We propose a spatial-temporal stochastic model for daily average surface temperature data. First, we build a model for a single spatial location, independently on the spatial information. The model includes trend, seasonality, and mean reversion, together with a seasonally dependent
variance of the residuals. The spatial dependency is modelled by a Gaussian random field. Empirical fitting to data collected in 16 measurement stations in Lithuania over more than 40 years shows that our model captures the seasonality in the autocorrelation of the squared residuals, a property
of temperature data already observed by other authors. We demonstrate through examples that our spatial-temporal model is applicable for prediction and classification.
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Spatial-temporal random field;
seasonally dependent variance;
Document Type: Research Article
Faculty of Medicine, Helse Øst Health Services Research Centre, University of Oslo, and Klaipeda University, Norway
Centre of Mathematics for Applications (CMA), University of Oslo and Agder University College, Norway
Vilnius University and Lithuanian Hydrometeorological Service, Lithuania
September 1, 2007