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.

本文提出一种新的深度顺序潜变量模型，通过高斯过程在时间上平滑演化的抽象低维表示来实现缺失数据的非线性降维和数据插值，旨在解决在医疗和金融等领域中常见的缺失数据问题。我们的方法优于传统的和基于深度学习的数据插值方法，并提供可解释的不确定性估计。