Modelling cross-dependencies between Spain’s regional tourism markets with an extension of the Gaussian process regression model

• Oscar Claveria ^[1] ; Enric Monte ^[2] ; Salvador Torra ^[1]
  1. [1] Universitat de Barcelona

     Universitat de Barcelona

     Barcelona, España

     
  2. [2] Universitat Politècnica de Catalunya

     Universitat Politècnica de Catalunya

     Barcelona, España

     
• Localización: SERIEs : Journal of the Spanish Economic Association, ISSN 1869-4195, Vol. 7, Nº. 3, 2016, págs. 341-357
• Idioma: inglés
• DOI: 10.1007/s13209-016-0144-7
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• Resumen

  • This study presents an extension of the Gaussian process regression model for multiple-input multiple-output forecasting. This approach allows modelling the cross-dependencies between a given set of input variables and generating a vectorial prediction. Making use of the existing correlations in international tourism demand to all seventeen regions of Spain, the performance of the proposed model is assessed in a multiple-step-ahead forecasting comparison. The results of the experiment in a multivariate setting show that the Gaussian process regression model significantly improves the forecasting accuracy of a multi-layer perceptron neural network used as a benchmark. The results reveal that incorporating the connections between different markets in the modelling process may prove very useful to refine predictions at a regional level.