基于交叉验证网格寻优支持向量机的产品销售预测
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摘要
综合考虑影响汽车销售的多种因素,运用交叉验证网格搜索优化支持向量机的惩罚系数和核函数参数,建立了适合汽车销售的预测模型.仿真实验结果表明,改进支持向量机优化汽车销售预测模型的预测效果比某公司当前采用的模型更佳,该模型具有较高的预测精度和较大的可信度,可为企业决策层提供较为准确的销售预测参考.
Considering various factors affecting automobile sales, the penalty coefficient and kernel function parameters of support vector machine are optimized by cross validation and grid search, and a prediction model suitable for automobile sales is established. The simulation results show that the forecasting effect of the improved support vector machine optimized automobile sales forecasting model is better than that of the current model adopted by a company. The model has higher forecasting accuracy and greater credibility, and can provide more accurate sales forecasting reference for enterprise decision-making level.
Considering various factors affecting automobile sales, the penalty coefficient and kernel function parameters of support vector machine are optimized by cross validation and grid search, and a prediction model suitable for automobile sales is established. The simulation results show that the forecasting effect of the improved support vector machine optimized automobile sales forecasting model is better than that of the current model adopted by a company. The model has higher forecasting accuracy and greater credibility, and can provide more accurate sales forecasting reference for enterprise decision-making level.
引文
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20罗小燕,陈慧明,卢小江,等.基于网格搜索与交叉验证的SVM磨机负荷预测.中国测试,2017,43(1):132-135,144.
21郭李娜,樊贵盛.基于网格搜索和交叉验证支持向量机的地表土壤容重预测.土壤通报,2018,49(3):512-518.
22亢生彩.网格搜索法SVM参数优化在主扇风机故障诊断中的应用.煤炭技术,2015,34(1):295-297.
23王琪,孙玉坤,倪福银,等.一种混合动力电动汽车电池荷电状态预测的新方法.电工技术学报,2016,31(9):189-196.[doi:10.3969/j.issn.1000-6753.2016.09.024]
2Yu Y,Choi TM,Hui CL.An intelligent fast sales forecasting model for fashion products.Expert Systems with Applications,2011,38(6):7373-7379.[doi:10.1016/j.eswa.2010.12.089]
3张闯,姜杨,吴铭,等.基于社会化媒体节点属性的信息预测.北京邮电大学学报,2012,35(4):24-27.[doi:10.3969/j.issn.1007-5321.2012.04.006]
4吴奇,严洪森.基于混沌ν-支持矢量机的产品销售预测模型.机械工程学报,2010,46(7):128-135.
5徐歆,严洪森.基于扩展的径向基函数核支持向量机的产品销售预测模型.计算机集成制造系统,2013,19(6):1343-1350.
6Vapnik VN.The Nature of Statistical Learning Theory.New York:Springer,2000.138-167.
7史峰,王小川,郁磊,等.MATLAB神经网络30个案例分析.北京:北京航空航天大学出版社,2010.
8杨洪,古世甫,崔明东,等.基于遗传优化的最小二乘支持向量机风电场风速短期预测.电力系统保护与控制,2011,39(11):44-48,61.[doi:10.7667/j.issn.1674-3415.2011.11.008]
9王宁,谢敏,邓佳梁,等.基于支持向量机回归组合模型的中长期降温负荷预测.电力系统保护与控制,2016,44(3):92-97.
10Gao X,Hou J.An improved SVM integrated GS-PCA fault diagnosis approach of Tennessee Eastman process.Neurocomputing,2016,174:906-911.[doi:10.1016/j.neucom.2015.10.018]
11Gencoglu MT,Uyar M.Prediction of flashover voltage of insulators using least squares support vector machines.Expert Systems with Applications,2009,36(7):10789-10798.[doi:10.1016/j.eswa.2009.02.021]
12纪昌明,周婷,向腾飞,等.基于网格搜索和交叉验证的支持向量机在梯级水电系统隐随机调度中的应用.电力自动化设备,2014,34(3):125-131.[doi:10.3969/j.issn.1006-6047.2014.03.021]
13罗公亮.从神经网络到支撑矢量机(上、中、下).冶金自动化,2001,25(5):1-5,2001,25(6):1-4,2002,26(1):1-5.
14王观玉,郭勇.支持向量机在电信客户流失预测中的应用研究.计算机仿真,2011,28(4):115-118,312.[doi:10.3969/j.issn.1006-9348.2011.04.028]
15张金玉,张炜.装备智能故障诊断与预测.北京:国防工业出版社,2013.
16付元元,任东.支持向量机中核函数及其参数选择研究.科技创新导报,2010,(9):6-7.[doi:10.3969/j.issn.1674-098X.2010.09.004]
17马向东,卢占庆,谭永彦,等.基于支持向量机的分类辨识方法及应用.控制工程,2016,23(5):768-772.
18王霞,王占岐,金贵,等.基于核函数支持向量回归机的耕地面积预测.农业工程学报,2014,30(4):204-211.[doi:10.3969/j.issn.1002-6819.2014.04.025]
19Najafi G,Ghobadian B,Moosavian A,et al.SVM and ANFIS for prediction of performance and exhaust emissions of a SI engine with gasoline-ethanol blended fuels.AppliedThermal Engineering,2016,95:186-203.[doi:10.1016/j.applthermaleng.2015.11.009]
20罗小燕,陈慧明,卢小江,等.基于网格搜索与交叉验证的SVM磨机负荷预测.中国测试,2017,43(1):132-135,144.
21郭李娜,樊贵盛.基于网格搜索和交叉验证支持向量机的地表土壤容重预测.土壤通报,2018,49(3):512-518.
22亢生彩.网格搜索法SVM参数优化在主扇风机故障诊断中的应用.煤炭技术,2015,34(1):295-297.
23王琪,孙玉坤,倪福银,等.一种混合动力电动汽车电池荷电状态预测的新方法.电工技术学报,2016,31(9):189-196.[doi:10.3969/j.issn.1000-6753.2016.09.024]