【摘要】 入境旅游客流量预测模型中输入变量的多少在一定程度上制约了模型的模拟速度和预测效果。首先,由主成分分析法对影响入境游客流量的指标进行综合分析从而确定出少数几个主要指标作为预测模型的输入变量,然后建立以主要指标为输入变量以客流量为输出变量的基于超松弛改进的最小二乘支持向量机预测模型。通过实验仿真,结果显示了基于PCA-SOR-LS-SVM的入境游客流量预测模型具有较好的预测精度和较强的推广价值。更多还原

【Abstract】 In forecast model on tourist flow of entering, the number of input variables to some extent constrains simulation speed and prediction of effects. Firstly, the key indicators are gained through analyzing the impact indicators of tourist flow using principal component analysis. Secondly, it is established the forecast model of Least Squares Support Vector Machine improved by successive overrelaxation based on input variables of key indicators and output variable of tourist flow. Through experimental simulation, the results show that the model of PCA-SOR-LS-SVM on tourist flow of entering has good prediction accuracy and strong promoted value.更多还原