Title :
Estimating Hurst Index Based On Wavelet
Author :
Wang, Lele ; Bian, Bao Jun ; Yuan, Gui Quin
Author_Institution :
Math Dept., Tongji Univ., Shanghai
Abstract :
Long range persistence has been observed in many fields. A variety of methods have been proposed to estimate Hurst index of non-stationary and stationary process, which has power- law decay. In this paper, non-stationary process (fractional Brownian motion) is transformed to a stationary process and the autocorrelation decay exponentially by using discrete wavelet transformation. Then a novel unbiased estimator is developed. Wavelet method not only effectively eliminates the trend of series, but also deals with the abrupt change of series. Even the series contains some noise, wavelet method can perform well. At last, by comparing with R/H method, we conclude that estimator based on wavelet is more robust and more exact than that on R/H.
Keywords :
Brownian motion; discrete wavelet transforms; Hurst index; discrete wavelet transformation; fractional Brownian motion; Autocorrelation; Brownian motion; Educational institutions; Frequency estimation; Gaussian processes; Investments; Motion estimation; Statistical distributions; Statistics; Wavelet coefficients;
Conference_Titel :
Wireless Communications, Networking and Mobile Computing, 2008. WiCOM '08. 4th International Conference on
Conference_Location :
Dalian
Print_ISBN :
978-1-4244-2107-7
Electronic_ISBN :
978-1-4244-2108-4
DOI :
10.1109/WiCom.2008.2300