LLM Agents for Time-Series: A Survey

Yilong Chen; Xiao Qin; Chenghao Liu; Liang Wu; Noelle I. Samia; Kaize Ding

LLM Agents for Time-Series: A Survey

Yilong Chen, Xiao Qin, Chenghao Liu, Liang Wu, Noelle I. Samia, Kaize Ding

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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.

Anthology ID:: 2026.findings-emnlp.367
Volume:: Findings of the Association for Computational Linguistics: EMNLP 2026
Month:: October
Year:: 2026
Address:: Budapest, Hungary
Editors:: Hung-Yi Lee, Sunipa Dev, Jessy Li, Desmond Elliott
Venue:: Findings
SIG:: SIGDAT
Publisher:: Association for Computational Linguistics
Note:
Pages:: 7687–7711
Language:
URL:: https://aclanthology.org/2026.findings-emnlp.367/
DOI:
Bibkey:
Cite (ACL):: Yilong Chen, Xiao Qin, Chenghao Liu, Liang Wu, Noelle I. Samia, and Kaize Ding. 2026. LLM Agents for Time-Series: A Survey. In Findings of the Association for Computational Linguistics: EMNLP 2026, pages 7687–7711, Budapest, Hungary. Association for Computational Linguistics.
Cite (Informal):: LLM Agents for Time-Series: A Survey (Chen et al., Findings 2026)
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PDF:: https://aclanthology.org/2026.findings-emnlp.367.pdf

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@inproceedings{chen-etal-2026-llm-agents,
    title = "{LLM} Agents for Time-Series: A Survey",
    author = "Chen, Yilong  and
      Qin, Xiao  and
      Liu, Chenghao  and
      Wu, Liang  and
      Samia, Noelle I.  and
      Ding, Kaize",
    editor = "Lee, Hung-Yi  and
      Dev, Sunipa  and
      Li, Jessy  and
      Elliott, Desmond",
    booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {EMNLP} 2026",
    month = oct,
    year = "2026",
    address = "Budapest, Hungary",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2026.findings-emnlp.367/",
    pages = "7687--7711",
    ISBN = "979-8-89176-462-0",
    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."
}

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%0 Conference Proceedings
%T LLM Agents for Time-Series: A Survey
%A Chen, Yilong
%A Qin, Xiao
%A Liu, Chenghao
%A Wu, Liang
%A Samia, Noelle I.
%A Ding, Kaize
%Y Lee, Hung-Yi
%Y Dev, Sunipa
%Y Li, Jessy
%Y Elliott, Desmond
%S Findings of the Association for Computational Linguistics: EMNLP 2026
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-462-0
%F chen-etal-2026-llm-agents
%X 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.
%U https://aclanthology.org/2026.findings-emnlp.367/
%P 7687-7711

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Markdown (Informal)

[LLM Agents for Time-Series: A Survey](https://aclanthology.org/2026.findings-emnlp.367/) (Chen et al., Findings 2026)

LLM Agents for Time-Series: A Survey (Chen et al., Findings 2026)

ACL

Yilong Chen, Xiao Qin, Chenghao Liu, Liang Wu, Noelle I. Samia, and Kaize Ding. 2026. LLM Agents for Time-Series: A Survey. In Findings of the Association for Computational Linguistics: EMNLP 2026, pages 7687–7711, Budapest, Hungary. Association for Computational Linguistics.