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    - AI Anomaly Detection Report (Template Triggers)
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Terminology

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.

Algorithm	Description
Spike	Predicts spikes in independent identically distributed (i.i.d.) time series based on adaptive kernel density estimations and martingale scores.
Change Point	Predicts change points in an independent identically distributed (i.i.d.) time series based on adaptive kernel density estimations and martingale scores.
Seasonality	This 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.

Option	Description
Negative	Only negative anomalies are detected.
Positive	Only positive anomalies are detected.
Two Sided	Both 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.

Option	Description
None	No martingale is used.
Power	The Power martingale is used.
Mixture	The 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