Time-Series-Reasoning-Survey

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Time-Series-Reasoning-Survey

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[TMLR 2026] A Survey of Reasoning and Agentic Systems in Time Series with Large Language Models

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

[![TMLR](https://img.shields.io/badge/TMLR-Accepted-success.svg)](https://openreview.net/forum?id=l3QW42g6u3)
[![arXiv](https://img.shields.io/badge/arXiv-2509.11575-b31b1b.svg)](https://arxiv.org/abs/2509.11575)
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![Topic](https://img.shields.io/badge/Time%20Series-Reasoning--LLMs-blueviolet)
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✨ If you find our survey useful, a star ⭐ on GitHub helps others discover it and keeps you updated on future releases.

## 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](#abbreviations--value-definitions)
- [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)
- [Non-Research Papers](#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)
| Full Name                  | Abbreviation |
|---------------------------|--------------|
| Task Decomposition        | T-Dec        |
| Verification and Critique | T-Ver        |
| Ensemble Selection        | T-Ens        |
| Tool Use                  | T-Tool       |
| Knowledge Access          | T-Know       |
| Multimodal Inputs         | T-Multi      |
| Agents                    | T-Agent      |
| LLM Alignment             | T-Align      |

### Primary Objective Values
| Full Name                               | Abbreviation |
|-----------------------------------------|--------------|
| Traditional Time Series Analysis        | Trad. TS Anal. |
| Explanation and Understanding           | Expl. & Und. |
| Causal Inference and Decision Making    | Causal Inf.  |
| Time Series Generation                  | TS Gen.      |

### Task Values
| Full Name                     | Abbreviation |
|------------------------------|--------------|
| Forecasting                  | Forc.        |
| Classification               | Class.       |
| Anomaly Detection            | Anom. Det.   |
| Segmentation                 | Segm.        |
| Multiple Tasks               | Mult. Tasks  |
| Temporal Question Answering  | Temp. QA     |
| Explanatory Diagnostics      | Expl. Diagn. |
| Structure Discovery          | Struct. Disc.|
| Autonomous Policy Learning   | Auto. Policy |
| Advisory Decision Support    | Adv. Dec. Supp. |
| Conditioned Synthesis        | Cond. Synth. |

### Attribute Tag Values (Legend)
| Tag(s)                             | Values / Meaning                                                |
|-----------------------------------|------------------------------------------------------------------|
| T-Dec, T-Ver, T-Ens, T-Tool, T-Know, T-Multi | `✔ = present`, empty = absent                           |
| T-Agent                            | `0 = no agent`, `1 = single agent`, `M = multiple agents`       |
| T-Align                            | `P = Prompting`, `S = Supervised fine-tuning`, `R = Reinforcement/preference alignment`, `H = Hybrid` |

