AnomalyDetectorClient Class

Definition

Initializes a new instance of the synchronous AnomalyDetectorClient type.

public final class AnomalyDetectorClient
Inheritance
java.lang.Object
AnomalyDetectorClient

Inherited Members

java.lang.Object.clone() java.lang.Object.equals(java.lang.Object) java.lang.Object.finalize() java.lang.Object.getClass() java.lang.Object.hashCode() java.lang.Object.notify() java.lang.Object.notifyAll() java.lang.Object.toString() java.lang.Object.wait() java.lang.Object.wait(long) java.lang.Object.wait(long,int)

Methods

deleteMultivariateModel(UUID modelId)

Delete an existing multivariate model according to the modelId.

deleteMultivariateModelWithResponse(UUID modelId, Context context)

Delete an existing multivariate model according to the modelId.

detectAnomaly(UUID modelId, DetectionRequest body)

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.

detectAnomalyWithResponse(UUID modelId, DetectionRequest body, Context context)

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.

detectChangePoint(ChangePointDetectRequest body)

Evaluate change point score of every series point.

detectChangePointWithResponse(ChangePointDetectRequest body, Context context)

Evaluate change point score of every series point.

detectEntireSeries(DetectRequest body)

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.

detectEntireSeriesWithResponse(DetectRequest body, Context context)

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.

detectLastPoint(DetectRequest body)

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.

detectLastPointWithResponse(DetectRequest body, Context context)

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.

exportModel(UUID modelId)

Export multivariate anomaly detection model based on modelId.

exportModelWithResponse(UUID modelId, Context context)

Export multivariate anomaly detection model based on modelId.

getDetectionResult(UUID resultId)

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

getDetectionResultWithResponse(UUID resultId, Context context)

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

getMultivariateModel(UUID modelId)

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

getMultivariateModelWithResponse(UUID modelId, Context context)

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

lastDetectAnomaly(UUID modelId, LastDetectionRequest body)

Synchronized API for anomaly detection.

lastDetectAnomalyWithResponse(UUID modelId, LastDetectionRequest body, Context context)

Synchronized API for anomaly detection.

listMultivariateModel(Integer skip, Integer top)

List models of a subscription.

listMultivariateModel(Integer skip, Integer top, Context context)

List models of a subscription.

trainMultivariateModel(ModelInfo body)

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.

trainMultivariateModelWithResponse(ModelInfo body, Context context)

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.

Applies to