UnivariateEntireDetectionResult Class

Definition

Namespace:

Azure.AI.AnomalyDetector

Assembly:

Azure.AI.AnomalyDetector.dll

Package:

Azure.AI.AnomalyDetector v3.0.0-preview.7

Source:

UnivariateEntireDetectionResult.cs

Response of the entire anomaly detection.

public class UnivariateEntireDetectionResult

type UnivariateEntireDetectionResult = class

Public Class UnivariateEntireDetectionResult

Inheritance

Object

UnivariateEntireDetectionResult

Properties

ExpectedValues	Expected value for each input point. The index of the array is consistent with the input series.
IsAnomaly	Anomaly properties for each input point. True means an anomaly (either negative or positive) has been detected. The index of the array is consistent with the input series.
IsNegativeAnomaly	Anomaly status in a negative direction for each input point. True means a negative anomaly has been detected. A negative anomaly means the point is detected as an anomaly and its real value is smaller than the expected one. The index of the array is consistent with the input series.
IsPositiveAnomaly	Anomaly status in a positive direction for each input point. True means a positive anomaly has been detected. A positive anomaly means the point is detected as an anomaly and its real value is larger than the expected one. The index of the array is consistent with the input series.
LowerMargins	Lower margin of each input point. LowerMargin is used to calculate lowerBoundary, which is equal to expectedValue - (100 - marginScale)*lowerMargin. Points between the boundary can be marked as normal ones on the client side. The index of the array is consistent with the input series.
Period	Frequency extracted from the series. Zero means no recurrent pattern has been found.
Severity	Severity score for each input point. The larger the value is, the more severe the anomaly is. For normal points, the severity is always 0.
UpperMargins	Upper margin of each input point. UpperMargin is used to calculate upperBoundary, which is equal to expectedValue + (100 - marginScale)*upperMargin. Anomalies in the response can be filtered by upperBoundary and lowerBoundary. Adjusting the marginScale value can help filter less significant anomalies on the client side. The index of the array is consistent with the input series.