A Survey of Reasoning and Agentic Systems in Time Series with Large Language Models

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Overview

Time series reasoning treats time as a first-class axis and integrates intermediate evidence into the answer itself.
This survey organizes the field along two levels:

• Reasoning Topology — execution structures:

  • Direct reasoning (single step)
  • Linear chain reasoning (sequential intermediate steps)
  • Branch-structured reasoning (exploration, feedback, and aggregation)

• Primary Objective — the main intent:

  • Traditional time series analysis (forecasting, classification, anomaly detection, segmentation)
  • Explanation and understanding (temporal QA, diagnostics, structure discovery)
  • Causal inference and decision making (counterfactuals, policy evaluation, decision support)
  • Time series generation (simulation, editing, synthesis)

We complement this two-level taxonomy with attribute tags that capture control-flow operators (decomposition, verification, ensembling), execution actors (tool use, agents), information sources (knowledge access, multimodality), and alignment regimes (prompting, supervised finetuning, reinforcement/preference, hybrid).

This repository curates and classifies both research papers and non-research contributions in the field.

• Research papers are categorized by reasoning topology and primary objective.
• Non-research papers include datasets & benchmarks (reasoning-first, reasoning-ready, general-purpose TS), as well as surveys, tutorials, and position/vision papers.

The following table presents the unified taxonomy and tags for all research papers we review, while non-research contributions are grouped separately for completeness.

Paper Counts

The distribution of surveyed papers, split into research and non-research categories:

Table of Contents

• Abbreviations & Value Definitions
• Direct Reasoning
• Linear Chain Reasoning
• Branch-Structured Reasoning
• Non-Research Papers

Abbreviations & Value Definitions

Covers: abbreviations and value definitions for primary objectives, task values, and attribute tags used in the curated research-paper taxonomy tables.

Non-research papers are listed without attribute tags.

Attribute Tag Headers (8 total)

Primary Objective Values

Task Values

Attribute Tag Values (Legend)

Research Papers for Direct Reasoning

Research Papers for Linear Chain Reasoning

Research Papers for Branch-Structured Reasoning

Non-Research Papers

How to Contribute

We welcome contributions to keep this table updated. Please follow these steps:

1. Decide Research vs. Non-Research

   • Research = new methods/analyses with experiments (papers with tasks, tags, etc.).
   • Non-Research = datasets/benchmarks, surveys/tutorials, position/vision papers.

2. If Research:

   • Pick exactly one Reasoning Topology and add the paper to that table:
     • Direct Reasoning → Research Papers for Direct Reasoning
     • Linear Chain Reasoning → Research Papers for Linear Chain Reasoning
     • Branch-Structured Reasoning → Research Papers for Branch-Structured Reasoning
   • Fill columns: Primary Objective, Task, and attribute tags
     (T-Dec, T-Ver, T-Ens, T-Tool, T-Know, T-Multi, T-Agent ∈ {0,1,M}, T-Align ∈ {P,S,R,H}).
   • One objective + one topology only. Keep tags consistent with the legends.

3. If Non-Research:

   • Add the paper under Non-Research Papers in the correct subgroup:
     • Reasoning-First Benchmarks
     • Reasoning-Ready Benchmarks
     • General-Purpose Time Series Benchmarks
     • Surveys and Tutorials
     • Position and Vision Papers
   • No attribute tags for non-research entries.

4. Formatting rules

   • Use the exact row format already in the tables:

     | [Paper Title](link) [Venue/Year] | <Type or columns per table> |
     

   • Keep venue/year brackets (e.g., [NeurIPS 2024], [arXiv 2025]).
   • Place new rows in chronological order (newest first) within each subgroup/table.

5. Open a PR

   • Title: Add <Paper Short Title> (<Year>)
   • In the PR body, state:
     • Research vs. non-research
     • Chosen reasoning topology (if research)
     • Primary objective, task, and tags you set
     • Any notes (e.g., multimodal inputs, tools used)

Citation

If you find this resource useful, please cite our survey.

@misc{chang2025surveyreasoningagenticsystems,
      title={A Survey of Reasoning and Agentic Systems in Time Series with Large Language Models}, 
      author={Ching Chang and Yidan Shi and Defu Cao and Wei Yang and Jeehyun Hwang and Haixin Wang and Jiacheng Pang and Wei Wang and Yan Liu and Wen-Chih Peng and Tien-Fu Chen},
      year={2025},
      eprint={2509.11575},
      archivePrefix={arXiv},
      primaryClass={cs.AI},
      url={https://arxiv.org/abs/2509.11575}, 
}