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.
CitacióClaveria, O., Monte, E., Torra Porras, S. Modelling cross-dependencies between Spain’s regional tourism markets with an extension of the Gaussian process regression model. "Series-Journal of the Spanish Economic Association", 16 Agost 2016, vol. 7, núm. 3, p. 341-357.