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AI Anomaly Detection Reports (Template Triggers)

Template Trigger AI Anomaly Detection Reports enable you to scan the History Database for Template Triggers, for example, generate a report that displays the days which the greatest trigger densities occurred. This report is typically used by network administrators that want to identify degraded performance trends.

How to create a Template Trigger Artificial Intelligence Report

  • From the Menu Bar select File | New. The Create New Object View displays.
  • From the Create New Object View, expand Reports.
  • Expand Report | Artificial Intelligence Reports then select AI Anomaly Detection (Template Triggers). The Properties View displays.
  • The Properties View contains either 4 configuration tabs.

The Options Tab

Use this view to configure the AI machine learning rules.

  • Use the Template drop-down so select the target Template.
  • Use the Type drop-down so select the machine learning algorithm to apply.
AlgorithmDescription
SpikePredicts spikes in independent identically distributed (i.i.d.) time series based on adaptive kernel density estimations and martingale scores.
Change PointPredicts change points in an independent identically distributed (i.i.d.) time series based on adaptive kernel density estimations and martingale scores.
SeasonalityThis method detects this predictable interval (or period) by adopting techniques of fourier analysis.
  • Use the Column definitions controls to specify the columns to include in the report.
    Note
    The previously assigned Select distict column are automatically added to the 3rd column in the table.
  • Use the Sort by drop-down to select the columns to sort by.
  • Use the Group by time span to group anomaly periods.

Spike

  • Use the Confidence slider to fine tune the anomaly detection confidence.
  • Use the Sub type drop-down to determine whether to detect positive or negative anomalies, or both.
OptionDescription
NegativeOnly negative anomalies are detected.
PositiveOnly positive anomalies are detected.
Two SidedBoth positive and negative anomalies are detected.

Source: Anomaly Side Enumeration

Change Point

  • Use the Confidence slider to fine tune the anomaly detection confidence.
  • Use the Sub type drop-down to select the martingale used for scoring.
OptionDescription
NoneNo martingale is used.
PowerThe Power martingale is used.
MixtureThe Mixture martingale is used.

Source: Martingale Type Enumeration

Seasonality

  • Use the Threshold slider to fine tune the anomaly detection threshold.
  • Use the Sensitivity slider to fine tune the anomaly detection sensitivity.
AI Anomaly Detection Options Properties View
AI Anomaly Detection Options Properties View

Related Topics

AI Reports

Reports