Predicting chaos with Lyapunov exponents: Zero plays no role in forecasting chaotic systems
Download full text from publisher
Other versions of this item:
- Dominique Guegan & Justin Leroux, 2010. "Predicting chaos with Lyapunov exponents: zero plays no role in forecasting chaotic systems," Documents de travail du Centre d'Economie de la Sorbonne 10019, Université Panthéon-Sorbonne (Paris 1), Centre d'Economie de la Sorbonne.
More about this item
KeywordsMonte Carlo simulations; Chua attractor; Rössler attractor; Lyapunov exponent; Lorenz attractor; prévisions Rössler; Chua; Lorenz; Chaos theory; forecasting;
- C15 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Statistical Simulation Methods: General
- C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
- C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
- C65 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Miscellaneous Mathematical Tools
NEP fieldsThis paper has been announced in the following NEP Reports:
- NEP-ECM-2010-03-28 (Econometrics)
StatisticsAccess and download statistics
All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:hal:cesptp:halshs-00462454. See general information about how to correct material in RePEc.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (CCSD). General contact details of provider: https://hal.archives-ouvertes.fr/ .
We have no references for this item. You can help adding them by using this form .