DetectEntireResponse interface

Package:

@azure/ai-anomaly-detector

The response of entire anomaly detection.

Properties

expectedValues	ExpectedValues contain expected value for each input point. The index of the array is consistent with the input series.
isAnomaly	IsAnomaly contains 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	IsNegativeAnomaly contains anomaly status in 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	IsPositiveAnomaly contain anomaly status in 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	LowerMargins contain lower margin of each input point. LowerMargin is used to calculate lowerBoundary, which equals to expectedValue - (100 - marginScale)*lowerMargin. Points between the boundary can be marked as normal ones in 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	The severity score for each input point. The larger the value is, the more sever the anomaly is. For normal points, the "severity" is always 0.
upperMargins	UpperMargins contain upper margin of each input point. UpperMargin is used to calculate upperBoundary, which equals to expectedValue + (100 - marginScale)*upperMargin. Anomalies in response can be filtered by upperBoundary and lowerBoundary. By adjusting marginScale value, less significant anomalies can be filtered in client side. The index of the array is consistent with the input series.

Property Details

expectedValues

ExpectedValues contain expected value for each input point. The index of the array is consistent with the input series.

expectedValues: number[]

Property Value

number[]

isAnomaly

IsAnomaly contains 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.

isAnomaly: boolean[]

Property Value

boolean[]

isNegativeAnomaly

IsNegativeAnomaly contains anomaly status in 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.

isNegativeAnomaly: boolean[]

Property Value

boolean[]

isPositiveAnomaly

IsPositiveAnomaly contain anomaly status in 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.

isPositiveAnomaly: boolean[]

Property Value

boolean[]

lowerMargins

LowerMargins contain lower margin of each input point. LowerMargin is used to calculate lowerBoundary, which equals to expectedValue - (100 - marginScale)*lowerMargin. Points between the boundary can be marked as normal ones in client side. The index of the array is consistent with the input series.

lowerMargins: number[]

Property Value

number[]

period

Frequency extracted from the series, zero means no recurrent pattern has been found.

period: number

Property Value

number

severity

The severity score for each input point. The larger the value is, the more sever the anomaly is. For normal points, the "severity" is always 0.

severity?: number[]

Property Value

number[]

upperMargins

UpperMargins contain upper margin of each input point. UpperMargin is used to calculate upperBoundary, which equals to expectedValue + (100 - marginScale)*upperMargin. Anomalies in response can be filtered by upperBoundary and lowerBoundary. By adjusting marginScale value, less significant anomalies can be filtered in client side. The index of the array is consistent with the input series.

upperMargins: number[]

Property Value

number[]