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

TL;DR

This paper explores how large language models can be leveraged to revolutionize time series analysis, enabling more efficient decision-making and new capabilities like modality switching and question answering.

Contribution

It highlights the potential of LLMs to transform time series analysis and discusses methods for integrating these models with existing time series techniques.

Findings

LLMs can facilitate time series modality switching

LLMs enable question answering in time series contexts

Potential for advancing towards artificial general intelligence in time series analysis

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.