系统梳理多变量时间序列预测中的通道建模策略，提供分类框架与未来方向。
A Comprehensive Survey of Deep Learning for Multivariate Time Series Forecasting: A Channel Strategy Perspective
- 从策略、机制、特征三层次构建通道建模分类体系
- 分析各类方法优劣，揭示跨通道相关性建模的关键路径
- 适合从事时序预测、数据融合研究的科研人员参考
多变量时间序列预测（MTSF）在经济、能源、交通等领域具有关键作用。近年来，深度学习在MTSF任务中表现出色。在MTSF中，建模不同通道间的相关性至关重要，利用相关通道信息可显著提升特定通道的预测精度。本文系统回顾了时间序列的通道建模策略，提出一个包含三个层级的分类体系：策略视角、机制视角和特征视角。在此基础上，对现有方法进行结构化分析，并深入探讨不同通道策略的优缺点。最后，总结并讨论若干未来研究方向，为后续研究提供指导。此外，维护了一个持续更新的GitHub仓库（https://github.com/decisionintelligence/CS4TS），收录本文所讨论的所有论文。
原文摘要 · Abstract (English)
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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