Tourism demand forecasting with different neural networks models

Oscar Claveria, Enric Monte and Salvador Torra ()
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Enric Monte: Department of Signal Theory and Communications, Polytechnic University of Catalunya (UPC)
Salvador Torra: Faculty of Economics, University of Barcelona

No 201321, IREA Working Papers from University of Barcelona, Research Institute of Applied Economics

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.

Keywords: tourism demand; forecasting; artificial neural networks; multi-layer perceptron; radial basis function; Elman networks; Catalonia. JEL classification: L83; C53; C45; R11 (search for similar items in EconPapers)
Pages: 23 pages
Date: 2013-11, Revised 2013-11
New Economics Papers: this item is included in nep-cmp and nep-for
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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Working Paper: Tourism demand forecasting with different neural networks models (2013)
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Persistent link: https://EconPapers.repec.org/RePEc:ira:wpaper:201321

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