  The imputation of missing values in multivariate time series (MTS) data is
critical in ensuring data quality and producing reliable data-driven predictive
models. Apart from many statistical approaches, a few recent studies have
proposed state-of-the-art deep learning methods to impute missing values in MTS
data. However, the evaluation of these deep methods is limited to one or two
data sets, low missing rates, and completely random missing value types. This
survey performs six data-centric experiments to benchmark state-of-the-art deep
imputation methods on five time series health data sets. Our extensive analysis
reveals that no single imputation method outperforms the others on all five
data sets. The imputation performance depends on data types, individual
variable statistics, missing value rates, and types. Deep learning methods that
jointly perform cross-sectional (across variables) and longitudinal (across
time) imputations of missing values in time series data yield statistically
better data quality than traditional imputation methods. Although
computationally expensive, deep learning methods are practical given the
current availability of high-performance computing resources, especially when
data quality and sample size are highly important in healthcare informatics.
Our findings highlight the importance of data-centric selection of imputation
methods to optimize data-driven predictive models.
