利用视觉模型进行时间序列分析：综述

摘要

时间序列分析领域经历了从传统自回归模型、深度学习模型，到近期Transformer及大型语言模型（LLMs）的鼓舞人心的发展历程。在此过程中，利用视觉模型进行时间序列分析的尝试虽已展开，但由于该领域内序列建模研究的主导地位，这些努力在学术界相对鲜为人知。然而，连续时间序列与LLMs离散令牌空间之间的差异，以及在多元时间序列中明确建模变量相关性所面临的挑战，已促使部分研究目光转向同样取得巨大成功的大型视觉模型（LVMs）和视觉语言模型（VLMs）。为填补现有文献的空白，本综述探讨了视觉模型在时间序列分析中相较于LLMs的优势，提供了现有方法的全面深入概览，通过双重视角的详细分类体系解答了关键研究问题，包括如何将时间序列编码为图像以及如何为各类任务建模图像化时间序列。此外，我们还探讨了该框架中预处理与后处理步骤所面临的挑战，并展望了未来利用视觉模型进一步推进时间序列分析的研究方向。

English

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.

利用视觉模型进行时间序列分析：综述

Harnessing Vision Models for Time Series Analysis: A Survey

摘要

Summary

Support