TS-Reasoner用领域知识增强大模型，实现多步时间序列推理与自动分析。
TS-Reasoner: Domain-Oriented Time Series Inference Agents for Reasoning and Automated Analysis
- 融合大模型推理与领域计算工具，构建可纠错的分析闭环。
- 在多步推理任务中超越通用大模型，提升分析精度与逻辑性。
- 适合需要复杂时间序列分析的金融、医疗等专业场景使用。
时间序列分析在现实应用中至关重要，但传统方法仅关注孤立任务，近期研究多局限于单步推理或自然语言回答。本文提出TS-Reasoner，一种面向领域的多步时间序列推理智能体。通过整合大语言模型推理能力、领域专用计算工具及错误反馈机制，实现具备领域知识、约束感知的符号推理与精确数值分析相结合的分析流程。我们在两个维度评估系统性能：(1) 基础时间序列理解，使用TimeSeriesExam基准；(2) 复杂多步推理，采用新构建的数据集，用于测试组合推理与计算精度。实验表明，该方法在基础概念理解与多步推理任务上均优于独立使用的通用大模型，凸显领域专用智能体在自动化时间序列推理与分析中的潜力。
原文摘要 · Abstract (English)
Time series analysis is crucial in real-world applications, yet traditional methods focus on isolated tasks only, and recent studies on time series reasoning remain limited to either single-step inference or are constrained to natural language answers. In this work, we introduce TS-Reasoner, a domain-specialized agent designed for multi-step time series inference. By integrating large language model (LLM) reasoning with domain-specific computational tools and an error feedback loop, TS-Reasoner enables domain-informed, constraint-aware analytical workflows that combine symbolic reasoning with precise numerical analysis. We assess the system's capabilities along two axes: (1) fundamental time series understanding assessed by TimeSeriesExam and (2) complex, multi-step inference evaluated by a newly proposed dataset designed to test both compositional reasoning and computational precision in time series analysis. Experiments show that our approach outperforms standalone general-purpose LLMs in both basic time series concept understanding as well as the multi-step time series inference task, highlighting the promise of domain-specialized agents for automating real-world time series reasoning and analysis.
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