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Discriminating chaotic and stochastic time series using permutation entropy and artificial neural networks

Author
Boaretto, B.; Budzinski, R.; Rossi, K.; Prado, T.; Lopes, S.; Masoller, C.
Type of activity
Journal article
Journal
Scientific reports
Date of publication
2021-08-04
Volume
11
First page
15789/1
Last page
15789/10
DOI
10.1038/s41598-021-95231-z
Project funding
Complex dynamical systems and advanced data analysis tools
Repository
http://hdl.handle.net/2117/352194 Open in new window
URL
https://www.nature.com/articles/s41598-021-95231-z Open in new window
Abstract
Extracting relevant properties of empirical signals generated by nonlinear, stochastic, and high-dimensional systems is a challenge of complex systems research. Open questions are how to differentiate chaotic signals from stochastic ones, and how to quantify nonlinear and/or high-order temporal correlations. Here we propose a new technique to reliably address both problems. Our approach follows two steps: first, we train an artificial neural network (ANN) with flicker (colored) noise to predict ...
Citation
Boaretto, B. [et al.]. Discriminating chaotic and stochastic time series using permutation entropy and artificial neural networks. "Scientific reports", 4 Agost 2021, vol. 11, p. 15789/1-15789/10.
Group of research
DONLL - Nonlinear dynamics, nonlinear optics and lasers

Participants

  • Boaretto, Bruno R.  (author)
  • Budzinski, Roberto C.  (author)
  • Rossi, Kalel L.  (author)
  • Prado, Thiago L.  (author)
  • Lopes, Sergio R.  (author)
  • Masoller Alonso, Cristina  (author)

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