train_multivariate_models
Creates, updates, deletes, gets or lists a train_multivariate_models resource.
Overview
| Name | train_multivariate_models |
| Type | Resource |
| Id | azure.ai_anomaly_detector.train_multivariate_models |
Fields
The following fields are returned by SELECT queries:
SELECT not supported for this resource, use SHOW METHODS to view available operations for the resource.
Methods
The following methods are available for this resource:
| Name | Accessible by | Required Params | Optional Params | Description |
|---|---|---|---|---|
train_multivariate_model | exec | endpoint, api_version, dataSource, startTime, endTime | Train a Multivariate Anomaly Detection Model. Create and train a multivariate anomaly detection model. The request must include a source parameter to indicate an externally accessible Azure blob storage URI.There are two types of data input: An URI pointed to an Azure blob storage folder which contains multiple CSV files, and each CSV file contains two columns, timestamp and variable. Another type of input is an URI pointed to a CSV file in Azure blob storage, which contains all the variables and a timestamp column. |
Parameters
Parameters can be passed in the WHERE clause of a query. Check the Methods section to see which parameters are required or optional for each operation.
| Name | Datatype | Description |
|---|---|---|
api_version | string | The service endpoint host (no scheme), e.g. myaccount.table.cosmos.azure.com:443 - value of the client ApiVersion parameter. (default: ) |
endpoint | string | The service endpoint host (no scheme), e.g. myaccount.table.cosmos.azure.com:443 - value of the client Endpoint parameter. (default: ) |
Lifecycle Methods
- train_multivariate_model
Train a Multivariate Anomaly Detection Model. Create and train a multivariate anomaly detection model. The request must include a source parameter to indicate an externally accessible Azure blob storage URI.There are two types of data input: An URI pointed to an Azure blob storage folder which contains multiple CSV files, and each CSV file contains two columns, timestamp and variable. Another type of input is an URI pointed to a CSV file in Azure blob storage, which contains all the variables and a timestamp column.
EXEC azure.ai_anomaly_detector.train_multivariate_models.train_multivariate_model
@endpoint='{{ endpoint }}' --required,
@api_version='{{ api_version }}' --required
@@json=
'{
"dataSource": "{{ dataSource }}",
"dataSchema": "{{ dataSchema }}",
"startTime": "{{ startTime }}",
"endTime": "{{ endTime }}",
"displayName": "{{ displayName }}",
"slidingWindow": {{ slidingWindow }},
"alignPolicy": "{{ alignPolicy }}",
"status": "{{ status }}",
"errors": "{{ errors }}",
"diagnosticsInfo": "{{ diagnosticsInfo }}"
}'
;