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附有周期项的二次多项式LASSO钟差预报模型
供稿: 李坤;王潜心;闵扬海;龚佑兴;苗伟;程彤 时间: 2022-05-10 次数:

李坤, 王潜心, 闵扬海,.附有周期项的二次多项式LASSO钟差预报模型[J].河南理工大学学报(自然科学版),2022,41(3):74-80.

LI K, WANG Q X, MIN Y H, et al.A clock offset prediction model with quadratic polynomial based on LASSO algorithm[J].Journal of Henan Polytechnic University(Natural Science) ,2022,41(3):74-80.

附有周期项的二次多项式LASSO钟差预报模型

李坤1, 王潜心1, 闵扬海2, 龚佑兴3, 苗伟1, 程彤1

1.中国矿业大学 环境与测绘学院,江苏 徐州 221116;2.苏州市房地产市场与交易管理中心,江苏 苏州 215002;3.国防科学技术大学 指挥军官基础教育学院,湖南 长沙 410072

摘要:为了解决最小二乘估计(least squares estimation LSQ)算法在处理高维度数据模型式时易产生模型过拟合、预报精度不高等问题,采用LASSO算法(least absolute shrinkage and selection operator)对附有周期项的二次多项式模型进行整体求解,分析6121824 h预报精度。结果表明,LASSO算法能有效避免模型参数求解的过拟合问题,极大提高二次多项式模型的预报精度,随着预报时间增加,LASSO算法优势愈加明显。

关键词:二次多项式;周期项;LASSO;最小二乘估计;过拟合

doi:10.16186/j.cnki.1673-9787.2020090051

基金项目:国家自然科学基金资助项目(41874039);江苏省自然科学基金资助项目(BK20191342

收稿日期:2020/09/11

修回日期:2020/11/03

出版日期:2022/05/15

A clock offset prediction model with quadratic polynomial based on LASSO algorithm

LI Kun1, WANG Qianxin1, MIN Yanghai2, GONG Youxing3, MIAO Wei1, CHENG Tong1

1.School of Environment and Geoinformatics China University of Mining and Technology Xuzhou  221116 Jiangsu China;2.Suzhou Real Estate Market and Transaction Management Center Suzhou  215002 Jiangsu China;3.School of Military Commanding Officer Basic EducationNational University of Defense TechnologyChangsha  410072HunanChina

Abstract: In order to solve the problems of model over-fitting and low prediction accuracy of the least squares estimation (LSQ) in processing high-dimensional data model formulas,the LASSO algorithm was used to solve the overall model of the quadratic polynomial model with periodic terms , and 6 ,12 ,18,24 h accuracy analysis of the prediction experiment respectively was carried out. The result showed that the LASSO algorithm could effectively avoid the over-fitting phenomenon in the solution of model parameters , greatly improve the prediction accuracy of the quadratic polynomial, and as the prediction time increased,the advantages of the lasso algorithm increased obviously.

Key words:quadratic polynomial;period term;LASSO;least squares estimation;over-fitting

  附有周期项的二次多项式LASSO钟差预报模型_李坤.pdf

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