AnomalyDetectorClient.DetectUnivariateLastPointAsync Method

Definition

Namespace:

Azure.AI.AnomalyDetector

Assembly:

Azure.AI.AnomalyDetector.dll

Package:

Azure.AI.AnomalyDetector v3.0.0-preview.7

Overloads

DetectUnivariateLastPointAsync(UnivariateDetectionOptions, CancellationToken)	Detect anomaly status of the latest point in time series.
DetectUnivariateLastPointAsync(RequestContent, RequestContext)	[Protocol Method] Detect anomaly status of the latest point in time series. This protocol method allows explicit creation of the request and processing of the response for advanced scenarios. Please try the simpler DetectUnivariateLastPointAsync(UnivariateDetectionOptions, CancellationToken) convenience overload with strongly typed models first.

DetectUnivariateLastPointAsync(UnivariateDetectionOptions, CancellationToken)

Source:

AnomalyDetectorClient.cs

Detect anomaly status of the latest point in time series.

public virtual System.Threading.Tasks.Task<Azure.Response<Azure.AI.AnomalyDetector.UnivariateLastDetectionResult>> DetectUnivariateLastPointAsync (Azure.AI.AnomalyDetector.UnivariateDetectionOptions options, System.Threading.CancellationToken cancellationToken = default);

abstract member DetectUnivariateLastPointAsync : Azure.AI.AnomalyDetector.UnivariateDetectionOptions * System.Threading.CancellationToken -> System.Threading.Tasks.Task<Azure.Response<Azure.AI.AnomalyDetector.UnivariateLastDetectionResult>>
override this.DetectUnivariateLastPointAsync : Azure.AI.AnomalyDetector.UnivariateDetectionOptions * System.Threading.CancellationToken -> System.Threading.Tasks.Task<Azure.Response<Azure.AI.AnomalyDetector.UnivariateLastDetectionResult>>

Public Overridable Function DetectUnivariateLastPointAsync (options As UnivariateDetectionOptions, Optional cancellationToken As CancellationToken = Nothing) As Task(Of Response(Of UnivariateLastDetectionResult))

Parameters

options

UnivariateDetectionOptions

Method of univariate anomaly detection.

cancellationToken

CancellationToken

The cancellation token to use.

Returns

Task<Response<UnivariateLastDetectionResult>>

Exceptions

ArgumentNullException

options is null.

Examples

This sample shows how to call DetectUnivariateLastPointAsync with required parameters.

var credential = new AzureKeyCredential("<key>");
var endpoint = new Uri("<https://my-service.azure.com>");
var client = new AnomalyDetectorClient(endpoint, credential);

var options = new UnivariateDetectionOptions(new TimeSeriesPoint[] 
{
    new TimeSeriesPoint(3.14f)
{
        Timestamp = DateTimeOffset.UtcNow,
    }
})
{
    Granularity = TimeGranularity.Yearly,
    CustomInterval = 1234,
    Period = 1234,
    MaxAnomalyRatio = 3.14f,
    Sensitivity = 1234,
    ImputeMode = ImputeMode.Auto,
    ImputeFixedValue = 3.14f,
};
var result = await client.DetectUnivariateLastPointAsync(options);

Remarks

This operation generates a model by using the points that you sent in to the API and based on all data to determine whether the last point is anomalous.

DetectUnivariateLastPointAsync(RequestContent, RequestContext)

Source:

AnomalyDetectorClient.cs

[Protocol Method] Detect anomaly status of the latest point in time series.

• This protocol method allows explicit creation of the request and processing of the response for advanced scenarios.
• Please try the simpler DetectUnivariateLastPointAsync(UnivariateDetectionOptions, CancellationToken) convenience overload with strongly typed models first.

public virtual System.Threading.Tasks.Task<Azure.Response> DetectUnivariateLastPointAsync (Azure.Core.RequestContent content, Azure.RequestContext context = default);

abstract member DetectUnivariateLastPointAsync : Azure.Core.RequestContent * Azure.RequestContext -> System.Threading.Tasks.Task<Azure.Response>
override this.DetectUnivariateLastPointAsync : Azure.Core.RequestContent * Azure.RequestContext -> System.Threading.Tasks.Task<Azure.Response>

Public Overridable Function DetectUnivariateLastPointAsync (content As RequestContent, Optional context As RequestContext = Nothing) As Task(Of Response)

Parameters

content

RequestContent

The content to send as the body of the request.

context

RequestContext

The request context, which can override default behaviors of the client pipeline on a per-call basis.

