摘要
针对传统灰色GM(1,1)预测模型维数确定困难、适用范围小和预测精度不高等局限性,提出了一种能处理复杂序列的动态的最佳维数GM(1,1)幂模型.最后以2003-2013年居民收入基尼系数为研究样本做预测分析,同时建立了传统GM(1,1)模型、经典GM(1,1)幂模型作为对比,结果表明:动态的最佳维数GM(1,1)幂模型的平均相对误差为0.08%,显著低于传统GM(1,1)模型的1.04%和经典GM(1,1)幂模型的0.85%.
Aiming at the limitations of traditional gray GM（1,1） prediction model in the difficult to determine the dimensions, the small scope of application and the low prediction accuracy, we propose a dynamic optimal dimension GM（1,1） power model which is able to handle complex sequences. Finally, we use the 2003-2013 residents income Gini coefficient as the research sample to predict, at the same time establish the traditional GM（1,1） model and the classic GM（1,1） power model as a comparison, the results show that the average relative error of the dynamic optimal dimension GM（1,1） power model is 0.08%, significantly lower than 1.04% of the traditional GM（1,1） model and 0.85% of the classic GM（1,1） power model.
出处
《数学的实践与认识》
北大核心
2015年第1期88-95,共8页
Mathematics in Practice and Theory
