【摘要】 传统SVR模型可预测房价的变化趋势,但不恰当的参数设置会影响预测的精度。本文针对北京市二手房同比价格指数的非线性变化特征,将蝙蝠算法引入到SVR模型中,使其对模型的三个参数进行优化设置,结合网络搜索数据,构建了BA-SVR&WSD混合模型,并给出了该模型算法的预测流程,通过引入多个基准预测模型和预测性能度量指标进行对比研究。结果表明:基于蝙蝠算法的SVR模型具有较好的泛化能力,预测效果更准确且预测精度更高,该预测方法也为北京市二手房价格的监测和调控提供了有价值的参考。更多还原

【Abstract】 Traditional SVR models can predict trends in house prices, but inappropriate parameter settings can affect the accuracy of predictions. In this paper, based on the nonlinear variation characteristics of Beijing second-hand house year-on-year price index, it introduces the Bat Algorithm(BA) into the SVR model to optimize the three parameters of the model. Combing with web search data(WSD), it builds the BA-SVR&WSD mixed model, and gives the prediction process of the model algorithm. It also carries out the comparison study by introducing multiple benchmark prediction models and predictive performance metrics. The research results show that the SVR model based on bat algorithm has better generalization ability,more accurate prediction effect and higher prediction accuracy. This prediction method also provides a valuable reference for the monitoring and regulation of second-hand houseinpricesin Beijing.更多还原