  Complex multivariate time series arise in many fields, ranging from computer
vision to robotics or medicine. Often we are interested in the independent
underlying factors that give rise to the high-dimensional data we are
observing. While many models have been introduced to learn such disentangled
representations, only few attempt to explicitly exploit the structure of
sequential data. We investigate the disentanglement properties of Gaussian
process variational autoencoders, a class of models recently introduced that
have been successful in different tasks on time series data. Our model exploits
the temporal structure of the data by modeling each latent channel with a GP
prior and employing a structured variational distribution that can capture
dependencies in time. We demonstrate the competitiveness of our approach
against state-of-the-art unsupervised and weakly-supervised disentanglement
methods on a benchmark task. Moreover, we provide evidence that we can learn
meaningful disentangled representations on real-world medical time series data.
