Dynamic models for spatiotemporal data

doi:10.1111/1467-9868.00305

Dynamic models for spatiotemporal data

2002-04-21

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DOI：10.1111/1467-9868.00305

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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.

Keyword:

Bayesian inference

locally weighted mixture

on-line inference

space-time modelling

state space models

Dynamic models for spatiotemporal data

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