2017
DOI: 10.1063/1.4978743
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Regenerating time series from ordinal networks

Abstract: Recently proposed ordinal networks not only afford novel methods of nonlinear time series analysis but also constitute stochastic approximations of the deterministic flow time series from which the network models are constructed. In this paper, we construct ordinal networks from discrete sampled continuous chaotic time series and then regenerate new time series by taking random walks on the ordinal network. We then investigate the extent to which the dynamics of the original time series are encoded in the ordi… Show more

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Cited by 42 publications
(27 citation statements)
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“…I would like to pay attention how the researches of Refs. [74,[90][91][92] will develop in the future.…”
Section: Discussionmentioning
confidence: 99%
“…I would like to pay attention how the researches of Refs. [74,[90][91][92] will develop in the future.…”
Section: Discussionmentioning
confidence: 99%
“…Although we will not explore recurrence networks in this paper we mention, for completeness, that a network with the topology of the attractor can be calculated from the recurrence plot (Donner et al 2010;McCullough et al 2017) as shown in Fig. 8.…”
Section: An Example: the Lorentz Attractor-quantification And Predictionmentioning
confidence: 99%
“…McCullough et al 74 construct ordinal networks from discretely sampled chaotic time series generated by dynamical systems and regenerate new time series by taking random walks on the corresponding ordinal network. They investigate the extent to which the dynamics of the original time series are encoded in the ordinal networks and retained through the process of regenerating new time series by using several distinct quantitative approaches.…”
Section: Network Analysis Of Flow Time Seriesmentioning
confidence: 99%