GP-VAE: Deep Probabilistic Time Series Imputation

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.

Accepted for publication at the 23rd International Conference on Artificial Intelligence and Statistics (AISTATS 2020)

References in corpus (13)

• Auto-Encoding Variational Bayes
• GAIN: Missing Data Imputation using Generative Adversarial Nets
• BRITS: Bidirectional Recurrent Imputation for Time Series
• Dynamic Word Embeddings
• Deep Kalman Filters
• SOM-VAE: Interpretable Discrete Representation Learning on Time Series
• MisGAN: Learning from Incomplete Data with Generative Adversarial Networks
• EDDI: Efficient Dynamic Discovery of High-Value Information with Partial VAE
• Scalable Generalized Dynamic Topic Models
• Scalable Gaussian Processes on Discrete Domains
• Structured Black Box Variational Inference for Latent Time Series Models
• Disentangled VAE Representations for Multi-Aspect and Missing Data
• Unsupervised Data Imputation via Variational Inference of Deep Subspaces

Cited by in corpus (2)

• tvGP-VAE: Tensor-variate Gaussian Process Prior Variational Autoencoder
• Dynamic Topic Language Model on Heterogeneous Children's Mental Health Clinical Notes