Comparison of Daubechies wavelets for Hurst parameter estimation

Ciftlikli C., Gezer A.

TURKISH JOURNAL OF ELECTRICAL ENGINEERING AND COMPUTER SCIENCES, vol.18, no.1, pp.117-128, 2010 (SCI-Expanded) identifier identifier

  • Publication Type: Article / Article
  • Volume: 18 Issue: 1
  • Publication Date: 2010
  • Doi Number: 10.3906/elk-0905-47
  • Journal Name: TURKISH JOURNAL OF ELECTRICAL ENGINEERING AND COMPUTER SCIENCES
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus, TR DİZİN (ULAKBİM)
  • Page Numbers: pp.117-128
  • Keywords: Daubechies Wavelets, Fractional Gaussian Noise, Hurst Estimation, Vanishing Moment
  • Kayseri University Affiliated: No

Abstract

Time scale dependence on the working nature of wavelet analysis makes it a valuable tool for Hurst parameter estimation Similar to other wavelet-based signal processing applications, the selection of a particular wavelet type and vanishing moment m wavelet based Hurst estimation is a challenging problem In this paper, we investigate the best Daubechies wavelet in wavelet based Hurst estimation for an exact self similar process, fractional Gaussian noise and how Daubechies vanishing moment affects the Hurst estimation accuracy Daubechies wavelets are preferred in analysis because increasing vanishing moment does not cause excessive increase of time support of Daubechies wavelets Thus, limited time support of wavelets reduces the border effects Results show that Daubechies wavelets with one vanishing moment (Daubechies 1) gives the best estimation result for short range dependent fractional Gaussian. noise Daubechies 2 is the best preference for long range dependent fractional Gaussian noise