UnivariateEntireDetectionResult Class

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]