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.

许多用于检测数据序列中的多个变化点的现有程序在频繁变化点的情况下都失败了。本文提出了一种新的变更点检测方法，旨在在不频繁和频繁的变更点设置中都能很好地工作。它由两个成分组成：一个是“Wild Binary Segmentation 2”（WBS2），一种递归算法，用于生成我们所说的变化点检测问题的“完整”解决方案路径，即一系列估计的嵌套模型，其中包含\(0, \ldots , T-1\)变化点，其中T是数据长度。另一个要素是一种新的模型选择程序，称为“Steepest Drop to Low Levels”（SDLL）。SDLL 准则作用于 WBS2 解决方案路径，并且与许多现有的针对变点问题的模型选择程序不同，它不是基于惩罚的，并且仅使用阈值作为某种离散的辅助检查。由此产生的 WBS2.SDLL 过程结合了这两种成分，被证明是一致的，并且在测试的频繁变化点场景中显着优于竞争对手。WBS2.SDLL 快速、易于编码并且不需要选择窗口或跨度参数。