Novel Martingale Approaches For Change Point Detection

Jonathan Etumusei; Jorge Martinez Carracedo; Sally McClean

Novel Martingale Approaches For Change Point Detection

Jonathan Etumusei, Jorge Martinez Carracedo, Sally McClean

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

Abstract

Existing algorithms are able to find changes in data streams but they often struggle to distinguish between a real change and noise. This fact limits the effectiveness of current algorithms. In this paper, we propose two methods using the martingale framework that are able to detect changes and minimise the noise effect in a labelled electromagnetic data set. Results show that the proposed methods make some improvements over the previous approaches within the martingale framework.

Original language	English
Title of host publication	20th International Conference on Intelligent Systems Design and Applications ( ISDA 2020 )
Editors	Ajith Abraham, Vincenzo Piuri, Niketa Gandhi, Patrick Siarry, Arturas Kaklauskas, Ana Madureira
Place of Publication	Online
Publisher	Springer
Pages	2113-27
Number of pages	690
Volume	1351
Edition	1
ISBN (Electronic)	978-3-030-71187-0
ISBN (Print)	978-3-030-71186-3
Publication status	Published (in print/issue) - 17 Dec 2020

Publication series

Name	Advances in Intelligent Systems and Computing
Publisher	Springer International Publishing
Volume	1351
ISSN (Print)	2194-5357

Keywords

Anomaly Detection
Electromagnetic Data
Martingales

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

Cite this

Etumusei, J., Martinez Carracedo, J., & McClean, S. (2020). Novel Martingale Approaches For Change Point Detection. In A. Abraham, V. Piuri, N. Gandhi, P. Siarry, A. Kaklauskas, & A. Madureira (Eds.), 20th International Conference on Intelligent Systems Design and Applications ( ISDA 2020 ) (1 ed., Vol. 1351, pp. 2113-27). Article 59 (Advances in Intelligent Systems and Computing; Vol. 1351). Springer.

Etumusei, Jonathan ; Martinez Carracedo, Jorge ; McClean, Sally. / Novel Martingale Approaches For Change Point Detection. 20th International Conference on Intelligent Systems Design and Applications ( ISDA 2020 ). editor / Ajith Abraham ; Vincenzo Piuri ; Niketa Gandhi ; Patrick Siarry ; Arturas Kaklauskas ; Ana Madureira. Vol. 1351 1. ed. Online : Springer, 2020. pp. 2113-27 (Advances in Intelligent Systems and Computing).

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abstract = "Existing algorithms are able to find changes in data streams but they often struggle to distinguish between a real change and noise. This fact limits the effectiveness of current algorithms. In this paper, we propose two methods using the martingale framework that are able to detect changes and minimise the noise effect in a labelled electromagnetic data set. Results show that the proposed methods make some improvements over the previous approaches within the martingale framework. ",

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Etumusei, J, Martinez Carracedo, J & McClean, S 2020, Novel Martingale Approaches For Change Point Detection. in A Abraham, V Piuri, N Gandhi, P Siarry, A Kaklauskas & A Madureira (eds), 20th International Conference on Intelligent Systems Design and Applications ( ISDA 2020 ). 1 edn, vol. 1351, 59, Advances in Intelligent Systems and Computing, vol. 1351, Springer, Online, pp. 2113-27.

Novel Martingale Approaches For Change Point Detection. / Etumusei, Jonathan; Martinez Carracedo, Jorge ; McClean, Sally.
20th International Conference on Intelligent Systems Design and Applications ( ISDA 2020 ). ed. / Ajith Abraham; Vincenzo Piuri; Niketa Gandhi; Patrick Siarry; Arturas Kaklauskas; Ana Madureira. Vol. 1351 1. ed. Online: Springer, 2020. p. 2113-27 59 (Advances in Intelligent Systems and Computing; Vol. 1351).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Novel Martingale Approaches For Change Point Detection

AU - Etumusei, Jonathan

AU - Martinez Carracedo, Jorge

AU - McClean, Sally

PY - 2020/12/17

Y1 - 2020/12/17

N2 - Existing algorithms are able to find changes in data streams but they often struggle to distinguish between a real change and noise. This fact limits the effectiveness of current algorithms. In this paper, we propose two methods using the martingale framework that are able to detect changes and minimise the noise effect in a labelled electromagnetic data set. Results show that the proposed methods make some improvements over the previous approaches within the martingale framework.

AB - Existing algorithms are able to find changes in data streams but they often struggle to distinguish between a real change and noise. This fact limits the effectiveness of current algorithms. In this paper, we propose two methods using the martingale framework that are able to detect changes and minimise the noise effect in a labelled electromagnetic data set. Results show that the proposed methods make some improvements over the previous approaches within the martingale framework.

KW - Anomaly Detection

KW - Electromagnetic Data

KW - Martingales

UR - http://10.1109/TPAMI.2010.48

UR - https://drive.google.com/file/d/1k_Zp_JONz5WJTFgqHzCNKmV4jEvp2pvI/view?usp=sharing

UR - https://drive.google.com/file/d/13rZa52FJdIVbrKcBNVHpaew5xbZMPGkI/view?usp=sharing

M3 - Conference contribution

SN - 978-3-030-71186-3

VL - 1351

T3 - Advances in Intelligent Systems and Computing

SP - 2113

EP - 2127

BT - 20th International Conference on Intelligent Systems Design and Applications ( ISDA 2020 )

A2 - Abraham, Ajith

A2 - Piuri, Vincenzo

A2 - Gandhi, Niketa

A2 - Siarry, Patrick

A2 - Kaklauskas, Arturas

A2 - Madureira, Ana

PB - Springer

CY - Online

ER -

Etumusei J, Martinez Carracedo J , McClean S. Novel Martingale Approaches For Change Point Detection. In Abraham A, Piuri V, Gandhi N, Siarry P, Kaklauskas A, Madureira A, editors, 20th International Conference on Intelligent Systems Design and Applications ( ISDA 2020 ). 1 ed. Vol. 1351. Online: Springer. 2020. p. 2113-27. 59. (Advances in Intelligent Systems and Computing).

Novel Martingale Approaches For Change Point Detection

Abstract

Publication series

Keywords

UN SDGs

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