## Research Papers for Direct Reasoning

## Research Papers for Linear Chain Reasoning

| &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;Paper&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; | Primary Objective | Task | T-Dec | T-Ver | T-Ens | T-Tool | T-Know | T-Multi | T-Agent | T-Align |
| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
| [Can Slow-thinking LLMs Reason Over Time? Empirical Studies in   Time Series Forecasting](https://arxiv.org/abs/2505.24511) [arXiv 2025] | Trad. TS Anal. | Forc. | ✔ | ✔ |  |  |  | ✔ | 0 | P |
| [Retrieval Augmented Time Series Forecasting](https://arxiv.org/abs/2411.08249) [arXiv 2024] | Trad. TS Anal. | Forc. |  |  |  | ✔ | ✔ |  | 0 | S |
| [TimeRAG: Boosting LLM Time Series Forecasting via   Retrieval-Augmented Generation](https://arxiv.org/abs/2412.16643) [arXiv 2024] | Trad. TS Anal. | Forc. |  |  |  | ✔ | ✔ |  | 0 | P |
| [Time Series Forecasting as Reasoning: A Slow-Thinking Approach   with Reinforced LLMs](https://arxiv.org/abs/2506.10630) [arXiv 2025] | Trad. TS Anal. | Forc. | ✔ |  |  |  |  |  | 0 | H |
| [Temporal Data Meets LLM - Explainable Financial Time Series   Forecasting](https://arxiv.org/abs/2306.11025) [arXiv 2023] | Trad. TS Anal. | Forc. | ✔ |  |  | ✔ | ✔ | ✔ | 0 | S |
| [CAARL: In-Context Learning for Interpretable Co-Evolving Time Series Forecasting](https://arxiv.org/abs/2604.18305) [arXiv 2026] | Trad. TS Anal. | Forc. | ✔ |  |  |  |  |  | 0 | P |
| [TableTime: Reformulating Time Series Classification as   Training-Free Table Understanding with Large Language Models](https://arxiv.org/abs/2411.15737) [arXiv 2024] | Trad. TS Anal. | Class. | ✔ |  | ✔ | ✔ |  |  | 0 | P |
| [A Picture is Worth A Thousand Numbers: Enabling LLMs Reason   about Time Series via Visualization](https://aclanthology.org/2025.naacl-long.383/) [NAACL 2025] | Trad. TS Anal. | Class. | ✔ |  |  |  |  | ✔ | 0 | P |
| [ZARA: Zero-shot Motion Time-Series Analysis via Knowledge and   Retrieval Driven LLM Agents](https://arxiv.org/abs/2508.04038) [arXiv 2025] | Trad. TS Anal. | Class. | ✔ |  |  | ✔ | ✔ |  | M | P |
| [TimeMaster: Training Time-Series Multimodal LLMs to Reason via   Reinforcement Learning](https://arxiv.org/abs/2506.13705) [arXiv 2025] | Trad. TS Anal. | Class. | ✔ | ✔ |  | ✔ |  | ✔ | 0 | H |
| [Towards Time-Series Reasoning with LLMs](https://openreview.net/forum?id=W0UTR3LLwj) [NeurIPS Workshop on Time   Series in the Age of Large Models 2024] | Trad. TS Anal. | Class. | ✔ |  |  |  |  | ✔ | 0 | S |
| [REALM: RAG-Driven Enhancement of Multimodal Electronic Health   Records Analysis via Large Language Models](https://arxiv.org/abs/2402.07016) [arXiv 2024] | Trad. TS Anal. | Class. | ✔ | ✔ |  | ✔ | ✔ | ✔ | 0 | P |
| [Harnessing Vision-Language Models for Time Series Anomaly   Detection](https://arxiv.org/abs/2506.06836) [arXiv 2025] | Trad. TS Anal. | Anom. Det. | ✔ | ✔ |  |  |  | ✔ | 0 | P |
| [Large Language Models can Deliver Accurate and Interpretable   Time Series Anomaly Detection](https://doi.org/10.1145/3711896.3737239) [KDD 2025] | Trad. TS Anal. | Anom. Det. | ✔ | ✔ |  | ✔ | ✔ |  | 0 | P |
| [Can LLMs Serve As Time Series Anomaly Detectors?](https://arxiv.org/abs/2408.03475) [arXiv 2024] | Trad. TS Anal. | Anom. Det. | ✔ |  |  |  |  |  | 0 | S |
