Deep Learning for Multivariate Time Series Imputation: A Survey

Wang, Jun; Du, Wenjie; Yang, Yiyuan; Qian, Linglong; Cao, Wei; Zhang, Keli; Wang, Wenjia; Liang, Yuxuan; Wen, Qingsong

doi:10.24963/ijcai.2025/1187

Deep Learning for Multivariate Time Series Imputation: A Survey

Jun Wang, Wenjie Du, Yiyuan Yang, Linglong Qian, Wei Cao, Keli Zhang, Wenjia Wang, Yuxuan Liang, Qingsong Wen

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Proceedings of the Thirty-Fourth International Joint Conference on Artificial Intelligence. Survey Track. Pages 10696-10704.

https://doi.org/10.24963/ijcai.2025/1187

Keywords

Machine Learning: ML: Time series and data streams
Machine Learning: ML: Attention models
Data Mining: DM: Mining data streams
Machine Learning: ML: Deep learning architectures

Abstract

Missing values are ubiquitous in multivariate time series (MTS) data, posing significant challenges for accurate analysis and downstream applications. In recent years, deep learning-based methods have successfully handled missing data by leveraging complex temporal dependencies and learned data distributions. In this survey, we provide a comprehensive summary of deep learning approaches for multivariate time series imputation (MTSI) tasks. We propose a novel taxonomy that categorizes existing methods based on two key perspectives: imputation uncertainty and neural network architecture. Furthermore, we summarize existing MTSI toolkits with a particular emphasis on the PyPOTS Ecosystem, which provides an integrated and standardized foundation for MTSI research. Finally, we discuss key challenges and future research directions, which give insight for further MTSI research. This survey aims to serve as a valuable resource for researchers and practitioners in the field of time series analysis and missing data imputation tasks. A well-maintained MTSI paper and tool list is available at https://github.com/WenjieDu/Awesome_Imputation.

Jun Wang, Wenjie Du, Yiyuan Yang, Linglong Qian, Wei Cao, Keli Zhang, Wenjia Wang, Yuxuan Liang, and Qingsong Wen. 2025. Deep Learning for Multivariate Time Series Imputation: A Survey. In Proceedings of the Thirty-Fourth International Joint Conference on Artificial Intelligence, IJCAI-25. IJCAI, 10696–10704. https://doi.org/10.24963/ijcai.2025/1187

@inproceedings{ijcai2025p1187,
  title     = {Deep Learning for Multivariate Time Series Imputation: A Survey},
  author    = {Wang, Jun and Du, Wenjie and Yang, Yiyuan and Qian, Linglong and Cao, Wei and Zhang, Keli and Wang, Wenjia and Liang, Yuxuan and Wen, Qingsong},
  booktitle = {Proceedings of the Thirty-Fourth International Joint Conference on
               Artificial Intelligence, {IJCAI-25}},
  publisher = {International Joint Conferences on Artificial Intelligence Organization},
  editor    = {James Kwok},
  pages     = {10696--10704},
  year      = {2025},
  month     = {8},
  note      = {Survey Track},
  doi       = {10.24963/ijcai.2025/1187},
  url       = {https://doi.org/10.24963/ijcai.2025/1187},
}

TY  - CPAPER
TI  - Deep Learning for Multivariate Time Series Imputation: A Survey
AU  - Wang, Jun
AU  - Du, Wenjie
AU  - Yang, Yiyuan
AU  - Qian, Linglong
AU  - Cao, Wei
AU  - Zhang, Keli
AU  - Wang, Wenjia
AU  - Liang, Yuxuan
AU  - Wen, Qingsong
T2  - Proceedings of the Thirty-Fourth International Joint Conference on Artificial Intelligence, IJCAI-25
PB  - International Joint Conferences on Artificial Intelligence Organization
PY  - 2025
SP  - 10696
EP  - 10704
DO  - 10.24963/ijcai.2025/1187
UR  - https://doi.org/10.24963/ijcai.2025/1187
ER  -