Tourism Demand Forecasting with Neural Network Models: Different Ways of Treating Information

Oscar Clavería · Enric Monte · Salvador Torra

131 citations0/0 files storedInternational Journal of Tourism Research

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Forecasting Techniques and Applications Neural Networks and Applications Hydrological Forecasting Using AI

Abstract

Abstract This paper aims to compare the performance of three different artificial neural network techniques for tourist demand forecasting: a multi‐layer perceptron, a radial basis function and an Elman network. We find that multi‐layer perceptron and radial basis function models outperform Elman networks. We repeated the experiment assuming different topologies regarding the number of lags used for concatenation so as to evaluate the effect of the memory on the forecasting results. We find that for higher memories, the forecasting performance obtained for longer horizons improves, suggesting the importance of increasing the dimensionality for long‐term forecasting. Copyright © 2014 John Wiley & Sons, Ltd.

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References

1998Forecasting with artificial neural networks:2002Tourism as a long-run economic growth factor: the Spanish case 1994Time series prediction: Forecasting the future and understanding the past 1991Time series forecasting using neural networks vs. Box- Jenkins methodology 2006Support vector regression with genetic algorithms in forecasting tourism demand 2000Back-propagation learning in improving the accuracy of neural network-based tourism demand forecasting 2003A comparison of three different approaches to tourist arrival forecasting 1999A neural network model to forecast Japanese demand for travel to Hong Kong

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