摘要
针对多变量时间序列(MTS)存在时间和变量两个维度,以及传统主成分分析(PCA)方法在MTS数据表示上的局限性,提出一种基于二维奇异值分解(2dSVD)和高斯混合模型(GMM)的MTS聚类算法。该文计算MTS的行-行和列-列协方差矩阵的特征向量,从时间和变量两个维度提取特征矩阵;用GMM从概率分布角度对特征矩阵进行聚类。数值实验结果表明,该方法对多变量时间序列具有更好的聚类效果。
Multi-variable time series(MTS)has two dimensions,time and variable,and traditional principal component analysis(PCA)is limited in MTS data representation.Therefore,a MTS clustering algorithm based on the two-dimensional singular value decomposition(2dSVD)and Gaussian mixture model(GMM)is proposed.We calculated the eigenvectors of the row-row and column-column covariance matrix of MTS,extracted the eigenmatrix from the two dimensions of time and variable,and then used GMM to cluster the eigenmatrix from the perspective of probability distribution.The experimental results demonstrate that the new algorithm can gain better clustering results on MTS.
作者
杨秋颖
翁小清
Yang Qiuying;Weng Xiaoqing(Information Technology College,Hebei University of Economics&Business,Shijiazhuang 050061,Hebei,China)
出处
《计算机应用与软件》
北大核心
2024年第3期283-289,327,共8页
Computer Applications and Software
关键词
二维奇异值分解
高斯混合模型
多变量时间序列聚类
Two-dimensional singular value decomposition
Gaussian mixture model
Multi-variate time series clustering