Tourism Demand Forecasting with Neural Network Models: Different Ways of Treating Information
Year: 2014
Type: article
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.
DOI: 10.1002/jtr.2016
Authors: Oscar Claveria, Enric Monte, Salvador Torra
Language: English
Field: Decision Sciences
Domain: Social Sciences
Open Access status: green
OpenAlex ID: W1908656564