Open this publication in new window or tab >>2013 (English)In: Proceedings of the 15th annual conference on Genetic and evolutionary computation, ACM Press, 2013, p. 119-120Conference paper, Published paper (Refereed)
Abstract [en]
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 \ea 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
Place, publisher, year, edition, pages
ACM Press, 2013
Keywords
Evolutionary Algorithms, Statistics, Break points
National Category
Computational Mathematics
Identifiers
urn:nbn:se:lnu:diva-28152 (URN)10.1145/2464576.2464635 (DOI)2-s2.0-84882359094 (Scopus ID)978-1-4503-1964-5 (ISBN)
Conference
Genetic and evolutionary computation conference (GECCO)Amsterdam, Netherlands — July 06 - 10, 2013
2013-08-142013-08-142014-04-23Bibliographically approved