches include representing sequences as plots, numerical embeddings, or symbolic temporal programs; combining language models with specialized time-series models; and using tools for retrieval, computation, and adaptive analysis. Training methods increasingly use synthetic tasks, supervised reasoning traces, and reinforcement learning with verifiable rewards, while benchmarks test skills such as event localization, cross-interval comparison, and forecast explanation. Results show that tools and structured representations can help, but long-context use, complex temporal reasoning, and grounding explanations in the actual data remain difficult, and general language-model ability alone is not enough for reliable performance.