Belief Renyi Divergence of Divergence and its Application in Time Series Classification

被引:11
|
作者
Zhang, Lang [1 ]
Xiao, Fuyuan [1 ]
机构
[1] Chongqing Univ, Sch Big Data & Software Engn, Chongqing, 401331, Peoples R China
基金
中国国家自然科学基金;
关键词
Complexity theory; Time series analysis; Time measurement; Evidence theory; Feature extraction; Measurement uncertainty; Classification algorithms; D-S evidence theory; uncertainty; belief Renyi divergence; belief divergence of divergence; complexity; time series analysis; classification; NETWORKS;
D O I
10.1109/TKDE.2024.3369719
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
Time series data contains the amount of information to reflect the development process and state of a subject. Especially, the complexity is a valuable factor to illustrate the feature of the time series. However, it is still an open issue to measure the complexity of sophisticated time series due to its uncertainty. In this study, based on the belief Renyi divergence, a novel time series complexity measurement algorithm, called belief Renyi divergence of divergence (BReDOD), is proposed. Specifically, the BReDOD algorithm takes the boundaries of time series value into account. What is more, according to the Dempster-Shafer (D-S) evidence theory, the time series is converted to the basic probability assignments (BPAs) and it measures the divergence of a divergence sequence. Then, the secondary divergence of the time series is figured out to represent the complexity of the time series. In addition, the BReDOD algorithm is applied to sets of cardiac inter-beat interval time series, which shows the superiority of the proposed method over classical machine learning methods and recent well-known works.
引用
收藏
页码:3670 / 3681
页数:12
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