Many existing procedures for detecting multiple change-points in data sequences fail in frequent-change-point scenarios. This article proposes a new change-point detection methodology designed to work well in both infrequent and frequent change-point settings. It is made up of two ingredients: one is “Wild Binary Segmentation 2” (WBS2), a recursive algorithm for producing what we call a ‘complete’ solution path to the change-point detection problem, i.e. a sequence of estimated nested models containing \(0, \ldots , T-1\) change-points, where T is the data length. The other ingredient is a new model selection procedure, referred to as “Steepest Drop to Low Levels” (SDLL). The SDLL criterion acts on the WBS2 solution path, and, unlike many existing model selection procedures for change-point problems, it is not penalty-based, and only uses thresholding as a certain discrete secondary check. The resulting WBS2.SDLL procedure, combining both ingredients, is shown to be consistent, and to significantly outperform the competition in the frequent change-point scenarios tested. WBS2.SDLL is fast, easy to code and does not require the choice of a window or span parameter.

用于检测数据序列中多个更改点的许多现有过程在频繁更改点的情况下会失败。本文提出了一种新的变更点检测方法，该方法旨在在不频繁和频繁的变更点设置中都能很好地工作。它由两种成分组成：一种是“野生二进制分割2”（WBS2），这是一种递归算法，用于产生我们称为“完整”解决方案的变更点检测问题的路径，即，一系列估计的嵌套模型包含\（0，\ ldots，T-1 \）更改点，其中T是数据长度。另一个因素是新的模型选择过程，称为“最深跌至低水平”（SDLL）。SDLL标准作用于WBS2解决方案路径，与许多现有的针对更改点问题的模型选择程序不同，它不是基于惩罚的，仅将阈值用作特定的离散二级检查。结果表明，结合了两种成分的WBS2.SDLL过程是一致的，并且在经过测试的频繁更改点方案中明显优于竞争对手。WBS2.SDLL快速，易于编码，并且不需要选择window或span参数。