Prédiction of Chaotic Time Series in the Presence of Measurement Error : The Importance of Initial Conditions
n this paper we argue that even if a dynamic relationship can be well described by a deterministic system, retrieving this relationship from an empirical time series has to take into account some, although possibly very small measurement error in the observations. Therefore, measuring the initial conditions for prediction may become much more difficult since one now has a combination of deterministic and stochastic elements. We introduce a partial smoothing estimator for estimating the unobserved initial conditions. We will show that this estimator allows to reduce the effects of measurement error for predictions although the reduction may be small in the presence of strong chaotic dynamics. This will be illustrated using the logistic map.
(This abstract was borrowed from another version of this item.)
|Date of creation:||1998|
|Contact details of provider:|| Postal: Bâtiment ENSAE, 5 rue Henry LE Chatelier, 91120 Palaiseau|
Phone: 01 41 17 60 81
Web page: http://crest.science
More information through EDIRC
When requesting a correction, please mention this item's handle: RePEc:crs:wpaper:98-02. 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: (Sri Srikandan)
If references are entirely missing, you can add them using this form.