An improved Hurst parameter estimator based on fractional Fourier transform
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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 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.
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- An improved Hurst parameter estimator based on fractional Fourier transform
Volume 43, Issue 3-4 , pp 197-206
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- Springer US
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- Fractional Fourier transform
- Fractional Gaussian noise
- Hurst parameter
- Long-range dependence
- Industry Sectors
- Author Affiliations
- 1. Center for Self-Organizing and Intelligent Systems (CSOIS), Department of Electrical and Computer Engineering, Utah State University, 4160 Old Main Hill, Logan, UT, 84322, USA
- 2. Phase Dynamics, Inc., 1251 Columbia Dr., Richardson, TX, 75081, USA
- 3. Department of Biological and Irrigational Engineering, Utah State University, 4105 Old Main Hill, Logan, UT, 84322-4105, USA