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Irregularly sampled time series commonly occur in several domains where theypresent a significant challenge to standard deep learning models. In thispaper, we propose a new deep learning framework for probabilistic interpolationof irregularly sampled time series that we call the Heteroscedastic TemporalVariational Autoencoder (HeTVAE). HeTVAE includes a novel input layer to encodeinformation about input observation sparsity, a temporal VAE architecture topropagate uncertainty due to input sparsity, and a heteroscedastic output layerto enable variable uncertainty in output interpolations. Our results show thatthe proposed architecture is better able to reflect variable uncertaintythrough time due to sparse and irregular sampling than a range of baseline andtraditional models, as well as recently proposed deep latent variable modelsthat use homoscedastic output layers.

Heteroscedastic Temporal Variational Autoencoder For Irregularly Sampled Time Series