The tourism industry, which benefits the transportation, accommodation, catering, entertainment and retailing sectors, has been blooming in the past few decades. The 20th century witnessed a steady increase in tourism all over the world. Each country wants to know its international visitors and tourism receipts in order to choose an appropriate strategy for its economic well-being. Hence, a reliable forecast is needed and plays a major role in tourism planning.
Support vector machine(SVM) is first applied in pattern recognition problem, however, introduction of ε- insensitive loss function by Vapnik, SVM has been developed in solving non-linear regression estimation problem, such new techniques called support vector regression(SVR). This study will apply support vector regression technique to construct the prediction model of tourism demand. In order to construct effective SVR model, we have to set SVR’s parameters carefully. This study proposes a new model called GA-SVR that searching for SVR’s optimal parameters through applies real-valued genetic algorithms, and uses the optimal parameters to construct SVR model. The conclusion in the forecasting the tourism demand study. SVR model shows its reliabilities and as a good prediction technique. Its generalization performance is even more accurate than neural networks. Moreover, in order to test the importance and understand the features of SVR model, this study implements sensitivity analysis technique, the analysis demonstrates that incorrectly selected parameters will lead the model’s results in the risk of over-fitting, or under-fitting.