一种分段在线支持向量回归算法  

Segmental online support vector regression algorithm

作　　者：刘大同[1] 彭宇[1] 彭喜元[1] 于江[2] 陈强[2] 

机构地区：[1]哈尔滨工业大学自动化测试与控制研究所,哈尔滨150080 [2]中国移动通信集团黑龙江有限公司,哈尔滨150028

出　　处：《仪器仪表学报》2010年第8期1732-1737,共6页Chinese Journal of Scientific Instrument

基　　金：高校博士点基金(20092302110013);中国移动科技发展项目(2008-09)资助项目

摘　　要：针对在线支持向量回归算法在提高时间序列预测执行效率的同时,其预测精度会有所下降的问题,提出一种分段在线支持向量回归的时间序列预测方法,通过缩减在线建模数据长度实现快速训练,并对在线支持向量回归模型进行分段存储,根据预测数据与子分段模型的匹配度,选取最优子分段模型预测输出,从而提高在线算法预测精度。通过对黑龙江移动通信话务量时间序列数据的实验结果表明,该算法既很好地保持了在线预测方法的运行效率,又通过分段使预测精度提高了5%～10%。To improve the poor precision of online support vector regression （Online SVR） when increasing operation efficiency in complicated and nonlinear time series prediction,this paper proposes a novel segmental online SVR algorithm for time series forecasting.Fast training speed is achieved by cutting the training data set short.A segmental strategy is applied and the online SVR model is stored in segments.The most suitable segmental model is selected to output the prediction value according to the matching degree between prediction neighborhood data and all the segmental models.As a result,the forecasting precision is improved.Experiment results with the data provided by China Mobile Communications Corporation Heilongjiang Co.Ltd.show that the proposed method can increase precision by 5% to 10% while keeping the operation efficiency.

关 键 词：时间序列预测 快速预测 在线支持向量回归 分段 话务量预测 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]