Presentation 1996/7/26
A Sequential Cross-correlation Using Higher Order Exponential Smoothing for Detecting Nonstationarity of Cross-correlation
Takayuki HIRANO,
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Abstract(in Japanese) (See Japanese page)
Abstract(in English) A Sequential method for the cross-correlation function has been presented in this paper. This method employs a higher order exponential smoothing method for obtaining cross-correlation sequentially from the instantaneous cross-correlation function which is the integrand of the ordinary cross-correlation function. This makes it possible to detect nonstationary viriations of the cross-correlation function faster and more accurately in comparison with the ordinary cross-correlation Because noise can be eliminated successfully, the transition process of the cross-correlation function value gets so smoother that can be useful to find the variation visually. Numerical simulation has shown there are some appropriate pairs of the smoothing constant an the order of the smoothing method to detect the cross-correlation function variations and to eliminate noise. This proposed method has been applied to actual economic data to detect cross-correlation function variations more effectively.
Keyword(in Japanese) (See Japanese page)
Keyword(in English) cross-correlation / exponential smoothing method / nonstationary correlation / on-line
Paper # DSP96-64,SST96-46,CS96-64
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Conference Information
Committee DSP
Conference Date 1996/7/26(1days)
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Paper Information
Registration To Digital Signal Processing (DSP)
Language JPN
Title (in Japanese) (See Japanese page)
Sub Title (in Japanese) (See Japanese page)
Title (in English) A Sequential Cross-correlation Using Higher Order Exponential Smoothing for Detecting Nonstationarity of Cross-correlation
Sub Title (in English)
Keyword(1) cross-correlation
Keyword(2) exponential smoothing method
Keyword(3) nonstationary correlation
Keyword(4) on-line
1st Author's Name Takayuki HIRANO
1st Author's Affiliation Department of Industrial Engineering and Management,Graduate School of Decision Science and Technology,Tokyo Institute of Technology()
Date 1996/7/26
Paper # DSP96-64,SST96-46,CS96-64
Volume (vol) vol.96
Number (no) 183
Page pp.pp.-
#Pages 6
Date of Issue