Affordable Access

Access to the full text

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.

Report this publication

Statistics

Seen <100 times