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:2019,28(5):1-9
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基于交叉验证网格寻优支持向量机的产品销售预测
(1.上海理工大学 能源与动力工程学院, 上海 200093;2.上海交通大学 机械与动力工程学院, 上海 200240)
Product Sale Forecast Based on Support Vector Machine Optimized by Cross Validation and Grid Search
(1.School of Energy and Power Engineering, University of Shanghai for Science & Technology, Shanghai 200093, China;2.School of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China)
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投稿时间:2018-11-28    修订日期:2018-12-18
中文摘要: 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.
Abstract: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.
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基金项目:国家自然科学基金(51506125)
引用文本:
张文雅,范雨强,韩华,张斌,崔晓钰.基于交叉验证网格寻优支持向量机的产品销售预测.计算机系统应用,2019,28(5):1-9
ZHANG Wen-Ya,FAN Yu-Qiang,HAN Hua,ZHANG Bin,CUI Xiao-Yu.Product Sale Forecast Based on Support Vector Machine Optimized by Cross Validation and Grid Search.COMPUTER SYSTEMS APPLICATIONS,2019,28(5):1-9

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