基于数据挖掘的组合预测模型的建模方法

Modelling Method of Combination Forecast Model Based on Data Mining

目的研究变量代换对非线性回归预测模型精度的影响,寻找能提高预测精度的建模方法。方法基于数据挖掘、空间变换和加权处理的组合方法,充分利用原始数据提供的信息。结果给出了基于数据挖掘的组合预测模型的建模方法。结论基于数据挖掘的组合预测模型的建模方法减少了变量代换带来的严重影响,充分利用了原始数据中的有用信息,较显著地提高了预测模型的精度。

Objective Research on variable substitution to non-linear regression forecast model precision＇s influence, and seek the modelling method that can improve the forecast precision. Methods Based on the data mining, the transform in space and the weighted processing combined method, make full use of information that the primary data provide. Results Given modelling method of combination forecast model based on the data mining. Conclusion Based on data mining＇s combination forecast model＇s modelling method can reduce the serious influence that the variable substitution brings and has fully used useful information in the primary data. It obviously improved the accuracy of the prediction model.