Extended Framework and Evaluation for Multivariate Streaming Anomaly Detection with Machine Learning

Andreas Koch, Michael Petry, Martin Werner

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

Streaming anomaly detection in multivariate time series is an important problem relevant for automatic monitoring of various devices. This paper tackles the problem of streaming anomaly detection by extending a framework for the purpose of incorporating model-based approaches and evaluating previously uncombined methods for a total number of 26 distinct machinelearning-based algorithms. The framework identifies four fundamental components inherent to many streaming anomaly detection algorithms and one or more methods are presented for each component. It is found that a simple and computationally less expensive strategy for detecting concept drift yields almost identical results to the ''KSWIN'' strategy, when applied to measuring concept drift in a training set relevant for training a machine learning model. A secondary experiment supports the effectiveness of finetuning a machine learning model after the detection of concept drift for the purpose of detecting anomalies.

Original languageEnglish
Title of host publicationProceedings - 2024 IEEE 40th International Conference on Data Engineering Workshops, ICDEW 2024
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages144-152
Number of pages9
ISBN (Electronic)9798350317152
DOIs
StatePublished - 2024
Event40th IEEE International Conference on Data Engineering Workshops, ICDEW 2024 - Utrecht, Netherlands
Duration: 13 May 202416 May 2024

Publication series

NameProceedings - 2024 IEEE 40th International Conference on Data Engineering Workshops, ICDEW 2024

Conference

Conference40th IEEE International Conference on Data Engineering Workshops, ICDEW 2024
Country/TerritoryNetherlands
CityUtrecht
Period13/05/2416/05/24

Keywords

  • Machine learning
  • anomaly detection
  • multivariate time series
  • online learning
  • stream mining

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