Clavería González, ÓscarMonte Moreno, EnricTorra Porras, Salvador2016-09-142017-08-312016-081869-4187https://hdl.handle.net/2445/101764This 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.17 p.application/pdfeng(c) Springer Verlag, 2016TurismeAnàlisi de regressióProcessos gaussiansXarxes neuronals (Informàtica)TourismRegression analysisGaussian processesNeural networks (Computer science)info:eu-repo/semantics/article6637332016-09-14info:eu-repo/semantics/openAccess