Online non-parametric changepoint detection with application to monitoring operational performance of network devices

Edward Austin, Gaetano Romano, Idris A. Eckley and Paul Fearnhead

Computational Statistics & Data Analysis, 2023, vol. 177, issue C

Abstract: Motivated by a telecommunications application where there are few computational constraints, a novel nonparametric algorithm, NUNC, is introduced to perform an online detection for changes in the distribution of data. Two variants are considered: the first, NUNC Local, detects changes within a sliding window. Conversely, NUNC Global, compares the current window of data to all of the historic information seen so far and makes use of an efficient update step so that this historic information does not need to be stored. To explore the properties of both algorithms, both real and simulated datasets are analysed. Furthermore, a theoretical result for the choice of test threshold to control the false alarm rate is presented, a result that could be applied in other binary segmentation change detection settings.

Keywords: Online changepoint detection; Non-parametric statistics; Network devices; NUNC (search for similar items in EconPapers)
Date: 2023
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Persistent link: https://EconPapers.repec.org/RePEc:eee:csdana:v:177:y:2023:i:c:s0167947322001311

DOI: 10.1016/j.csda.2022.107551

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