Online Graph-Based Change-Point Detection for High Dimensional Data
This addresses a rarely studied problem in online change-point detection for high-dimensional applications like finance and IoT, offering a method with timely detection capabilities.
The paper tackles the challenge of online change-point detection in high-dimensional data by proposing a novel graph-based algorithm that uses a graph-spanning ratio similarity measure, demonstrating high detection power while controlling false alarm rates at nominal levels in numerical studies.
Online change-point detection (OCPD) is important for application in various areas such as finance, biology, and the Internet of Things (IoT). However, OCPD faces major challenges due to high-dimensionality, and it is still rarely studied in literature. In this paper, we propose a novel, online, graph-based, change-point detection algorithm to detect change of distribution in low- to high-dimensional data. We introduce a similarity measure, which is derived from the graph-spanning ratio, to test statistically if a change occurs. Through numerical study using artificial online datasets, our data-driven approach demonstrates high detection power for high-dimensional data, while the false alarm rate (type I error) is controlled at a nominal significant level. In particular, our graph-spanning approach has desirable power with small and multiple scanning window, which allows timely detection of change-point in the online setting.