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...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
Public Library of Science (PLoS)
2015-01-01
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| Series: | PLoS ONE |
| Online Access: | https://doi.org/10.1371/journal.pone.0129161 |
| _version_ | 1819123355816558592 |
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| author | Qinglei Li Zuntao Fu Naiming Yuan |
| author_facet | Qinglei Li Zuntao Fu Naiming Yuan |
| author_sort | Qinglei Li |
| collection | DOAJ |
| 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. |
| first_indexed | 2024-12-22T07:07:02Z |
| format | Article |
| id | doaj.art-68b3dd3450dd4380ac5f98d30901453e |
| institution | Directory Open Access Journal |
| issn | 1932-6203 |
| language | English |
| last_indexed | 2024-12-22T07:07:02Z |
| publishDate | 2015-01-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS ONE |
| spelling | doaj.art-68b3dd3450dd4380ac5f98d30901453e2022-12-21T18:34:39ZengPublic Library of Science (PLoS)PLoS ONE1932-62032015-01-01106e012916110.1371/journal.pone.0129161Beyond Benford's Law: Distinguishing Noise from Chaos.Qinglei LiZuntao FuNaiming YuanDeterminism 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.https://doi.org/10.1371/journal.pone.0129161 |
| spellingShingle | Qinglei Li Zuntao Fu Naiming Yuan Beyond Benford's Law: Distinguishing Noise from Chaos. PLoS ONE |
| 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 |
| url | https://doi.org/10.1371/journal.pone.0129161 |
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