摘要
针对组合预测传统熵值赋权方法可能存在大样本下权重差异不大的不足,引入演化聚类分析技术,提出一类组合预测改进的熵值赋权模型.在确定好单项预测方法和结果后,通过对所有单项预测结果给出的绝对误差信息进行聚类分析,得到单项预测方法下误差信息在不同类中的离散分布,给出单项预测结果在整体和局部两个层面的差异比较工具,进而利用得到的离散分布应用熵权模型给出改进的熵值赋权方法.结合美元兑日元的汇率数据分析表明了所提出方法的可行性和有效性.
In order to overcome lower difference among weights in combination forecasting with large sample determined by traditional entropy weighting methods,a modified entropy weighting method is developed by introducing the evolutionary automatic clustering analysis.When single forecasting approaches and predictions are fixed,all absolute errors are clustered and diverse discrete distributions of absolute errors corresponding to single forecasting approaches among clusters are obtained,which are tools for further comparisons among the single forecasting methods.Then,a modified entropy weighting method is given using the obtained discrete distributions.By using a numerical study with exchange rate between dollar and yen,the feasibility and effectiveness of the developed method are illustrated.
作者
陶志富
朱家明
刘金培
陈华友
TAO Zhi-fu;ZHU Jia-ming;LIU Jin-pei;CHEN Hua-you(School of Economics,Anhui University,Hefei 230601,China;School of Mathematical Sciences,Anhui University,Hefei 230601,China;School of Business,Anhui University,Hefei 230601,China)
出处
《控制与决策》
EI
CSCD
北大核心
2020年第2期410-416,共7页
Control and Decision
基金
国家自然科学基金项目(71701001,71771001,71871001,61502003)
安徽省哲学社会科学规划项目(AHSKQ2016D13)
安徽大学博士科研启动基金项目.
关键词
改进熵权
聚类分析
组合预测
汇率
modified entropy weighting method
cluster analysis
combination forecasting
exchange rate
