A New Combining Prediction Method of visitor numbers at Shanghai Expo

摘要：

Forecast of visitor numbers to the large-scale activities is the key issue of collective behaviors analysis and control. At present,forecasting visitor numbers is mainly based on traditional research approach or sole artificial neural network technology. Recent study results show that combining forecast model approach enjoys more precise forecast than monomial forecast approach. In this paper,a new forecast approach based on inflexion point was proposed. Then,we combined BP neural network and the inflexion approach to make comprehensive analysis and to predict visitor numbers to Shanghai Expo per day. Experimental results indicate that the proposed combining approach is feasible and effective in forecast of the visitor numbers,and is more precise in terms of monomial forecast method. Respectively,the average relative error of combining model is 0.1085. 0.1177,0.1875 less than that of inflexion model. BP model and ARIMA model.

关键词： forecasting BP model inflexion model visitor numbers combining model

作者: Shusheng Li Rong Xie Li Song Xiaokang Yang Wenjun Zhang

作者单位: Institute of Image Communication and Information Processing Shanghai Digital Media Processing and Tr Institute of Image Communication andInformation Processing Shanghai Digital Media Processing and Tra

会议类型: 国际会议

会议名称: 2011 International Conference on Opto-Electronics Engineering and Information Science(2011光电电子工程与信息科学国际会议 ICOEIS 2011)

会议地点: 西安

会议语种:英文

页码: 673-677

在线出版日期: 2011-12-23（万方平台首次上网日期，不代表论文的发表时间）