Online Identification of Recurring Changepoints

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Nandan Sriranga, Anthony Trezza, Saikiran Bulusu, Donald J. Bucci, Pramod K. Varshney

Published: 2022, Last Modified: 27 Jan 2026IEEECONF 2022EveryoneRevisionsBibTeXCC BY-SA 4.0
Abstract: This work aims at accurately detecting recurring changes as quickly as possible after their occurrence. We consider a general framework in which the changepoints and the duration of the data in each state after a change, are unknown. Assuming that the statistical model of the data is known, we adopt the windowed-cumulative sum (W-CUSUM) test to solve this problem. In this work, we also propose the False-Alarm Density (FADE) and False-Alarm Correction Time (FACT) metrics to characterize the false-alarm performance of the test. We present simulation results for a Gaussian mean-shift problem to demonstrate the performance of the W-CUSUM test.