Please use this identifier to cite or link to this item: http://hdl.handle.net/2445/57643
Title: Tourism demand forecasting with neural network models : Different ways of treating information
Author: Clavería González, Óscar
Monte Moreno, Enric
Torra Porras, Salvador
Keywords: Previsió econòmica
Turisme
Desenvolupament econòmic
Economic forecasting
Tourism
Economic development
Issue Date: Oct-2015
Publisher: Wiley-Blackwell
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.
Note: Versió preprint del document publicat a: http://dx.doi.org/10.1002/jtr.2016
It is part of: International Journal of Tourism Research, 2015, vol.17, num. 5, pgs. 492-500
Related resource: http://dx.doi.org/10.1002/jtr.2016
URI: http://hdl.handle.net/2445/57643
ISSN: 1099-2340
Appears in Collections:Articles publicats en revistes (Econometria, Estadística i Economia Aplicada)

Files in This Item:
File Description SizeFormat 
Int J of Tour Research (2015) - Vol 17 Issue (5) - pp 492-500.pdf124.36 kBAdobe PDFView/Open


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.