Beyond Benford's Law: Distinguishing Noise from Chaos.
Where this comes from
- Record sourced from PubMed, PMID 26030809.
- Also identified by DOI 10.1371/journal.pone.0129161 and PMC identifier 4452586.
- Licence recorded as CC BY.
- The licence permits redistribution, so the abstract is shown in full and the full text is available from the publisher.
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.
Medical subject headings
- Data Interpretation, Statistical
- Information Management
- Models, Theoretical
- Noise