AnomalyDetector class

Initializes a new instance of the AnomalyDetector class.

new AnomalyDetector(endpoint: string, options?: AnomalyDetectorOptionalParams)

Parameters

apiVersion: string

Property Value

Inherited From AnomalyDetectorContext.apiVersion

endpoint: string

Property Value

Inherited From AnomalyDetectorContext.endpoint

Delete an existing multivariate model according to the modelId

function deleteMultivariateModel(modelId: string, options?: AnomalyDetectorDeleteMultivariateModelOptionalParams): Promise<RestResponse>

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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>

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Evaluate change point score of every series point

function detectChangePoint(body: DetectChangePointRequest, options?: AnomalyDetectorDetectChangePointOptionalParams): Promise<AnomalyDetectorDetectChangePointResponse>

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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>

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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>

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Export multivariate anomaly detection model based on modelId

function exportModel(modelId: string, options?: AnomalyDetectorExportModelOptionalParams): Promise<AnomalyDetectorExportModelResponse>

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Get multivariate anomaly detection result based on resultId returned by the DetectAnomalyAsync api

function getDetectionResult(resultId: string, options?: AnomalyDetectorGetDetectionResultOptionalParams): Promise<AnomalyDetectorGetDetectionResultResponse>

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Get detailed information of multivariate model, including the training status and variables used in the model.

function getMultivariateModel(modelId: string, options?: AnomalyDetectorGetMultivariateModelOptionalParams): Promise<AnomalyDetectorGetMultivariateModelResponse>

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Synchronized API for anomaly detection.

function lastDetectAnomaly(modelId: string, body: LastDetectionRequest, options?: AnomalyDetectorLastDetectAnomalyOptionalParams): Promise<AnomalyDetectorLastDetectAnomalyResponse>

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List models of a subscription

function listMultivariateModel(options?: AnomalyDetectorListMultivariateModelOptionalParams): PagedAsyncIterableIterator<AnomalyDetectorClientModelSnapshot, AnomalyDetectorClientModelSnapshot[], PageSettings>

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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>

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Send an HTTP request that is populated using the provided OperationSpec.

function sendOperationRequest(operationArguments: OperationArguments, operationSpec: OperationSpec, callback?: ServiceCallback<any>): Promise<RestResponse>

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Inherited From AnomalyDetectorContext.sendOperationRequest

Send the provided httpRequest.

function sendRequest(options: RequestPrepareOptions | WebResourceLike): Promise<HttpOperationResponse>

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Inherited From AnomalyDetectorContext.sendRequest