基于季节SVR-PSO的旅游客流量预测模型研究

准确的旅游客流量预测对旅游风景区有着决定性的意义.受多种原因影响，旅游客流量预测不仅呈现复杂非线性特点，而且显示出典型的季节性趋势，尤其在旅游旺季.文章提出一种季节支持向量回归（seasonal support vector regression，SSVR）和粒子群算法（particle swarm optimization，PSO）结合模型，即SSVR-PSO，实现对旅游客流量的预测.来自国内著名5A级风景区黄山2008-2011年最新月客流量数据仿真结果显示，SSVR-PSO模型预测精度明显高于SVR-PSO、SVR-GA、BPNN、ARIMA等方法，是进行旅游客流量预测的有效工具.

Forecasting tourism flow based on seasonal PSO-SVR model

Accurate tourism flow forecasting has always been the most important issues in the scenic spots. Due to various influences, tourism flow forecasting reveals not only a rather complex nonlinear characteristics but also seasonal trend, particularly during tourism peak periods. This article presents a tourism flow forecasting model that combines the seasonal support vector regression model with particle swarm optimization algorithm (SSVR-PSO), to forecast tourism flow. Finally, the new monthly data sets from a famous 5A scenic spot Mount Huangshan during 2008-2011 are simulated repeatedly. Empirical results demonstrate that a SSVR-PSO approach is an effective way to forecast tourism flow, outperforming SVR-PSO, SVR-GA, BPNN and ARIMA.