Evolved kernel method for time series Conference Paper

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Overview

abstract

• An evolutionary algorithm for parameter estimation of a kernel method for noisy and irregularly sampled time series is presented. We aim to estimate the time delay between time series coming from gravitational lensing in astronomy. The parameters to estimate include the delay, the width of kernels or smoothing, and a regularization parameter. We use mixed types to represent variables within the evolutionary algorithm. The algorithm is tested on several artificial data sets, and also on real astronomical observations. The performance of our method is compared with the most popular methods for time delay estimation. An statistical analysis of results is given, where the results of our approach are more accurate and significant than those of other methods. © Springer-Verlag Berlin Heidelberg 2007.

authors

• Cuevas Tello Juan Carlos

publication date

• 2007-01-01

published in

• Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)  Journal

Research

keywords

• Evolutionary algorithms; Genetic algorithms; Kernel methods; Machine learning; Time series Data structures; Learning systems; Parameter estimation; Time delay; Time series analysis; Gravitational lensing; Kernel methods; Regularization parameters; Evolutionary algorithms

Identity

Digital Object Identifier (DOI)

• 10.1007/978-3-540-76631-5_53

Additional Document Info

start page

• 559

end page

• 569

volume

• 4827 LNAI