An improved Hurst parameter estimator based on fractional Fourier transform
An improved Hurst parameter estimator based on fractional Fourier transform
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DOI：
10.1007/s11235-009-9207-4
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发表时间：
2010-04
影响因子：
2.5
通讯作者：
Y. Chen;Rongtao Sun;Anhong Zhou
中科院分区：
文献类型：
--
作者：
Y. Chen;Rongtao Sun;Anhong Zhou
提出了一种基于分数阶傅里叶变换（FrFT）的时间序列长相关性（LRD）估计方法。LRD的程度可以用Hurst参数来表征。本文提出的基于FrFT的Hurst参数估计方法可以在大数据量下有效地实现。我们使用分数高斯噪声（FGN），通常具有长程依赖与已知的赫斯特参数来测试建议的赫斯特参数估计的准确性。为了证明所提出的估计器的优点，其他一些现有的Hurst参数估计方法，如基于小波的方法和基于色散分析的全局估计，进行了比较。所提出的估计器可以处理很长的实验时间序列本地实现一个可靠的估计赫斯特参数。
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.