A Novel Hybrid Methodology for Anomaly Detection in Time Series

A Novel Hybrid Methodology for Anomaly Detection in Time Series

Abstract Numerous research methods have been developed to detect anomalies in the areas of security and risk analysis. In healthcare, there are numerous use cases where anomaly detection is relevant. For example, early detection of sepsis is one such use case. Early treatment of sepsis is cost effec...

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Bibliographic Details
Main Authors: Lejla Begic Fazlic, Ahmed Halawa, Anke Schmeink, Robert Lipp, Lukas Martin, Arne Peine, Marlies Morgen, Thomas Vollmer, Stefan Winter, Guido Dartmann
Format: Article
Language:English
Published: Springer 2022-07-01
Series:International Journal of Computational Intelligence Systems
Subjects:
Anomaly detection
Classification
Self Organizing Maps (SOM)
Linear Discriminant Analysis (LDA)
Online Access:https://doi.org/10.1007/s44196-022-00100-w

Internet

https://doi.org/10.1007/s44196-022-00100-w

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