Title: Recurrent Neural Filters: Learning Independent Bayesian Filtering Steps for Time Series Prediction,
Abstract: 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.