| [Large language models can be zero-shot anomaly detectors for   time series?](https://arxiv.org/abs/2405.14755) [DSAA 2024] | Trad. TS Anal. | Anom. Det. |  |  | ✔ |  |  |  | 0 | P |
| [Large-model-based smart agent for time series anomaly   detection in power systems](https://www.sciencedirect.com/science/article/pii/S0957417425025345) [Expert Systems with   Applications 2025] | Trad. TS Anal. | Anom. Det. |  | ✔ |  |  |  |  | 1 | P |
| [LEMAD: LLM-Empowered Multi-Agent System for Anomaly Detection   in Power Grid Services](https://www.mdpi.com/2079-9292/14/15/3008) [Electronics 2025] | Trad. TS Anal. | Anom. Det. | ✔ |  |  |  |  | ✔ | M | P |
| [A Time Series Multitask Framework Integrating a Large Language   Model, Pre-Trained Time Series Model, and Knowledge Graph](https://arxiv.org/abs/2503.07682) [arXiv 2025] | Trad. TS Anal. | Mult. Tasks |  |  |  | ✔ | ✔ | ✔ | 0 | P |
| [Agentic Retrieval-Augmented Generation for Time Series   Analysis](https://arxiv.org/abs/2408.14484) [arXiv 2024] | Trad. TS Anal. | Mult. Tasks |  |  |  | ✔ | ✔ |  | M | H |
| [Inferring Events from Time Series using Language Models](https://arxiv.org/abs/2503.14190) [arXiv 2025] | Expl. & Und. | Temp. QA | ✔ |  |  |  |  | ✔ | 0 | H |
| [Large Language Models Can Learn Temporal Reasoning](https://aclanthology.org/2024.acl-long.563/) [ACL 2024] | Expl. & Und. | Temp. QA | ✔ | ✔ |  |  |  |  | 0 | S |
| [TempoGPT: Enhancing Time Series Reasoning via Quantizing   Embedding](https://arxiv.org/abs/2501.07335) [arXiv 2025] | Expl. & Und. | Expl. Diagn. | ✔ |  |  |  |  | ✔ | 0 | S |
| [Time Series Language Model for Descriptive Caption Generation](https://arxiv.org/abs/2501.01832) [arXiv 2025] | Expl. & Und. | Expl. Diagn. |  |  | ✔ | ✔ |  | ✔ | 0 | S |
| [Visual Analysis of Time Series Data for Multi-Agent Systems   Driven by Large Language Models](https://doi.org/10.1145/3712335.3712410) [SPCNC 2024] | Expl. & Und. | Expl. Diagn. | ✔ |  |  | ✔ | ✔ | ✔ | M | P |
| [A Multimodal Foundation Agent for Financial Trading:   Tool-Augmented, Diversified, and Generalist](https://doi.org/10.1145/3637528.3671801) [KDD 2024] | Causal Inf. | Auto. Policy | ✔ | ✔ |  | ✔ | ✔ | ✔ | 1 | P |
| [FINMEM: A Performance-Enhanced LLM Trading Agent with Layered   Memory and Character Design](https://openreview.net/forum?id=sstfVOwbiG) [ICLR Workshop on Large   Language Model (LLM) Agents 2024] | Causal Inf. | Auto. Policy | ✔ | ✔ |  | ✔ | ✔ | ✔ | 1 | P |
| [Open-TI: Open Traffic Intelligence with Augmented Language   Model](https://arxiv.org/abs/2401.00211) [International Journal of   Machine Learning and Cybernetics 2024] | Causal Inf. | Auto. Policy | ✔ |  |  | ✔ |  |  | M | P |
| [SocioDojo: Building Lifelong Analytical Agents with Real-world   Text and Time Series](https://openreview.net/forum?id=s9z0HzWJJp) [ICLR 2024] | Causal Inf. | Adv. Dec. Supp. | ✔ | ✔ |  | ✔ | ✔ | ✔ | M | P |
| [GenG: An LLM-based Generic Time Series Data Generation   Approach for Edge Intelligence via Cross-domain Collaboration](https://doi.org/10.1109/INFOCOMWKSHPS61880.2024.10620716) [INFOCOM Wksps 2024] | TS Gen. | Cond. Synth. | ✔ |  |  |  |  | ✔ | 0 | S |
| [Using Gen AI Agents With GAE And VAE To Enhance Resilience Of   Us Markets](https://ssrn.com/abstract=5123068) [SSRN 2025] | TS Gen. | Cond. Synth. |  | ✔ |  | ✔ | ✔ |  | 0 | P |

