Quantifying the Dynamical Complexity of Chaotic Time Series

Antonio Politi

Research output: Contribution to journalLetterpeer-review

38 Citations (Scopus)
9 Downloads (Pure)

Abstract

A powerful approach is proposed for the characterization of chaotic signals. It is based on the combined use of two classes of indicators: (i) the probability of suitable symbolic sequences (obtained from the ordinal patterns of the corresponding time series); (ii) the width of the corresponding cylinder sets. This way, much information can be extracted and used to quantify the complexity of a given signal. As an example of the potentiality of the method, I introduce a modified permutation entropy which allows for quantitative estimates of the Kolmogorov-Sinai entropy in hyperchaotic models, where other methods would be unpractical. As a by-product, estimates of the fractal dimension of the underlying attractors are possible as well.
Original languageEnglish
Article number144101
JournalPhysical Review Letters
Volume118
Issue number4
DOIs
Publication statusPublished - 7 Apr 2017

Bibliographical note

Acknowledgements
The author wishes to acknowledge G. Giacomelli, M. Mulansky, and L. Ricci for early discussions.

Fingerprint

Dive into the research topics of 'Quantifying the Dynamical Complexity of Chaotic Time Series'. Together they form a unique fingerprint.

Cite this