The imputation of missing values in multivariate time series data has been
explored using a few recently proposed deep learning methods. The evaluation of
these state-of-the-art methods is limited to one or two data sets, low missing
rates, and completely random missing value types. These limited experiments do
not comprehensively evaluate imputation methods on realistic data scenarios
with varying missing rates and not-at-random missing types. This survey takes a
data-centric approach to benchmark state-of-the-art deep imputation methods
across five time series health data sets and six experimental conditions. 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. In this
context, state-of-the-art methods jointly perform cross-sectional (across
variables) and longitudinal (across time) imputations of missing values in time
series data. However, variables with high cross-correlation can be better
imputed by cross-sectional imputation methods alone. In contrast, the ones with
time series sensor signals may be better imputed by longitudinal imputation
methods alone. The findings of this study emphasize the importance of
considering data specifics when choosing a missing value imputation method for
multivariate time series data.

本文以五个时间序列健康数据集和六个实验条件为基准，展开数据中心的方法来评估最先进的深度插补方法，发现没有单一的插补方法在所有五个数据集上表现最佳，插补表现取决于数据类型，变量统计，缺失率和类型，这表明在选择多变量时间序列数据的缺失值插补方法时要考虑数据的特定情况。