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latestSwagger 2.02026-07-13103553.7 KB

Train a Multivariate Anomaly Detection Model

Create and train a multivariate anomaly detection model. The request must include a source parameter to indicate an Azure Blob Storage URI that's accessible to the service. There are two types of data input. The Blob Storage URI can point to an Azure Blob Storage folder that contains multiple CSV files, where each CSV file has two columns, time stamp and variable. Or the Blob Storage URI can point to a single blob that contains a CSV file that has all the variables and a time stamp column. The model object will be created and returned in the response, but the training process happens asynchronously. To check the training status, call GetMultivariateModel with the modelId value and check the status field in the modelInfo object.

post/multivariate/models

Request body

dataSourcestring uri required

Source link to the input data to indicate an accessible Azure Storage URI. It either points to an Azure Blob Storage folder or points to a CSV file in Azure Blob Storage, based on your data schema selection.

dataSchema'OneTable' | 'MultiTable'

Data schema of the input data source. The default is OneTable.

startTimestring date-time required

Start date/time of training data, which should be in ISO 8601 format.

endTimestring date-time required

End date/time of training data, which should be in ISO 8601 format.

displayNamestring

Display name of the model. Maximum length is 24 characters.

slidingWindowinteger

Number of previous time stamps that will be used to detect whether the time stamp is an anomaly or not.

status'CREATED' | 'RUNNING' | 'READY' | 'FAILED'

Response

The request has succeeded and a new resource has been created as a result.

modelIdstring uuid required

Universally Unique Identifier

createdTimestring date-time required

Date and time (UTC) when the model was created.

lastUpdatedTimestring date-time required

Date and time (UTC) when the model was last updated.