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A multivariate neural network approach to tourism demand forecasting

Autor
Claveria, O.; Monte, E.; Torra Porras, Salvador
Tipus d'activitat
Document cientificotècnic
Data
2013-12-04
Codi
RePEc:aqr:wpaper:201410
Repositori
http://hdl.handle.net/2117/23086 Obrir en finestra nova
URL
http://www.ub.edu/irea/working_papers/2014/201417.pdf Obrir en finestra nova
Resum
This study compares the performance of different Artificial Neural Networks models for tourist demand forecasting in a multiple-output framework. We test the forecasting accuracy of three different types of architectures: a multi-layer perceptron network, a radial basis function network and an Elman neural network. We use official statistical data of inbound international tourism demand to Catalonia (Spain) from 2001 to 2012. By means of cointegration analysis we find that growth rates of touris...
Citació
Claveria, O.; Monte, E.; Torra, S. "A multivariate neural network approach to tourism demand forecasting". 2013.
Paraules clau
Artificial neural networks. JEL classification: L83, C45, C53, Cointegration, Forecasting, Multiple-output, R11, 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