Anomaly Detector v1.1-preview.1

The Anomaly Detection service detects anomalies automatically in time series data. It supports several functionalities, one is for detecting anomalies in single time-series, including entire, last and change point detection. The other is detecting anomalies in multiple time-series. With univariate anomaly detection ability, business customers can discover incidents and establish a logic flow for root cause analysis. The multivariate anomaly detection APIs in Anomaly Detector analyze dependencies and inter-correlations between different signals. It enables customers to gather a group of related time-series and detect failures with a wholistic view. To ensure online service quality is one of the main reasons we developed this service. Our team is dedicated to continuing to improve the anomaly detection service to provide precise results.

This Multivariate Anomaly Detection is currently available in:

  • East US - eastus.api.cognitive.microsoft.com
  • East US 2 - eastus2.api.cognitive.microsoft.com
  • South Central US - southcentralus.api.cognitive.microsoft.com
  • UK South - uksouth.api.cognitive.microsoft.com
  • West Europe - westeurope.api.cognitive.microsoft.com
  • West US 2 - westus2.api.cognitive.microsoft.com

Multivariate Anomaly Detection - Detect Multivariate Anomaly Sync

Detect multivariate anomaly with the trained model Id and the multivariate time-series data in request body. This API returns the anomalies in the given data.

Select the testing console in the region where you created your resource:

Open API testing console

Request URL

Request parameters

string

Request headers

string
Media type of the body sent to the API.
string
Subscription key which provides access to this API. Found in your Cognitive Services accounts.

Request body

The request to detect latest several points.

{
  "variables": [
    {
      "name": "variable_1",
      "timestamps": [
        "string"
      ],
      "values": [
        0
      ]
    }
  ],
  "detectingPoints": 2
}
{
  "required": [
    "detectingPoints",
    "variables"
  ],
  "properties": {
    "variables": {
      "type": "array",
      "description": "variables",
      "items": {
        "required": [
          "name",
          "timestamps",
          "values"
        ],
        "properties": {
          "name": {
            "type": "string",
            "description": "variable name",
            "example": "variable_1"
          },
          "timestamps": {
            "type": "array",
            "description": "timestamps",
            "items": {
              "type": "string"
            }
          },
          "values": {
            "type": "array",
            "description": "values",
            "items": {
              "type": "number"
            }
          }
        },
        "type": "object"
      }
    },
    "detectingPoints": {
      "type": "integer",
      "description": "number of timestamps on which the model detects",
      "example": 2
    }
  },
  "type": "object"
}

