Please use this identifier to cite or link to this item: http://hdl.handle.net/10419/158560
Authors: 
Claveria, Oscar
Monte, Enric
Torra, Salvador
Year of Publication: 
2016
Citation: 
[Journal:] SERIEs - Journal of the Spanish Economic Association [ISSN:] 1869-4195 [Volume:] 7 [Year:] 2016 [Issue:] 3 [Pages:] 341-357
Abstract: 
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.
Subjects: 
Machine learning
Gaussian process regression
Neural networks
Multiple-input multiple-output (MIMO)
Economic forecasting
Tourism demand
JEL: 
C45
C51
C53
C63
E27
L83
Persistent Identifier of the first edition: 
Creative Commons License: 
http://creativecommons.org/licenses/by/4.0/
Document Type: 
Article

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