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Forecasting chaotic systems: the role of local Lyapunov exponents

Dominique Guegan and Justin Leroux

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://halshs.archives-ouvertes.fr/halshs-00259238v2
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Published in Documents de travail du Centre d'Economie de la Sorbonne 2008.14 - ISSN : 1955-611X. 2008

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