Quantifying the Dynamical Complexity of Chaotic Time Series

Antonio Politi

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36 Citations (Scopus)
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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
Issue number4
Publication statusPublished - 7 Apr 2017


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