Please use this identifier to cite or link to this item: http://hdl.handle.net/2445/57831
Title: Tourism demand forecasting with different neural networks models
Author: Clavería González, Óscar
Monte Moreno, Enric
Torra Porras, Salvador
Keywords: Previsió econòmica
Turisme
Desenvolupament econòmic
Economic forecasting
Tourism
Economic development
Issue Date: 2013
Publisher: Universitat de Barcelona. Institut de Recerca en Economia Aplicada Regional i Pública
Abstract: This paper aims to compare the performance of different Artificial Neural Networks techniques for tourist demand forecasting. We test the forecasting accuracy of three different types of architectures: a multi-layer perceptron, a radial basis function and an Elman network. We also evaluate the effect of the memory by repeating the experiment assuming different topologies regarding the number of lags introduced. We used tourist arrivals from all the different countries of origin to Catalonia from 2001 to 2012. We find that multi-layer perceptron and radial basis function models outperform Elman networks, being the radial basis function architecture the one providing the best forecasts when no additional lags are incorporated. These results indicate the potential existence of instabilities when using dynamic networks for forecasting purposes. We also 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: Reproducció del document publicat a: http://www.ub.edu/irea/working_papers/2013/201321.pdf
It is part of: IREA – Working Papers, 2013, IR13/021
AQR – Working Papers, 2013, AQR13/13
URI: http://hdl.handle.net/2445/57831
ISSN: 2014-1254
Appears in Collections:Documents de treball (Institut de Recerca en Economia Aplicada Regional i Pública (IREA))
AQR (Grup d’Anàlisi Quantitativa Regional) – Working Papers
Documents de treball / Informes (Econometria, Estadística i Economia Aplicada)

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