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docs: change sorting of anomaly deteciton
Signed-off-by: Artem Navoiev <tenmozes@gmail.com>
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# sort: 4
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sort: 4
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weight: 4
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title: CHANGELOG
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menu:
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docs:
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identifier: "vmanomaly-changelog"
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parent: "anomaly-detection"
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sort: 4
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weight: 4
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aliases:
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- /anomaly-detection/CHANGELOG.html
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# sort: 3
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sort: 3
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weight: 3
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title: FAQ
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@ -7,7 +7,6 @@ menu:
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identifier: "vmanomaly-faq"
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parent: "anomaly-detection"
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weight: 3
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sort: 3
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aliases:
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- /anomaly-detection/FAQ.html
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---
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# sort: 14
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title: VictoriaMetrics Anomaly Detection
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title: Anomaly Detection
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weight: 0
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disableToc: true
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- /anomaly-detection.html
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# VictoriaMetrics Anomaly Detection
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# Anomaly Detection
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In the dynamic and complex world of system monitoring, VictoriaMetrics Anomaly Detection, being a part of our [Enterprise offering](https://victoriametrics.com/products/enterprise/), stands as a pivotal tool for achieving advanced observability. It empowers SREs and DevOps teams by automating the intricate task of identifying abnormal behavior in time-series data. It goes beyond traditional threshold-based alerting, utilizing machine learning techniques to not only detect anomalies but also minimize false positives, thus reducing alert fatigue. By providing simplified alerting mechanisms atop of [unified anomaly scores](/anomaly-detection/components/models/models.html#vmanomaly-output), it enables teams to spot and address potential issues faster, ensuring system reliability and operational efficiency.
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# sort: 1
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sort: 1
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title: Components
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weight: 0
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weight: 1
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menu:
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docs:
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identifier: "vmanomaly-components"
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parent: "anomaly-detection"
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weight: 0
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sort: 1
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aliases:
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- /anomaly-detection/components/
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title: Guides
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weight: 2
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# sort: 2
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sort: 2
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menu:
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docs:
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identifier: "anomaly-detection-guides"
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parent: "anomaly-detection"
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weight: 2
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sort: 2
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aliases:
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- /anomaly-detection/guides.html
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---
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