UnivariateDetectionOptions Class

The request of entire or last anomaly detection.

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

Inheritance

azure.ai.anomalydetector._model_base.Model

UnivariateDetectionOptions

Constructor

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

Variables

Name	Description
series	list[TimeSeriesPoint] Time series data points. Points should be sorted by timestamp in ascending order to match the anomaly detection result. If the data is not sorted correctly or there is duplicated timestamp, the API will not work. In such case, an error message will be returned. Required.
granularity	str or TimeGranularity Optional argument, can be one of yearly, monthly, weekly, daily, hourly, minutely, secondly, microsecond or none. If granularity is not present, it will be none by default. If granularity is none, the timestamp property in time series point can be absent. Known values are: "yearly", "monthly", "weekly", "daily", "hourly", "minutely", "secondly", "microsecond", and "none".
custom_interval	int Custom Interval is used to set non-standard time interval, for example, if the series is 5 minutes, request can be set as {"granularity":"minutely", "customInterval":5}.
period	int Optional argument, periodic value of a time series. If the value is null or does not present, the API will determine the period automatically.
max_anomaly_ratio	float Optional argument, advanced model parameter, max anomaly ratio in a time series.
sensitivity	int Optional argument, advanced model parameter, between 0-99, the lower the value is, the larger the margin value will be which means less anomalies will be accepted.
impute_mode	str or ImputeMode Used to specify how to deal with missing values in the input series, it's used when granularity is not "none". Known values are: "auto", "previous", "linear", "fixed", "zero", and "notFill".
impute_fixed_value	float Used to specify the value to fill, it's used when granularity is not "none" and imputeMode is "fixed".

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

custom_interval

Custom Interval is used to set non-standard time interval, for example, if the series is 5 minutes, request can be set as {"granularity":"minutely", "customInterval":5}.

custom_interval: int | None

granularity

Optional argument, can be one of yearly, monthly, weekly, daily, hourly, minutely, secondly, microsecond or none. If granularity is not present, it will be none by default. If granularity is none, the timestamp property in time series point can be absent. Known values are: "yearly", "monthly", "weekly", "daily", "hourly", "minutely", "secondly", "microsecond", and "none".

granularity: str | _models.TimeGranularity | None

impute_fixed_value

Used to specify the value to fill, it's used when granularity is not "none" and imputeMode is "fixed".

impute_fixed_value: float | None

impute_mode

Used to specify how to deal with missing values in the input series, it's used when granularity is not "none". Known values are: "auto", "previous", "linear", "fixed", "zero", and "notFill".

impute_mode: str | _models.ImputeMode | None

max_anomaly_ratio

Optional argument, advanced model parameter, max anomaly ratio in a time series.

max_anomaly_ratio: float | None

period

Optional argument, periodic value of a time series. If the value is null or does not present, the API will determine the period automatically.

period: int | None

sensitivity

Optional argument, advanced model parameter, between 0-99, the lower the value is, the larger the margin value will be which means less anomalies will be accepted.

sensitivity: int | None

series

Time series data points. Points should be sorted by timestamp in ascending order to match the anomaly detection result. If the data is not sorted correctly or there is duplicated timestamp, the API will not work. In such case, an error message will be returned. Required.

series: List[_models.TimeSeriesPoint]