Deep Amortized Variational Inference for Multivariate Time Series Imputation with Latent Gaussian Process Models

Vincent Fortuin, Dmitry Baranchuk, Gunnar Rätsch, Stephan Mandt

Keywords: amortized inference, time series, gaussian processes, imputation, missing data, variational autoencoder

TL;DR: We perform amortized variational inference on a latent Gaussian process model to achieve superior imputation performance on multivariate time series with missing data.

Abstract: Multivariate time series with missing values are common in areas such as healthcare and finance, and have grown in number and complexity over the years. This raises the question whether deep learning methodologies can outperform classical data imputation methods in this domain. However, naive applications of deep learning fall short in giving reliable confidence estimates and lack interpretability. We propose a new deep sequential latent variable model for dimensionality reduction and data imputation. Our modeling assumption is simple and interpretable: the high dimensional time series has a lower-dimensional representation which evolves smoothly in time according to a Gaussian process. The non-linear dimensionality reduction in the presence of missing data is achieved using a VAE approach with a novel structured variational approximation. We demonstrate that our approach outperforms several classical and deep learning-based data imputation methods on high-dimensional data from the domains of computer vision and healthcare, while additionally improving the smoothness of the imputations and providing interpretable uncertainty estimates.