多阶灰色支持向量机集成预测模型研究

对灰色预测模型GM(1,1)和支持向量机SVM预测模型进行分析,提出了多阶灰色支持向量机集成预测模型Dm_GM(1,1)-SVM。通过多阶缓冲算子改进灰色预测模型的预测精度,对最终预测值的各个相关指标进行预测;同时,采用粒子群优化算法对支持向量机模型进行径向基核参数和惩罚参数寻优,得到最佳参数对(c,g),从而确定支持向量机的最佳回归模型;最后将各指标预测值作为支持向量机模型的输入,依据预测模型和预测模型的输入值求得预测结果。实验实例表明,多阶灰色支持向量机集成模型和传统的预测模型相比,在本例中预测精度更高,说明多阶灰色预测模型和支持向量机模型相结合在解决实际预测问题中具有实用价值。

An integrated prediction model using multi-stage gray model and support vector machine

A multi-stage gray support vector machine ensemble prediction model (Dm_GM(1,1) SVM) is presented by analyzing the gray model GM (1,1) and the support vector machine model (SVM).The prediction accuracy of gray model is improved through multi stage buffer operators to predict various relevant indicators.Meanwhile, the particle swarm optimization algorithm is used to find the optimal parameters of the support vector machine model, which include RBF kernel parameters and penalty parameters and the optimal pair is (c, g).Thus,the optimal support vector machine regression model is determined. Finally the final output value is predicted by inputting the predictive value of each indicator to support the vector machine model.The results show that Dm_GM (1,1) SVM has a higher prediction accuracy compared with the gray prediction model and the BP neural network prediction model in this case,and that multi-stage gray forecasting model combined with support vector machine model has a practical value in solving practical prediction problems.