Complex network approach for recurrence analysis of time series

By Norbert Marwan, Jonathan F. Donges, Yong Zou, Reik V. Donner, Jürgen Kurths. 2009

555 citations recorded by the source corpus.

We propose a novel approach for analysing time series using complex network theory. We identify the recurrence matrix (calculated from time series) with the adjacency matrix of a complex network and apply measures for the characterisation of complex networks to this recurrence matrix. By using the logistic map, we illustrate the potential of these complex network measures for the detection of dynamical transitions. Finally, we apply the proposed approach to a marine palaeo-climate record and identify the subtle changes to the climate regime.

Subfields: Computer Networks and Communications, Economics and Econometrics, Statistical and Nonlinear Physics

View Paper: https://www.semanticscholar.org/paper/b0fc171b86b0999ffac0184231172d0931fafa81

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Citations in the corpus

Wavelet Multiresolution Complex Network for Analyzing Multivariate Nonlinear Time Series (2017, background): “…applications have been achieved in different research fields [Wang et al., 2016; Zhang et al., 2006; 2008; Xu et al., 2008; Lacasa et al., 2008; Marwan et al., 2009; Majhi et al., 2016; Wang et al., 2015; Xiang et al., 2012; Marwan & Kurths, 2015; Peng et al., 2013; Wang et al., 2012; Huang et…”
Statistical properties of visibility graph of energy dissipation rates in three-dimensional fully developed turbulence (2009, background): “ber 23, 2018 its four nearest neighbors to form a complex network [11]. There are also other mapping methods from time series to networks based on n-tuples of ﬂuctuations [12–14] or recurr ence plots [15, 16]. Lacasa et al. proposed the visibility graph algorithm, which can map all types of time ser”
Recurrence networks—a novel paradigm for nonlinear time series analysis (2009, methodology): “Examples include the detection of dynamical transitions between periodic and chaotic behaviour in maps [43], continuous dynamical systems [85] or the analysis of protein folding [89, 90].”
Complex network approaches to nonlinear time series analysis (2019, methodology): “Consequently, we approach theminimum possible value of L̂ = 1 [52], whereas chaotic orbits typically lead to larger L̂.”