重庆理工大学学报：自然科学2012，Vol.26Issue(2)：118-121,4.
一种基于时序可变加权系数的组合预测模型
A Combination Forecasting Model Based on Time Series Variable Weighting Coefficient
摘要
Abstract
To improve the forecasting model＇ s accuracy, this paper presented an optimized combina- tion forecasting model based on analysis of time series variable weighting coefficient. With the Least Sum of Square Error as the standard, this study established a combination forecasting model whose variable weighting coefficient is nonnegative, and calculated the optimized weighting coefficient of in- dividual forecasting model in different time. Using the ARMA model, the study predicted the variable weighting coefficients of individual forecasting models and figured out the combination forecasting model. The example showed that this combination forecasting model is better than individuals. As a result, it can make the best of the information of individuals and lead to obtaining least Sum of Square Error of the model. Therefore, the model is more accurate and applicable.关键词
变权组合预测/时间序列/指数模型/灰色预测Key words
variable weighting coefficient combination forecasting/time series/exponent model/grey forecasting model分类
数理科学引用本文复制引用
王金山,杨国超..一种基于时序可变加权系数的组合预测模型[J].重庆理工大学学报：自然科学,2012,26(2):118-121,4.
