on fractional Fourier transform

Abstract A fractional Fourier transform (FrFT) based es-timation 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 para-meters to test the accuracy of the proposed Hurst parame-ter estimator. For justifying the advantage of the proposed estimator, some other existing Hurst parameter estimation methods, such as wavelet-based method and a global estima-tor based on dispersional analysis, are compared. The pro-posed estimator can process the very long experimental tim