Detecting multiple generalized change-points by isolating single ones

Andreas Anastasiou () and Piotr Fryzlewicz
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Andreas Anastasiou: University of Cyprus
Piotr Fryzlewicz: The London School of Economics and Political Science

Metrika: International Journal for Theoretical and Applied Statistics, 2022, vol. 85, issue 2, No 1, 174 pages

Abstract: Abstract We introduce a new approach, called Isolate-Detect (ID), for the consistent estimation of the number and location of multiple generalized change-points in noisy data sequences. Examples of signal changes that ID can deal with are changes in the mean of a piecewise-constant signal and changes, continuous or not, in the linear trend. The number of change-points can increase with the sample size. Our method is based on an isolation technique, which prevents the consideration of intervals that contain more than one change-point. This isolation enhances ID’s accuracy as it allows for detection in the presence of frequent changes of possibly small magnitudes. In ID, model selection is carried out via thresholding, or an information criterion, or SDLL, or a hybrid involving the former two. The hybrid model selection leads to a general method with very good practical performance and minimal parameter choice. In the scenarios tested, ID is at least as accurate as the state-of-the-art methods; most of the times it outperforms them. ID is implemented in the R packages IDetect and breakfast, available from CRAN.

Keywords: Segmentation; Symmetric interval expansion; Threshold criterion; Schwarz information criterion; SDLL (search for similar items in EconPapers)
Date: 2022
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DOI: 10.1007/s00184-021-00821-6

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