自底向上扫描的多变点检测
Multiple change-point detection via bottom-up scanning
- KAIST（韩国科学技术院）
- Ewha Womans University（梨花女子大学）
- Durham University（杜伦大学）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
针对高维序列多变点检测，提出基于图的自底向上框架gBottomup，通过合并与不合并规则自适应估计变点数量，在频繁变点场景优于自顶向下方法，并具有良好计算性能。
AI中文摘要：
我们研究了高维序列的非参数多变点检测问题，旨在识别底层分布发生变化的时间点。尽管许多现有方法在变点分离良好时表现优异，但当结构突变密集聚集时，其性能可能会下降。为应对这一挑战，我们提出了gBottomup，一种基于图的自底向上框架，用于高维环境下的多变点检测。gBottomup通过提议合并相邻分段，并利用结合绝对显著性与相对局部异质性的不合并规则进行验证，从而构建层次化分割，自适应地细化分区并估计变点数量。模拟结果表明，gBottomup在各种结构配置下表现可靠，尤其在频繁变点设置中效果显著，而现有的自顶向下方法可能在此类情况下失去敏感性。运行时间实验表明，与基于图的自顶向下替代方法相比，gBottomup具有有利的计算性能。我们通过对S&P 500数据集的分析来展示所提出的方法。
英文摘要：
We study nonparametric multiple change-point detection for high-dimensional sequences, aiming to identify time points at which the underlying distribution changes. While many existing methods perform well when change-points are well-separated, their performance can deteriorate when structural breaks are densely clustered. To address this challenge, we propose gBottomup, a graph-based bottom-up framework for multiple change-point detection in high-dimensional settings. gBottomup constructs a hierarchical segmentation by proposing merges of adjacent segments and verifying them through an unmerge rule that combines absolute significance with relative local heterogeneity, thereby adaptively refining partitions and estimating the number of change-points. Simulation results demonstrate that gBottomup performs reliably across a range of structural configurations and is particularly effective in frequent change-point settings, where existing top-down procedures may lose sensitivity. Runtime experiments indicate favorable computational performance relative to graph-based top-down alternatives. We illustrate the proposed method through an analysis of a S&P 500 dataset.
