Hard c-means transition network for the analysis of multivariate time series

Transition networks have extended the existing concept of nonlinear time series analysis and offered new insights into the dynamical system analysis based on time series data. Existing methods mainly focus on one-dimensional time series. However, due to the complexity of real-world systems, the dyna...

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Bibliographic Details
Published in:Nonlinear dynamics 2024-05, Vol.112 (10), p.8393-8413
Main Authors: Yang, Guangyu, Long, Dafeng, Wang, Kai, Xia, Shuyan
Format: Article
Language:English
Subjects:
Automotive Engineering
Classical Mechanics
Control
Dynamical Systems
Electrocardiography
Engineering
Mechanical Engineering
Multivariate analysis
Original Paper
Sequences
Systems analysis
Time series
Vibration
Citations: Items that this one cites
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Summary:Transition networks have extended the existing concept of nonlinear time series analysis and offered new insights into the dynamical system analysis based on time series data. Existing methods mainly focus on one-dimensional time series. However, due to the complexity of real-world systems, the dynamic interrelation within multichannel data sequences of synchronous observation has attracted increasingly attentions. Thus, further study is needed to extend the transition network method from univariate time series to multivariate time series. In this paper, we propose two multivariate time series analysis methods. The first is multivariate hard c-means transition network which maps different time series into transition networks with the same size so that the time series characteristics can be captured more effectively by network measures. The second is combined hard c-means transition network whose size is different for various time series. The noise resistance, time cost and time series characterization ability of the proposed methods are illustrated by studying multivariate fractal series and coupled Rössler system. Finally, the application to multivariate electrocardiogram (ECG) data demonstrates the effectiveness of the proposed methods for distinguishing different heart conditions.
ISSN:0924-090X
1573-269X
DOI:10.1007/s11071-024-09523-w