利用視覺模型進行時間序列分析：綜述

Harnessing Vision Models for Time Series Analysis: A Survey

February 13, 2025

作者: Jingchao Ni, Ziming Zhao, ChengAo Shen, Hanghang Tong, Dongjin Song, Wei Cheng, Dongsheng Luo, Haifeng Chen

cs.AI

摘要

時間序列分析領域見證了從傳統自回歸模型、深度學習模型，到近期Transformer架構及大型語言模型（LLMs）的振奮人心發展。在此過程中，利用視覺模型進行時間序列分析的努力雖未間斷，但由於該領域對序列建模的集中研究，這些工作較少受到社群關注。然而，LLMs離散標記空間與連續時間序列之間的差異，以及多元時間序列中變量相關性顯式建模的挑戰，已將部分研究焦點轉向同樣取得顯著成功的大型視覺模型（LVMs）和視覺語言模型（VLMs）。為填補現有文獻的空白，本綜述探討了視覺模型在時間序列分析中相較於LLMs的優勢。它從雙重視角出發，提供了對現有方法的全面深入概述，並通過細緻的分類體系解答了關鍵研究問題，包括如何將時間序列編碼為圖像，以及如何針對不同任務對圖像化的時間序列進行建模。此外，我們還探討了這一框架中前後處理步驟面臨的挑戰，並展望了未來利用視覺模型進一步推進時間序列分析的方向。

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.