Abstract
Support vector machines (SVMs) have been successfully applied to solve nonlinear regression and times series problems. However, the application of SVMs for tourist forecasting has not been widely explored. Furthermore, most SVM models are applied for solving univariate forecasting problems. Therefore, this investigation examines the feasibility of SVMs with backpropagation neural networks in forecasting tourism demand influenced by different factors. A numerical example from an existing study is used to demonstrate the performance of tourist forecasting. Experimental results indicate that the proposed model outperforms other approaches for forecasting tourism demand.
This research was conducted with the support of National Science Council NSC 94-2213-E-260-023.
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Pai, PF., Hong, WC., Lin, CS. (2005). Forecasting Tourism Demand Using a Multifactor Support Vector Machine Model. In: Hao, Y., et al. Computational Intelligence and Security. CIS 2005. Lecture Notes in Computer Science(), vol 3801. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11596448_75
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DOI: https://doi.org/10.1007/11596448_75
Publisher Name: Springer, Berlin, Heidelberg
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