Response 200

Detect multivariate anomaly successfully

{
  "variableStates": [
    {
      "variable": "string",
      "filledNARatio": 0.0,
      "effectiveCount": 0,
      "startTime": "string",
      "endTime": "string"
    }
  ],
  "results": [
    {
      "timestamp": "2020-01-01T00:00:00Z",
      "value": {
        "isAnomaly": true,
        "severity": 0.8,
        "score": 0.3,
        "interpretation": [
          {
            "variable": "string",
            "contributionScore": 0.0,
            "correlationChanges": {
              "changedVariables": [
                "string"
              ],
              "changedValues": [
                0.0
              ]
            }
          }
        ]
      },
      "errors": [
        {
          "code": "string",
          "message": "string"
        }
      ]
    }
  ]
}
{
  "properties": {
    "variableStates": {
      "type": "array",
      "items": {
        "properties": {
          "variable": {
            "type": "string",
            "description": "Variable name."
          },
          "filledNARatio": {
            "type": "number",
            "description": "Proportion of NaN values filled of the variable.",
            "minimum": 0,
            "maximum": 1
          },
          "effectiveCount": {
            "type": "integer",
            "description": "Number of effective points counted."
          },
          "startTime": {
            "type": "string",
            "format": "date-time",
            "description": "Start time of the variable."
          },
          "endTime": {
            "type": "string",
            "format": "date-time",
            "description": "End time of the variable."
          }
        },
        "type": "object"
      }
    },
    "results": {
      "type": "array",
      "items": {
        "required": [
          "timestamp"
        ],
        "properties": {
          "timestamp": {
            "type": "string",
            "format": "date-time",
            "description": "timestamp",
            "example": "2020-01-01T00:00:00Z"
          },
          "value": {
            "required": [
              "isAnomaly",
              "score",
              "severity"
            ],
            "properties": {
              "isAnomaly": {
                "type": "boolean",
                "description": "True if an anomaly is detected at the current timestamp.",
                "example": true
              },
              "severity": {
                "type": "number",
                "description": "Indicates the significance of the anomaly. The higher the severity, the more significant the anomaly.",
                "example": 0.8,
                "minimum": 0,
                "maximum": 1
              },
              "score": {
                "type": "number",
                "description": "Raw score from the model.",
                "example": 0.3,
                "minimum": 0,
                "maximum": 2
              },
              "interpretation": {
                "type": "array",
                "items": {
                  "properties": {
                    "variable": {
                      "type": "string"
                    },
                    "contributionScore": {
                      "type": "number"
                    },
                    "correlationChanges": {
                      "properties": {
                        "changedVariables": {
                          "type": "array",
                          "description": "correlated variables",
                          "items": {
                            "type": "string"
                          }
                        },
                        "changedValues": {
                          "type": "array",
                          "description": "changes in correlation",
                          "items": {
                            "type": "number"
                          }
                        }
                      },
                      "type": "object"
                    }
                  },
                  "type": "object"
                }
              }
            },
            "type": "object"
          },
          "errors": {
            "type": "array",
            "description": "Error message for the current timestamp",
            "items": {
              "required": [
                "code",
                "message"
              ],
              "properties": {
                "code": {
                  "type": "string",
                  "description": "The error code."
                },
                "message": {
                  "type": "string",
                  "description": "The message explaining the error reported by the service."
                }
              },
              "type": "object"
            }
          }
        },
        "type": "object"
      }
    }
  },
  "type": "object"
}

Response 500

Internal Server Error.

Response 400

{
  "code": "string",
  "message": "string"
}
{
  "required": [
    "code",
    "message"
  ],
  "properties": {
    "code": {
      "type": "string",
      "description": "The error code."
    },
    "message": {
      "type": "string",
      "description": "The message explaining the error reported by the service."
    }
  },
  "type": "object"
}

Response 405

Method Not Allowed.

Response 404

Model not found

{
  "code": "string",
  "message": "string"
}
{
  "required": [
    "code",
    "message"
  ],
  "properties": {
    "code": {
      "type": "string",
      "description": "The error code."
    },
    "message": {
      "type": "string",
      "description": "The message explaining the error reported by the service."
    }
  },
  "type": "object"
}

Code samples

@ECHO OFF

curl -v -X POST "https://chinaeast2.api.cognitive.azure.cn/anomalydetector/v1.1-preview.1/multivariate/models/{modelId}/last/detect"
-H "Content-Type: application/json"
-H "Ocp-Apim-Subscription-Key: {subscription key}"

--data-ascii "{body}" 
using System;
using System.Net.Http.Headers;
using System.Text;
using System.Net.Http;
using System.Web;

namespace CSHttpClientSample
{
    static class Program
    {
        static void Main()
        {
            MakeRequest();
            Console.WriteLine("Hit ENTER to exit...");
            Console.ReadLine();
        }
        
        static async void MakeRequest()
        {
            var client = new HttpClient();
            var queryString = HttpUtility.ParseQueryString(string.Empty);

            // Request headers
            client.DefaultRequestHeaders.Add("Ocp-Apim-Subscription-Key", "{subscription key}");

            var uri = "https://chinaeast2.api.cognitive.azure.cn/anomalydetector/v1.1-preview.1/multivariate/models/{modelId}/last/detect?" + queryString;

