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  • 2020fischphd

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Novel methods for anomaly detection

Research output: Thesis › Doctoral Thesis

Published

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Novel methods for anomaly detection. / Fisch, Alex.
Lancaster University, 2020. 293 p.

Research output: Thesis › Doctoral Thesis

Harvard

Fisch, A 2020, 'Novel methods for anomaly detection', PhD, Lancaster University. https://doi.org/10.17635/lancaster/thesis/1131

APA

Fisch, A. (2020). Novel methods for anomaly detection. [Doctoral Thesis, Lancaster University]. Lancaster University. https://doi.org/10.17635/lancaster/thesis/1131

Vancouver

Fisch A. Novel methods for anomaly detection. Lancaster University, 2020. 293 p. doi: 10.17635/lancaster/thesis/1131

Author

Fisch, Alex. / Novel methods for anomaly detection. Lancaster University, 2020. 293 p.

Bibtex

@phdthesis{c164279d3e784eefb75a0680bc36c965,
title = "Novel methods for anomaly detection",
abstract = "Anomaly detection is of increasing importance in the data rich world of today. It can be applied to a broad range of challenges ranging from fault detection to fraud prevention and cyber-security. Many of these application require algorithms which are very scalable, as well as accurate, due to large data volumes and/or limited computational resources.This thesis contributes three novel approaches to the field of anomaly detection.The first contribution, Collective And Point Anomalies (CAPA) detects and distinguishes between both collective and point anomalies in linear time. The second contribution, MultiVariate Collective And Point Anomalies (MVCAPA) extends CAPA to the multivariate setting. The third contribution is a novel particle based kalman filter which detects and distinguished between additive outliers and innovative outliers.",
author = "Alex Fisch",
year = "2020",
doi = "10.17635/lancaster/thesis/1131",
language = "English",
publisher = "Lancaster University",
school = "Lancaster University",

}

RIS

TY - BOOK

T1 - Novel methods for anomaly detection

AU - Fisch, Alex

PY - 2020

Y1 - 2020

N2 - Anomaly detection is of increasing importance in the data rich world of today. It can be applied to a broad range of challenges ranging from fault detection to fraud prevention and cyber-security. Many of these application require algorithms which are very scalable, as well as accurate, due to large data volumes and/or limited computational resources.This thesis contributes three novel approaches to the field of anomaly detection.The first contribution, Collective And Point Anomalies (CAPA) detects and distinguishes between both collective and point anomalies in linear time. The second contribution, MultiVariate Collective And Point Anomalies (MVCAPA) extends CAPA to the multivariate setting. The third contribution is a novel particle based kalman filter which detects and distinguished between additive outliers and innovative outliers.

AB - Anomaly detection is of increasing importance in the data rich world of today. It can be applied to a broad range of challenges ranging from fault detection to fraud prevention and cyber-security. Many of these application require algorithms which are very scalable, as well as accurate, due to large data volumes and/or limited computational resources.This thesis contributes three novel approaches to the field of anomaly detection.The first contribution, Collective And Point Anomalies (CAPA) detects and distinguishes between both collective and point anomalies in linear time. The second contribution, MultiVariate Collective And Point Anomalies (MVCAPA) extends CAPA to the multivariate setting. The third contribution is a novel particle based kalman filter which detects and distinguished between additive outliers and innovative outliers.

U2 - 10.17635/lancaster/thesis/1131

DO - 10.17635/lancaster/thesis/1131

M3 - Doctoral Thesis

PB - Lancaster University

ER -