## Research Papers for Branch-Structured Reasoning

## Non-Research Papers

| &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;Paper&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; | Type |
| --- | --- |
| [Evaluating Large Language Models on Time Series Feature   Understanding: A Comprehensive Taxonomy and Benchmark](https://aclanthology.org/2024.emnlp-main.1204/) [EMNLP 2024] | Reasoning-First Benchmarks |
| [Implicit Reasoning in Deep Time Series Forecasting](https://openreview.net/forum?id=PFZiJwX9j5) [NeurIPS Workshop on Time   Series in the Age of Large Models 2024] | Reasoning-First Benchmarks |
| [Investigating Compositional Reasoning in Time Series   Foundation Models](https://arxiv.org/abs/2502.06037) [arXiv 2025] | Reasoning-First Benchmarks |
| [Evaluating System 1 vs. 2 Reasoning Approaches for Zero-Shot   Time Series Forecasting: A Benchmark and Insights](https://arxiv.org/abs/2503.01895) [arXiv 2025] | Reasoning-First Benchmarks |
| [MTBench: A Multimodal Time Series Benchmark for Temporal   Reasoning and Question Answering](https://arxiv.org/abs/2503.16858) [arXiv 2025] | Reasoning-First Benchmarks |
| [Time-MQA: Time Series Multi-Task Question Answering with   Context Enhancement](https://aclanthology.org/2025.acl-long.1437/) [ACL 2025] | Reasoning-First Benchmarks |
| [PUB: Plot Understanding Benchmark and Dataset for Evaluating   Large Language Models on Synthetic Visual Data Interpretation](https://arxiv.org/abs/2409.02617) [arXiv 2024] | Reasoning-First Benchmarks |
| [Context is Key: A Benchmark for Forecasting with Essential   Textual Information](https://arxiv.org/abs/2410.18959) [ICML 2025] | Reasoning-First Benchmarks |
| [TimeSeriesGym: A Scalable Benchmark for (Time Series) Machine   Learning Engineering Agents](https://arxiv.org/abs/2505.13291) [arXiv 2025] | Reasoning-First Benchmarks |
| [SocioDojo: Building Lifelong Analytical Agents with Real-world   Text and Time Series](https://openreview.net/forum?id=s9z0HzWJJp) [ICLR 2024] | Reasoning-First Benchmarks |
| [Inferring Events from Time Series using Language Models](https://arxiv.org/abs/2503.14190) [arXiv 2025] | Reasoning-First Benchmarks |
| [Intervention-Aware Forecasting: Breaking Historical Limits   from a System Perspective](https://arxiv.org/abs/2405.13522) [arXiv 2024] | Reasoning-First Benchmarks |
| [ITFormer: Bridging Time Series and Natural Language for   Multi-Modal QA with Large-Scale Multitask Dataset](https://openreview.net/forum?id=GByP03IitA) [ICML 2025] | Reasoning-First Benchmarks |
| [AssetOpsBench: Benchmarking AI Agents for Task Automation in Industrial Asset Operations and Maintenance](https://arxiv.org/abs/2506.03828) [arXiv 2025] | Reasoning-First Benchmarks |
| [GEM: Empowering MLLM for Grounded ECG Understanding with Time   Series and Images](https://arxiv.org/abs/2503.06073) [arXiv 2025] | Reasoning-Ready Benchmarks |
| [See it, Think it, Sorted: Large Multimodal Models are Few-shot   Time Series Anomaly Analyzers](https://arxiv.org/abs/2411.02465) [arXiv 2024] | Reasoning-Ready Benchmarks |
| [GPT4MTS: Prompt-Based Large Language Model for Multimodal   Time-Series Forecasting](https://ojs.aaai.org/index.php/AAAI/article/view/30383) [AAAI 2024] | Reasoning-Ready Benchmarks |
| [Multi-Modal Forecaster: Jointly Predicting Time Series and   Textual Data](https://arxiv.org/abs/2411.06735) [arXiv 2024] | Reasoning-Ready Benchmarks |
| [Retrieval-augmented Large Language Models for Financial Time   Series Forecasting](https://arxiv.org/abs/2502.05878) [arXiv 2025] | Reasoning-Ready Benchmarks |
| [TEMPO: Prompt-based Generative Pre-trained Transformer for   Time Series Forecasting](https://openreview.net/forum?id=YH5w12OUuU) [ICLR 2024] | Reasoning-Ready Benchmarks |
| [Time Travel is Cheating: Going Live with DeepFund for   Real-Time Fund Investment Benchmarking](https://arxiv.org/abs/2505.11065) [arXiv 2025] | Reasoning-Ready Benchmarks |
| [Momentor: Advancing Video Large Language Model with   Fine-Grained Temporal Reasoning](https://arxiv.org/abs/2402.11435) [ICML 2024] | Reasoning-Ready Benchmarks |
| [MoTime: A Dataset Suite for Multimodal Time Series Forecasting](https://arxiv.org/abs/2505.15072) [arXiv 2025] | Reasoning-Ready Benchmarks |
| [Time-IMM: A Dataset and Benchmark for Irregular Multimodal   Multivariate Time Series](https://arxiv.org/abs/2506.10412) [NeurIPS 2025] | Reasoning-Ready Benchmarks |
| [Time-MMD: Multi-Domain Multimodal Dataset for Time Series   Analysis](https://openreview.net/forum?id=fuD0h4R1IL) [NeurIPS 2024] | Reasoning-Ready Benchmarks |
| [TSFM-Bench: A Comprehensive and Unified Benchmark of   Foundation Models for Time Series Forecasting](https://doi.org/10.1145/3711896.3737442) [KDD 2025] | Reasoning-Ready Benchmarks |
