UnivariateLastDetectionResult Class
The response of last anomaly detection.
All required parameters must be populated in order to send to Azure.
- Inheritance
-
azure.ai.anomalydetector._model_base.ModelUnivariateLastDetectionResult
Constructor
UnivariateLastDetectionResult(*args: Any, **kwargs: Any)
Variables
Name | Description |
---|---|
period
|
Frequency extracted from the series, zero means no recurrent pattern has been found. Required. |
suggested_window
|
Suggested input series points needed for detecting the latest point. Required. |
expected_value
|
Expected value of the latest point. Required. |
upper_margin
|
Upper margin of the latest point. UpperMargin is used to calculate upperBoundary, which equals to expectedValue + (100 - marginScale)*upperMargin. If the value of latest point is between upperBoundary and lowerBoundary, it should be treated as normal value. By adjusting marginScale value, anomaly status of latest point can be changed. Required. |
lower_margin
|
Lower margin of the latest point. LowerMargin is used to calculate lowerBoundary, which equals to expectedValue - (100 - marginScale)*lowerMargin. Required. |
is_anomaly
|
Anomaly status of the latest point, true means the latest point is an anomaly either in negative direction or positive direction. Required. |
is_negative_anomaly
|
Anomaly status in negative direction of the latest point. True means the latest point is an anomaly and its real value is smaller than the expected one. Required. |
is_positive_anomaly
|
Anomaly status in positive direction of the latest point. True means the latest point is an anomaly and its real value is larger than the expected one. Required. |
severity
|
The severity score for the last 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_value
Expected value of the latest point. Required.
expected_value: float
is_anomaly
Anomaly status of the latest point, true means the latest point is an anomaly either in negative direction or positive direction. Required.
is_anomaly: bool
is_negative_anomaly
Anomaly status in negative direction of the latest point. True means the latest point is an anomaly and its real value is smaller than the expected one. Required.
is_negative_anomaly: bool
is_positive_anomaly
Anomaly status in positive direction of the latest point. True means the latest point is an anomaly and its real value is larger than the expected one. Required.
is_positive_anomaly: bool
lower_margin
Lower margin of the latest point. LowerMargin is used to calculate lowerBoundary, which equals to expectedValue - (100 - marginScale)*lowerMargin. Required.
lower_margin: float
period
Frequency extracted from the series, zero means no recurrent pattern has been found. Required.
period: int
severity
The severity score for the last input point. The larger the value is, the more sever the anomaly is. For normal points, the "severity" is always 0.
severity: float | None
suggested_window
Suggested input series points needed for detecting the latest point. Required.
suggested_window: int
upper_margin
Upper margin of the latest point. UpperMargin is used to calculate upperBoundary, which equals to expectedValue + (100 - marginScale)*upperMargin. If the value of latest point is between upperBoundary and lowerBoundary, it should be treated as normal value. By adjusting marginScale value, anomaly status of latest point can be changed. Required.
upper_margin: float