Title: Detecting multiple generalised change-points by isolating single ones
Authors: Andreas Anastasiou - University of Cyprus (Cyprus) [presenting]
Piotr Fryzlewicz - London School of Economics (United Kingdom)
Abstract: A new approach is introduced, called Isolate-Detect (ID), for the consistent estimation of the number and location of multiple generalised 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 in the trend, accompanied by discontinuities or not, in the piecewise-linear model. The method is based on an isolation technique, which prevents the consideration of intervals that contain more than one change-point. This isolation allows for detection in the presence of frequent changes of possibly small magnitudes. Thresholding and model selection through an information criterion are the two stopping rules are described. A hybrid of both criteria leads to a general method with very good practical performance and minimal parameter choice. Examples from both simulated and real-life data will be given; ID is at least as accurate as the state-of-the-art methods.