In this paper, we present a new deep learning architecture for addressing the
problem of supervised learning with sparse and irregularly sampled multivariate
time series. The architecture is based on the use of a semi-parametric
interpolation network followed by the application of a prediction network. The
interpolation network allows for information to be shared across multiple
dimensions of a multivariate time series during the interpolation stage, while
any standard deep learning model can be used for the prediction network. This
work is motivated by the analysis of physiological time series data in
electronic health records, which are sparse, irregularly sampled, and
multivariate. We investigate the performance of this architecture on both
classification and regression tasks, showing that our approach outperforms a
range of baseline and recently proposed models.

本文提出了一种新的深度学习架构，以解决稀疏和不规则采样的多变量时间序列的监督学习问题，该架构基于半参数插值网络，其允许在插值阶段跨多个时间序列维度共享信息，同时预测网络可以使用任何标准的深度学习模型。我们研究了该架构在分类和回归任务中的性能，并表明我们的方法优于一系列基线模型和最近提出的模型。本研究的动机是对在电子健康记录中的生理时间序列数据的分析，这些数据稀疏、不规则采样和多元。