Dynamic models for spatiotemporal data

Jonathan R. Stroud, Peter Müller, Bruno Sansó

Journal of the Royal Statistical Society: Series B (Statistical Methodology) · 2001 · Volume 63 · Issue 4

Abstract

We propose a model for non-stationary spatiotemporal data. To account for spatial variability, we model the mean function at each time period as a locally weighted mixture of linear regressions. To incorporate temporal variation, we allow the regression coefficients to change through time. The model is cast in a Gaussian state space framework, which allows us to include temporal components such as trends, seasonal effects and autoregressions, and permits a fast implementation and full probabilistic inference for the parameters, interpolations and forecasts. To illustrate the model, we apply it to two large environmental data sets: tropical rainfall levels and Atlantic Ocean temperatures.

Keywords

Bayesian inference, Locally weighted mixture, On-line inference, Space–time modelling, State space models.

Enable JavaScript for citation tools and additional paper interactions.