Detecting smoothness in noisy time series

Journal Article · Sat Jun 01 00:00:00 EDT 1996 · AIP Conference Proceedings

DOI:https://doi.org/10.1063/1.51053· OSTI ID:401073

Cawley, R; Hsu, G; Salvino, L W ^[1]

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1. Information Sciences and Systems Branch, Naval Surface Warfare Center, Dahlgren Division, White Oak, 10901 New Hamsphire Avenue, Silver Spring, Maryland 20903-5640 (United States)

We describe the role of chaotic noise reduction in detecting an underlying smoothness in a dataset. We have described elsewhere a general method for assessing the presence of determinism in a time series, which is to test against the class of datasets producing smoothness (i.e., the null hypothesis is determinism). In order to reduce the likelihood of a false call, we recommend this kind of analysis be applied first to a time series whose deterministic origin is at question. We believe this step should be taken before implementing other methods of dynamical analysis and measurement, such as correlation dimension or Lyapounov spectrum. {copyright} {ital 1996 American Institute of Physics.}

• OSTI ID: 401073
• Report Number(s): CONF-950730-; ISSN 0094-243X; TRN: 96:029450
• Journal Information: AIP Conference Proceedings, Vol. 375, Issue 1; Conference: 3. technical conference on nonlinear dynamics (chaos) and full spectrum processing, Mystic, CT (United States), 10-13 Jul 1995; Other Information: PBD: Jun 1996
• Country of Publication: United States
• Language: English

Related Subjects

66 PHYSICS
TIME-SERIES ANALYSIS
DYNAMICS
NOISE
DATA ANALYSIS
CORRELATION FUNCTIONS
LANGEVIN EQUATION
HENON MAPPING