Regime Learning for Differentiable Particle Filters
This addresses a specific limitation in sequential Monte Carlo methods for regime-switching systems, but it is incremental as it builds on existing differentiable particle filter frameworks.
The paper tackles the problem of learning both individual regimes and the switching process in state-space models with differentiable particle filters, achieving competitive performance compared to previous state-of-the-art algorithms in numerical experiments.
Differentiable particle filters are an emerging class of models that combine sequential Monte Carlo techniques with the flexibility of neural networks to perform state space inference. This paper concerns the case where the system may switch between a finite set of state-space models, i.e. regimes. No prior approaches effectively learn both the individual regimes and the switching process simultaneously. In this paper, we propose the neural network based regime learning differentiable particle filter (RLPF) to address this problem. We further design a training procedure for the RLPF and other related algorithms. We demonstrate competitive performance compared to the previous state-of-the-art algorithms on a pair of numerical experiments.