The application of support vector machines to forecast tourist arrivals in Barbados: An empirical study

Abstract

Accurate tourist demand forecasting systems are essential in tourism planning, particularly in tourism-based countries. Artificial neural networks are attracting attention to forecast tourist arrivals due to their general nonlinear mapping capabilities. Unlike most conventional neural network models, which are based on the empirical risk minimization principle, support vector machines (SVMs) apply the structural risk minimization principle to minimize an upper bound of the generalization error, rather than minimizing the training error. This investigation presents an SVM model with genetic algorithms to forecast the tourist arrivals. Genetic algorithms (GAs) are used to determine free parameters in the SVM model. Empirical results that involve tourist arrival data for Barbados reveal that the proposed model outperforms other approaches in the literature. [PUBLICATION ABSTRACT]