摘要
为了提高销售预测的准确性,建立了组合销售预测模型。历史销售数据是非线性、时变的时间序列,可看成由线性和非线性2部分组成。用ARMA模型预测线性部分,用BP_Ada Boost模型预测非线性部分,然后将2部分预测结果叠加得到销售预测结果。该组合模型克服了单纯采用ARMA模型预测结果精度低的问题,也克服了单纯使用BP神经网络模型容易陷入局部极小值的问题。经实验对比表明,采用组合预测模型能够更加准确、全面地反应销售规律,提高了销售预测的准确性。
In order to improve the accuracy of prediction, a combined sales prediction model is established.The historical sales data is nonlinear, time-varying time series.It consists of two parts, the linear and nonlinear.By using the ARMA model, the lin-ear part can be predicted while the nonlinear part can be predicted by using BP_AdaBoost model.Then the two prediction results are added together.The combination model overcomes the problem of low accuracy by using ARMA model alone.What’ s more, it also overcomes the problems that BP neural network model is easy to fall into local minimum.The experiments show that the combination model can improve the accuracy of sales prediction and reflect market rules more accurately and comprehensively.
出处
《计算机与现代化》
2015年第2期14-18,共5页
Computer and Modernization
基金
国家高新技术研究发展计划(2009AA062802)
国家自然科学基金资助项目(60473125)
中国石油(CNPC)石油科技中青年创新基金资助项目(05E7013)
国家重大专项子课题(G5800-08-ZS-WX)
关键词
BP神经网络
ADABOOST算法
预测
组合模型
ARMA
ARMA model
BP neural network
AdaBoost algorithm
prediction
combination model
