摘要
针对铁矿粉库存量预测问题,结合灰色系统模型与时间序列模型的优点,提出一种基于多模型集成的库存量集成预测方法。根据库存量历史数据,分别建立基于残差修正的等维新息GM(1,1)模型与自回归积分移动平均模型ARIMA(p,d,q);采用基于信息熵的方法对2种模型进行加权集成;分别采用单一模型与集成模型对铁矿粉库存量进行预测。仿真验证结果表明:集成预测模型实现库存量的准确预测,在3种模型中预测结果最好。
To realize effective prediction for iron mine powder,a multi-model based integrated prediction strategy was presented,which makes use of gray system model and time series model.According to the historical records on iron mine powder,two independent prediction models,the residual based equal dimension new information GM（1,1）,and auto regressive integrated moving average model（ARIMA（p,d,q））,were constructed respectively.Applying information entropy method,both models were weighted integrated to realize multi-model based integrated prediction.Finally,the integrated prediction model and both single prediction models were tested to predict iron mine powder.The comparison results show that the integrated prediction model is of higher precision than the other two.
出处
《中南大学学报（自然科学版）》
EI
CAS
CSCD
北大核心
2011年第11期3399-3407,共9页
Journal of Central South University:Science and Technology
基金
国家高技术研究计划("863"计划)项目(2008AA04Z128
2009AA04Z157)
关键词
库存量预测
GM(1
1)模型
ARIMA模型
集成模型
inventories prediction
gray model GM（1
1）
auto regressive integrated moving average model（ARIMA（p
d
q））
integrated model
