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计算机系统应用:2020,29(4):18-23
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正则化和交叉验证在组合预测模型中的应用
(安徽财经大学 统计与应用数学学院, 蚌埠 233030)
Application of Regularization and Cross-Validation in Combination Forecasting Model
(Institute of Statistics and Applied Mathematics, Anhui University of Finance and Economics, Bengbu 233030, China)
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投稿时间:2019-06-16    修订日期:2019-07-12
中文摘要: 组合预测模型的权重确定方式对于提高模型精度至关重要,为研究正则化与交叉验证是否能改善组合预测模型的预测效果,提出将正则化和交叉验证应用于基于最小二乘法的组合预测模型.通过在组合模型的最优化求解中分别加入L1L2范数正则化项,并对数据集进行留一交叉验证后发现:L1L2范数正则化都对组合模型的预测精度具有改善效果,且L1范数正则化比L2范数正则化对组合预测模型的改善效果更好,并且参与组合预测的单项预测模型越多,正则化的改善效果越好,交叉验证对组合预测模型的改善效果则与给定实验数据量呈现正相关.
Abstract:To determine the weight of the combined forecasting model is very important to improve the accuracy of the model. Applying the regularization and cross-validation to the combined forecasting model based on the least squares method is for studying whether the regularization and cross-validation can improve the prediction effect of the combined forecasting model. It is carried out by adding the L1 and L2 norm regularization terms to the optimization solution of the combined model and using leave-one-out-cross-validation in the data set. The result shows that both the L1 and L2 norm regularization can improve prediction accuracy of the combined model to a certain degree. Moreover, the L1 norm regularization is better than the L2 norm regularization for the combined forecasting model, and the more single forecasting models participating in the combined forecasting, the better the regularization improvement effect. In addition, there is a positive correlation between the cross-validation improvement effect and amount of experimental data given.
文章编号:7254     中图分类号:    文献标志码:
基金项目:国家社会科学基金(13CTJ006);安徽省教育厅高校人文社会科学重点研究项目(SK2018A0431);安徽财经大学研究生科研创新基金(ACYC2017236);安徽财经大学重点科研基金(ACKY1713ZDB)
引用文本:
张欣怡,袁宏俊.正则化和交叉验证在组合预测模型中的应用.计算机系统应用,2020,29(4):18-23
ZHANG Xin-Yi,YUAN Hong-Jun.Application of Regularization and Cross-Validation in Combination Forecasting Model.COMPUTER SYSTEMS APPLICATIONS,2020,29(4):18-23

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