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Interface DetectEntireResponse

Package version

The response of entire anomaly detection.

Hierarchy

  • DetectEntireResponse

Index

Properties

expectedValues

expectedValues: number[]

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

isAnomaly

isAnomaly: boolean[]

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: boolean[]

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: boolean[]

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: number[]

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

period: number

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

Optional severity

severity: number[]

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: number[]

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.

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