摘要
为了解决全局支持向量回归机(Global-SVR)在大样本数据集中计算效率低下的问题,将局部支持向量回归机与灰色系统理论有机结合,并利用灰色关联度作为局部邻域函数构造灰色局部支持向量回归机(GL-SVR),该做法具有一定的理论价值.优化过程中采用留一法评估学习机的泛化性能,利用模式搜索法选择模型参数.真实的股价涨跌幅预测实验结果表明,该方法既加快了运算速度,又提高了预测精度.
Due to much time consumption of global support vector regression (Global-SVR) for large sample size data,grey local SVR (GL-SVR) combined grey relational grade regarded as neighbourhood function with local support vector regression is considered. To optimize the machine,based on leave-one-out errors,pattern search method is adopted for model selection. Experiments are carried out on a real stock price change forecasting with GL-SVR and the results show that the proposed approach can not only speed up the computing speed,but also improve the prediction accuracy.
出处
《控制与决策》
EI
CSCD
北大核心
2010年第3期399-403,共5页
Control and Decision
关键词
局部支持向量回归
灰色关联度
股价涨跌幅
Local support vector regression
Grey relational grade
Stock price change