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  A direct method to detect deterministic and stochastic properties of data

Prado, T. L., Boaretto, B. R. R., Corso, G., dos Santos Lima, G. Z., Kurths, J., Lopes, S. R. (2022): A direct method to detect deterministic and stochastic properties of data. - New Journal of Physics, 24, 033027.
https://doi.org/10.1088/1367-2630/ac5057

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 Creators:
Prado, Thiago Lima1, Author
Boaretto, Bruno Rafael Reichert1, Author
Corso, Gilberto1, Author
dos Santos Lima, Gustavo Zampier1, Author
Kurths, Jürgen2, Author              
Lopes, Sergio Roberto1, Author
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1External Organizations, ou_persistent22              
2Potsdam Institute for Climate Impact Research, ou_persistent13              

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 Abstract: A fundamental question of data analysis is how to distinguish noise corrupted deterministic chaotic dynamics from time-(un)correlated stochastic fluctuations when just short length data is available. Despite its importance, direct tests of chaos vs stochasticity in finite time series still lack of a definitive quantification. Here we present a novel approach based on recurrence analysis, a nonlinear approach to deal with data. The main idea is the identification of how recurrence microstates and permutation patterns are affected by time reversibility of data, and how its behavior can be used to distinguish stochastic and deterministic data. We demonstrate the efficiency of the method for a bunch of paradigmatic systems under strong noise influence, as well as for real-world data, covering electronic circuit, sound vocalization and human speeches, neuronal activity, heart beat data, and geomagnetic indexes. Our results support the conclusion that the method distinguishes well deterministic from stochastic fluctuations in simulated and empirical data even under strong noise corruption, finding applications involving various areas of science and technology. In particular, for deterministic signals, the quantification of chaotic behavior may be of fundamental importance because it is believed that chaotic properties of some systems play important functional roles, opening doors to a better understanding and/or control of the physical mechanisms behind the generation of the signals.

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Language(s): eng - English
 Dates: 2022-03-182022-03
 Publication Status: Finally published
 Pages: 20
 Publishing info: -
 Table of Contents: -
 Rev. Type: Peer
 Identifiers: DOI: 10.1088/1367-2630/ac5057
MDB-ID: No data to archive
PIKDOMAIN: RD4 - Complexity Science
Organisational keyword: RD4 - Complexity Science
Research topic keyword: Nonlinear Dynamics
Model / method: Nonlinear Data Analysis
Working Group: Network- and machine-learning-based prediction of extreme events
OATYPE: Gold Open Access
 Degree: -

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Title: New Journal of Physics
Source Genre: Journal, SCI, Scopus, p3, oa
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Pages: - Volume / Issue: 24 Sequence Number: 033027 Start / End Page: - Identifier: CoNE: https://publications.pik-potsdam.de/cone/journals/resource/1911272
Publisher: IOP Publishing