Rights statement: This is the author’s version of a work that was accepted for publication in Computational Statistics & Data Analysis. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Computational Statistics & Data Analysis, 177, 2022 DOI: 10.1016/j.csda.2022.107551
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Final published version
Research output: Contribution to Journal/Magazine › Journal article › peer-review
Research output: Contribution to Journal/Magazine › Journal article › peer-review
}
TY - JOUR
T1 - Online non-parametric changepoint detection with application to monitoring operational performance of network devices
AU - Austin, Edward
AU - Romano, Gaetano
AU - Eckley, Idris
AU - Fearnhead, Paul
N1 - This is the author’s version of a work that was accepted for publication in Computational Statistics & Data Analysis. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Computational Statistics & Data Analysis, 177, 2022 DOI: 10.1016/j.csda.2022.107551
PY - 2023/1/31
Y1 - 2023/1/31
N2 - 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.
AB - 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.
KW - Online changepoint detection
KW - Non-parametric statistics
KW - Network devices
KW - NUNC
U2 - 10.1016/j.csda.2022.107551
DO - 10.1016/j.csda.2022.107551
M3 - Journal article
VL - 177
JO - Computational Statistics and Data Analysis
JF - Computational Statistics and Data Analysis
SN - 0167-9473
M1 - 107551
ER -