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Estimating time delays between irregularly sampled time series

Cuevas Tello, Juan Carlos (2007)
Ph.D. thesis, University of Birmingham.

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Abstract

The time delay estimation between time series is a real-world problem in gravitational lensing, an area of astrophysics. Lensing is the most direct method of measuring the distribution of matter, which is often dark, and the accurate measurement of time delays set the scale to measure distances over cosmological scales. For our purposes, this means that we have to estimate a time delay between two or more noisy and irregularly sampled time series. Estimations have been made using statistical methods in the astrophysics literature, such as interpolation, dispersion analysis, discrete correlation function, Gaussian processes and Bayesian method, among others. Instead, this thesis proposes a kernel-based approach to estimating the time delay, which is inspired by kernel methods in the context of statistical and machine learning. Moreover, our methodology is evolved to perform model selection, regularisation and time delay estimation globally and simultaneously. Experimental results show that this approach is one of the most accurate methods for gaps (missing data) and distinct noise levels. Results on artificial and real data are shown.

Type of Work:Ph.D. thesis.
Supervisor(s):Tino, Peter
School/Faculty:Schools (1998 to 2008) > School of Computer Science
Department:Computer Science
Keywords:Machine Learning, Evolutionary Computation, Kernel Methods, Time Series, Gravitational Lensing, Astronomy
Subjects:QA75 Electronic computers. Computer science
QB Astronomy
Institution:University of Birmingham
Library Catalogue:Check for printed version of this thesis
ID Code:88
This unpublished thesis/dissertation is copyright of the author and/or third parties. The intellectual property rights of the author or third parties in respect of this work are as defined by The Copyright Designs and Patents Act 1988 or as modified by any successor legislation. Any use made of information contained in this thesis/dissertation must be in accordance with that legislation and must be properly acknowledged. Further distribution or reproduction in any format is prohibited without the permission of the copyright holder.
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