PLoS ONE, 10, e0129161.
ISSN/ISBN: Not available at this time. DOI: 10.1371/journal.pone.0129161
Abstract: 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.
Bibtex:
@article {,
AUTHOR = {Qinglei Li and Zuntao Fu and Naiming Yuan},
TITLE = {Beyond Benford's Law: Distinguishing Noise from Chaos},
JOURNAL = {PLoS ONE},
YEAR = {2015},
VOLUME = {10},
DOI = {10.1371/journal.pone.0129161,
URL = {http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0129161},
}
Reference Type: Journal Article
Subject Area(s): Dynamical Systems, Physics