Novel Martingale Approaches For Change Point Detection

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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.
Original languageEnglish
Title of host publication20th International Conference on Intelligent Systems Design and Applications ( ISDA 2020 )
EditorsAjith Abraham, Vincenzo Piuri, Niketa Gandhi, Patrick Siarry, Arturas Kaklauskas, Ana Madureira
Place of PublicationOnline
PublisherSpringer
Pages2113-27
Number of pages690
Volume1351
Edition1
ISBN (Electronic)978-3-030-71187-0
ISBN (Print)978-3-030-71186-3
Publication statusPublished - 17 Dec 2020

Publication series

NameAdvances in Intelligent Systems and Computing
PublisherSpringer International Publishing
Volume1351
ISSN (Print)2194-5357

Keywords

  • Anomaly Detection
  • Electromagnetic Data
  • Martingales

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