面向旅游突发事件的客流量混合预测方法研究

doi:10.16381/j.cnki.issn1003-207x.2017.05.020

Abstract

Abstract: Because of sudden explosiveness and destructiveness as well as information asymmetry caused by tourism emergency events, the tourist flow deviates from original patterns and presents nonlinear and linear features, which causes a great difficulty to tourist flow forecasting. Traditional forecasting methods cannot solve this complicated problem. The article proposes a kind of tourist flow hybrid forecasting model for tourism emergency events which include two methods. One method is Support Vector Regression (SVR). It has good ability to deal with nonlinear and small sample problems and has been successfully used in many forecasting fields by researchers. The other method is Autoregressive Integrated Moving Average (ARIMA) which can deal with linear problem easily. At same time, the three parameters C,ε,σ of SVR affect the accuracy of forecast. A kind of Chaos Particle Swarm Optimization (CPSO) is used in the article. By the local search ability of Chaotic Local Search(CLS) as well as global search ability of Adaptive Inertia Weight Factor (AIWF) in CPSO, the optimal parameters C,ε,σ of SVR can be found effectively.
The detail process of tourist flow hybrid forecasting model is as follow. Firstly SVR is used to forecast tourist flow during emergencies. Meanwhile, CPSO is implemented to select the SVR parameters; Secondly ARIMA model is provided to forecast residual sequence of forecasting values. Finally two predicted values will be added, which leads to the final predicted values.
Data set from Mount Huangshan during Wenchuan Earthquakes period are used to validate the effectiveness of the hybrid models. The number of the data is from February 12, 2008 to June 12, 2008, including the daily tourist flow and daily tourist flow before eight o'clock. The results show that the hybrid approaches are significantly higher in accuracy than CPSO-SVR and PSO-SVR., which provide an effective choice to tourism emergency events flow forecasting as well as similar industries facing the same situation.Next researches will focus on tourist flow forecasting under the background of big data.

Key words: SVR, ARIMA, CPSO, tourism emergeney everts, tourist floco forecasting

CLC Number:

TP181

CHEN Rong, LIANG Chang-yong, LU Wen-xing, DONG Jun-feng, GE Li-xin. The Research of Tourist Flow Hybrid Forecasting Model for Tourism Emergency Events[J]. Chinese Journal of Management Science, 2017, 25(5): 167-174.

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The Research of Tourist Flow Hybrid Forecasting Model for Tourism Emergency Events

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