azure.ai.anomalydetector.aio package

class azure.ai.anomalydetector.aio.AnomalyDetectorClient(credential: azure.core.credentials.AzureKeyCredential, endpoint: str, **kwargs: Any)[source]

The Anomaly Detector API detects anomalies automatically in time series data. It supports two kinds of mode, one is for stateless using, another is for stateful using. In stateless mode, there are three functionalities. Entire Detect is for detecting the whole series with model trained by the time series, Last Detect is detecting last point with model trained by points before. ChangePoint Detect is for detecting trend changes in time series. In stateful mode, user can store time series, the stored time series will be used for detection anomalies. Under this mode, user can still use the above three functionalities by only giving a time range without preparing time series in client side. Besides the above three functionalities, stateful model also provide group based detection and labeling service. By leveraging labeling service user can provide labels for each detection result, these labels will be used for retuning or regenerating detection models. Inconsistency detection is a kind of group based detection, this detection will find inconsistency ones in a set of time series. By using anomaly detector service, business customers can discover incidents and establish a logic flow for root cause analysis.

Parameters
async close() → None[source]
async detect_change_point(body: azure.ai.anomalydetector.models._models_py3.ChangePointDetectRequest, **kwargs) → azure.ai.anomalydetector.models._models_py3.ChangePointDetectResponse

Detect change point for the entire series.

Evaluate change point score of every series point.

Parameters

body (ChangePointDetectRequest) – Time series points and granularity is needed. Advanced model parameters can also be set in the request if needed.

Keyword Arguments

cls (callable) – A custom type or function that will be passed the direct response

Returns

ChangePointDetectResponse, or the result of cls(response)

Return type

ChangePointDetectResponse

Raises

~azure.core.exceptions.HttpResponseError

async detect_entire_series(body: azure.ai.anomalydetector.models._models_py3.DetectRequest, **kwargs) → azure.ai.anomalydetector.models._models_py3.EntireDetectResponse

Detect anomalies for the entire series in batch.

This operation generates a model using an entire series, each point is detected with the same model. With this method, points before and after a certain point are used to determine whether it is an anomaly. The entire detection can give user an overall status of the time series.

Parameters

body (DetectRequest) – Time series points and period if needed. Advanced model parameters can also be set in the request.

Keyword Arguments

cls (callable) – A custom type or function that will be passed the direct response

Returns

EntireDetectResponse, or the result of cls(response)

Return type

EntireDetectResponse

Raises

~azure.core.exceptions.HttpResponseError

async detect_last_point(body: azure.ai.anomalydetector.models._models_py3.DetectRequest, **kwargs) → azure.ai.anomalydetector.models._models_py3.LastDetectResponse

Detect anomaly status of the latest point in time series.

This operation generates a model using points before the latest one. With this method, only historical points are used to determine whether the target point is an anomaly. The latest point detecting operation matches the scenario of real-time monitoring of business metrics.

Parameters

body (DetectRequest) – Time series points and period if needed. Advanced model parameters can also be set in the request.

Keyword Arguments

cls (callable) – A custom type or function that will be passed the direct response

Returns

LastDetectResponse, or the result of cls(response)

Return type

LastDetectResponse

Raises

~azure.core.exceptions.HttpResponseError