文件名称:HOW_TO_REVIEW_A_PAPER
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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
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
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