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Anomaly Detection in SIGMA data

Research output: Contribution to conference - Without ISBN/ISSN › Conference paper

Forthcoming

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Anomaly Detection in SIGMA data. / Ward, Kes; McGarry, Luke; Pyke, Caroline.
2023.

Research output: Contribution to conference - Without ISBN/ISSN › Conference paper

Harvard

Ward, K, McGarry, L & Pyke, C 2023, 'Anomaly Detection in SIGMA data'.

APA

Ward, K., McGarry, L., & Pyke, C. (in press). Anomaly Detection in SIGMA data.

Vancouver

Ward K, McGarry L, Pyke C. Anomaly Detection in SIGMA data. 2023.

Author

Ward, Kes ; McGarry, Luke ; Pyke, Caroline. / Anomaly Detection in SIGMA data.

Bibtex

@conference{69beee0b97154ff8aa4eed525c05e71a,
title = "Anomaly Detection in SIGMA data",
abstract = "The Poisson Functional Online Cumulative Sum (Poisson-FOCuS) method is a method for solving the likelihood ratio test of Poisson(λ) null against Poisson(μλ) alternative where μ>1, i.e. searching for an increase in count. This can be thought of as equivalent to testing all possible anomaly start points τ<T at each timestep T, giving a computationally efficient way to analyse count anomalies that occur over intervals of time. We run the Poisson-FOCuS method on SIGMA data, with an additional adjustment to remove anomaly tail traces, and report the results.",
author = "Kes Ward and Luke McGarry and Caroline Pyke",
year = "2023",
month = oct,
day = "9",
language = "English",

}

RIS

TY - CONF

T1 - Anomaly Detection in SIGMA data

AU - Ward, Kes

AU - McGarry, Luke

AU - Pyke, Caroline

PY - 2023/10/9

Y1 - 2023/10/9

N2 - The Poisson Functional Online Cumulative Sum (Poisson-FOCuS) method is a method for solving the likelihood ratio test of Poisson(λ) null against Poisson(μλ) alternative where μ>1, i.e. searching for an increase in count. This can be thought of as equivalent to testing all possible anomaly start points τ<T at each timestep T, giving a computationally efficient way to analyse count anomalies that occur over intervals of time. We run the Poisson-FOCuS method on SIGMA data, with an additional adjustment to remove anomaly tail traces, and report the results.

AB - The Poisson Functional Online Cumulative Sum (Poisson-FOCuS) method is a method for solving the likelihood ratio test of Poisson(λ) null against Poisson(μλ) alternative where μ>1, i.e. searching for an increase in count. This can be thought of as equivalent to testing all possible anomaly start points τ<T at each timestep T, giving a computationally efficient way to analyse count anomalies that occur over intervals of time. We run the Poisson-FOCuS method on SIGMA data, with an additional adjustment to remove anomaly tail traces, and report the results.

M3 - Conference paper

ER -