基于2DSVD的多变量时间序列半监督分类  

Semi-Supervised Classification of Multivariate Time Series Based on Two-Dimensional Singular Value Decomposition

作　　者：单中南 翁小清[1] 马超红 SHAN Zhong-Nan;WENG Xiao-Qing;MA Chao-Hong(Information Technology College,Hebei University of Economics and Business,Shijiazhuang 050061,China)

机构地区：[1]河北经贸大学信息技术学院

出　　处：《计算机系统应用》2019年第11期153-160,共8页Computer Systems & Applications

摘　　要：目前时间序列半监督分类研究主要集中在单变量时间序列,由于多变量时间序列(MTS)变量之间存在复杂关系, MTS的半监督分类研究比较少.针对这种情况,提出一种基于二维奇异值分解的MTS半监督分类方法,该方法首先计算行-行以及列-列协方差矩阵的特征向量,然后从MTS样本中提取特征矩阵;特征矩阵的行数以及列数不仅比原MTS样本低,而且还清晰地考虑了MTS样本的二维特性.在10个MTS数据集上的实验结果表明,该方法的分类性能显著地好于使用扩展Frobenius范数、中心序列、以及基于一维奇异值分解的半监督分类方法.At present, semi-supervised classification research of time series mainly focuses on univariate time series, due to the complex relationship between Multivariate Time Series(MTS) variables, there is less research on semi-supervised classification of MTS. In view of this, we proposes a semi-supervised MTS classification method based on TwoDimensional Singular Value Decomposition(2 DSVD), which first computes the eigenvectors of row-row and columncolumn covariance matrices, and then extracts feature matrices from MTS samples. The number of rows and columns of the feature matrix is not only lower than the original MTS sample, but also clearly considers the two-dimensional nature of the MTS sample. The experimental results on 10 MTS datasets show that the semi-supervised classification performance of this method is significantly better than the method using extended Frobenius norm, center sequence, and based on one dimensional singular value decomposition.

关 键 词：多变量时间序列 二维奇异值分解(2DSVD) 半监督分类 

分 类 号：O15[理学—数学]