Articles | Volume 7, issue 1/2
Nonlin. Processes Geophys., 7, 111–116, 2000
https://doi.org/10.5194/npg-7-111-2000
Nonlin. Processes Geophys., 7, 111–116, 2000
https://doi.org/10.5194/npg-7-111-2000

  30 Jun 2000

30 Jun 2000

Wavelet filtering of chaotic data

M. Grzesiak M. Grzesiak
  • Space Research Center of Polish Academy of Sciences, Warsaw, Poland

Abstract. Satisfactory method of removing noise from experimental chaotic data is still an open problem. Normally it is necessary to assume certain properties of the noise and dynamics, which one wants to extract, from time series. The wavelet based method of denoising of time series originating from low-dimensional dynamical systems and polluted by the Gaussian white noise is considered. Its efficiency is investigated by comparing the correlation dimension of clean and noisy data generated for some well-known dynamical systems. The wavelet method is contrasted with the singular value decomposition (SVD) and finite impulse response (FIR) filter methods.