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Fast Detection of Structural Breaks
Technical University of Denmark, Denmark. (DTU Compute)
Linnaeus University, Faculty of Technology, Department of Mathematics.
2014 (English)In: Proceedings of COMPSTAT 2014, 21th International Conference on Computational Statistics, Geneva, August 19-22, 2014 / [ed] Manfred Gilli, Gil Gonzalez-Rodriguez & Alicia Nieto-Reyes, The International Statistical Institute, 2014, p. 9-16Conference paper, Published paper (Refereed)
Abstract [en]

A fundamental task in the analysis of time series is to detect structural breaks. A break indicates a significant change in the behaviour of the series. One method to formalise the notion of a break point, is to fit statistical models piecewise to the series. To find break points, the endpoints of the pieces are varied as is their number. A structural break is indicated by a significant change of the model parameters in adjacent pieces. Both, varying the pieces and repeatedly fitting models to them, are usually computationally very expensive. By combining genetic algorithms with a preprocessing of the time series we design a very fast algorithm for structural break detection. It reduces the time for model-fitting from linear to logarithmic in the length of the series. We show how this method can be used to find structural breaks for time series which are piecewise generated by AR(p)-models. Moreover, we introduce a nonparametric model for which the speed-up can also be achieved. Additionally we briefly present simulation results which demonstrate the manifold applications of these methods. A reference implementation is available at http://www2.imm.dtu.dk/~pafi/StructBreak/index.html

Place, publisher, year, edition, pages
The International Statistical Institute, 2014. p. 9-16
National Category
Probability Theory and Statistics
Research subject
Natural Science, Mathematics
Identifiers
URN: urn:nbn:se:lnu:diva-34517ISBN: 978-2-8399-1347-8 (print)OAI: oai:DiVA.org:lnu-34517DiVA, id: diva2:720613
Conference
The 21th International Conference on Computational Statistics (COMPSTAT), Geneva, August 19-22, 2014
Available from: 2014-06-01 Created: 2014-06-01 Last updated: 2019-06-19Bibliographically approved

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Hilbert, Astrid

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CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf