2022
DOI: 10.21203/rs.3.rs-2120136/v1
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Online Time-series Anomaly Detection: A Survey of Modern Model-based Approaches

Abstract: This survey provides an extensive overview of the state-of-the-art model-based online semi-supervised and unsupervised anomaly detection algorithms used on multivariate time series. It also outlines the most popular benchmark datasets used in literature, as well as a novel taxonomy where a distinction between online and offline, and training and inference is made. To achieve this, almost 50 peer-reviewed publications are surveyed and categorised into different model families to paint a clear picture of the ano… Show more

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Cited by 2 publications
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“…In these practical settings, sequence patterns often need to be assessed in real-time under limited computational resources and training data. The online applicability, which enables inference while data are being recorded and streamed, makes real-time assessment feasible [7]. Moreover, unsupervised or semi-supervised learning approaches are valuable when large amounts of labeled data are not readily available [8].…”
Section: Introductionmentioning
confidence: 99%
“…In these practical settings, sequence patterns often need to be assessed in real-time under limited computational resources and training data. The online applicability, which enables inference while data are being recorded and streamed, makes real-time assessment feasible [7]. Moreover, unsupervised or semi-supervised learning approaches are valuable when large amounts of labeled data are not readily available [8].…”
Section: Introductionmentioning
confidence: 99%