A Comprehensive Survey of Deep Learning for Multivariate Time Series Forecasting: A Channel Strategy Perspective

Xiangfei Qiu, Hanyin Cheng, Xingjian Wu, Junkai Lu, Jilin Hu +3 more
2/15/2025
cs.LG

Official code: decisionintelligence/CS4TS (50 stars, last commit 2026-01-19).

Abstract

Multivariate Time Series Forecasting (MTSF) plays a crucial role across diverse fields, ranging from economic, energy, to traffic. In recent years, deep learning has demonstrated outstanding performance in MTSF tasks. In MTSF, modeling the correlations among different channels is critical, as leveraging information from other related channels can significantly improve the prediction accuracy of a specific channel. This study systematically reviews the channel modeling strategies for time series and proposes a taxonomy organized into three hierarchical levels: the strategy perspective, the mechanism perspective, and the characteristic perspective. On this basis, we provide a structured analysis of these methods and conduct an in-depth examination of the advantages and limitations of different channel strategies. Finally, we summarize and discuss some future research directions to provide useful research guidance. Moreover, we maintain an up-to-date Github repository (https://github.com/decisionintelligence/CS4TS) which includes all the papers discussed in the survey.

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A Comprehensive Survey of Deep Learning for Multivariate Time Series Forecasting: A Channel Strategy Perspective

502Feb 11, 20259 months ago

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Cite this paper

@article{qiu2025comprehensive,
  title  = {A Comprehensive Survey of Deep Learning for Multivariate Time Series Forecasting: A Channel Strategy Perspective},
  author = {Xiangfei Qiu and Hanyin Cheng and Xingjian Wu and Junkai Lu and Jilin Hu and Chenjuan Guo and Christian S. Jensen and Bin Yang},
  year   = {2025},
  eprint = {2502.10721},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2502.10721v3}
}

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