Deep Learning for Multivariate Time Series Imputation: A Survey
Jun Wang Wenjie Du Wei Cao Keli Zhang Wenjia Wang Yuxuan Liang Qingsong Wen
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}.