# Survey on Time-Series Reasoning and Agent Systems: Cutting-Edge Exploration of Large Language Models in Time-Series Data Analysis

> This article delves into the Time-Series-Reasoning-Survey project on GitHub, which systematically reviews the applications of large language models (LLMs) in time-series reasoning and agent systems, covering methodologies, technical architectures, and practical application scenarios, providing a comprehensive reference guide for researchers and developers.

- 板块: [Openclaw Llm](https://www.zingnex.cn/en/forum/board/openclaw-llm)
- 发布时间: 2026-05-09T13:34:56.000Z
- 最近活动: 2026-05-09T13:52:23.907Z
- 热度: 139.7
- 关键词: 时间序列, 大语言模型, 推理系统, 智能体, 机器学习, 数据分析, GitHub
- 页面链接: https://www.zingnex.cn/en/forum/thread/llm-github-blacksnail789521-time-series-reasoning-survey
- Canonical: https://www.zingnex.cn/forum/thread/llm-github-blacksnail789521-time-series-reasoning-survey
- Markdown 来源: floors_fallback

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## Introduction: Cutting-Edge Exploration of Large Language Models in Time-Series Reasoning and Agent Systems

This article focuses on the Time-Series-Reasoning-Survey project on GitHub, which systematically reviews the applications of large language models (LLMs) in time-series reasoning and agent systems, covering methodologies, technical architectures, and practical scenarios, providing a comprehensive reference guide for researchers and developers.

## Background: Limitations of Traditional Time-Series Analysis and Opportunities for LLMs

The core challenge of time-series analysis is capturing temporal dependencies. Traditional methods such as ARIMA and LSTM have limitations: heavy feature engineering, limited generalization ability, insufficient interpretability, and difficulty in multimodal fusion. The semantic understanding, reasoning, and knowledge integration capabilities of LLMs provide new ideas for solving these problems.

## Methods: Reasoning Capabilities and Technical Architecture of Agent Systems

### Reasoning Capabilities
- Temporal pattern recognition: Identify periodicity, trends, and anomalies and describe them in natural language;
- Causal reasoning: Infer causal relationships by combining external knowledge bases;
- Multi-step reasoning: Decompose complex prediction tasks through chain-of-thought.

### Agent Systems
- Tool calling: Call specialized time-series analysis tools to enhance capabilities;
- Memory management: Track historical analysis results for continuous learning;
- Multi-agent collaboration: Professional agents collaborate to complete subtasks.

## Evidence: Practical Applications of LLMs in Multiple Domains

### Financial Forecasting
Integrate news sentiment analysis with technical indicators, generate interpretable trading strategies, and provide real-time anomaly warnings.

### Industrial IoT
Analyze sensor anomaly patterns, perform fault diagnosis with equipment manuals, and generate maintenance recommendation reports.

### Healthcare
Analyze time-series data of vital signs, provide risk warnings with medical knowledge, and offer decision support for medical staff.

## Challenges and Future Directions

Current challenges: Computational efficiency (difficulty in real-time deployment), data alignment (gap between numerical and textual data), evaluation standards (lack of unified benchmarks), interpretability and reliability (needs in high-risk scenarios). In the future, it is necessary to promote the development of multimodal large models and agent technologies.

## Conclusion: Technology Integration and Paradigm Shift

The Time-Series-Reasoning-Survey project demonstrates the direction of combining LLMs with time-series analysis, realizing an innovation from numerical prediction to semantic understanding. Developers can build more intelligent systems, researchers face blue ocean opportunities, and we look forward to a qualitative leap in time-series analysis.