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.

我们引入了一种称为隔离检测（ID）的新方法，用于对噪声数据序列中多个广义变化点的数量和位置进行一致估计。 ID 可以处理的信号变化示例包括分段恒定信号均值的变化以及线性趋势中连续或不连续的变化。变化点的数量可以随着样本大小的增加而增加。我们的方法基于一种隔离技术，该技术防止考虑包含多个变化点的区间。这种隔离提高了 ID 的准确性，因为它允许在存在可能小幅度的频繁变化的情况下进行检测。在 ID 中，模型选择是通过阈值、信息标准、SDLL 或涉及前两者的混合来执行的。混合模型选择导致了一种具有非常好的实用性能和最少参数选择的通用方法。在测试的场景中，ID 至少与最先进的方法一样准确；大多数时候它的表现都超过了他们。 ID 在 R 包IDetect和breakfast中实现，可从 CRAN 获得。