基于麻雀搜索优化SVR模型的房地产价格研究OA

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.