[2510.07432] TS-Agent: Understanding and Reasoning Over Raw Time Series via Iterative Insight Gathering

Computer Science > Artificial Intelligence arXiv:2510.07432 (cs) [Submitted on 8 Oct 2025 (v1), last revised 6 Apr 2026 (this version, v2)] Title:TS-Agent: Understanding and Reasoning Over Raw Time Series via Iterative Insight Gathering Authors:Penghang Liu, Elizabeth Fons, Annita Vapsi, Mohsen Ghassemi, Svitlana Vyetrenko, Daniel Borrajo, Vamsi K. Potluru, Manuela Veloso View a PDF of the paper titled TS-Agent: Understanding and Reasoning Over Raw Time Series via Iterative Insight Gathering, by Penghang Liu and 7 other authors View PDF HTML (experimental) Abstract:Large language models (LLMs) exhibit strong symbolic and compositional reasoning, yet they struggle with time series question answering as the data is typically transformed into an LLM-compatible modality, e.g., serialized text, plotted images, or compressed time series embeddings. Such conversions impose representation bottlenecks, often require cross-modal alignment or finetuning, and can exacerbate hallucination and knowledge leakage. To address these limitations, we propose TS-Agent, an agentic, tool-grounded framework that uses LLMs strictly for iterative evidence-based reasoning, while delegating statistical and structural extraction to time series analytical tools operating on raw sequences. Our framework solves time series tasks through an evidence-driven agentic process: (1) it alternates between thinking, tool execution, and observation in a ReAct-style loop, (2) records intermediate results in an expl...