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

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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.

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Series-Journal Of The Spanish Economic Association, 2016, vol. 7, num. 3, p. 341-357

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Articles publicats en revistes  (Econometria, Estadística i Economia Aplicada)

# metadata.dc.title[].value
Modelling cross-dependencies between Spain's regional tourism markets with an extension of the Gaussian process regression model