文件名称:HOW_TO_REVIEW_A_PAPER
下载
别用迅雷、360浏览器下载。
如迅雷强制弹出，可右键点击选“另存为”。
失败请重下，重下不扣分。
如迅雷强制弹出，可右键点击选“另存为”。
失败请重下，重下不扣分。
介绍说明－－下载内容均来自于网络，请自行研究使用
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
(系统自动生成,下载前可以参看下载内容)
下载文件列表
HOW_TO_REVIEW_A_PAPER.pdf