Modelling cross-dependencies between Spain’s regional tourism markets with an extension of the Gaussian process regression model
- Authors
- Type
- Published Article
- Journal
- SERIEs
- Publisher
- Springer-Verlag
- Publication Date
- Jun 26, 2016
- Volume
- 7
- Identifiers
- DOI: 10.1007/s13209-016-0144-7
- Source
- MyScienceWork
- License
- Green
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.