Hybridizing GMDH and least squares SVM support vector machine for forecasting tourism demand.
作者： Ani Shabri , Puteh Saad , Ruhaidah Samsudin
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摘要: In this paper, we proposed a novel hybrid group method of data handling least squares support vector machine (GLSSVM) algorithm, which combines the theory (GMDH) with (LSSVM). With GMDH is used to determine inputs LSSVM and model works as time series forecasting. The aim study examine feasibility in tourism demand forecasting by comparing it model. tourist arrivals Johor Malaysia during 1970 2008 were employed set. comparison modeling results demonstrate that outperforms than two other nonlinear approaches models.
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