摘要
GOM(1,1)模型同GM(1,1)模型一样存在背景值构造的不足.分析已有以累加序列为齐次指数序列推导出的背景值构造形式存在的问题,以背景值几何思想建立两种加权背景值构造,分别为齐次指数序列下的定权背景值构造和近似齐次指数序列下的变权背景值构造.实例应用结果显示加权背景值构造下的GOM(1,1)模型不仅可以完全拟合齐次指数序列,近似齐次指数序列下建模的预测精度也提高了.
The construction of background value is deficient in GOM（1,1） model as in GM（1,1） model. The paper analyzes the deficiency of the existing background value, which is obtained under the premise that the accumulated data are homogenous index sequence. Two types of weighted background value are proposed based on the geometric definition of background value, which are constant weighted background value for homogenous exponential sequence and variable weighted background value for the data sequences which satisfy to the homogenous exponential rule. For case studies, measurement results show that with weighted background value GOM （1, 1） model can completely fit homogenous exponential sequence and its forecasting precision is improved when modeling for the data sequences which satisfy to the homogenous exponential rule.
出处
《杭州师范大学学报（自然科学版）》
CAS
2010年第1期33-36,共4页
Journal of Hangzhou Normal University(Natural Science Edition)
关键词
GOM(1
1)模型
加权
背景值
齐次指数序列
GOM（1,1） model
weighted
background value
homogenous exponential sequence
