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Tourism demand forecasting with neural network models : Different ways of treating information

By Óscar Clavería González, Enric Monte Moreno and Salvador Torra Porras


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

Topics: Previsió econòmica, Turisme, Desenvolupament econòmic, Economic forecasting, Tourism, Economic development
Publisher: Wiley-Blackwell
Year: 2014
DOI identifier: 10.1002/jtr.2016
OAI identifier:

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