基于优化组合模型的航空货邮市场需求预测 ——来自2007—2017年全球及中国面板数据的实证分析

航空货邮周转量是评价航空物流业发展的一个重要指标,传统预测方法在复杂系统的表现较差。在趋势外推法、多项式回归和灰色预测的基础上,引进诱导有序加权平均(IOWA)算子组合预测模型。结果表明,该方法对航空货邮周转量的预测效果比其他方法更好,以2018年全球数据进行检验,预测误差仅有4.72%。通过预测结果进一步对比分析全球及中国航空货邮市场的发展情况及未来趋势,为中国航空物流业的转型升级以及经济结构的优化发展提供参考与启示。

Demand Forecast of Air Cargo and Mail Market Based on Optimized Combination Model:Empirical Analysis of Global and Chinese Panel Data from 2007 to 2017

Air cargo and mail turnover is an important index to evaluate the development of aviation logistics industry. Traditional prediction methods perform poorly in the prediction of complex systems. Therefore, based on the trend extrapolation method, polynomial regression and grey prediction, this paper introduces the induced ordered weighted average operator combination prediction model. The results show that this method has a better forecasting effect on air cargo and mail turnover than other methods, and the prediction error is only 4.72% when it is tested with 2018 global data. The status and future trends of the global and Chinese air cargo and mail markets is further compared and analyzed through the prediction results, which will provide some suggestions and implications for the upgrading of China''s aviation logistics industry and the optimization and development of the national economic structure.