De-noising with wavelets method in chaotic time series: application in climatology, energy and finance

Dominique Guegan (1) , Kebira Hoummyia (1)

1 IDHE - Institutions et Dynamiques Historiques de l'Economie

In this paper, we compre the time fresuency deconvolution method with the wavelets method. We apply our results on several dynamical systems and show the capability of the wavelet's method to reconstruct the attractor of a chaotic time series? We de-noise different data sets in order to rebuilt their attractor using the wavelets method. Tha applications concern temperatures, wind fluctuations, electricity spot prices and exchange rates.

Mots clés: Chaos; deconvolution; meteorology -returns

Domaines: Economies et finances; Probabilités [math.PR]; Statistiques [math.ST]

Dominique Guegan, Kebira Hoummyia. De-noising with wavelets method in chaotic time series: application in climatology, energy and finance. Proceedings of SPIE, the International Society for Optical Engineering, 2005, 5848, pp.174 - 185. ⟨halshs-00180873⟩