| By: |
Oscar Claveria (Faculty of Economics, University of Barcelona);
Enric Monte (Department of Signal Theory and Communications, Polytechnic University of Catalunya);
Salvador Torra (Faculty of Economics, University of Barcelona) |
| Abstract: |
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 tourist arrivals from all
different countries share a common stochastic trend, which leads us to apply a
multivariate out-of-sample forecasting comparison. When comparing the
forecasting accuracy of the different techniques for each visitor market and
for different forecasting horizons, we find that radial basis function models
outperform multi-layer perceptron and Elman networks. We repeat the experiment
assuming different topologies regarding the number of lags used for
concatenation so as to evaluate the effect of the memory on the forecasting
results, and we find no significant differences when additional lags are
incorporated. These results reveal the suitability of hybrid models such as
radial basis functions that combine supervised and unsupervised learning for
economic forecasting with seasonal data. |
| Keywords: |
forecasting; tourism demand; cointegration; multiple-output; artificial neural networks. JEL classification: L83; C53; C45; R11 |
| Date: |
2014–05 |
| URL: |
http://d.repec.org/n?u=RePEc:aqr:wpaper:201410&r=tur |