Position Paper: 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
Time series analysis is essential for comprehending the complexities inherent
in various real-world 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 modality switching and time series 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.