# Detecting multiple generalized change-points by isolating single ones

## TL;DR

The paper presents Isolate-Detect (ID), a novel method for accurately identifying multiple change-points in noisy data sequences, capable of handling various signal changes and outperforming existing methods.

## Contribution

Introduction of the Isolate-Detect (ID) method, a new approach for consistent estimation of multiple change-points with enhanced accuracy and minimal parameter tuning.

## Key findings

- ID outperforms state-of-the-art methods in various scenarios.
- ID effectively detects change-points with small magnitudes.
- The method is implemented in R packages IDetect and breakfast.

## 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.