GP-VAE: Deep Probabilistic Time Series Imputation
This addresses data imputation challenges in fields like healthcare and finance, offering a novel method for reliable confidence estimates and interpretability, though it is incremental in combining existing techniques.
The paper tackled the problem of imputing missing values in multivariate time series, proposing a deep probabilistic model that outperformed classical and deep learning methods on high-dimensional data from computer vision and healthcare, providing improved smoothness and interpretable uncertainty estimates.
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.