期刊名称：SERIEs: Journal of the Spanish Economic Association
印刷版ISSN：1869-4187
电子版ISSN：1869-4195
出版年度：2016
卷号：7
期号：3
页码：341-357
DOI：10.1007/s13209-016-0144-7
出版社：Springer Berlin / Heidelberg
摘要：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.
