  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.
