Research on Forecasting Optimization of Tourist Arrivals in Scenic Areas Based on Monthly Data——A Case Study of Huangshan Scenic Areas
利用黄山24年月度游客数据，比较9种预测方法（含ARIMA、LSSVM等），以RMSE、MAE等指标优化选择，发现ARIMA在多数指标上最优，LSSVM在MAPE上最优，为景区管理决策提供参考。
Based on 24 years'monthly data of tourist arrivals in Huangshan Scenic Areas,9 forecasting methods including widely used time series prediction methods,Least Square Support Vector Machine(LSSVM),recently popular conbined prediction method,and etal.,are adopted to predict tourist arrivals of 2-year time scale,and optimized by Root Mean Square Error(RMSE),Mean Absolute Error(MAE),Mean Absolute Percentage Error(MAPE) and Theil Inequality Coefficient(TIC) in order that the proper method is chosed to help provide decision support for managers.The result shows autoregressive integrated moving average ARIMA perform best in RMSE,MAE,and TIC,LSSVM perform best in MAPE,but other prediction methods don't present obvious advantage.