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Anomaly Detection. Time Series AR Deployment

Anomaly detection Time series analysis Auto-regressive models IoT Internet of Things
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This workflow deploys a previously trained auto-regressive model for anomaly detection: - Select the date for deployment. Two months of its past values must be available. - Loop over each frequency column - Apply the previously trained auto-regressive model to the data - Calculate 1st level alarms based on the prediction errors - Calculate 2nd level alarms as the moving average of the 1st level alarms - Trigger an action if a 2nd level alarm is active

Used extensions & nodes

Created with KNIME Analytics Platform version 4.5.1
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    KNIME Base nodes Trusted extension

    KNIME AG, Zurich, Switzerland

    Version 4.5.1

    KNIME profile image
    knime
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    KNIME Javasnippet Trusted extension

    KNIME AG, Zurich, Switzerland

    Version 4.5.0

    KNIME profile image
    knime
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    KNIME Math Expression (JEP) Trusted extension

    KNIME AG, Zurich, Switzerland

    Version 4.5.0

    KNIME profile image
    knime
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    KNIME Quick Forms Trusted extension

    KNIME AG, Zurich, Switzerland

    Version 4.5.0

    KNIME profile image
    knime
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    KNIME ServerSpace Trusted extension

    KNIME AG, Zurich, Switzerland

    Version 4.14.1

    KNIME profile image
    knime
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    KNIME Timeseries nodes Trusted extension

    KNIME AG, Zurich, Switzerland

    Version 4.5.0

    KNIME profile image
    knime
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