Forecasting chaotic systems: the role of local Lyapunov exponents

Dominique Guegan () and Justin Leroux
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Dominique Guegan: Centre d'Economie de la Sorbonne et Paris School of Economics, https://cv.archives-ouvertes.fr/dominique-guegan

Documents de travail du Centre d'Economie de la Sorbonne from Université Panthéon-Sorbonne (Paris 1), Centre d'Economie de la Sorbonne

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 to proposed method can readily be applied to other non-parametric predictors

Keywords: Chaos theory; Lyapunov exponent; logistic map; Monte Carlo simulations (search for similar items in EconPapers)
JEL-codes: C15 C22 C53 C65 (search for similar items in EconPapers)
Pages: 12 pages
Date: 2008-02, Revised 2008-09
New Economics Papers: this item is included in nep-for and nep-ore
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ftp://mse.univ-paris1.fr/pub/mse/CES2008/B08014.pdf (application/pdf)

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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Persistent link: https://EconPapers.repec.org/RePEc:mse:cesdoc:b08014

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