Harnessing Vision Models for Time Series Analysis: A Survey
Authors: name: Jingchao Ni, name: Ziming Zhao, name: ChengAo Shen, name: Hanghang Tong, name: Dongjin Song, name: Wei Cheng, name: Dongsheng Luo, name: Haifeng Chen

Time series analysis has witnessed the inspiring development from traditional
autoregressive models, deep learning models, to recent Transformers and Large
Language Models (LLMs). Efforts in leveraging vision models for time series
analysis have also been made along the way but are less visible to the
community due to the predominant research on sequence modeling in this domain.
However, the discrepancy between continuous time series and the discrete token
space of LLMs, and the challenges in explicitly modeling the correlations of
variates in multivariate time series have shifted some research attentions to
the equally successful Large Vision Models (LVMs) and Vision Language Models
(VLMs). To fill the blank in the existing literature, this survey discusses the
advantages of vision models over LLMs in time series analysis. It provides a
comprehensive and in-depth overview of the existing methods, with dual views of
detailed taxonomy that answer the key research questions including how to
encode time series as images and how to model the imaged time series for
various tasks. Additionally, we address the challenges in the pre- and
post-processing steps involved in this framework and outline future directions
to further advance time series analysis with vision models.