Permutation entropy of of weakly noise-affected

Leonardo Ricci, Antonio Politi* (Corresponding Author)

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

2 Citations (Scopus)
3 Downloads (Pure)


We analyze the permutation entropy of deterministic chaotic signals affected by a weak observational noise. We investigate the scaling dependence of the entropy increase on both the noise amplitude and the window length used to encode the time series. In order to shed light on the scenario, we perform a multifractal analysis, which allows highlighting the emergence of many poorly populated symbolic sequences generated by the stochastic fluctuations. We finally make use of this information to reconstruct the noiseless permutation entropy. While this approach works quite well for Hénon and tent maps, it is much less effective in the case of hyperchaos. We argue about the underlying motivations.
Original languageEnglish
Article number54
Number of pages10
Issue number1
Publication statusPublished - 28 Dec 2021

Bibliographical note

L.R. and A.P. contributed equally to this work.
Funding: This research received no external funding.

Data Availability Statement

Data sharing not applicable.


  • entropy
  • ordinal patterns
  • multifractal analysis


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