This work presents a Markov switching model with a time-varying transition matrix to forecast
intra-hour photovoltaic (PV) power output, aiming at providing forecasting flexibility. First, the …

In this paper, the particle filtering problem is investigated for a class of nonlinear/non-
Gaussian systems with energy harvesting sensors subject to randomly occurring sensor …

The upcoming 6G technology is expected to operate in near-field (NF) radiating conditions
thanks to high-frequency and electrically large antenna arrays. Although several studies …

Differentiable particle filters are an emerging class of particle filtering methods that use
neural networks to construct and learn parametric state-space models. In real-world …

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 …

In many science and engineering problems, we observe high-dimensional data acquired
sequentially. At each time instant, these data correspond to one of a predefined number of …

We consider the challenge of estimating the model parameters and latent states of general
state-space models within a Bayesian framework. We extend the commonly applied particle …

We propose a sequential Monte Carlo algorithm for parameter learning when the studied
model exhibits random discontinuous jumps in behaviour. To facilitate the learning of high …

The use of time series for sequential online prediction (SOP) has long been a research topic,
but achieving robust and computationally efficient SOP with non-stationary time series …

This letter is concerned with multi-modal data fusion (MMDF) under unexpected modality
failures in nonlinear non-Gaussian dynamic processes. An efficient framework to tackle this …