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

Autor
Claveria, O.; Monte, E.; Torra Porras, Salvador
Tipus d'activitat
Article en revista
Revista
International journal of tourism research
Data de publicació
2014-07-21
Volum
17
Número
3
Pàgina inicial
209
Pàgina final
312
DOI
https://doi.org/10.1002/jtr.2016 Obrir en finestra nova
Repositori
http://hdl.handle.net/2117/27523 Obrir en finestra nova
URL
http://onlinelibrary.wiley.com/doi/10.1002/jtr.2016/abstract Obrir en finestra nova
Resum
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 high...
Citació
Claveria, 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.
Paraules clau
Artificial neural networks, Elman networks, Forecasting, Multi-layer perceptron, Radial basis function, Tourism demand
Grup de recerca
IDEAI-UPC Intelligent Data Science and Artificial Intelligence
TALP - Centre de Tecnologies i Aplicacions del Llenguatge i la Parla
VEU - Grup de Tractament de la Parla

Participants