An improved wavelet de-noising method for time series analysis

  • Authors:

  • Yan-fang Sang;Dong Wang;Ji-chun Wu

  • Affiliations:

  • Department of Hydrosciences, School of Earth Sciences and Engineering, Nanjing University, Nanjing, P. R. China;Department of Hydrosciences, School of Earth Sciences and Engineering, Nanjing University, Nanjing, P. R. China;Department of Hydrosciences, School of Earth Sciences and Engineering, Nanjing University, Nanjing, P. R. China

  • Venue:
  • FSKD'09 Proceedings of the 6th international conference on Fuzzy systems and knowledge discovery - Volume 2
  • Year:
  • 2009

Quantified Score

Hi-index 0.00

Visualization

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 denoising 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 denoising 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.