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Tourism demand forecasting with different neural networks models

By Óscar Clavería González, Enric Monte Moreno and Salvador Torra Porras

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

Topics: Previsió econòmica, Turisme, Desenvolupament econòmic, Economic forecasting, Tourism, Economic development
Publisher: Universitat de Barcelona. Institut de Recerca en Economia Aplicada Regional i Pública
OAI identifier: oai:recercat.cat:2072/301794
Provided by: RECERCAT
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