A novel shapelet transformation method for classification of multivariate time series with dynamic discriminative subsequence and application in anode current signals  

一种基于动态鉴别性序列的多变量时间序列分类方法及在阳极电流信号上的应用（英文）

作　　者：WAN Xiao-xue CHEN Xiao-fang GUI Wei-hua YUE Wei-chao XIE Yong-fang 万晓雪;陈晓方;桂卫华;岳伟超;谢永芳(School of Automation, Central South University)

机构地区：[1]School of Automation,Central South University,Changsha 410083,China

出　　处：《Journal of Central South University》2020年第1期114-131,共18页中南大学学报（英文版）

基　　金：Projects(61773405,61725306,61533020)supported by the National Natural Science Foundation of China;Project(2018zzts583)supported by the Fundamental Research Funds for the Central Universities,China

摘　　要：Classification of multi-dimension time series(MTS) plays an important role in knowledge discovery of time series. Many methods for MTS classification have been presented. However, most of these methods did not consider the kind of MTS whose discriminative subsequence was not restricted to one dimension and dynamic. In order to solve the above problem, a method to extract new features with extended shapelet transformation is proposed in this study. First, key features is extracted to replace k shapelets to calculate distance, which are extracted from candidate shapelets with one class for all dimensions. Second, feature of similarity numbers as a new feature is proposed to enhance the reliability of classification. Third, because of the time-consuming searching and clustering of shapelets, distance matrix is used to reduce the computing complexity. Experiments are carried out on public dataset and the results illustrate the effectiveness of the proposed method. Moreover, anode current signals(ACS) in the aluminum reduction cell are the aforementioned MTS, and the proposed method is successfully applied to the classification of ACS.多变量时间序列的分类方法是时间序列知识发现的重要组成部分。因此,提出了多种多变量时间序列分类方法。然而,大部分的多变量时间序列方法都没有考虑鉴别性特征不受维度限制的时间序列。因此,本文提出了一种基于shapelet转换的特征提取方法。首先,从同一类别中的所有维度的候选shapelet中提取核心特征,它代替k个shapelet计算距离。其次,利用相似数量特征去加强分类的可靠性。最后,为缩短搜索和聚类shapelet的时间使用了距离矩阵。基于公共数据集的实验结果表明了该方法的有效性,且将实验结果成功地应用于阳极电流信号的分类。

关 键 词：anode current signals key features distance matrix feature of similarity numbers shapelet transformation 

分 类 号：TN911.7[电子电信—通信与信息系统] O211.61[电子电信—信息与通信工程]