基于MIV-GA-BP神经网络的我国棉价预测研究
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摘要
探究了MIV-GA-BP神经网络模型对我国棉花价格预测的情况。以国家棉花价格指数B作为棉价反映指标,选取了棉花产量、进口量、消费量等13个影响棉花价格的因素,采用平均影响值(MIV)、遗传算法(GA)与BP神经网络相结合的方法,按照15%的淘汰率进行筛选,得出我国棉价波动的主要影响因素,并在此基础上构建了MIV-GA-BP神经网络模型。以2015年1月—2017年12月的3128B棉花月度平均价格为样本数据,进行MIV-GA-BP模型拟合精度评估和预测精度评估。认为:基于MIV-GA-BP神经网络模型拟合精度良好,预测精度较高,训练样本可反映99%的样本特征。
The situation of CN Cotton Bby using MIV-GA-BP neural network model was discussed.The national CN Cotton B was used as reflection index of cotton prices.13 factors affecting cotton prices such as cotton output,import output and consumption output were selected.The combination method of mean impact value(MIV),genetic algorithm(GA)and BP neural network was screened according to the elimination rate of 15%.Main influencing factors of CN Cotton B were obtained.MIV-GA-BP neural network model was constructed on this basis.The monthly average data of 3128 Bcotton price from January 2015 to December 2017 were taken as sample data,fitting precision and forecasting accuracy of MIV-GA-BP neural network model were evaluated.It is considered that fitting accuracy of MIV-GA-BP neural network model is better and prediction accuracy of the model is higher.The training sample can reflect 99% sample characteristics.
The situation of CN Cotton Bby using MIV-GA-BP neural network model was discussed.The national CN Cotton B was used as reflection index of cotton prices.13 factors affecting cotton prices such as cotton output,import output and consumption output were selected.The combination method of mean impact value(MIV),genetic algorithm(GA)and BP neural network was screened according to the elimination rate of 15%.Main influencing factors of CN Cotton B were obtained.MIV-GA-BP neural network model was constructed on this basis.The monthly average data of 3128 Bcotton price from January 2015 to December 2017 were taken as sample data,fitting precision and forecasting accuracy of MIV-GA-BP neural network model were evaluated.It is considered that fitting accuracy of MIV-GA-BP neural network model is better and prediction accuracy of the model is higher.The training sample can reflect 99% sample characteristics.
引文
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[7]张兆同,余潜.灰色关联分析与RBF神经网络在中国棉花价格预测中的应用研究[J].价格月刊,2017(9):31-36.
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[11]史峰,王辉,郁磊,等.MATLAB智能算法30个案例分析[M].北京:北京航空航天大学出版社,2011.
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[15]高欣宇,余国新.对我国棉花期货价格预测的方法研究——基于EGARCH-EWMA模型与ARIMA模型比较[J].价格理论与实践,2014(12):85-87.
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