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.

翻译：多变量时间序列数据中的缺失值插补对于确保数据质量和构建可靠的数据驱动预测模型至关重要。除多种统计方法外，近年来的研究提出了基于深度学习的先进方法来插补多变量时间序列数据中的缺失值。然而，这些深度学习方法的评估通常局限于少量数据集、低缺失率和完全随机缺失类型。本综述通过六项以数据为中心的实验，对五种时间序列健康数据集上的先进深度插补方法进行了基准测试。我们的广泛分析表明，没有任何单一插补方法能在所有五个数据集上优于其他方法。插补性能取决于数据类型、单个变量统计特性、缺失值比率和类型。与传统插补方法相比，能同时对时间序列数据中的横截面（跨变量）和纵向（跨时间）缺失值进行插补的深度学习方法，在统计上可获得更优的数据质量。尽管深度学习方法的计算成本较高，但在当前高性能计算资源可用的背景下，尤其在医疗信息学中数据质量和样本量极为重要时，这类方法具有实用性。我们的研究结果强调了以数据为中心选择插补方法以优化数据驱动预测模型的重要性。