Bitte verwenden Sie diesen Link, um diese Publikation zu zitieren, oder auf sie als Internetquelle zu verweisen: https://hdl.handle.net/10419/158560 
Erscheinungsjahr: 
2016
Quellenangabe: 
[Journal:] SERIEs - Journal of the Spanish Economic Association [ISSN:] 1869-4195 [Volume:] 7 [Issue:] 3 [Publisher:] Springer [Place:] Heidelberg [Year:] 2016 [Pages:] 341-357
Verlag: 
Springer, Heidelberg
Zusammenfassung: 
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.
Schlagwörter: 
Machine learning
Gaussian process regression
Neural networks
Multiple-input multiple-output (MIMO)
Economic forecasting
Tourism demand
JEL: 
C45
C51
C53
C63
E27
L83
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