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

Bryan Lim; Stefan Zohren; Stephen Roberts

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arXiv · 1901.08096

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

Bryan Lim·Stefan Zohren·Stephen Roberts

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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 not only improve the accuracy of one-step-ahead forecasts while providing realistic uncertainty estimates, but also facilitate multistep prediction through the separation of encoder stages.

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BibTeX RIS

Bryan Lim, Stefan Zohren, Stephen Roberts. 2020-09-27. Recurrent Neural Filters: Learning Independent Bayesian Filtering Steps for Time Series Prediction. https://arxiv.org/abs/1901.08096

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