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A Spatial-temporal Model for Temperature with Seasonal Variance

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  • Jurate saltyte Benth
  • Fred Espen Benth
  • Paulius Jalinskas
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    Abstract

    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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    Bibliographic Info

    Article provided by Taylor & Francis Journals in its journal Journal of Applied Statistics.

    Volume (Year): 34 (2007)
    Issue (Month): 7 ()
    Pages: 823-841

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    Handle: RePEc:taf:japsta:v:34:y:2007:i:7:p:823-841

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    Related research

    Keywords: Spatial-temporal random field; temperature; seasonally dependent variance;

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    Cited by:
    1. Šaltytė Benth, Jūratė & Benth, Fred Espen, 2012. "A critical view on temperature modelling for application in weather derivatives markets," Energy Economics, Elsevier, vol. 34(2), pages 592-602.
    2. Benth, Fred Espen & Saltyte Benth, Jurate, 2009. "Dynamic pricing of wind futures," Energy Economics, Elsevier, vol. 31(1), pages 16-24, January.

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