Forecasting chaotic systems : the role of local Lyapunov exponents.

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.

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Year of publication:	2008-02
Authors:	Guegan, Dominique ; Leroux, Justin
Institutions:	Centre d'Économie de la Sorbonne, Université Paris 1 (Panthéon-Sorbonne)
Subject:	Chaos theory \| Lyapunov exponent \| logistic map \| Monte Carlo simulations

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Extent:	application/pdf
Series:	Documents de travail du Centre d'Economie de la Sorbonne. - ISSN 1955-611X.
Type of publication:	Book / Working Paper
Notes:	12 pages
Classification:	C15 - Statistical Simulation Methods; Monte Carlo Methods ; C22 - Time-Series Models ; C53 - Forecasting and Other Model Applications ; C65 - Miscellaneous Mathematical Tools
Source:	RePEc - Research Papers in Economics

Persistent link: https://www.econbiz.de/10005670880