Detecting multiple generalized change-points by isolating single ones

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.

arXiv comment30 pages for the main article and 31 pages for the supplementary material. There are also 23 tables and 9 figures in total

References in corpus (6)

• Optimal detection of changepoints with a linear computational cost
• Selective review of offline change point detection methods
• Wild binary segmentation for multiple change-point detection
• An Algorithm for Optimal Partitioning of Data on an Interval
• Adaptive piecewise polynomial estimation via trend filtering
• Narrowest-Over-Threshold Detection of Multiple Change-points and Change-point-like Features

Cited by in corpus (2)

• Geometric-Based Pruning Rules For Change Point Detection in Multiple Independent Time Series
• Data-adaptive structural change-point detection via isolation