Returns

Task<Response>

The response returned from the service.

Exceptions

ArgumentNullException

content is null.

RequestFailedException

Service returned a non-success status code.

Examples

This sample shows how to call DetectUnivariateLastPointAsync with required request content, and how to parse the result.

var credential = new AzureKeyCredential("<key>");
var endpoint = new Uri("<https://my-service.azure.com>");
var client = new AnomalyDetectorClient(endpoint, credential);

var data = new {
    series = new[] {
        new {
            value = 123.45f,
        }
    },
};

Response response = await client.DetectUnivariateLastPointAsync(RequestContent.Create(data));

JsonElement result = JsonDocument.Parse(response.ContentStream).RootElement;
Console.WriteLine(result.GetProperty("period").ToString());
Console.WriteLine(result.GetProperty("suggestedWindow").ToString());
Console.WriteLine(result.GetProperty("expectedValue").ToString());
Console.WriteLine(result.GetProperty("upperMargin").ToString());
Console.WriteLine(result.GetProperty("lowerMargin").ToString());
Console.WriteLine(result.GetProperty("isAnomaly").ToString());
Console.WriteLine(result.GetProperty("isNegativeAnomaly").ToString());
Console.WriteLine(result.GetProperty("isPositiveAnomaly").ToString());

This sample shows how to call DetectUnivariateLastPointAsync with all request content, and how to parse the result.

var credential = new AzureKeyCredential("<key>");
var endpoint = new Uri("<https://my-service.azure.com>");
var client = new AnomalyDetectorClient(endpoint, credential);

var data = new {
    series = new[] {
        new {
            timestamp = "2022-05-10T14:57:31.2311892-04:00",
            value = 123.45f,
        }
    },
    granularity = "yearly",
    customInterval = 1234,
    period = 1234,
    maxAnomalyRatio = 123.45f,
    sensitivity = 1234,
    imputeMode = "auto",
    imputeFixedValue = 123.45f,
};

Response response = await client.DetectUnivariateLastPointAsync(RequestContent.Create(data), new RequestContext());

JsonElement result = JsonDocument.Parse(response.ContentStream).RootElement;
Console.WriteLine(result.GetProperty("period").ToString());
Console.WriteLine(result.GetProperty("suggestedWindow").ToString());
Console.WriteLine(result.GetProperty("expectedValue").ToString());
Console.WriteLine(result.GetProperty("upperMargin").ToString());
Console.WriteLine(result.GetProperty("lowerMargin").ToString());
Console.WriteLine(result.GetProperty("isAnomaly").ToString());
Console.WriteLine(result.GetProperty("isNegativeAnomaly").ToString());
Console.WriteLine(result.GetProperty("isPositiveAnomaly").ToString());
Console.WriteLine(result.GetProperty("severity").ToString());

Remarks

This operation generates a model using the points that you sent into the API, and based on all data to determine whether the last point is anomalous.

Below is the JSON schema for the request and response payloads.

Request Body:

Schema for UnivariateDetectionOptions:

{
  series: [
    {
      timestamp: string (date & time), # Optional.
      value: number, # Required.
    }
  ], # Required.
  granularity: "yearly" | "monthly" | "weekly" | "daily" | "hourly" | "minutely" | "secondly" | "microsecond" | "none", # Optional.
  customInterval: number, # Optional.
  period: number, # Optional.
  maxAnomalyRatio: number, # Optional.
  sensitivity: number, # Optional.
  imputeMode: "auto" | "previous" | "linear" | "fixed" | "zero" | "notFill", # Optional.
  imputeFixedValue: number, # Optional.
}

Response Body:

Schema for UnivariateLastDetectionResult:

{
  period: number, # Required.
  suggestedWindow: number, # Required.
  expectedValue: number, # Required.
  upperMargin: number, # Required.
  lowerMargin: number, # Required.
  isAnomaly: boolean, # Required.
  isNegativeAnomaly: boolean, # Required.
  isPositiveAnomaly: boolean, # Required.
  severity: number, # Optional.
}