Deep Learning for Multivariate Time Series Imputation: A Survey
<|reference_start|>Deep Learning for Multivariate Time Series Imputation: A Survey: The ubiquitous missing values cause the multivariate time series data to be partially observed, destroying the integrity of time series and hindering the effective time series data analysis. Recently deep learning imputation methods have demonstrated remarkable success in elevating the quality of corrupted time series data, subsequently enhancing performance in downstream tasks. In this paper, we conduct a comprehensive survey on the recently proposed deep learning imputation methods. First, we propose a taxonomy for the reviewed methods, and then provide a structured review of these methods by highlighting their strengths and limitations. We also conduct empirical experiments to study different methods and compare their enhancement for downstream tasks. Finally, the open issues for future research on multivariate time series imputation are pointed out. All code and configurations of this work, including a regularly maintained multivariate time series imputation paper list, can be found in the GitHub repository~\url{https://github.com/WenjieDu/Awesome\_Imputation}.<|reference_end|>
@article{wang2024deep,
    title={Deep Learning for Multivariate Time Series Imputation: A Survey},
    author={Jun Wang, Wenjie Du, Wei Cao, Keli Zhang, Wenjia Wang, Yuxuan Liang,
  Qingsong Wen},
    journal={arXiv preprint arXiv:2402.04059},
    year={2024},
    archivePrefix={arXiv},
    eprint={2402.04059},
    primaryClass={cs.LG cs.AI}
}