Recurrent Neural Filters: Learning Independent Bayesian Filtering Steps for Time Series Prediction

Bryan Lim, Stefan Zohren, Stephen Roberts

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 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 the decoupled representations learnt improve the accuracy of one-step-ahead forecasts while providing realistic uncertainty estimates, and also facilitate multistep prediction through the separation of encoder stages.