Recurrence analysis is a powerful tool to appraise the nonlinear dynamics of complex systems and delineate the inherent laminar, divergent, or transient behaviors. Oftentimes, the effectiveness of recurrence quantification hinges upon the accurate reconstruction of the state space from a univariate time series with a uniform sampling rate. Few, if any, existing approaches quantify the recurrence properties from a short-term time series, particularly those sampled at a non-uniform rate, which are fairly ubiquitous in studies of rare or extreme events. This paper presents a novel intrinsic recurrence quantification analysis to portray the recurrence behaviors in complex dynamical systems with only short-term observations. As opposed to the traditional recurrence analysis, the proposed approach represents recurrence dynamics of a short-term time series in an intrinsic state space formed by proper rotations, attained from intrinsic time-scale decomposition (ITD) of the short time series. It is shown that intrinsic recurrence quantification analysis (iRQA), patterns harnessed from the corresponding recurrence plot, captures the underlying nonlinear and nonstationary dynamics of those short time series. In addition, as ITD does not require uniform sampling of the time series, iRQA is also applicable to unevenly spaced temporal data. Our findings are corroborated in two case studies: change detection in the Lorenz time series and early-stage identification of atrial fibrillation using short-term electrocardiogram signals.
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September 2020
Research Article|
September 01 2020
Intrinsic recurrence quantification analysis of nonlinear and nonstationary short-term time series Available to Purchase
Abdulrahman Shamsan
;
Abdulrahman Shamsan
1
Department of Systems Science and Industrial Engineering, State University of New York at Binghamton
, Binghamton, New York 13902, USA
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Xiaodan Wu;
Xiaodan Wu
2
Smart Health Laboratory, Hebei University of Technology
, Tianjin 300000, China
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Pengyu Liu;
Pengyu Liu
2
Smart Health Laboratory, Hebei University of Technology
, Tianjin 300000, China
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Changqing Cheng
Changqing Cheng
a)
1
Department of Systems Science and Industrial Engineering, State University of New York at Binghamton
, Binghamton, New York 13902, USA
a)Author to whom correspondence should be addressed: [email protected]
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Abdulrahman Shamsan
1
Xiaodan Wu
2
Pengyu Liu
2
Changqing Cheng
1,a)
1
Department of Systems Science and Industrial Engineering, State University of New York at Binghamton
, Binghamton, New York 13902, USA
2
Smart Health Laboratory, Hebei University of Technology
, Tianjin 300000, China
a)Author to whom correspondence should be addressed: [email protected]
Chaos 30, 093104 (2020)
Article history
Received:
March 04 2020
Accepted:
August 14 2020
Citation
Abdulrahman Shamsan, Xiaodan Wu, Pengyu Liu, Changqing Cheng; Intrinsic recurrence quantification analysis of nonlinear and nonstationary short-term time series. Chaos 1 September 2020; 30 (9): 093104. https://doi.org/10.1063/5.0006537
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