[1]喻沩舸,吴华瑞,彭程.基于Lasso回归的北京地区黄瓜价格波动原因分析[J].北方园艺,2020,44(12):165-170.[doi:10.11937/bfyy.20193336]
　YU Weige,WU Huarui,PENG Cheng.Cause Analysis of Cucumber Price Fluctuation in Beijing Based on Lasso Regression[J].Northern Horticulture,2020,44(12):165-170.[doi:10.11937/bfyy.20193336]
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基于Lasso回归的北京地区黄瓜价格波动原因分析

《北方园艺》[ISSN:1001-0009/CN:23-1247/S] 卷: 第44卷 期数: 2020年12 页码: 165-170 栏目: 出版日期: 2020-06-30
Title:
Cause Analysis of Cucumber Price Fluctuation in Beijing Based on Lasso Regression
作者:
喻沩舸1; 2; 吴华瑞2; 3; 4; 彭程2; 3; 4
（1.首都师范大学 信息工程学院，北京 100037；2.国家农业信息化工程技术研究中心，北京 100097；3.北京农业信息技术研究中心，北京 100097；4.农业农村部 农业信息软硬件产品质量检测重点实验室，北京 100097）
Author(s):
YU Weige1; 2; WU Huarui2; 3; 4; PENG Cheng2; 3; 4
(1.School of Information Engineering,Capital Normal University,Beijing 100037;2.National Agricultural Information Engineering Technology Research Center,Beijing 100097;3.Beijing Agricultural Information Technology Research Center,Beijing 100097;4.Key Laboratory for Quality Testing of Agricultural Information Software and Hardware Products,Ministry of Agriculture and Rural Affairs,Beijing 100097)
关键词:
Lasso回归; 蔬菜价格; 多元线性回归模型; 多重共线性
Keywords:
Lasso regrassion; vegetable prices; multiple linear regression model; multicollinearity
DOI:
10.11937/bfyy.20193336
文献标志码:
A
摘要:
近年来蔬菜市场价格波动幅度较大，为探究蔬菜价格波动剧烈的原因，以北京地区黄瓜为例，对2010—2017年价格波动规律进行实证分析基础上探究了Lasso回归方法的适用性，并使用Lasso回归方法对黄瓜数据进行建模并求解。该方法剔除了相关度较小的因素，获取主要影响因素及其相关度。相较于常用的最小二乘法求解，多重共线性判定条件值仅有19.66，拟合系数达0.844 8，证明Lasso回归模型适用于蔬菜价格原因分析且相比于传统方法有更好的表现，可为进一步开展蔬菜价格的预测提供参考依据。
Abstract:
In recent years,the price of vegetables had fluctuated greatly.The study used cucumber in Beijing to explor the applicability of Lasso regression method based on empirical analysis of price fluctuations from 2010 to 2017,in order to explore the causes of sharp fluctuations in vegetable prices.Used Lasso regression method to model and solve cucumber data.The method eliminated the factors with small correlation degree and obtained the main influencing factors and their correlation degree.Compared with the commonly used least square method,the multicollinearity determination condition value was only 19.66,and the fitting coefficient reached 0.844 8.The result proved that Lasso regression model was suitable for vegetable price cause analysis and had better performance than traditional methods.This study could provide basis for further vegetable price prediction.

参考文献/References:

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备注/Memo

第一作者简介：喻沩舸（1993-），男，硕士研究生，研究方向为智能应用技术。E-mail:yuwg@nercita.org.cn.责任作者：吴华瑞（1975-），男，博士，研究员，现主要从事农业智能系统等研究工作。E-mail:wuhr@nercita.org.cn.基金项目：北京市自然科学基金资助项目（4172025）；国家自然科学基金资助项目（41501418）；北京市农林科学院2019年度科研创新平台建设资助项目（PT2019-28）。收稿日期：2019-12-11

更新日期/Last Update: 2020-09-02