Modelling cross-dependencies between Spain's regional tourism markets with an extension of the Gaussian process regression model
This incremental improvement helps regional tourism planners by refining predictions through better modeling of market connections.
The study tackled forecasting international tourism demand across Spain's 17 regions by extending Gaussian process regression to model cross-dependencies, resulting in significantly improved accuracy over a neural network benchmark.
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.