{
  "opencollection": "1.0.0",
  "info": {
    "name": "Microsoft Azure AccessControlClient AccessConnector Multivariate API",
    "version": "2020-08-01-preview"
  },
  "items": [
    {
      "info": {
        "name": "Multivariate",
        "type": "folder"
      },
      "items": [
        {
          "info": {
            "name": "Microsoft Azure Get Multivariate Anomaly Detection Result",
            "type": "http"
          },
          "http": {
            "method": "GET",
            "url": "{{baseUrl}}/multivariate/detect-batch/:resultId",
            "params": [
              {
                "name": "resultId",
                "value": "",
                "type": "path",
                "description": "ID of a batch detection result."
              }
            ]
          },
          "docs": "For asynchronous inference, get a multivariate anomaly detection result based on the<br>resultId value that the BatchDetectAnomaly API returns."
        },
        {
          "info": {
            "name": "Microsoft Azure List Multivariate Models",
            "type": "http"
          },
          "http": {
            "method": "GET",
            "url": "{{baseUrl}}/multivariate/models",
            "params": [
              {
                "name": "skip",
                "value": "",
                "type": "query",
                "description": "The number of result items to skip."
              },
              {
                "name": "top",
                "value": "",
                "type": "query",
                "description": "The number of result items to return."
              }
            ]
          },
          "docs": "List models of a resource."
        },
        {
          "info": {
            "name": "Microsoft Azure Train A Multivariate Anomaly Detection Model",
            "type": "http"
          },
          "http": {
            "method": "POST",
            "url": "{{baseUrl}}/multivariate/models"
          },
          "docs": "Create and train a multivariate anomaly detection model. The request must<br>include a source parameter to indicate an Azure Blob<br>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<br>Storage folder that contains multiple CSV files, where each CSV file has<br>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<br>time stam"
        },
        {
          "info": {
            "name": "Microsoft Azure Get Multivariate Model",
            "type": "http"
          },
          "http": {
            "method": "GET",
            "url": "{{baseUrl}}/multivariate/models/:modelId",
            "params": [
              {
                "name": "modelId",
                "value": "",
                "type": "path",
                "description": "Model identifier."
              }
            ]
          },
          "docs": "Get detailed information about the multivariate model, including the training status<br>and variables used in the model."
        },
        {
          "info": {
            "name": "Microsoft Azure Delete Multivariate Model",
            "type": "http"
          },
          "http": {
            "method": "DELETE",
            "url": "{{baseUrl}}/multivariate/models/:modelId",
            "params": [
              {
                "name": "modelId",
                "value": "",
                "type": "path",
                "description": "Model identifier."
              }
            ]
          },
          "docs": "Delete an existing multivariate model according to the modelId value."
        },
        {
          "info": {
            "name": "Microsoft Azure Detect Multivariate Anomaly",
            "type": "http"
          },
          "http": {
            "method": "POST",
            "url": "{{baseUrl}}/multivariate/models/:modelId:detect-batch",
            "params": [
              {
                "name": "modelId",
                "value": "",
                "type": "path",
                "description": "Model identifier."
              }
            ]
          },
          "docs": "Submit a multivariate anomaly detection task with the modelId value of a trained model<br>and inference data. The input schema should be the same with the training<br>request. The request will finish asynchronously and return a resultId value to<br>query the detection result. The request should be a source link to indicate an<br>externally accessible Azure Storage URI that either points to an Azure Blob<br>Storage folder or points to a CSV file in Azure Blob Storage."
        },
        {
          "info": {
            "name": "Microsoft Azure Detect Anomalies In The Last Point Of The Request Body",
            "type": "http"
          },
          "http": {
            "method": "POST",
            "url": "{{baseUrl}}/multivariate/models/:modelId:detect-last",
            "params": [
              {
                "name": "modelId",
                "value": "",
                "type": "path",
                "description": "Model identifier."
              }
            ]
          },
          "docs": "Submit a multivariate anomaly detection task with the modelId value of a trained model<br>and inference data. The inference data should be put into the request body in<br>JSON format. The request will finish synchronously and return the detection<br>immediately in the response body."
        }
      ]
    }
  ],
  "bundled": true
}