Para acceder a los documentos con el texto completo, por favor, siga el siguiente enlace: http://hdl.handle.net/2117/27523

Tourism demand forecasting with neural network models: different ways of treating information
Claveria, Oscar; Monte Moreno, Enrique; Torra Porras, Salvador
Universitat Politècnica de Catalunya. Departament de Teoria del Senyal i Comunicacions; Universitat Politècnica de Catalunya. VEU - Grup de Tractament de la Parla
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.
Peer Reviewed
Àrees temàtiques de la UPC::Informàtica::Intel·ligència artificial
Àrees temàtiques de la UPC::Informàtica
Neural networks (Computer science)
Perceptrons
Tourism demand
Forecasting
Artificial neural networks
Multi-layer perceptron
Radial basis function
Elman networks
Xarxes neuronals (Informàtica)
Perceptrons
Attribution-NonCommercial-NoDerivs 3.0 Spain
http://creativecommons.org/licenses/by-nc-nd/3.0/es/
info:eu-repo/semantics/publishedVersion
Artículo
John Wiley & Sons
         

Mostrar el registro completo del ítem

Documentos relacionados

Otros documentos del mismo autor/a

Claveria, Oscar; Monte Moreno, Enrique; Torra, Salvador
Monte Moreno, Enrique; Hernández Pajares, Manuel; García Rigo, Alberto; Beniguel, Yannick; Orús Pérez, Raul; Prieto Cerdeira, Roberto; Schlueter, Stefan
Monte Moreno, Enrique; Hernández Pajares, Manuel