基于Copula函数特征筛选的电力物资供应商投标价格预测
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华北电力大学 经济与管理学院

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F252;F272

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国家重点研发计划项目 “制造业多价值链协同数据空间设计理论与方法”(2020YFB1707802);国家自然科学基金资助项目“促进大规模新能源消纳的电力市场管理研究”(71804045)


Tender price prediction of power materials suppliers based on Copula function feature selection
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School of Economics and Management,North China Electric Power University

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    摘要:

    分析电力物资供应商投标价格特征是招标企业估计投标价格并制定合理采购策略的基础。传统的Spearmen秩相关系数特征筛选方法只单纯考虑了特征的数值排列顺序,不能挖掘特征变量与目标的内在关联关系,会导致预测效果差。本研究在电力企业招标价格预测建模中引入Copula函数,通过特征变量与目标变量的联合概率分布来分析变量间的相依关系。首先确定特征变量与目标变量的边缘分布,进行Copula参数估计,选取合适的Copula函数并计算相关系数来筛选供应商投标价格的特征,并以多种预测方法进行预测来验证引入Copula函数后预测精度是否提升。结果表明引入Copula函数进行特征筛选后,预测精度更高、效果更好。

    Abstract:

    Analyzing the characteristics of power material suppliers’ tender price is the basis for purchase enterprises to estimate tender price and formulate reasonable purchasing strategy. The traditional Spearman rank correlation coefficient feature selection method only considered the numerical order of features, and cannot mine the intrinsic relationship between feature variables and targets, which will lead to poor prediction effect. Copula function was introduced into the forecasting model of material tender price of power enterprises, and the dependent relationship between variables was analyzed through the joint probability distribution of characteristic variables and target variables. Firstly, the marginal distribution of characteristic variables and target variables were determined, and the copula parameters were estimated. Then, the appropriate copula function was selected and the correlation coefficient was calculated to screen the characteristics of the suppliers’ tender price. Finally, a variety of prediction methods were used to verify whether the prediction accuracy was improved after the introduction of Copula function. The results show that the copula function has higher prediction accuracy and better effect.

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刘达,刘雨萌,许晓敏.基于Copula函数特征筛选的电力物资供应商投标价格预测[J].技术经济,2021,40(10):1-9.

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  • 收稿日期:2021-06-25
  • 最后修改日期:2021-10-09
  • 录用日期:2021-09-13
  • 在线发布日期: 2021-11-18
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