A non subjective approach to the GP algorithm for analysing noisy time series

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dc.creatorHarikrishnan, K. P.
dc.creatorMisra, R.
dc.creatorAmbika, G.
dc.creatorKembhavi, A. K.
dc.date2006-03-11
dc.date.accessioned2026-07-07T11:28:14Z
dc.date.available2026-07-07T11:28:14Z
dc.descriptionWe present an adaptation of the standard Grassberger-Proccacia (GP) algorithm for estimating the Correlation Dimension of a time series in a non subjective manner. The validity and accuracy of this approach is tested using different types of time series, such as, those from standard chaotic systems, pure white and colored noise and chaotic systems added with noise. The effectiveness of the scheme in analysing noisy time series, particularly those involving colored noise, is investigated. An interesting result we have obtained is that, for the same percentage of noise addition, data with colored noise is more distinguishable from the corresponding surrogates, than data with white noise. As examples for real life applications, analysis of data from an astrophysical X-ray object and human brain EEG, are presented.
dc.descriptionAccepted for publication in Physica D. A numerical code which implements the scheme is available at http://www.iucaa.ernet.in/~rmisra/NLD
dc.identifierhttps://arxiv.org/abs/nlin/0603024
dc.identifierhttp://arxiv.org/abs/nlin/0603024
dc.identifierPhysicaD215:137-145,2006
dc.identifierdoi:10.1016/j.physd.2006.01.027
dc.identifier.urihttp://salesiana.dossiersoluciones.com/handle/123456789/196378
dc.subjectChaotic Dynamics
dc.subjectAstrophysics
dc.titleA non subjective approach to the GP algorithm for analysing noisy time series
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