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Multiscale spectral analysis for detecting short and long range change points in time series

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  • Olsen, Lena Ringstad
  • Chaudhuri, Probal
  • Godtliebsen, Fred
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    Abstract

    Identifying short and long range change points in an observed time series that consists of stationary segments is a common problem. These change points mark the time boundaries of the segments where the time series leaves one stationary state and enters another. Due to certain technical advantages, analysis is carried out in the frequency domain to identify such change points in the time domain. What is considered as a change may depend on the time scale. The results of the analysis are displayed in the form of graphs that display change points on different time horizons (time scales), which are observed to be statistically significant. The methodology is illustrated using several simulated and real time series data. The method works well to detect change points and illustrates the importance of analysing the time series on different time horizons.

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    Bibliographic Info

    Article provided by Elsevier in its journal Computational Statistics & Data Analysis.

    Volume (Year): 52 (2008)
    Issue (Month): 7 (March)
    Pages: 3310-3330

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    Handle: RePEc:eee:csdana:v:52:y:2008:i:7:p:3310-3330

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    Web page: http://www.elsevier.com/locate/csda

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    1. Oigard, Tor Arne & Rue, Havard & Godtliebsen, Fred, 2006. "Bayesian multiscale analysis for time series data," Computational Statistics & Data Analysis, Elsevier, vol. 51(3), pages 1719-1730, December.
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    11. Ligges, Uwe & Weihs, Claus & Hasse-Becker, Petra, 2002. "Detection of locally stationary segments in time series: Algorithms and applications," Technical Reports 2002,11, Technische Universität Dortmund, Sonderforschungsbereich 475: Komplexitätsreduktion in multivariaten Datenstrukturen.
    12. Jushan Bai, 1997. "Estimation Of A Change Point In Multiple Regression Models," The Review of Economics and Statistics, MIT Press, vol. 79(4), pages 551-563, November.
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