On Estimation of Hurst Scaling Exponent through Discrete Wavelets

We study the scaling behavior of the fluctuations, as extracted through wavelet coefficients based on discrete wavelets. The analysis is carried out on a variety of physical data sets, as well as Gaussian white noise and binomial multi-fractal model time series and the results are compared with cont...

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Bibliographic Details
Published in:arXiv.org 2008-04
Main Authors: Manimaran, P, Panigrahi, Prasanta K, Parikh, Jitendra C
Format: Article
Language:English
Subjects:
Coefficients
Fractal models
Scaling
Time series
Variations
Wavelet analysis
Wavelet transforms
White noise
Online Access:Get full text
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Summary:We study the scaling behavior of the fluctuations, as extracted through wavelet coefficients based on discrete wavelets. The analysis is carried out on a variety of physical data sets, as well as Gaussian white noise and binomial multi-fractal model time series and the results are compared with continuous wavelet based average wavelet coefficient method. It is found that high-pass coefficients of wavelets, belonging to the Daubechies family are quite good in estimating the true power in the fluctuations in a non-stationary time series. Hence, the fluctuation functions based on discrete wavelet coefficients find the Hurst scaling exponents accurately.
ISSN:2331-8422