DetectRequest Class

The request of entire or last anomaly detection.

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

Inheritance

Model

DetectRequest

Constructor

DetectRequest(*, series: List[azure.ai.anomalydetector.models._models_py3.TimeSeriesPoint], granularity: Optional[Union[str, azure.ai.anomalydetector.models._anomaly_detector_client_enums.TimeGranularity]] = None, custom_interval: Optional[int] = None, period: Optional[int] = None, max_anomaly_ratio: Optional[float] = None, sensitivity: Optional[int] = None, impute_mode: Optional[Union[str, azure.ai.anomalydetector.models._anomaly_detector_client_enums.ImputeMode]] = None, impute_fixed_value: Optional[float] = None, **kwargs)

Variables

series

list[TimeSeriesPoint]

Required. 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.

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. Possible values include: "yearly", "monthly", "weekly", "daily", "hourly", "minutely", "secondly", "microsecond", "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". Possible values include: "auto", "previous", "linear", "fixed", "zero", "notFill".

impute_fixed_value

float

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