An Improved Wavelet De-noising Method for Time Series Analysis

Author

Sang, Yan-Fang ; Wang, Dong ; Wu, Ji-Chun

Author_Institution

Dept. of Hydrosciences, Nanjing Univ., Nanjing, China

Volume

2

fYear

2009

fDate

14-16 Aug. 2009

Firstpage

517

Lastpage

521

Abstract

On the basis of discussing some key problems about wavelet de-noising as: choice of reasonable wavelet function, determination of reasonable wavelet coefficients thresholds and choice of suitable threshold processing-means, an improved wavelet de-noising method has been proposed. Then by Monte-Carlo tests, the validity of this method is verified. Analyses results show that compared with traditional methods (FT, SURE and MINMAX), this improved wavelet de-noising method is more accurate and reliable. Furthermore, because of based on information entropy theories to choose the reasonable wavelet coefficients thresholds, the de-noising results by the improved method are the global optimum.

Keywords

Monte Carlo methods; time series; wavelet transforms; Monte-Carlo tests; information entropy theories; time series analysis; wavelet denoising method; Discrete wavelet transforms; Fuzzy systems; Minimax techniques; Noise reduction; Spectral analysis; Testing; Time series analysis; Wavelet analysis; Wavelet coefficients; Wiener filter;

fLanguage

English

Publisher

ieee

Conference_Titel

Fuzzy Systems and Knowledge Discovery, 2009. FSKD '09. Sixth International Conference on

Conference_Location

Tianjin

Print_ISBN

978-0-7695-3735-1

Type

conf

DOI

10.1109/FSKD.2009.75

Filename

5359502