Modelling cross-dependencies between Spain's regional tourism markets with an extension of the Gaussian process regression model - Oscar Claveria; Enric Monte; Salvador Torra (SERIEs - Journal of the Spanish Economic Association, 2016)
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.
Keywords: C45; C51; C53; C63; E27; L83; 330; Machine learning; Gaussian process regression; Neural networks; Multiple-input multiple-output (MIMO); Economic forecasting; Tourism demand