@inproceedings{c812fe0f6d264004be9072f6194b52c5,
title = "Online Identification of Recurring Changepoints",
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.",
keywords = "Changepoint Detection, False-Alarm, Windowed cumulative-sum",
author = "Nandan Sriranga and Anthony Trezza and Saikiran Bulusu and Bucci, {Donald J.} and Varshney, {Pramod K.}",
note = "Publisher Copyright: {\textcopyright} 2022 IEEE.; 56th Asilomar Conference on Signals, Systems and Computers, ACSSC 2022 ; Conference date: 31-10-2022 Through 02-11-2022",
year = "2022",
doi = "10.1109/IEEECONF56349.2022.10051943",
language = "English (US)",
series = "Conference Record - Asilomar Conference on Signals, Systems and Computers",
publisher = "IEEE Computer Society",
pages = "426--430",
editor = "Matthews, {Michael B.}",
booktitle = "56th Asilomar Conference on Signals, Systems and Computers, ACSSC 2022",
address = "United States",
}