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

• URL: http://arxiv.org/abs/2402.02713v2
• Date: Sat, 1 Jun 2024 06:42:09 GMT
• Title: Position: What Can Large Language Models Tell Us about Time Series Analysis
• Authors: Ming Jin, Yifan Zhang, Wei Chen, Kexin Zhang, Yuxuan Liang, Bin Yang, Jindong Wang, Shirui Pan, Qingsong Wen,
• Abstract summary: We argue that current large language models (LLMs) have the potential to revolutionize time series analysis. Such advancement could unlock a wide range of possibilities, including time series modality switching and question answering.
• Score: 69.70906014827547
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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