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

Paper content:
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.
Multivariate medical time series, consisting of multiple correlated univariate time series or channels, give rise to two distinct ways of imputing missing information: (1) by exploiting temporal correlations within each channel, and (2) by exploiting correlations across channels, for example by using lower-dimensional representations of the data.
An ideal imputation model for medical time series should take both of these sources of information into account.
Another desirable property of such models is to offer a probabilistic interpretation, allowing for uncertainty estimation.
Unfortunately, current imputation approaches fall short with respect to at least one of these desiderata.
While there are many time-tested statistical methods for multivariate time series analysis (e.g., Gaussian processes (Roberts et al., 2013) ), these methods are generally not applicable when features are missing.
On the other hand, classical methods for time series imputation often do not take the potentially complex interactions between the different channels into account (Little and Rubin, 2002; Pedersen et al., 2017) .
Finally, recent work has explored the use of non-linear dimensionality reduction using variational autoencoders for i.i.d. data points with missing values (Ainsworth et al., 2018; Ma et al., 2018; Nazabal et al., 2018) , but this work has not considered temporal data and strategies for sharing statistical strength across time.
A more comprehensive analysis of existing approaches and their shortcomings is deferred to the appendix (Sec. A).
In this paper, we propose an architecture that combines deep variational autoencoders (VAEs) with Gaussian process (GP) to efficiently model the latent dynamics at multiple time scales.
Moreover, our inference approach makes use of efficient structured variational approximations, where we fit another multivariate Gaussian process in order to approximate the intractable true posterior.
We make the following contributions:
• A new model.
We propose a VAE architecture for multivariate time series imputation with a GP prior in the latent space to capture temporal dynamics.
• Efficient inference.
We use a structured variational approximation that models posterior correlations in the time domain.
• Benchmarking on real-world data.
We carry out extensive comparisons to classical imputation methods as well as state-of-the-art deep learning approaches, and perform experiments on data from different domains.

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