Package version
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>")
);Url to an Azure Anomaly Detector service endpoint
Used to authenticate requests to the service.
Used to configure the Form Recognizer client.
Delete an existing multivariate model according to the modelId
Model identifier.
The options parameters.
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.
Model identifier.
Detect anomaly request
The options parameters.
Evaluate change point score of every series point
Time series points and granularity is needed. Advanced model parameters can also be set in the request if needed.
The options parameters.
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.
Time series points and period if needed. Advanced model parameters can also be set in the request.
The options parameters.
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.
Time series points and period if needed. Advanced model parameters can also be set in the request.
The options parameters.
Export multivariate anomaly detection model based on modelId
Model identifier.
The options parameters.
Get multivariate anomaly detection result based on resultId returned by the DetectAnomalyAsync api
Result identifier.
The options parameters.
Get detailed information of multivariate model, including the training status and variables used in the model.
Model identifier.
The options parameters.
List models of a subscription
The options parameters.
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.
Training request
The options parameters.
Generated using TypeDoc
Client class for interacting with Azure Anomaly Detector service.