Sequential Bayesian inference for spatio-temporal models of temperature and humidity data

TL;DR

This paper introduces a sequential Bayesian spatio-temporal model for forecasting temperature and humidity at multiple locations, utilizing coupled dynamic linear models and Gaussian processes, with an efficient, parallelizable inference algorithm.

Contribution

It presents a novel, efficient sequential inference algorithm for spatio-temporal models, improving forecast accuracy and computational scalability for environmental sensor data.

Findings

Model accurately captures temperature and humidity dynamics.

Algorithm achieves good forecast accuracy.

Enhanced efficiency and parallelization of inference process.

Abstract

We develop a spatio-temporal model to forecast sensor output at five locations in North East England. The signal is described using coupled dynamic linear models, with spatial effects specified by a Gaussian process. Data streams are analysed using a stochastic algorithm which sequentially approximates the parameter posterior through a series of reweighting and resampling steps. An iterated batch importance sampling scheme is used to circumvent particle degeneracy through a resample-move step. The algorithm is modified to make it more efficient and parallisable. The model is shown to give a good description of the underlying process and provide reasonable forecast accuracy.