UnivariateEntireDetectionResult Class

Expected value for each input point. The index of the array is consistent with the input series.

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.

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.

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.

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.

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

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.

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.