Uncertainty-Aware Time Series Anomaly Detection

Uncertainty-Aware Time Series Anomaly Detection

Traditional anomaly detection methods in time series data often struggle with inherent uncertainties like noise and missing values. Indeed, current approaches mostly focus on quantifying epistemic uncertainty and ignore data-dependent uncertainty. However, consideration of noise in data is important...

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Bibliographic Details
Main Authors:	Paul Wiessner, Grigor Bezirganyan, Sana Sellami, Richard Chbeir, Hans-Joachim Bungartz
Format:	Article
Language:	English
Published:	MDPI AG 2024-10-01
Series:	Future Internet
Subjects:	anomaly detection time series uncertainty quantification deep neural networks bayesian network
Online Access:	https://www.mdpi.com/1999-5903/16/11/403
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