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Title: Modelling cross-dependencies between Spain's regional tourism markets with an extension of the Gaussian process regression model
Author: Clavería González, Óscar
Monte Moreno, Enric
Torra Porras, Salvador
Keywords: Turisme
Anàlisi de regressió
Processos gaussians
Xarxes neuronals (Informàtica)
Regression analysis
Gaussian processes
Neural networks (Computer science)
Issue Date: Aug-2016
Publisher: Springer Verlag
Abstract: 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.
Note: Versió postprint del document publicat a:
It is part of: Series-Journal Of The Spanish Economic Association, 2016, vol. 7, num. 3, p. 341-357
Related resource:
ISSN: 1869-4187
Appears in Collections:Articles publicats en revistes (Econometria, Estadística i Economia Aplicada)

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