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Title: Transforming time series into complex networks
Authors: Small, M 
Zhang, J 
Xu, X 
Keywords: Chaos
Chaotic dynamics
Complex networks
Nonlinear time series
Issue Date: 2009
Publisher: Springer
Source: Lecture notes of the Institute for Computer Sciences, Social Informatics, and Telecommunications Engineering, 2009, v. 5 LNICST, no. PART 2, p. 2078-2089 How to cite?
Journal: Lecture notes of the Institute for Computer Sciences, Social Informatics, and Telecommunications Engineering 
Abstract: We introduce transformations from time series data to the domain of complex networks which allow us to characterise the dynamics underlying the time series in terms of topological features of the complex network. We show that specific types of dynamics can be characterized by a specific prevalence in the complex network motifs. For example, low dimensional chaotic flows with one positive Lyapunov exponent form a single family while noisy non-chaotic dynamics and hyper-chaos are both distinct. We find that the same phenomena is also true for discrete maplike data. These algorithms provide a new way of studying chaotic time series and equip us with a wide range of statistical measures previously not available in the field of nonlinear time series analysis.
Description: 1st International Conference on Complex Sciences: Theory and Applications, Complex 2009, Shanghai, 23-25 February 2009
ISBN: 3642024688
ISSN: 1867-8211
EISSN: 1867-822X
DOI: 10.1007/978-3-642-02469-6_84
Appears in Collections:Conference Paper

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