Modeling and International Tourism Demand Forecasting for Taiwan：Using Artificial Neural Networks
Date Issued
2009
Date
2009
Author(s)
Lee, Yi-Hsuan
Abstract
Tourism demand is important for policy-making. The study of tourism demand is attracting more and more attention. Research has shown multiple factors may have an impact on tourism demand. However, few studies have been done on applying the multiple factors to forecast tourism demand. The purpose of this thesis is to determine if the model applying artificial neural network would be more suitable for forecasting international tourism demand. The models were established for tourist from Japan to Taiwan. There were three artificial neural network models developed for forecasting tourism demand. The models are including self-organizing map (SOM), radial basis function (RBF), and back-propagation (BP). Rather, factors were specified which, on the basis of previous studies, were applied to the models. The data was collected from 1979 to 2006.Results indicated that SOM models did show the best prediction results and compared to traditional model also show the better result. Exchange rate was the most significant factor for international tourism demand.
Subjects
tourism demand
artificial neural network
forecasting model
SOM
BP
RBF
Type
thesis
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