梳理大模型代理在时间序列任务中的应用与设计思路
LLM Agents for Time-Series: A Survey
- 按时间序列问题类型分类，而非技术组件
- 涵盖预测推理、异常检测、决策支持等四类任务
- 适合研究时间序列智能系统的设计者参考
基于大模型的智能体正被广泛用于时间序列问题，但其设计差异较大。本综述提出以问题为导向的分类体系，按时间序列任务类型组织现有系统，分为预测与推理、增强与合成、异常检测与诊断、决策支持四类。每类分析任务需求如何影响智能体架构、工具使用和记忆设计。总结代表性数据集与环境，对比同类设置下的模型性能。整体为设计面向时间序列的大模型智能体提供任务导向指导，并指明未来研究空白。
原文摘要 · Abstract (English)
LLM-based agents are increasingly being developed for time-series problems, but their design choices vary substantially across task settings. This survey adopts a problem-driven taxonomy that organizes these systems by the time-series problems they address rather than by isolated technical components. We group existing systems into four categories: forecasting and reasoning, augmentation and synthesis, anomaly detection and diagnosis, and decision support. Within each category, we examine how task requirements shape agent architecture, tool use, and memory design. We further summarize representative datasets and environments, and compare reported model performance under shared or closely related settings. Overall, this survey offers a task-oriented guide to designing LLM-based agents for time-series problems and identifies open gaps for future work.
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