AnomalyDetectorClient class
Client class for interacting with Azure Anomaly Detector service.
- Extends
Constructors
| Anomaly |
Creates an instance of AnomalyDetectorClient. Example usage: |
Inherited Properties
| api |
|
| endpoint |
Inherited Methods
| delete |
Delete an existing multivariate model according to the modelId |
| detect |
Submit detection multivariate anomaly task with the trained model of modelId, the input schema should be the same with the training request. Thus request will be complete asynchronously and will return a resultId for querying the detection result.The request should be a source link to indicate an externally accessible Azure storage Uri (preferably a Shared Access Signature Uri). All time-series used in generate the model must be zipped into one single file. Each time-series will be as follows: the first column is timestamp and the second column is value. |
| detect |
Evaluate change point score of every series point |
| detect |
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. |
| detect |
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. |
| export |
Export multivariate anomaly detection model based on modelId |
| get |
Get multivariate anomaly detection result based on resultId returned by the DetectAnomalyAsync api |
| get |
Get detailed information of multivariate model, including the training status and variables used in the model. |
| last |
Synchronized API for anomaly detection. |
| list |
List models of a subscription |
| send |
Send an HTTP request that is populated using the provided OperationSpec. |
| send |
Send the provided httpRequest. |
| train |
Create and train a multivariate anomaly detection model. The request must include a source parameter to indicate an externally accessible Azure storage Uri (preferably a Shared Access Signature Uri). All time-series used in generate the model must be zipped into one single file. Each time-series will be in a single CSV file in which the first column is timestamp and the second column is value. |
Constructor Details
AnomalyDetectorClient(string, TokenCredential | KeyCredential, PipelineOptions)
Creates an instance of AnomalyDetectorClient.
Example usage:
import { AnomalyDetectorClient, AzureKeyCredential } from "@azure/ai-anomaly-detector";
const client = new AnomalyDetectorClient(
"<service endpoint>",
new AzureKeyCredential("<api key>")
);
new AnomalyDetectorClient(endpointUrl: string, credential: TokenCredential | KeyCredential, options?: PipelineOptions)Parameters
- endpointUrl
-
string
Url to an Azure Anomaly Detector service endpoint
- credential
Used to authenticate requests to the service.
- options
- PipelineOptions
Used to configure the Form Recognizer client.
Inherited Property Details
apiVersion
endpoint
Inherited Method Details
deleteMultivariateModel(string, AnomalyDetectorDeleteMultivariateModelOptionalParams)
Delete an existing multivariate model according to the modelId
function deleteMultivariateModel(modelId: string, options?: AnomalyDetectorDeleteMultivariateModelOptionalParams): Promise<RestResponse>Parameters
- modelId
-
string
Model identifier.
The options parameters.
Returns
Promise<RestResponse>
Inherited From AnomalyDetector.deleteMultivariateModel
detectAnomaly(string, DetectionRequest, AnomalyDetectorDetectAnomalyOptionalParams)
Submit detection multivariate anomaly task with the trained model of modelId, the input schema should be the same with the training request. Thus request will be complete asynchronously and will return a resultId for querying the detection result.The request should be a source link to indicate an externally accessible Azure storage Uri (preferably a Shared Access Signature Uri). All time-series used in generate the model must be zipped into one single file. Each time-series will be as follows: the first column is timestamp and the second column is value.
function detectAnomaly(modelId: string, body: DetectionRequest, options?: AnomalyDetectorDetectAnomalyOptionalParams): Promise<AnomalyDetectorDetectAnomalyResponse>Parameters
- modelId
-
string
Model identifier.
- body
- DetectionRequest
Detect anomaly request
The options parameters.
Returns
Promise<AnomalyDetectorDetectAnomalyResponse>
Inherited From AnomalyDetector.detectAnomaly
detectChangePoint(DetectChangePointRequest, AnomalyDetectorDetectChangePointOptionalParams)
Evaluate change point score of every series point
function detectChangePoint(body: DetectChangePointRequest, options?: AnomalyDetectorDetectChangePointOptionalParams): Promise<AnomalyDetectorDetectChangePointResponse>Parameters
Time series points and granularity is needed. Advanced model parameters can also be set in the request if needed.
The options parameters.
Returns
Inherited From AnomalyDetector.detectChangePoint
detectEntireSeries(DetectRequest, AnomalyDetectorDetectEntireSeriesOptionalParams)
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.
function detectEntireSeries(body: DetectRequest, options?: AnomalyDetectorDetectEntireSeriesOptionalParams): Promise<AnomalyDetectorDetectEntireSeriesResponse>Parameters
- body
- DetectRequest
Time series points and period if needed. Advanced model parameters can also be set in the request.
The options parameters.
