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

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

2006-03-11

arxiv.org/abs/nlin/0603024v1

PhysicaD215:137-145,2006

Nonlinear Sciences

Chaotic Dynamics

Accepted for publication in Physica D. A numerical code which implements the scheme is available at http://www.iucaa.ernet.in/

Scientific paper

10.1016/j.physd.2006.01.027

We 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.

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