, Volume 43, Issue 3-4, pp 197-206
Date: 23 Oct 2009

An improved Hurst parameter estimator based on fractional Fourier transform

Rent the article at a discount

Rent now

* Final gross prices may vary according to local VAT.

Get Access

Abstract

A fractional Fourier transform (FrFT) based estimation method is introduced in this paper to analyze the long range dependence (LRD) in time series. The degree of LRD can be characterized by the Hurst parameter. The FrFT-based estimation of Hurst parameter proposed in this paper can be implemented efficiently allowing very large data set. We used fractional Gaussian noises (FGN) which typically possesses long-range dependence with known Hurst parameters to test the accuracy of the proposed Hurst parameter estimator. For justifying the advantage of the proposed estimator, some other existing Hurst parameter estimation methods, such as wavelet-based method and a global estimator based on dispersional analysis, are compared. The proposed estimator can process the very long experimental time series locally to achieve a reliable estimation of the Hurst parameter.

Submitted June 2008. Revised Oct. 2008. For final submission to “Special Issue on Traffic Modeling, Its Computations and Applications” of Telecommunication Systems. Guest Editors: Professors Ming Li and Pierre Borgnat.