Prediction of Chaotic Time Series in the Presence of Measurement Error: the Importance of Initial Conditions

Dominique Guegan () and Rolf Tschernig ()
Additional contact information
Dominique Guegan: Département Mathématiques Mécanique et Informatique - URCA - Université de Reims Champagne-Ardenne

Post-Print from HAL

Abstract: 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.

Keywords: chaos; deterministic dynamics; estimation; initial conditions; measurement error; prediction; smoothing; nearest neighbors (search for similar items in EconPapers)
Date: 2001-07
References: Add references at CitEc
Citations:

Published in Statistics and Computing, 2001, 11 (3), pp.277-284. ⟨10.1023/A:1016608506110⟩

There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it.

Related works:
Working Paper: Prédiction of Chaotic Time Series in the Presence of Measurement Error: The Importance of Initial Conditions (1998)
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:hal:journl:halshs-00194303

DOI: 10.1023/A:1016608506110

Access Statistics for this paper

More papers in Post-Print from HAL
Bibliographic data for series maintained by CCSD ().