Characterizing complex time-series from the scaling of prediction error

This thesis concerns characterizing complex time series from the scaling of prediction error. We use the global modeling technique of radial basis function approximation to build models from a state-space reconstruction of a time series that otherwise appears complicated or random (i.e. aperiodic, irregular). Prediction error as a function of prediction horizon is obtained from the model using the direct method. The relationship between the underlying dynamics of the time series and the logarithmic scaling of prediction error as a function of prediction horizon is investigated. We use this relationship to characterize the dynamics of both a model chaotic system and physical data from the optic tectum of an attentive pigeon exhibiting the important phenomena of nonstationary neuronal oscillations in response to visual stimuli.

Name thesis:degree_name

Ph.D.

Level thesis:degree_level

Dissertation

Discipline thesis:degree_discipline

Physics

Grantor

University of Illinois at Urbana-Champaign

Year dc:date

2011

Author dc:creator

• Hinrichs, Brant Eric

Contributors dc:contributor

• Packard, Norman H.
• Chialvo, Dante

• Biology, Biostatistics
• Statistics
• Physics, General

Statement dc:rights

• Copyright 1994 Hinrichs, Brant Eric

Language dc:language

eng

Identifier

AAI9512394
(UMI)AAI9512394

OAI identifier oai:identifier

oai:www.ideals.illinois.edu:2142/22722