“Tourism demand forecasting with different neural networks models”
AbstractThis 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.
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Bibliographic InfoPaper provided by University of Barcelona, Regional Quantitative Analysis Group in its series AQR Working Papers with number 201313.
Length: 23 pages
Date of creation: Nov 2013
Date of revision: Nov 2013
tourism demand; forecasting; artificial neural networks; multi-layer perceptron; radial basis function; Elman networks; Catalonia. JEL classification: L83; C53; C45; R11;
Other versions of this item:
- Oscar Claveria & Enric Monte & Salvador Torra, 2013. "“Tourism demand forecasting with different neural networks models”," IREA Working Papers 201321, University of Barcelona, Research Institute of Applied Economics, revised Nov 2013.
This paper has been announced in the following NEP Reports:
- NEP-ALL-2013-11-29 (All new papers)
- NEP-CMP-2013-11-29 (Computational Economics)
- NEP-FOR-2013-11-29 (Forecasting)
- NEP-TUR-2013-11-29 (Tourism Economics)
Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:
- Witt, Stephen F. & Witt, Christine A., 1995. "Forecasting tourism demand: A review of empirical research," International Journal of Forecasting, Elsevier, vol. 11(3), pages 447-475, September.
- Claveria, Oscar & Torra, Salvador, 2014. "Forecasting tourism demand to Catalonia: Neural networks vs. time series models," Economic Modelling, Elsevier, vol. 36(C), pages 220-228.
- Zhang, Guoqiang & Eddy Patuwo, B. & Y. Hu, Michael, 1998. "Forecasting with artificial neural networks:: The state of the art," International Journal of Forecasting, Elsevier, vol. 14(1), pages 35-62, March.
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