电子科技2024，Vol.37Issue(1)：1-8,8.DOI:10.16180/j.cnki.issn1007-7820.2024.01.001
基于麻雀搜索优化SVR模型的房地产价格研究
Research on Real Estate Price Index Based on Sparrow Search Optimization SVR Model
摘要
Abstract
In order to solve the data acquisition lag problem of traditional economic indicators as an influencing factor of housing prices,and the uncertainty of parameter selection in the machine learning model when predicting housing prices,the network search data is used as the explanatory variable of the house price index,and Sparrow Search Algorithm(SSA)is used to establish the SSA-SVR(Support Vector Regression)model to optimize the penal-ty factor C of SVR and the parameter g of the RBF(Radical Basic Function)kernel function in this study.Compari-son among the established SSA-SVR model with PSO(Particle Swarm Optimization)-SVR,GA(Genetic Algo-rithm)-SVR,WOA(Whale Optimization Algorithm)-SVR,GS(Grid Search)-SVR and benchmark SVR show that the correlation coefficient of SSA-SVR(0.99),root mean square error(6.71),mean absolute error(5.24),mean square error(45.13)and mean absolute percentage error(0.26%)are better than those of the other five mod-els.The results show that the SVR model optimized by the sparrow search algorithm has better global optimization a-bility in housing price prediction,which can improve the prediction accuracy and prediction ability of the model.关键词
麻雀搜索算法/优化算法/SVR模型/数据滞后性/参数不确定性/网络搜索数据/房地产价格指数/房价预测Key words
sparrow search algorithm/optimization algorithm/SVR model/data lag/parameter uncertainty/net-work search data/real estate price index/house price forecast分类
信息技术与安全科学引用本文复制引用
兰瑞杰,孟维高,耿进强..基于麻雀搜索优化SVR模型的房地产价格研究[J].电子科技,2024,37(1):1-8,8.基金项目
河北省社会科学基金(HB18GL021)Social Science Foundation of Hebei(HB18GL021) （HB18GL021）
