摘要
为提高预测模型的精度,提出了一种基于灰色模型和支持向量机的组合预测模型。该模型将累加序列中每个数据分别作为初始条件建立灰色模型,然后对各个灰色模型进行最优组合,通过支持向量机建立最优预测模型,并以成都市1993—2001年航空旅客运输量进行仿真预测实验。实验结果表明,相对于传统灰色模型和灰色神经网络模型,基于灰色模型和支持向量机的组合预测模型提高了预测精度。
In order to improve the accuracy of the forecasting model,a combination forecasting model based on grey model and support vector machine is presented.Each data of the accumulated sequence is used for the initial condition to establish the grey model.Then the optimal combination of the grey model is built,and the optimal prediction model is established by support vector machine.The simulation prediction experiment is conducted according to 1993-2001 air travel passenger transportation volume in Chengdu.The experimental result shows that compared with the traditional grey model and the grey neural network model,the combination forecasting model based on grey model and support vector machine effectively improves the prediction precision.
出处
《桂林电子科技大学学报》
2015年第5期416-418,423,共4页
Journal of Guilin University of Electronic Technology
基金
广西教育厅科研项目(KY2015YB113)
桂林电子科技大学研究生教育创新计划(YJCXS201553)
关键词
灰色模型
支持向量机
时间序列预测
grey model
support vector machine
time series forecasting
