Title:	Time Series Analysis informed by Dynamical Systems Theory
Authors:	Schumacher, Johannes
Thesis advisor:	Prof. Dr. Gordon Pipa
Thesis referee:	Prof. Dr. Frank Jäkel
Abstract:	This thesis investigates time series analysis tools for prediction, as well as detection and characterization of dependencies, informed by dynamical systems theory. Emphasis is placed on the role of delays with respect to information processing in dynamical systems, as well as with respect to their effect in causal interactions between systems. The three main features that characterize this work are, first, the assumption that time series are measurements of complex deterministic systems. As a result, functional mappings for statistical models in all methods are justified by concepts from dynamical systems theory. To bridge the gap between dynamical systems theory and data, differential topology is employed in the analysis. Second, the Bayesian paradigm of statistical inference is used to formalize uncertainty by means of a consistent theoretical apparatus with axiomatic foundation. Third, the statistical models are strongly informed by modern nonlinear concepts from machine learning and nonparametric modeling approaches, such as Gaussian process theory. Consequently, unbiased approximations of the functional mappings implied by the prior system level analysis can be achieved. Applications are considered foremost with respect to computational neuroscience but extend to generic time series measurements.
URL:	https://osnadocs.ub.uni-osnabrueck.de/handle/urn:nbn:de:gbv:700-2015061113245
Subject Keywords:	Statistics; Time Series Analysis; Dynamical Systems; Machine Learning; Neuroscience
Issue Date:	11-Jun-2015
License name:	Namensnennung - Nicht-kommerziell - Weitergabe unter gleichen Bedingungen 3.0 Unported
License url:	http://creativecommons.org/licenses/by-nc-sa/3.0/
Type of publication:	Dissertation oder Habilitation [doctoralThesis]
Appears in Collections:	FB08 - E-Dissertationen

This item is licensed under a Creative Commons License