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Position: What Can Large Language Models Tell Us about Time Series Analysis

Ming Jin, Yifan Zhang, Wei Chen, Kexin Zhang, Yuxuan Liang, Bin Yang, Jindong Wang, Shirui Pan, Qingsong Wen

TL;DR

This position paper argues that large language models can serve as a unifying hub for time series analysis, enabling data/knowledge enhancement, improved prediction, and autonomous time-series reasoning via agents. It categorizes integration approaches into data-based enhancers, model-based enhancers, and LLM-centered predictors and discusses tuning-based versus non-tuning strategies, including the promise and challenges of LLM-enabled time series agents. Empirical insights illustrate both the capabilities and limitations of current LLMs in time series tasks, emphasizing interpretability, risk of hallucination, and the need for robust knowledge integration. The work outlines a roadmap toward trustworthy, multi-modal, and agent-based time series systems, and it calls for careful consideration of accountability, privacy, and costs as these technologies mature.

Abstract

Time series analysis is essential for comprehending the complexities inherent in various realworld systems and applications. Although large language models (LLMs) have recently made significant strides, the development of artificial general intelligence (AGI) equipped with time series analysis capabilities remains in its nascent phase. Most existing time series models heavily rely on domain knowledge and extensive model tuning, predominantly focusing on prediction tasks. In this paper, we argue that current LLMs have the potential to revolutionize time series analysis, thereby promoting efficient decision-making and advancing towards a more universal form of time series analytical intelligence. Such advancement could unlock a wide range of possibilities, including time series modality switching and question answering. We encourage researchers and practitioners to recognize the potential of LLMs in advancing time series analysis and emphasize the need for trust in these related efforts. Furthermore, we detail the seamless integration of time series analysis with existing LLM technologies and outline promising avenues for future research.

Position: What Can Large Language Models Tell Us about Time Series Analysis

TL;DR

This position paper argues that large language models can serve as a unifying hub for time series analysis, enabling data/knowledge enhancement, improved prediction, and autonomous time-series reasoning via agents. It categorizes integration approaches into data-based enhancers, model-based enhancers, and LLM-centered predictors and discusses tuning-based versus non-tuning strategies, including the promise and challenges of LLM-enabled time series agents. Empirical insights illustrate both the capabilities and limitations of current LLMs in time series tasks, emphasizing interpretability, risk of hallucination, and the need for robust knowledge integration. The work outlines a roadmap toward trustworthy, multi-modal, and agent-based time series systems, and it calls for careful consideration of accountability, privacy, and costs as these technologies mature.

Abstract

Time series analysis is essential for comprehending the complexities inherent in various realworld systems and applications. Although large language models (LLMs) have recently made significant strides, the development of artificial general intelligence (AGI) equipped with time series analysis capabilities remains in its nascent phase. Most existing time series models heavily rely on domain knowledge and extensive model tuning, predominantly focusing on prediction tasks. In this paper, we argue that current LLMs have the potential to revolutionize time series analysis, thereby promoting efficient decision-making and advancing towards a more universal form of time series analytical intelligence. Such advancement could unlock a wide range of possibilities, including time series modality switching and question answering. We encourage researchers and practitioners to recognize the potential of LLMs in advancing time series analysis and emphasize the need for trust in these related efforts. Furthermore, we detail the seamless integration of time series analysis with existing LLM technologies and outline promising avenues for future research.
Paper Structure (33 sections, 3 equations, 9 figures, 1 table)

This paper contains 33 sections, 3 equations, 9 figures, 1 table.

Table of Contents

  1. Introduction
  2. Why This Position Paper?
  3. Contributions:
  4. Background
  5. Time Series Analysis
  6. Data Modality.
  7. Analytical Tasks.
  8. Large Language Models
  9. Basic Concept.
  10. Emergent Abilities of LLMs.
  11. Research Roadmap
  12. LLM-assisted Enhancer for Time Series
  13. Data-based Enhancer
  14. Model-based Enhancer
  15. Discussion
  16. ...and 18 more sections

Figures (9)

  • Figure 1: Across a myriad of time series analytical domains, the integration of time series and LLMs demonstrates potential in solving complex real-world problems.
  • Figure 2: Task-solving capability boundaries on the roles of LLMs for time series analysis: as data/model enhancers, effective predictors, or next-generation agents.
  • Figure 3: A roadmap of time series analysis delineating four generations of models based on their task-solving capabilities.
  • Figure 4: Categories of LLM-centered predictor.
  • Figure 5: Confusion matrix of HAR classification.
  • ...and 4 more figures