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

Dominique Guegan () and Kebira Hoummyia
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Dominique Guegan: IDHE - Institutions et Dynamiques Historiques de l'Economie - ENS Cachan - École normale supérieure - Cachan - UP1 - Université Paris 1 Panthéon-Sorbonne - UP8 - Université Paris 8 Vincennes-Saint-Denis - UPN - Université Paris Nanterre - CNRS - Centre National de la Recherche Scientifique
Kebira Hoummyia: IDHE - Institutions et Dynamiques Historiques de l'Economie - ENS Cachan - École normale supérieure - Cachan - UP1 - Université Paris 1 Panthéon-Sorbonne - UP8 - Université Paris 8 Vincennes-Saint-Denis - UPN - Université Paris Nanterre - CNRS - Centre National de la Recherche Scientifique

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Abstract: 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.

Keywords: Chaos; deconvolution; meteorology -returns (search for similar items in EconPapers)
Date: 2005
Note: View the original document on HAL open archive server: https://shs.hal.science/halshs-00180873
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Published in Proceedings of SPIE, the International Society for Optical Engineering, 2005, 5848, pp.174 - 185

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Persistent link: https://EconPapers.repec.org/RePEc:hal:journl:halshs-00180873

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