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- Title
Hard c-means transition network for the analysis of multivariate time series.
- Authors
Yang, Guangyu; Long, Dafeng; Wang, Kai; Xia, Shuyan
- Abstract
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.
- Subjects
TIME series analysis; DYNAMICAL systems; SYSTEM analysis; NONLINEAR dynamical systems
- Publication
Nonlinear Dynamics, 2024, Vol 112, Issue 10, p8393
- ISSN
0924-090X
- Publication type
Article
- DOI
10.1007/s11071-024-09523-w