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Proceedings Paper

De-noising with wavelets method in chaotic time series: application in climatology, energy, and finance
Author(s): Dominique Guegan; Kebira Hoummiya
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Paper Abstract

In this paper, in order to de-noise a chaotic signal, we compare the time-frequency deconvolution method with the wavelets method. We apply our results on different dynamical systems and show the capability of wavelets' method to reconstruct the attractor of a chaotic time series. Then, we de-noise different data sets in order to re-built their attractor using the wavelets method. The applications concern temperatures and wind fluctuations, electricity spot prices and financial data sets.

Paper Details

Date Published: 23 May 2005
PDF: 12 pages
Proc. SPIE 5848, Noise and Fluctuations in Econophysics and Finance, (23 May 2005); doi: 10.1117/12.620301
Show Author Affiliations
Dominique Guegan, Ecole Normale Superieure de Cachan, CNRS (France)
Kebira Hoummiya, Ecole Normale Superieure de Cachan, CNRS (France)


Published in SPIE Proceedings Vol. 5848:
Noise and Fluctuations in Econophysics and Finance
Derek Abbott; Jean-Philippe Bouchaud; Xavier Gabaix; Joseph L. McCauley, Editor(s)

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