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.

This paper presents a new deep learning architecture for addressing the problem of supervised learning with sparse and irregularly sampled multivariate time series. Proposes a framework for making predictions on sparse, irregularly sampled time-series data using an interpolation module that models the missing values in using smooth interpolation, non-smooth interpolation, and intensity.  Solves the problem of supervised learning with sparse and irregularly sampled multivariate time series using a semi-parametric interpolation network followed by a prediction network.