| [Well Googled is Half Done: Multimodal Forecasting of New   Fashion Product Sales with Image-based Google Trends](https://arxiv.org/abs/2109.09824) [Journal of Forecasting 2024] | Reasoning-Ready Benchmarks |
| [Time-RA: Towards Time Series Reasoning for Anomaly with LLM   Feedback](https://arxiv.org/abs/2507.15066) [arXiv 2025] | Reasoning-Ready Benchmarks |
| [A Picture is Worth A Thousand Numbers: Enabling LLMs Reason   about Time Series via Visualization](https://aclanthology.org/2025.naacl-long.383/) [NAACL 2025] | General-Purpose Time Series Benchmarks |
| [Are Language Models Actually Useful for Time Series   Forecasting?](https://openreview.net/forum?id=DV15UbHCY1) [NeurIPS 2024] | General-Purpose Time Series Benchmarks |
| [Can Large Language Models Adequately Perform Symbolic   Reasoning Over Time Series?](https://arxiv.org/abs/2508.03963) [arXiv 2025] | General-Purpose Time Series Benchmarks |
| [Can LLMs Understand Time Series Anomalies?](https://openreview.net/forum?id=LGafQ1g2D2) [ICLR 2025] | General-Purpose Time Series Benchmarks |
| [Can Multimodal LLMs Perform Time Series Anomaly Detection?](https://arxiv.org/abs/2502.17812) [arXiv 2025] | General-Purpose Time Series Benchmarks |
| [TimeSeriesExam: A Time Series Understanding Exam](https://openreview.net/forum?id=oXRX7ABgLz) [NeurIPS Workshop on Time   Series in the Age of Large Models 2024] | General-Purpose Time Series Benchmarks |
| [Can LLMs Serve As Time Series Anomaly Detectors?](https://arxiv.org/abs/2408.03475) [arXiv 2024] | General-Purpose Time Series Benchmarks |
| [Language Models Still Struggle to Zero-shot Reason about Time   Series](https://aclanthology.org/2024.findings-emnlp.201/) [EMNLP 2024] | General-Purpose Time Series Benchmarks |
| [Chat-TS: Enhancing Multi-Modal Reasoning Over Time-Series and   Natural Language Data](https://arxiv.org/abs/2503.10883) [arXiv 2025] | General-Purpose Time Series Benchmarks |
| [ChatTS: Aligning Time Series with LLMs via Synthetic Data for   Enhanced Understanding and Reasoning](https://arxiv.org/abs/2412.03104) [VLDB 2025] | General-Purpose Time Series Benchmarks |
| [Domain-Oriented Time Series Inference Agents for Reasoning and   Automated Analysis](https://arxiv.org/abs/2410.04047) [arXiv 2025] | General-Purpose Time Series Benchmarks |
| [FinBen: An Holistic Financial Benchmark for Large Language   Models](https://openreview.net/forum?id=loDHZstVP6) [NeurIPS 2024] | General-Purpose Time Series Benchmarks |
| [FinTSB: A Comprehensive and Practical Benchmark for Financial   Time Series Forecasting](https://arxiv.org/abs/2502.18834) [arXiv 2025] | General-Purpose Time Series Benchmarks |
| [Foundation Models for Time Series Analysis: A Tutorial and   Survey](https://doi.org/10.1145/3637528.3671451) [KDD 2024] | Surveys and Tutorials |
| [Large Language Models for Forecasting and Anomaly Detection: A   Systematic Literature Review](https://arxiv.org/abs/2402.10350) [arXiv 2024] | Surveys and Tutorials |
| [Empowering Time Series Analysis with Synthetic Data: A Survey   and Outlook in the Era of Foundation Models](https://arxiv.org/abs/2503.11411) [arXiv 2025] | Surveys and Tutorials |
| [Integrating Artificial Intelligence Agents with the Internet   of Things for Enhanced Environmental Monitoring: Applications in Water   Quality and Climate Data](https://www.mdpi.com/2079-9292/14/4/696) [Electronics 2025] | Surveys and Tutorials |
| [LLMs Meet Cross-Modal Time Series Analytics: Overview and   Directions](https://arxiv.org/abs/2507.10620) [arXiv 2025] | Surveys and Tutorials |
| [Are Language Models Actually Useful for Time Series   Forecasting?](https://openreview.net/forum?id=DV15UbHCY1) [NeurIPS 2024] | Position and Vision Papers |
| [Context parroting: A simple but tough-to-beat baseline for   foundation models in scientific machine learning](https://arxiv.org/abs/2505.11349) [arXiv 2025] | Position and Vision Papers |
| [Position: Empowering Time Series Reasoning with Multimodal   LLMs](https://arxiv.org/abs/2502.01477) [arXiv 2025] | Position and Vision Papers |
| [Position: What Can Large Language Models Tell Us about Time   Series Analysis](https://arxiv.org/abs/2402.02713) [ICML 2024] | Position and Vision 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](#research-papers-for-direct-reasoning)
     - Linear Chain Reasoning → [Research Papers for Linear Chain Reasoning](#research-papers-for-linear-chain-reasoning)
     - Branch-Structured Reasoning → [Research Papers for 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.
```bibtex
@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}, 
}

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