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

Qiu, Xiangfei; Cheng, Hanyin; Wu, Xingjian; Lu, Junkai; Hu, Jilin; Guo, Chenjuan; Jensen, Christian S.; Yang, Bin

doi:10.24963/ijcai.2026/887

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, Chenjuan Guo, Christian S. Jensen, Bin Yang

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Proceedings of the Thirty-Fifth International Joint Conference on Artificial Intelligence. Survey Track. Pages 7995-8003.

https://doi.org/10.24963/ijcai.2026/887

Keywords

Machine Learning: Time series and data streams
Data Mining: Mining spatial and/or temporal data

Abstract

Multivariate Time Series Forecasting (MTSF) plays a crucial role across diverse fields, ranging from economics to 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 which includes all the papers discussed in the survey.

Xiangfei Qiu, Hanyin Cheng, Xingjian Wu, Junkai Lu, Jilin Hu, Chenjuan Guo, Christian S. Jensen, and Bin Yang. 2026. A Comprehensive Survey of Deep Learning for Multivariate Time Series Forecasting: A Channel Strategy Perspective. In Proceedings of the Thirty-Fifth International Joint Conference on Artificial Intelligence, IJCAI-26. IJCAI, 7995–8003. https://doi.org/10.24963/ijcai.2026/887

@inproceedings{ijcai2026p887,
  title     = {A Comprehensive Survey of Deep Learning for Multivariate Time Series Forecasting: A Channel Strategy Perspective},
  author    = {Qiu, Xiangfei and Cheng, Hanyin and Wu, Xingjian and Lu, Junkai and Hu, Jilin and Guo, Chenjuan and Jensen, Christian S. and Yang, Bin},
  booktitle = {Proceedings of the Thirty-Fifth International Joint Conference on
               Artificial Intelligence, {IJCAI-26}},
  publisher = {International Joint Conferences on Artificial Intelligence Organization},
  editor    = {Diego Calvanese},
  pages     = {7995--8003},
  year      = {2026},
  month     = {8},
  note      = {Survey Track},
  doi       = {10.24963/ijcai.2026/887},
  url       = {https://doi.org/10.24963/ijcai.2026/887},
}

TY  - CPAPER
TI  - A Comprehensive Survey of Deep Learning for Multivariate Time Series Forecasting: A Channel Strategy Perspective
AU  - Qiu, Xiangfei
AU  - Cheng, Hanyin
AU  - Wu, Xingjian
AU  - Lu, Junkai
AU  - Hu, Jilin
AU  - Guo, Chenjuan
AU  - Jensen, Christian S.
AU  - Yang, Bin
T2  - Proceedings of the Thirty-Fifth International Joint Conference on Artificial Intelligence, IJCAI-26
PB  - International Joint Conferences on Artificial Intelligence Organization
PY  - 2026
SP  - 7995
EP  - 8003
DO  - 10.24963/ijcai.2026/887
UR  - https://doi.org/10.24963/ijcai.2026/887
ER  -