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 |
URI: | http://hdl.handle.net/2445/57643 |
Related resource: | http://dx.doi.org/10.1002/jtr.2016 |
ISSN: | 1099-2340 |
Appears in Collections: | Articles publicats en revistes (Econometria, Estadística i Economia Aplicada) |
Files in This Item:
File | Description | Size | Format | |
---|---|---|---|---|
Int J of Tour Research (2015) - Vol 17 Issue (5) - pp 492-500.pdf | 124.36 kB | Adobe PDF | View/Open |
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.