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