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~https://github.com/WenjieDu/Awesome\_Imputation.

通过对最近提出的深度学习插补方法进行综合调查，本文首先提出了一种分类法，然后通过强调其优势和局限性对这些方法进行了结构化的审查。同时，我们还进行了实证实验，研究了不同方法并比较了它们对下游任务的改进效果。最后，指出了未来研究多变量时间序列插补的开放问题。