We introduce a representation space to contrast chaotic with stochastic dynamics. Following the complex network representation of a time series through ordinal pattern transitions, we propose to assign each system a position in a two-dimensional plane defined by the permutation entropy of the network (global network quantifier) and the minimum value of the permutation entropy of the nodes (local network quantifier). The numerical analysis of representative chaotic maps and stochastic systems shows that the proposed approach is able to distinguish linear from non-linear dynamical systems by different planar locations. Additionally, we show that this characterization is robust when observational noise is considered. Experimental applications ...
Computational chaos reports the artificial generation or suppression of chaotic behaviour in digital...
Abstract. Complex systems are commonly modeled using nonlinear dynamical systems. These models are o...
Author name used in this publication: M. Small2005-2006 > Academic research: refereed > Publication ...
By appealing to a long list of different nonlinear maps we review the characterization of time serie...
One of the most important aspects of time series is their degree of stochasticity vs. chaoticity. Si...
By appealing to a long list of different nonlinear maps we review the characterization of ...
Extracting relevant properties of empirical signals generated by nonlinear, stochastic, and high-dim...
Recently proposed ordinal networks not only afford novel methods of nonlinear time series analysis b...
Strategies based on the extraction of measures from ordinal patterns transformation, such as probabi...
This paper aims at introducing the Lempel–Ziv permutation complexity vs. permutation entropy plane (...
As effective representations of complex systems, complex networks have attracted scholarly attention...
Permutation entropy contains the information about the temporal structure associated with the underl...
We investigate a generalised version of the recently proposed ordinal partition time series to netwo...
Chaotic systems share with stochastic processes several properties that make them almost undistingui...
Recently a new framework has been proposed to explore the dynamics of pseudoperiodic time series by ...
Computational chaos reports the artificial generation or suppression of chaotic behaviour in digital...
Abstract. Complex systems are commonly modeled using nonlinear dynamical systems. These models are o...
Author name used in this publication: M. Small2005-2006 > Academic research: refereed > Publication ...
By appealing to a long list of different nonlinear maps we review the characterization of time serie...
One of the most important aspects of time series is their degree of stochasticity vs. chaoticity. Si...
By appealing to a long list of different nonlinear maps we review the characterization of ...
Extracting relevant properties of empirical signals generated by nonlinear, stochastic, and high-dim...
Recently proposed ordinal networks not only afford novel methods of nonlinear time series analysis b...
Strategies based on the extraction of measures from ordinal patterns transformation, such as probabi...
This paper aims at introducing the Lempel–Ziv permutation complexity vs. permutation entropy plane (...
As effective representations of complex systems, complex networks have attracted scholarly attention...
Permutation entropy contains the information about the temporal structure associated with the underl...
We investigate a generalised version of the recently proposed ordinal partition time series to netwo...
Chaotic systems share with stochastic processes several properties that make them almost undistingui...
Recently a new framework has been proposed to explore the dynamics of pseudoperiodic time series by ...
Computational chaos reports the artificial generation or suppression of chaotic behaviour in digital...
Abstract. Complex systems are commonly modeled using nonlinear dynamical systems. These models are o...
Author name used in this publication: M. Small2005-2006 > Academic research: refereed > Publication ...