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Deep Rao-Blackwellised Particle Filters for Time Series Forecasting
Richard Kurle · Syama Sundar Rangapuram · Emmanuel de Bézenac · Stephan Günnemann · Jan Gasthaus

Thu Dec 10 09:00 AM -- 11:00 AM (PST) @ Poster Session 5 #1370

This work addresses efficient inference and learning in switching Gaussian linear dynamical systems using a Rao-Blackwellised particle filter and a corresponding Monte Carlo objective. To improve the forecasting capabilities, we extend this classical model by conditionally linear state-to-switch dynamics, while leaving the partial tractability of the conditional Gaussian linear part intact. Furthermore, we use an auxiliary variable approach with a decoder-type neural network that allows for more complex non-linear emission models and multivariate observations. We propose a Monte Carlo objective that leverages the conditional linearity by computing the corresponding conditional expectations in closed-form and a suitable proposal distribution that is factorised similarly to the optimal proposal distribution. We evaluate our approach on several popular time series forecasting datasets as well as image streams of simulated physical systems. Our results show improved forecasting performance compared to other deep state-space model approaches.

Author Information

Richard Kurle (Technical University of Munich)
Syama Sundar Rangapuram (Amazon Research)
Emmanuel de Bézenac (Sorbonne Université)
Stephan Günnemann (Technical University of Munich)
Jan Gasthaus (Amazon / AWS)

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