动态随机树贝叶斯集成回归模型研究

Dynamic Random Tree Bayesian Ensemble Regression Model

针对目前的动态贝叶斯网络主要用于时间序列的因果分析和分类预测,缺少将动态贝叶斯网络用于回归计算方面研究的情况,结合随机树生成、回归变量的离散化、类变量的数量化、类的满条件概率计算和加权平均回归计算等建立动态随机树贝叶斯回归模型,并通过集成(平均)来提高回归模型的泛化能力,使用期货数据进行实验,实验结果显示,动态随机树贝叶斯集成回归模型具有良好的回归可靠性.

At present,the dynamic Bayesian network is mainly used for causality analysis and classification prediction of time series and the research on the dynamic Bayesian networks in regression computing is needed. In this paper,we combine the generation of random tree,the discretization of regression variable,the quantification of class variable,the full conditional probability calculation of a class,weighted mean regression calculation and so on to set up a dynamic random tree Bayesian regression model and improve it’ s generalization ability by model average. We use futures data to carry out experiments. Experimental results show that the dynamic random tree Bayesian ensemble regression model has good regression reliability.