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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...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2015
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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. |
format | Online Article Text |
id | pubmed-4452586 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2015 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
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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