Recurrent Neural Filters: Learning Independent Bayesian Filtering Steps for Time Series Prediction
  Despite the recent popularity of deep generative state space models, few
comparisons have been made between network architectures and the inference
steps of the Bayesian filtering framework -- with most models simultaneously
approximating both state transition and update steps with a single recurrent
neural network (RNN). In this paper, we introduce the Recurrent Neural Filter
(RNF), a novel recurrent variational autoencoder architecture that learns
distinct representations for each Bayesian filtering step, captured by a series
of encoders and decoders. Testing this on three real-world time series
datasets, we demonstrate that decoupling representations not only improves the
accuracy of one-step-ahead forecasts while providing realistic uncertainty
estimates, but also facilitates multistep prediction through the separation of
encoder stages.
