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Forecasting chaotic systems : the role of local Lyapunov exponents

  • Dominique Guegan

    ()

    (CES - Centre d'économie de la Sorbonne - CNRS : UMR8174 - Université Paris I - Panthéon-Sorbonne, EEP-PSE - Ecole d'Économie de Paris - Paris School of Economics - Ecole d'Économie de Paris)

  • Justin Leroux

    ()

    (HEC - Institute for Applied Economics - HEC MONTRÉAL, CIRPEE - Centre Interuniversitaire sur le Risque, les Politiques Economiques et l'Emploi)

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.

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Paper provided by HAL in its series Université Paris1 Panthéon-Sorbonne (Post-Print and Working Papers) with number halshs-00259238.

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Date of creation: Feb 2008
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Handle: RePEc:hal:cesptp:halshs-00259238
Note: View the original document on HAL open archive server: http://halshs.archives-ouvertes.fr/halshs-00259238
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  1. Oliver Linton & Mototsugu Shintani, 2002. "Nonparametric neutral network estimation of lyapunov exponents and a direct test for chaos," LSE Research Online Documents on Economics 58170, London School of Economics and Political Science, LSE Library.
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