title: Harnessing Vision Models for Time Series Analysis: A Survey
summary: 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.
paper_authors._id: 67b5f3e30e7fed1190f29f80
paper_authors.name: Jingchao Ni
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-02-19T15:14:31.563Z
paper_authors._id: 67b5efbe38c175486e2869b9
paper_authors.avatarUrl: /avatars/64a698259033bb8ac324e57c557a9aa9.svg
paper_authors.fullname: Jingchao Ni
paper_authors.type: user
paper_authors.user: nijingchao
paper_authors._id: 67b5f3e30e7fed1190f29f81
paper_authors.name: Ziming Zhao
paper_authors._id: 67b5f3e30e7fed1190f29f82
paper_authors.name: ChengAo Shen
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-02-20T09:36:57.535Z
paper_authors._id: 66c2a909a9425c872d5213f4
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/66c2a909a9425c872d5213f4/u-iodlEs2bGzbLwv-cqKx.jpeg
paper_authors.fullname: ChengAo Shen
paper_authors.type: user
paper_authors.user: ChengAoShen
paper_authors._id: 67b5f3e30e7fed1190f29f83
paper_authors.name: Hanghang Tong
paper_authors._id: 67b5f3e30e7fed1190f29f84
paper_authors.name: Dongjin Song
paper_authors._id: 67b5f3e30e7fed1190f29f85
paper_authors.name: Wei Cheng
paper_authors._id: 67b5f3e30e7fed1190f29f86
paper_authors.name: Dongsheng Luo
paper_authors._id: 67b5f3e30e7fed1190f29f87
paper_authors.name: Haifeng Chen
paper_title: Harnessing Vision Models for Time Series Analysis: A Survey
paper_summary: 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.
paper_ai_summary: Survey explores the advantages of using vision models over language models in time series analysis, addressing encoding techniques and challenges.
paper_ai_keywords: autoregressive models deep learning Transformers Large Language Models Large Vision Models Vision Language Models multivariate time series time series encoding pre-processing post-processing