Forecasting holiday daily tourist flow based on seasonal support vector regression with adaptive genetic algorithm
Abstract— Forecasting holiday daily tourist flow based on seasonal support vector regression with adaptive genetic algorithm. Accurate holiday daily tourist ﬂow forecasting is always the most important issue in tourism industry. However, it is found that holiday daily tourist ﬂow demonstrates a complex nonlinear characteristic and obvious seasonal tendency from different periods of holidays as well as the seasonal nature of climates. Support vector regression < Final Year Projects 2016 > SVR has been widely applied to deal with nonlinear time series forecasting problems, but it suffers from the critical parameters selection and the inﬂuence of seasonal tendency. An approach which hybridizes SVR model with adaptive genetic algorithm (AGA) and the seasonal index adjustment, namely AGA-SSVR, to forecast holiday daily tourist ﬂow. In addition, holiday daily tourist ﬂow data from 2008 to 2012 for Mountain Huangshan in China are employed as numerical examples to validate the performance of the proposed model. The experimental results indicate that the AGA-SSVR model is an effective approach with more accuracy than the other alternative models including AGA-SVR and back-propagation neural network (BPNN).
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