基于组合模型的农产品物价预测算法
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摘要
当今时代,科学技术高速发展,涌现出一批新技术,数据挖掘、机器学习等新科学领域被深入研究,众多智能算法逐渐出现,同时被应用到了不同的领域中.本文构建了一种基于BP (Back Propagation)神经网络和SVR(Support Vector Regression)支持向量回归机的组合模型.依托于农产品价格数据进行实例验证分析,结果表明相对于单一的预测模型, BP-SVR-BP组合模型在预测精度上有了很大的提升,拟合效果更加逼近真实数据曲线,能够客观真实的反应农产品物价变化规律.
Nowadays, with the rapid development of science and technology, a number of new technologies have emerged. New scientific fields such as data mining and machine learning have been deeply studied. Many intelligent algorithms have emerged and applied to different fields. This paper constructs a combined model based on BP(Back Propagation) neural network and SVR(Support Vector Regression). Based on the agricultural product price data, the example verification analysis shows that compared with the single prediction model, the BP-SVR-BP prediction model has greatly improved the prediction accuracy. The fitting effect is closer to the real data curve, which can objectively and truly reflect the law of agricultural product price changes.
Nowadays, with the rapid development of science and technology, a number of new technologies have emerged. New scientific fields such as data mining and machine learning have been deeply studied. Many intelligent algorithms have emerged and applied to different fields. This paper constructs a combined model based on BP(Back Propagation) neural network and SVR(Support Vector Regression). Based on the agricultural product price data, the example verification analysis shows that compared with the single prediction model, the BP-SVR-BP prediction model has greatly improved the prediction accuracy. The fitting effect is closer to the real data curve, which can objectively and truly reflect the law of agricultural product price changes.
引文
1陈咏梅.基于BP-ARIMA组合模型的福建滨海旅游市场规模预测.厦门理工学院学报, 2018, 26(4):22–26.
2 马健,盛魁,董辉.基于改进型误差函数的复值BP神经网络对股票价格的预测研究.长春师范大学学报, 2018, 37(6):85 –90.
3朱叶,喻晓玲.基于组合模型的兵团农产品物流市场需求预测.塔里木大学学报, 2016, 28(2):52–60.[doi:10.3969/j.issn.1009-0568.2016.02.010]
4 刘金源,王寿喜,李婵.城市燃气日负荷PCA-GM-BPNN组合预测模型.天然气与石油, 2018, 36(5):13–19.[doi:10.3969/j.issn.1006-5539.2018.05.003]
5吴叶,刘婷婷,方少勇.基于MIV-GA-BP神经网络的我国棉价预测研究.棉纺织技术, 2018, 46(7):77–80.[doi:10.3969/j.issn.1001-7415.2018.07.020]
6 江粉桃.非线性方法的时间序列组合模型在农产品价格预测中的应用研究[硕士学位论文].兰州:兰州交通大学,2018.
2 马健,盛魁,董辉.基于改进型误差函数的复值BP神经网络对股票价格的预测研究.长春师范大学学报, 2018, 37(6):85 –90.
3朱叶,喻晓玲.基于组合模型的兵团农产品物流市场需求预测.塔里木大学学报, 2016, 28(2):52–60.[doi:10.3969/j.issn.1009-0568.2016.02.010]
4 刘金源,王寿喜,李婵.城市燃气日负荷PCA-GM-BPNN组合预测模型.天然气与石油, 2018, 36(5):13–19.[doi:10.3969/j.issn.1006-5539.2018.05.003]
5吴叶,刘婷婷,方少勇.基于MIV-GA-BP神经网络的我国棉价预测研究.棉纺织技术, 2018, 46(7):77–80.[doi:10.3969/j.issn.1001-7415.2018.07.020]
6 江粉桃.非线性方法的时间序列组合模型在农产品价格预测中的应用研究[硕士学位论文].兰州:兰州交通大学,2018.