AnomalyDetectorClient.DetectUnivariateEntireSeries Method

Definition

Namespace:

Azure.AI.AnomalyDetector

Assembly:

Azure.AI.AnomalyDetector.dll

Package:

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

Overloads

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

DetectUnivariateEntireSeries(UnivariateDetectionOptions, CancellationToken)

Source:

AnomalyDetectorClient.cs

Detect anomalies for the entire series in batch.

public virtual Azure.Response<Azure.AI.AnomalyDetector.UnivariateEntireDetectionResult> DetectUnivariateEntireSeries (Azure.AI.AnomalyDetector.UnivariateDetectionOptions options, System.Threading.CancellationToken cancellationToken = default);

abstract member DetectUnivariateEntireSeries : Azure.AI.AnomalyDetector.UnivariateDetectionOptions * System.Threading.CancellationToken -> Azure.Response<Azure.AI.AnomalyDetector.UnivariateEntireDetectionResult>
override this.DetectUnivariateEntireSeries : Azure.AI.AnomalyDetector.UnivariateDetectionOptions * System.Threading.CancellationToken -> Azure.Response<Azure.AI.AnomalyDetector.UnivariateEntireDetectionResult>

Public Overridable Function DetectUnivariateEntireSeries (options As UnivariateDetectionOptions, Optional cancellationToken As CancellationToken = Nothing) As Response(Of UnivariateEntireDetectionResult)

Parameters

options

UnivariateDetectionOptions

Method of univariate anomaly detection.

cancellationToken

CancellationToken

The cancellation token to use.

Returns

Response<UnivariateEntireDetectionResult>

Exceptions

ArgumentNullException

options is null.

Examples

This sample shows how to call DetectUnivariateEntireSeries 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 = client.DetectUnivariateEntireSeries(options);

Remarks

This operation generates a model with 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's an anomaly. The entire detection can give the user an overall status of the time series.

DetectUnivariateEntireSeries(RequestContent, RequestContext)

Source:

AnomalyDetectorClient.cs

[Protocol Method] Detect anomalies for the entire series in batch.

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

public virtual Azure.Response DetectUnivariateEntireSeries (Azure.Core.RequestContent content, Azure.RequestContext context = default);

abstract member DetectUnivariateEntireSeries : Azure.Core.RequestContent * Azure.RequestContext -> Azure.Response
override this.DetectUnivariateEntireSeries : Azure.Core.RequestContent * Azure.RequestContext -> Azure.Response

Public Overridable Function DetectUnivariateEntireSeries (content As RequestContent, Optional context As RequestContext = Nothing) As 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

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 DetectUnivariateEntireSeries 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 = client.DetectUnivariateEntireSeries(RequestContent.Create(data));

JsonElement result = JsonDocument.Parse(response.ContentStream).RootElement;
Console.WriteLine(result.GetProperty("period").ToString());
Console.WriteLine(result.GetProperty("expectedValues")[0].ToString());
Console.WriteLine(result.GetProperty("upperMargins")[0].ToString());
Console.WriteLine(result.GetProperty("lowerMargins")[0].ToString());
Console.WriteLine(result.GetProperty("isAnomaly")[0].ToString());
Console.WriteLine(result.GetProperty("isNegativeAnomaly")[0].ToString());
Console.WriteLine(result.GetProperty("isPositiveAnomaly")[0].ToString());

This sample shows how to call DetectUnivariateEntireSeries 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 = client.DetectUnivariateEntireSeries(RequestContent.Create(data), new RequestContext());

JsonElement result = JsonDocument.Parse(response.ContentStream).RootElement;
Console.WriteLine(result.GetProperty("period").ToString());
Console.WriteLine(result.GetProperty("expectedValues")[0].ToString());
Console.WriteLine(result.GetProperty("upperMargins")[0].ToString());
Console.WriteLine(result.GetProperty("lowerMargins")[0].ToString());
Console.WriteLine(result.GetProperty("isAnomaly")[0].ToString());
Console.WriteLine(result.GetProperty("isNegativeAnomaly")[0].ToString());
Console.WriteLine(result.GetProperty("isPositiveAnomaly")[0].ToString());
Console.WriteLine(result.GetProperty("severity")[0].ToString());

Remarks

This operation generates a model with 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.

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 UnivariateEntireDetectionResult:

{
  period: number, # Required.
  expectedValues: [number], # Required.
  upperMargins: [number], # Required.
  lowerMargins: [number], # Required.
  isAnomaly: [boolean], # Required.
  isNegativeAnomaly: [boolean], # Required.
  isPositiveAnomaly: [boolean], # Required.
  severity: [number], # Optional.
}