基于μσ-DWC特征和树结构M-SVM的多维时间序列分类  

Multivariate time series classification based onμσ-DWC feature and tree-structured M-SVM

作　　者：谭海龙[1] 刘康玲[1] 金鑫[1] 石向荣[2] 梁军[1] 

机构地区：[1]浙江大学控制科学与工程学系,浙江杭州310027 [2]浙江财经大学信息管理系,浙江杭州310018

出　　处：《浙江大学学报（工学版）》2015年第6期1061-1069,1100,共10页Journal of Zhejiang University：Engineering Science

基　　金：国家自然科学基金资助项目(61174114);教育部博士点基金优先领域资助项目(20120101130016);浙江省公益性技术应用研究计划资助项目(2014C31019);浙江省网络媒体云处理与分析工程技术中心资助项目(2012E10023-7)

摘　　要：为了实现多维时间序列的分类,提出基于统计量-小波系数(μσ-DWC)的序列特征提取方法和新型树结构多分类支持向量机M-SVM模型.分类算法的实现过程如下:利用该特征提取方法将原始多维时间序列映射到特征空间,获得原始序列的压缩表示,即特征向量;得到训练集的特征向量表示之后,训练和构建树结构M-SVM模型;提取未知序列的特征向量并输入已训练完成的树结构M-SVM模型,得到未知序列的类标号,完成分类.实验结果表明:该算法比传统的分类方法具有更高的分类准确率和预测速度,同时可以保证较理想的训练速度.Aiming at the realization of multivariate time series classification, a feature extraction method based on dimension statistics-wavelet coefficients （μσ-DWC） and a new classification model based on multi- class support vector machine （M-SVM） with tree structure were proposed. The classification algorithm was realized as follows. Firstly, map original multivariate time series to feature space by the proposed feature extraction method. The compressed representation of original time series, namely eigenvector, was obtained. Secondly, tree-structured M-SVM model was trained and constructed after getting the eigenvector representation of training set. Finally, the eigenvector of an unknown time series was extracted and put into the trained tree-structured M-SVM model. Thus, the classification process was completed and the class label of the unknown time series was obtained. The experimental results showed that the proposed algorithm provided higher classification accuracy and faster prediction speed than traditional classification methods, and gave relatively ideal training speed at the same time.

关 键 词：多维时间序列 特征提取 小波系数 多分类支持向量机(M-SVM) 树结构 

分 类 号：TP181[自动化与计算机技术—控制理论与控制工程]