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UnivariateEntireDetectionResult Class

  • Reference

The response of entire anomaly detection.

All required parameters must be populated in order to send to Azure.

Inheritance
azure.ai.anomalydetector._model_base.Model
UnivariateEntireDetectionResult

Constructor

UnivariateEntireDetectionResult(*args: Any, **kwargs: Any)

Variables

Name Description
period
int

Frequency extracted from the series, zero means no recurrent pattern has been found. Required.
expected_values
list[float]

ExpectedValues contain expected value for each input point. The index of the array is consistent with the input series. Required.
upper_margins
list[float]

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. Required.
lower_margins
list[float]

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. Required.
is_anomaly
list[bool]

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. Required.
is_negative_anomaly
list[bool]

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. Required.
is_positive_anomaly
list[bool]

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. Required.
severity
list[float]

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.

Methods

clear
copy
get
items
keys
pop
popitem
setdefault
update
values

clear 

clear() -> None

copy 

copy()

get 

get(key: str, default: Any = None) -> Any

Parameters

Name Description
key
Required
default
Default value: None

items 

items() -> ItemsView

keys 

keys() -> KeysView

pop 

pop(key: ~typing.Any, default: ~typing.Any = <object object>) -> Any

Parameters

Name Description
key
Required
default

popitem 

popitem() -> Tuple[str, Any]

setdefault 

setdefault(key: ~typing.Any, default: ~typing.Any = <object object>) -> Any

Parameters

Name Description
key
Required
default

update 

update(*args: Any, **kwargs: Any) -> None

values 

values() -> ValuesView

Attributes

expected_values

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

expected_values: List[float]

is_anomaly

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. Required.

is_anomaly: List[bool]

is_negative_anomaly

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. Required.

is_negative_anomaly: List[bool]

is_positive_anomaly

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. Required.

is_positive_anomaly: List[bool]

lower_margins

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. Required.

lower_margins: List[float]

period

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

period: int

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: List[float] | None

upper_margins

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. Required.

upper_margins: List[float]