abstract

Evolutionary algorithms for the detection of structural breaks in time series: extended abstract

Authors:

Benjamin Doerr,

Paul Fischer,

Astrid Hilbert,

Carsten WittAuthors Info & Claims

GECCO '13 Companion: Proceedings of the 15th annual conference companion on Genetic and evolutionary computation

Pages 119 - 120

https://doi.org/10.1145/2464576.2464635

Published: 06 July 2013 Publication History

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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}

References

[1]

R. Davis, T. Lee, and G. Rodriguez-Yam. Break detection for a class of nonlinear time series models. J. of Time Series Analysis, 29:834--867, 2008.

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[2]

T. Jansen and C. Zarges. Analysis of evolutionary algorithms: From computational complexity analysis to algorithm engineering. In Proc. of the 11th ACM SIGEVO Workshop on Foundations of Genetic Algorithms (FOGA 2011), pages 1--14. ACM Press, 2011.

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Evolutionary algorithms for the detection of structural breaks in time series: extended abstract
1. Mathematics of computing
  1. Probability and statistics
    1. Statistical paradigms

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Published In

GECCO '13 Companion: Proceedings of the 15th annual conference companion on Genetic and evolutionary computation

July 2013

1798 pages

ISBN:9781450319645

DOI:10.1145/2464576

Editor:
Christian Blum
IKERBASQUE and University of the Basque Country UPV/EHU, Spain
,
General Chair:
Enrique Alba
University of Malaga, Spain

Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 06 July 2013

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Conference

GECCO '13

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SIGEVO

GECCO '13: Genetic and Evolutionary Computation Conference

July 6 - 10, 2013

Amsterdam, The Netherlands

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Overall Acceptance Rate 1,669 of 4,410 submissions, 38%

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