GP-VAE: Deep Probabilistic Time Series Imputation

TL;DR

GP-VAE introduces a deep probabilistic model that leverages Gaussian processes and variational autoencoders to improve imputation, interpretability, and uncertainty estimation in high-dimensional time series with missing data.

Contribution

It presents a novel structured variational approximation within a VAE framework that models smooth low-dimensional latent trajectories with Gaussian processes for time series imputation.

Findings

Outperforms classical and deep learning imputation methods

Provides reliable uncertainty estimates and improved smoothness

Effective in healthcare and computer vision datasets

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.