Bagging in Tourism Demand Modeling and Forecasting
研究将自助聚合（bagging）方法引入旅游需求建模与预测，结合自动化变量选择过程，基于六个客源市场到澳大利亚的游客到达数据，证明bagging能有效提升预测精度。
This study introduces bootstrap aggregation (bagging) in modeling and forecasting tourism demand. The aim is to improve the forecast accuracy of predictive regressions while considering fully automated variable selection processes which are particularly useful in industry applications. The procedures considered for variable selection is the general-to-specific (GETS) approach based on statistical inference and stepwise search procedures based on a measure of predictive accuracy (MPA). The evidence based on tourist arrivals from six source markets to Australia overwhelmingly suggests that bagging is effective for improving the forecasting accuracy of the models considered.