LLM Agents for Time-Series: A Survey

A new survey from arXiv (2608.26226v1) organizes LLM-based agents for time-series problems into four categories: forecasting and reasoning, augmentation and synthesis, anomaly detection and diagnosis, and decision support, examining how task requirements shape agent architecture, tool use, and memory design. The survey summarizes representative datasets and environments and compares reported model performance under shared settings, offering a task-oriented guide and identifying open gaps for future work.

arXiv:2608.26226v1 Announce Type: new Abstract: 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.