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Multivariate time series classification via potential relationship between samples

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基于样本间潜在关系的多变量时间序列分类

Journal Information

J. Hefei Univ. Tech. (Nat. Sci.), Dec.2023, Vol.46 No.12: 1642-1650

DOI: 10.3969/j.issn.1003-5060.2023.12.009

Authors

TANG Shengtang, WU Gongqing, TAI Changyang, YANG Ze, ZHANG Zan

(School of Computer Science and Information Engineering, Hefei University of Technology, Hefei 230601, China)

Abstract and Keywords

Abstract: The task of multivariate time series (MTS) classification aims to determine the label of MTS samples. MTS data have rich relationship information such as temporal relationship and sample similarity relationship. However, the existing methods fail to make full use of these relationship information, which makes it difficult to improve the classification performance. For this reason, this paper proposes an MTS classification method based on graph convolutional network (GCN), which improves the classification performance by mining the potential relationship between samples. Firstly, in order to effectively represent the sample relationship, rules of building graph based on sample similarity are designed to model the samples, which can map the potential relationship information of samples into a graph space. Then, a classification model based on graph convolution is proposed, which captures the potential sample relationship conducive to classification by aggregating sample features, and updates them to the sample's own feature vector to improve the classification accuracy. Extensive experiments on eleven public datasets show that the proposed method is superior to twelve comparison methods, which shows that the proposed method provides a new approach for dealing with the problem of time series classification. It really has an important influence on the classification results by mining the potential relationship between time series data for classification.

Keywords: multivariate time series classification; sample similarity; graph convolutional network(GCN); potential relationship; feature aggregation

作者信息

唐胜唐，吴共庆，台昌杨，杨泽，张赞

（合肥工业大学计算机与信息学院，安徽合肥230601）

摘要和关键词

摘要: 多变量时间序列（multivariate time series，MTS）分类任务旨在确定多变量时间序列样本的标签。多变量时间序列数据存在时序关系和样本相似性关系等丰富的关系信息，然而现有的算法未能充分利用关系信息导致分类性能难以提升。基于此，文章提出一种基于图卷积网络（graph convolutional network，GCN）的多变量时间序列分类方法，通过挖掘样本间的潜在关系来提高分类性能。为了有效表示样本关系，设计基于样本相似度的构图规则，对样本数据进行建模从而将样本的时序特征和潜在关系信息映射到图空间中，提出基于图卷积的分类模型，通过聚合样本特征来捕获有利于分类的潜在样本关系，更新到样本自身特征向量以提升分类精度。在11个公共数据集上的大量实验结果表明，该文所提算法优于12种对比算法，可见通过挖掘时间序列数据之间潜在的关系用于分类对分类结果具有重要影响，从而为处理时间序列分类问题提供一种新的途径。

关键词: 多变量时间序列分类；样本相似度；图卷积网络（GCN）；潜在关系；特征聚合

Funding

国家自然科学基金资助项目（61906059）；国家自然科学基金国际（地区）合作与交流资助项目（62120106008）

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