IAnomalyDetectorClient.EntireDetectWithHttpMessagesAsync Method

Reference

Definition

Namespace:: Microsoft.Azure.CognitiveServices.AnomalyDetector

Assembly:: Microsoft.Azure.CognitiveServices.AnomalyDetector.dll

Package:: Microsoft.Azure.CognitiveServices.AnomalyDetector v1.0.0

Important

Some information relates to prerelease product that may be substantially modified before it’s released. Microsoft makes no warranties, express or implied, with respect to the information provided here.

Detect anomalies for the entire series in batch.

public System.Threading.Tasks.Task<Microsoft.Rest.HttpOperationResponse<Microsoft.Azure.CognitiveServices.AnomalyDetector.Models.EntireDetectResponse>> EntireDetectWithHttpMessagesAsync (Microsoft.Azure.CognitiveServices.AnomalyDetector.Models.Request body, System.Collections.Generic.Dictionary<string,System.Collections.Generic.List<string>> customHeaders = default, System.Threading.CancellationToken cancellationToken = default);

abstract member EntireDetectWithHttpMessagesAsync : Microsoft.Azure.CognitiveServices.AnomalyDetector.Models.Request * System.Collections.Generic.Dictionary<string, System.Collections.Generic.List<string>> * System.Threading.CancellationToken -> System.Threading.Tasks.Task<Microsoft.Rest.HttpOperationResponse<Microsoft.Azure.CognitiveServices.AnomalyDetector.Models.EntireDetectResponse>>

Public Function EntireDetectWithHttpMessagesAsync (body As Request, Optional customHeaders As Dictionary(Of String, List(Of String)) = Nothing, Optional cancellationToken As CancellationToken = Nothing) As Task(Of HttpOperationResponse(Of EntireDetectResponse))

Parameters

body: Request

Time series points and period if needed. Advanced model parameters can also be set in the request.

customHeaders: Dictionary<String,List<String>>

The headers that will be added to request.

cancellationToken: CancellationToken

The cancellation token.

Returns

Task<HttpOperationResponse<EntireDetectResponse>>

Remarks

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.

Applies to

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