Anomaly Detection Output

An Anomaly Detection Output is a structured computational detection output that can represent anomaly identification results through various representation formats.

• AKA: Anomaly Detection Result, Outlier Detection Output, Abnormality Detection Response.
• Context:
  • It can typically indicate Anomaly Presence through binary flags or continuous scores.
  • It can typically provide Temporal Information through timestamp annotations.
  • It can typically include Confidence Levels through probability estimates.
  • It can typically specify Anomaly Locations through index references.
  • It can typically convey Detection Metadata through auxiliary information fields.
  • ...
  • It can often categorize Anomaly Types through classification labels.
  • It can often quantify Severity Levels through impact assessment scores.
  • It can often suggest Root Causes through diagnostic information.
  • It can often recommend Remediation Actions through response suggestions.
  • ...
  • It can range from being a Simple Anomaly Detection Output to being a Complex Anomaly Detection Output, depending on its anomaly detection output information richness.
  • It can range from being a Binary Anomaly Detection Output to being a Multi-Class Anomaly Detection Output, depending on its anomaly detection output classification granularity.
  • It can range from being a Point-Wise Anomaly Detection Output to being a Segment-Wise Anomaly Detection Output, depending on its anomaly detection output spatial resolution.
  • It can range from being a Raw Anomaly Detection Output to being a Post-Processed Anomaly Detection Output, depending on its anomaly detection output refinement level.
  • It can range from being a Machine-Readable Anomaly Detection Output to being a Human-Readable Anomaly Detection Output, depending on its anomaly detection output presentation format.
  • ...
  • It can enable Performance Evaluation through anomaly detection output comparison.
  • It can support Alert Generation through anomaly detection output threshold checking.
  • It can facilitate Visualization through anomaly detection output rendering.
  • It can inform Decision Making through anomaly detection output interpretation.
  • It can drive System Responses through anomaly detection output action triggering.
  • ...
• Example(s):
  • Scalar Anomaly Detection Outputs, such as:
    • Anomaly Score Output with continuous value range.
    • Binary Classification Output indicating normal or abnormal.
    • Probability Output representing anomaly likelihood.
  • Structured Anomaly Detection Outputs, such as:
    • JSON Anomaly Report with hierarchical fields.
    • Time Series Anomaly Label Sequence with temporal annotations.
    • Tabular Anomaly Output in row-column format.
  • Interpretable Anomaly Detection Outputs, such as:
    • Natural Language Explanation describing anomaly characteristics.
    • Interpretable Time Series Anomaly Detection Output with human-readable insights.
    • Visual Anomaly Report using graphical representations.
  • Composite Anomaly Detection Outputs, such as:
    • Multi-Level Anomaly Output with hierarchical details.
    • Ensemble Detection Output combining multiple detector results.
    • Contextual Anomaly Report including environmental factors.
  • ...
• Counter-Example(s):
  • Raw Data Stream, which lacks anomaly assessment.
  • Statistical Summary, which lacks anomaly-specific information.
  • Prediction Output, which forecasts future values rather than detecting anomalies.
• See: Detection Output, Classification Output, Machine Learning Output, Anomaly Detection Method, Anomaly Detection Framework, Alert Message, Diagnostic Output, Evaluation Result, Data Analysis Output.

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