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We introduce a new approach, called Isolate-Detect (ID), for the consistentestimation of the number and location of multiple generalized change-points innoisy data sequences. Examples of signal changes that ID can deal with arechanges in the mean of a piecewise-constant signal and changes, continuous ornot, in the linear trend. The number of change-points can increase with thesample size. Our method is based on an isolation technique, which prevents theconsideration of intervals that contain more than one change-point. Thisisolation enhances ID's accuracy as it allows for detection in the presence offrequent changes of possibly small magnitudes. In ID, model selection iscarried out via thresholding, or an information criterion, or SDLL, or a hybridinvolving the former two. The hybrid model selection leads to a general methodwith very good practical performance and minimal parameter choice. In thescenarios 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 packagesIDetect and breakfast, available from CRAN.

Detecting multiple generalized change-points by isolating single ones