Tourism demand forecasting with neural network models: different ways of treating information
PublisherJohn Wiley & Sons
Rights accessRestricted access - publisher's policy
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.
CitationClaveria, O.; Monte, E.; Torra Porras, S. Tourism demand forecasting with neural network models: different ways of treating information. "International Journal of Tourism Research", 21 Juliol 2014, vol. 2014.
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