In this work we introduce a new framework for performing temporal predictions
 in the presence of uncertainty. It is based on a simple idea of disentangling com-
 ponents of the future state which are predictable from those which are inherently
 unpredictable, and encoding the unpredictable components into a low-dimensional
 latent variable which is fed into the forward model. Our method uses a simple su-
 pervised training objective which is fast and easy to train. We evaluate it in the
 context of video prediction on multiple datasets and show that it is able to consi-
 tently generate diverse predictions without the need for alternating minimization
 over a latent space or adversarial training.
A simple and easy to train method for multimodal prediction in time series.  This paper introduces a times-series prediction model that that learns a deterministic mapping and trains another net to predict future frames given the input and residual error from the first network. The paper proposes a model for prediction under uncertainty where they separate out deterministic component prediction and uncertain component prediction.