Stochastic noises of fiber optic gyroscope （FOG） mainly contain white noise and fractal noise whose long-term dependent component causes FOG a rather slow drift. In order to eliminate this component, a two-step filt...Stochastic noises of fiber optic gyroscope （FOG） mainly contain white noise and fractal noise whose long-term dependent component causes FOG a rather slow drift. In order to eliminate this component, a two-step filtering methodology is proposed. Firstly, fractional differencing （FD） method is introduced to trans-form fractal noise into fractional white noise based on the estima-tion of Hurst exponent for long-term dependent fractal process, which together with the existing white noise make up of a gener-alized white noise. Further, an improved denoising algorithm of wavelet maxima is developed to suppress the generalized white noise. Experimental results show that the basic noise terms of FOG greatly decrease, and especially the slow drift is restrained effectively. The proposed methodology provides a promising ap-proach for filtering long-term dependent fractal noise.展开更多
This paper proposes a long memory analysis based on wavelet transform of financial data. This method treats return series and volatility series in the stock market as a fractional differenced noise process, and analyz...This paper proposes a long memory analysis based on wavelet transform of financial data. This method treats return series and volatility series in the stock market as a fractional differenced noise process, and analyzes it by MODWT（maximal overlap discrete wavelet transform）. The result shows there is a lineal relationship between wavelet variance logarithm and scale logarithm, so a long memory parameter can be obtained by using the relationship. This method is proved to be effective and feasible by analyzing the return series and volatility series of composite indexes of Shanghai and Shenzhen stock market.展开更多
基金supported by Aviation Science Foundation(20070851011).
文摘Stochastic noises of fiber optic gyroscope （FOG） mainly contain white noise and fractal noise whose long-term dependent component causes FOG a rather slow drift. In order to eliminate this component, a two-step filtering methodology is proposed. Firstly, fractional differencing （FD） method is introduced to trans-form fractal noise into fractional white noise based on the estima-tion of Hurst exponent for long-term dependent fractal process, which together with the existing white noise make up of a gener-alized white noise. Further, an improved denoising algorithm of wavelet maxima is developed to suppress the generalized white noise. Experimental results show that the basic noise terms of FOG greatly decrease, and especially the slow drift is restrained effectively. The proposed methodology provides a promising ap-proach for filtering long-term dependent fractal noise.
文摘This paper proposes a long memory analysis based on wavelet transform of financial data. This method treats return series and volatility series in the stock market as a fractional differenced noise process, and analyzes it by MODWT（maximal overlap discrete wavelet transform）. The result shows there is a lineal relationship between wavelet variance logarithm and scale logarithm, so a long memory parameter can be obtained by using the relationship. This method is proved to be effective and feasible by analyzing the return series and volatility series of composite indexes of Shanghai and Shenzhen stock market.
