Deep Learning for Multivariate Time Series Imputation: A Survey
It addresses the problem of missing values in multivariate time series data for researchers and practitioners, providing a resource for the field, but is incremental as a survey.
This survey comprehensively summarizes deep learning approaches for multivariate time series imputation, proposing a novel taxonomy based on imputation uncertainty and neural network architecture and highlighting the PyPOTS Ecosystem as a standardized toolkit.
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 are available at https://github.com/WenjieDu/Awesome_Imputation.