Online Multiple Changepoint Detection with False Discovery Rate Control

Technological advances have led to the emergence of an increasing number of applications requiring the analysis of datastreams, that are characterized by an indefinitely long and time-evolving sequence, particularly in the healthcare domain. In such applications, the status of a stream can alternate, possibly many times, between a regular status and an irregular status. Consequently, it is necessary to develop statistical methodologies that constantly detect multiple changepoints in an online manner. While we may employ conventional methods of sequential change detection to trigger signals after the change occurs, no online procedure is available to quantify the uncertainty of the detected changes. In this work, we fill this gap by framing online multiple changepoint detection into an online multiple testing problem and proposing a new framework to test the null hypothesis that there is no change between neighboring signalled points. To obtain valid p-values for online multiple testing, we propose a data-fission-based procedure that is a simple yet effective way of dealing with the post-detection uncertainty quantification. It is shown that popular online false discovery rate control methods with those p-values can achieve finite-sample false discovery rate control. We evaluate the proposed method in simulation studies. The method is applied to health monitoring dataset, alleviating the false alarm issue in online data analysis.