Forecasting chaotic systems: The role of local Lyapunov exponents

Dominique Guegan (dominique.guegan@univ-paris1.fr) and Justin Leroux (justin.leroux@hec.ca)
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Dominique Guegan: PSE - Paris School of Economics - UP1 - Université Paris 1 Panthéon-Sorbonne - ENS-PSL - École normale supérieure - Paris - PSL - Université Paris Sciences et Lettres - EHESS - École des hautes études en sciences sociales - ENPC - École nationale des ponts et chaussées - CNRS - Centre National de la Recherche Scientifique - INRAE - Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement, CES - Centre d'économie de la Sorbonne - UP1 - Université Paris 1 Panthéon-Sorbonne - CNRS - Centre National de la Recherche Scientifique
Justin Leroux: HEC Montréal - HEC Montréal

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Abstract: We propose a novel methodology for forecasting chaotic systems which is based on exploiting the information conveyed by the local Lyapunov exponents of a system. This information is used to correct for the inevitable bias of most non-parametric predictors. Using simulated data, we show that gains in prediction accuracy can be substantial.

Keywords: chaotic; systems (search for similar items in EconPapers)
Date: 2009-09
Note: View the original document on HAL open archive server: https://shs.hal.science/halshs-00431726v2
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Published in Chaos, Solitons & Fractals, 2009, 41 (5), pp.2401-2404. ⟨10.1016/j.chaos.2008.09.017⟩

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Persistent link: https://EconPapers.repec.org/RePEc:hal:journl:halshs-00431726

DOI: 10.1016/j.chaos.2008.09.017

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