Improving Univariate Time Series Anomaly Detection Through Automatic Algorithm Selection and Human-in-the-Loop False-Positive Removement

Improving Univariate Time Series Anomaly Detection Through Automatic Algorithm Selection and Human-in-the-Loop False-Positive Removement

The existence of a time series anomaly detection method that performs well for all domains is a myth. Given a massive library of available methods, how can one select the best method for their application? An extensive evaluation of every anomaly detection method is not feasible. Many existing anoma...

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Bibliographic Details
Main Authors: Cynthia Freeman, Ian Beaver, Abdullah Mueen
Format: Article
Language:English
Published: LibraryPress@UF 2021-04-01
Series:Proceedings of the International Florida Artificial Intelligence Research Society Conference
Online Access:https://journals.flvc.org/FLAIRS/article/view/128543
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