带遗忘因子的递推GM(1,1)预测模型  

Recursive GM (1,1) Forecasting Model with Forgetting Factor

作　　者：赵建堂[1] ZHAO Jian-tang(College of Mathematics and Information Science,Xianyang Normal University,Xianyang 712000,China)

机构地区：[1]咸阳师范学院数学与信息科学学院

出　　处：《数学的实践与认识》2019年第24期148-153,共6页Mathematics in Practice and Theory

基　　金：陕西省教育厅项目(No.11JK0513)

摘　　要：针对有新信息逐步引入的灰色预测模型,为区分新旧数据对模型的贡献度,提出一种带遗忘因子的递推灰色预测模型.通过在目标函数中增加遗忘因子,实现新旧建模数据的动态加权,达到逐步遗忘旧信息的目的.给出了灰色预测模型参数的递推估计算法,将新获信息实时加入,以提升模型预测精度.网络流量的递推GM(1,1)预测模型建立及数值仿真,验证了改进算法的有效性.New information can effectively improve the prediction accuracy of the forecasting model,in order to reduce computational complexity of traditional grey forecasting model,and to distinguish the contribution of old and new data of the model,proposed a recursive grey forecasting model with forgetting factor.By adding forgetting factor into the objective function,the dynamic weight of the old and the new data can be given,and the previous information can be gradually forgotten.The recursive estimation algorithm of grey forecasting model parameters is given,and the new information is added in real time to improve the prediction accuracy of the model.The effectiveness of the improved algorithm is verified by the empirical analysis of Network traffic forecasting model.

关 键 词：灰色预测 GM(1 1) 递推 遗忘因子 网络流量 

分 类 号：N941.5[自然科学总论—系统科学]