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Deep learning for clustering of multivariate short time series with potentially many missing values

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This repository contains code for a new method for clustering multivariate short time series with potentially many missing values (published here), a setting commonly encountered in the analysis of longitudinal clinical data, but generally still poorly addressed in the literature. The method is based on a variational autoencoder with a Gaussian mixture prior (with a latent loss as described here), extended with LSTMs for modeling multivariate time series, as well as implicit imputation and loss re-weighting for directly dealing with (potentially many) missing values.

The code was written using

(1) Python 3.6 and Tensorflow 1.10.1 (directory tensorflow1), and

(2) Python 3.8 and Tensorflow 2.3.1 (directory tensorflow2).

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Deep learning for clustering of multivariate short time series with potentially many missing values

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