Evolutionary Algorithms for the Detection of Structural Breaks in Time Series

Benjamin Doerr, Paul Fischer, Astrid Hilbert, Carsten Witt

Research output: Chapter in Book/Report/Conference proceedingArticle in proceedingsResearchpeer-review

Abstract

Detecting structural breaks is an essential task for the statistical analysis of time series, for example, for fitting parametric models to it. In short, structural breaks are points in time at which the behavior of the time series changes. Typically, no solid background knowledge of the time series under consideration is available. Therefore, a black-box optimization approach is our method of choice for detecting structural breaks. We describe a evolutionary algorithm framework which easily adapts to a large number of statistical settings. The experiments on artificial and real-world time series show that the algorithm detects break points with high precision and is computationally very efficient.
A reference implementation is availble at the following address: http://www2.imm.dtu.dk/~pafi/SBX/launch.html

Original languageEnglish
Title of host publicationProceeding of the fifteenth annual conference companion on Genetic and evolutionary computation
PublisherAssociation for Computing Machinery
Publication date2013
Pages119-120
ISBN (Print)978-1-4503-1964-5
DOIs
Publication statusPublished - 2013
Event2013 Genetic and Evolutionary Computation Conference - Amsterdam, Netherlands
Duration: 6 Jul 201310 Jul 2013
http://www.sigevo.org/gecco-2013/

Conference

Conference2013 Genetic and Evolutionary Computation Conference
CountryNetherlands
CityAmsterdam
Period06/07/201310/07/2013
Internet address

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