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 FrFTbased

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