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Modelling cross-dependencies between Spain’s regional tourism markets with an extension of the Gaussian process regression model

Oscar Claveria, Enric Monte () and Salvador Torra ()
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Enric Monte: Polytechnic University of Catalunya
Salvador Torra: University of Barcelona

SERIEs: Journal of the Spanish Economic Association, 2016, vol. 7, issue 3, No 3, 357 pages

Abstract: 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.

Keywords: Machine learning; Gaussian process regression; Neural networks; Multiple-input multiple-output (MIMO); Economic forecasting; Tourism demand (search for similar items in EconPapers)
JEL-codes: C45 C51 C53 C63 E27 L83 (search for similar items in EconPapers)
Date: 2016
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Citations: View citations in EconPapers (6)

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DOI: 10.1007/s13209-016-0144-7

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