Characterizing pseudoperiodic time series through the complex network approach

By J. Zhang, Junfeng Sun, Xiaodong Luo, Kai Zhang, Tomomichi Nakamura, Michael Small. 2008

178 citations recorded by the source corpus.

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

View Paper: https://www.semanticscholar.org/paper/78000258b5d9a3cbf0c9f3fe96f68143fbeca94a

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In Computer Networks and Communications

In Economics and Econometrics

In Statistical and Nonlinear Physics

Citations in the corpus

Wavelet Multiresolution Complex Network for Analyzing Multivariate Nonlinear Time Series (2017, background): “…incorporated into the analysis of time series and many successful 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;…”
Statistical properties of visibility graph of energy dissipation rates in three-dimensional fully developed turbulence (2009, methodology): “into networks have emerged. Zhang et al. introduced a method to deal with the pseudoperiodictime series and found that the structure of the correspondingnetwork depended on the dynamics of the series [8, 9]. In this method, the nodes correspond directly to cycles in the time series, and the edges ar”
Complex network approaches to nonlinear time series analysis (2019, background): “As a consequence, the networks constructed with these methods will have characteristic and distinct properties: linear and periodic systems have cycle networks that appear randomly,while chaotic and nonlinear systems generate highly structured networks [49,55].”
Complex network approach for recurrence analysis of time series (2009, methodology): “is actually not necessary to consider the phase space trajectory as a network. Recently, very ﬁrst steps in the direction of bridging complex network theory and recurrence analysis have been reported [31, 32]. In these works, the local properties of phase space trajectories have been studied using c”
Recurrence-based time series analysis by means of complex network methods (2010, methodology): “In [Zhang & Small, 2006; Zhang et al. , 2008] the preferred method for deﬁning cycles is splitting the trajectory at peaks (or equally troughs).”
Recurrence networks—a novel paradigm for nonlinear time series analysis (2009, background): “It shall be noted that a generalization to weighted networks (as partially studied in [33, 34]) is straightforward if the recurrence matrix is replaced by the associated distance matrix between pairs of states.”