AnomalyDetector class
- Extends
Constructors
| Anomaly |
Initializes a new instance of the AnomalyDetector class. |
Inherited Properties
| api |
|
| endpoint |
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 |
| 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. |
Inherited Methods
| send |
Send an HTTP request that is populated using the provided OperationSpec. |
| send |
Send the provided httpRequest. |
Constructor Details
AnomalyDetector(string, AnomalyDetectorOptionalParams)
Initializes a new instance of the AnomalyDetector class.
new AnomalyDetector(endpoint: string, options?: AnomalyDetectorOptionalParams)Parameters
- endpoint
-
string
Supported Cognitive Services endpoints (protocol and hostname, for example: https://westus2.api.cognitive.microsoft.com).
- options
- AnomalyDetectorOptionalParams
The parameter options
Inherited Property Details
apiVersion
endpoint
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>
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>
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
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
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
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>
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
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
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
listMultivariateModel(AnomalyDetectorListMultivariateModelOptionalParams)
List models of a subscription
function listMultivariateModel(options?: AnomalyDetectorListMultivariateModelOptionalParams): PagedAsyncIterableIterator<AnomalyDetectorClientModelSnapshot, AnomalyDetectorClientModelSnapshot[], PageSettings>Parameters
The options parameters.
Returns
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 Method Details
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 AnomalyDetectorContext.sendOperationRequest
sendRequest(RequestPrepareOptions | WebResourceLike)
Send the provided httpRequest.
function sendRequest(options: RequestPrepareOptions | WebResourceLike): Promise<HttpOperationResponse>Parameters
- options
Returns
Promise<HttpOperationResponse>
Inherited From AnomalyDetectorContext.sendRequest
Azure SDK for JavaScript
Feedback
Coming soon: Throughout 2024 we will be phasing out GitHub Issues as the feedback mechanism for content and replacing it with a new feedback system. For more information see: https://aka.ms/ContentUserFeedback.
Submit and view feedback for