Forecasting chaotic systems: the role of local Lyapunov exponents

Dominique Guegan () and Justin Leroux
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Dominique Guegan: CES - Centre d'économie de la Sorbonne - UP1 - Université Paris 1 Panthéon-Sorbonne - CNRS - Centre National de la Recherche Scientifique, 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

Université Paris1 Panthéon-Sorbonne (Post-Print and Working Papers) from HAL

Abstract: We propose a novel methodology for forecasting chaotic systems which is based on the nearest-neighbor predictor and improves upon it by incorporating local Lyapunov exponents to correct for its inevitable bias. Using simulated data, we show that gains in prediction accuracy can be substantial. The general intuition behind the proposed method can readily be applied to other non-parametric predictors.

Keywords: Monte Carlo simulations; Chaos theory; Lyapunov exponent; logistic map; Monte Carlo simulations.; Systèmes chaotiques; exposants de Lyapunov; fonction logistique; méthodes de simulations de Monte Carlo. (search for similar items in EconPapers)
Date: 2008-02
Note: View the original document on HAL open archive server: https://shs.hal.science/halshs-00259238v2
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Published in 2008

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Related works:
Working Paper: Forecasting chaotic systems: The role of local Lyapunov exponents (2009)
Working Paper: Forecasting chaotic systems: the role of local Lyapunov exponents (2008)
Working Paper: Forecasting chaotic systems: The role of local Lyapunov exponents (2007)
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