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Beyond Benford's Law: Distinguishing Noise from Chaos

Determinism and randomness are two inherent aspects of all physical processes. Time series from chaotic systems share several features identical with those generated from stochastic processes, which makes them almost undistinguishable. In this paper, a new method based on Benford's law is desig...

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Detalles Bibliográficos
Autores principales: Li, Qinglei, Fu, Zuntao, Yuan, Naiming
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2015
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4452586/
https://www.ncbi.nlm.nih.gov/pubmed/26030809
http://dx.doi.org/10.1371/journal.pone.0129161
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author Li, Qinglei
Fu, Zuntao
Yuan, Naiming
author_facet Li, Qinglei
Fu, Zuntao
Yuan, Naiming
author_sort Li, Qinglei
collection PubMed
description Determinism and randomness are two inherent aspects of all physical processes. Time series from chaotic systems share several features identical with those generated from stochastic processes, which makes them almost undistinguishable. In this paper, a new method based on Benford's law is designed in order to distinguish noise from chaos by only information from the first digit of considered series. By applying this method to discrete data, we confirm that chaotic data indeed can be distinguished from noise data, quantitatively and clearly.
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spelling pubmed-44525862015-06-09 Beyond Benford's Law: Distinguishing Noise from Chaos Li, Qinglei Fu, Zuntao Yuan, Naiming PLoS One Research Article Determinism and randomness are two inherent aspects of all physical processes. Time series from chaotic systems share several features identical with those generated from stochastic processes, which makes them almost undistinguishable. In this paper, a new method based on Benford's law is designed in order to distinguish noise from chaos by only information from the first digit of considered series. By applying this method to discrete data, we confirm that chaotic data indeed can be distinguished from noise data, quantitatively and clearly. Public Library of Science 2015-06-01 /pmc/articles/PMC4452586/ /pubmed/26030809 http://dx.doi.org/10.1371/journal.pone.0129161 Text en © 2015 Li et al http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited.
spellingShingle Research Article
Li, Qinglei
Fu, Zuntao
Yuan, Naiming
Beyond Benford's Law: Distinguishing Noise from Chaos
title Beyond Benford's Law: Distinguishing Noise from Chaos
title_full Beyond Benford's Law: Distinguishing Noise from Chaos
title_fullStr Beyond Benford's Law: Distinguishing Noise from Chaos
title_full_unstemmed Beyond Benford's Law: Distinguishing Noise from Chaos
title_short Beyond Benford's Law: Distinguishing Noise from Chaos
title_sort beyond benford's law: distinguishing noise from chaos
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4452586/
https://www.ncbi.nlm.nih.gov/pubmed/26030809
http://dx.doi.org/10.1371/journal.pone.0129161
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