Returns
Inherited From AnomalyDetector.detectEntireSeries
detectLastPoint(DetectRequest, AnomalyDetectorDetectLastPointOptionalParams)
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.
function detectLastPoint(body: DetectRequest, options?: AnomalyDetectorDetectLastPointOptionalParams): Promise<AnomalyDetectorDetectLastPointResponse>Parameters
- body
- DetectRequest
Time series points and period if needed. Advanced model parameters can also be set in the request.
The options parameters.
Returns
Inherited From AnomalyDetector.detectLastPoint
exportModel(string, AnomalyDetectorExportModelOptionalParams)
Export multivariate anomaly detection model based on modelId
function exportModel(modelId: string, options?: AnomalyDetectorExportModelOptionalParams): Promise<AnomalyDetectorExportModelResponse>Parameters
- modelId
-
string
Model identifier.
The options parameters.
Returns
Promise<AnomalyDetectorExportModelResponse>
Inherited From AnomalyDetector.exportModel
getDetectionResult(string, AnomalyDetectorGetDetectionResultOptionalParams)
Get multivariate anomaly detection result based on resultId returned by the DetectAnomalyAsync api
function getDetectionResult(resultId: string, options?: AnomalyDetectorGetDetectionResultOptionalParams): Promise<AnomalyDetectorGetDetectionResultResponse>Parameters
- resultId
-
string
Result identifier.
The options parameters.
Returns
Inherited From AnomalyDetector.getDetectionResult
getMultivariateModel(string, AnomalyDetectorGetMultivariateModelOptionalParams)
Get detailed information of multivariate model, including the training status and variables used in the model.
function getMultivariateModel(modelId: string, options?: AnomalyDetectorGetMultivariateModelOptionalParams): Promise<AnomalyDetectorGetMultivariateModelResponse>Parameters
- modelId
-
string
Model identifier.
The options parameters.
Returns
Inherited From AnomalyDetector.getMultivariateModel
lastDetectAnomaly(string, LastDetectionRequest, AnomalyDetectorLastDetectAnomalyOptionalParams)
Synchronized API for anomaly detection.
function lastDetectAnomaly(modelId: string, body: LastDetectionRequest, options?: AnomalyDetectorLastDetectAnomalyOptionalParams): Promise<AnomalyDetectorLastDetectAnomalyResponse>Parameters
- modelId
-
string
Model identifier.
- body
- LastDetectionRequest
Request for last detection.
The options parameters.
Returns
Inherited From AnomalyDetector.lastDetectAnomaly
listMultivariateModel(AnomalyDetectorListMultivariateModelOptionalParams)
List models of a subscription
function listMultivariateModel(options?: AnomalyDetectorListMultivariateModelOptionalParams): PagedAsyncIterableIterator<AnomalyDetectorClientModelSnapshot, AnomalyDetectorClientModelSnapshot[], PageSettings>Parameters
The options parameters.
Returns
PagedAsyncIterableIterator<AnomalyDetectorClientModelSnapshot, AnomalyDetectorClientModelSnapshot[], PageSettings>
Inherited From AnomalyDetector.listMultivariateModel
sendOperationRequest(OperationArguments, OperationSpec, ServiceCallback<any>)
Send an HTTP request that is populated using the provided OperationSpec.
function sendOperationRequest(operationArguments: OperationArguments, operationSpec: OperationSpec, callback?: ServiceCallback<any>): Promise<RestResponse>Parameters
- operationArguments
- OperationArguments
The arguments that the HTTP request's templated values will be populated from.
- operationSpec
- OperationSpec
The OperationSpec to use to populate the httpRequest.
- callback
-
ServiceCallback<any>
The callback to call when the response is received.
Returns
Promise<RestResponse>
Inherited From AnomalyDetector.sendOperationRequest
sendRequest(RequestPrepareOptions | WebResourceLike)
Send the provided httpRequest.
function sendRequest(options: RequestPrepareOptions | WebResourceLike): Promise<HttpOperationResponse>Parameters
- options
Returns
Promise<HttpOperationResponse>
Inherited From AnomalyDetector.sendRequest
trainMultivariateModel(AnomalyDetectorClientModelInfo, AnomalyDetectorTrainMultivariateModelOptionalParams)
Create and train a multivariate anomaly detection model. The request must include a source parameter to indicate an externally accessible Azure storage Uri (preferably a Shared Access Signature Uri). All time-series used in generate the model must be zipped into one single file. Each time-series will be in a single CSV file in which the first column is timestamp and the second column is value.
function trainMultivariateModel(body: AnomalyDetectorClientModelInfo, options?: AnomalyDetectorTrainMultivariateModelOptionalParams): Promise<AnomalyDetectorTrainMultivariateModelResponse>Parameters
Training request
The options parameters.
Returns
Inherited From AnomalyDetector.trainMultivariateModel
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