            HttpResponseMessage response;

            // Request body
            byte[] byteData = Encoding.UTF8.GetBytes("{body}");

            using (var content = new ByteArrayContent(byteData))
            {
               content.Headers.ContentType = new MediaTypeHeaderValue("< your content type, i.e. application/json >");
               response = await client.PostAsync(uri, content);
            }

        }
    }
}	
// // This sample uses the Apache HTTP client from HTTP Components (http://hc.apache.org/httpcomponents-client-ga/)
import java.net.URI;
import org.apache.http.HttpEntity;
import org.apache.http.HttpResponse;
import org.apache.http.client.HttpClient;
import org.apache.http.client.methods.HttpGet;
import org.apache.http.client.utils.URIBuilder;
import org.apache.http.impl.client.HttpClients;
import org.apache.http.util.EntityUtils;

public class JavaSample 
{
    public static void main(String[] args) 
    {
        HttpClient httpclient = HttpClients.createDefault();

        try
        {
            URIBuilder builder = new URIBuilder("https://chinaeast2.api.cognitive.azure.cn/anomalydetector/v1.1-preview.1/multivariate/models/{modelId}/last/detect");


            URI uri = builder.build();
            HttpPost request = new HttpPost(uri);
            request.setHeader("Content-Type", "application/json");
            request.setHeader("Ocp-Apim-Subscription-Key", "{subscription key}");


            // Request body
            StringEntity reqEntity = new StringEntity("{body}");
            request.setEntity(reqEntity);

            HttpResponse response = httpclient.execute(request);
            HttpEntity entity = response.getEntity();

            if (entity != null) 
            {
                System.out.println(EntityUtils.toString(entity));
            }
        }
        catch (Exception e)
        {
            System.out.println(e.getMessage());
        }
    }
}

<!DOCTYPE html>
<html>
<head>
    <title>JSSample</title>
    <script src="http://ajax.googleapis.com/ajax/libs/jquery/1.9.0/jquery.min.js"></script>
</head>
<body>

<script type="text/javascript">
    $(function() {
        var params = {
            // Request parameters
        };
      
        $.ajax({
            url: "https://chinaeast2.api.cognitive.azure.cn/anomalydetector/v1.1-preview.1/multivariate/models/{modelId}/last/detect?" + $.param(params),
            beforeSend: function(xhrObj){
                // Request headers
                xhrObj.setRequestHeader("Content-Type","application/json");
                xhrObj.setRequestHeader("Ocp-Apim-Subscription-Key","{subscription key}");
            },
            type: "POST",
            // Request body
            data: "{body}",
        })
        .done(function(data) {
            alert("success");
        })
        .fail(function() {
            alert("error");
        });
    });
</script>
</body>
</html>
#import <Foundation/Foundation.h>

int main(int argc, const char * argv[])
{
    NSAutoreleasePool * pool = [[NSAutoreleasePool alloc] init];
    
    NSString* path = @"https://chinaeast2.api.cognitive.azure.cn/anomalydetector/v1.1-preview.1/multivariate/models/{modelId}/last/detect";
    NSArray* array = @[
                         // Request parameters
                         @"entities=true",
                      ];
    
    NSString* string = [array componentsJoinedByString:@"&"];
    path = [path stringByAppendingFormat:@"?%@", string];

    NSLog(@"%@", path);

    NSMutableURLRequest* _request = [NSMutableURLRequest requestWithURL:[NSURL URLWithString:path]];
    [_request setHTTPMethod:@"POST"];
    // Request headers
    [_request setValue:@"application/json" forHTTPHeaderField:@"Content-Type"];
    [_request setValue:@"{subscription key}" forHTTPHeaderField:@"Ocp-Apim-Subscription-Key"];
    // Request body
    [_request setHTTPBody:[@"{body}" dataUsingEncoding:NSUTF8StringEncoding]];
    
    NSURLResponse *response = nil;
    NSError *error = nil;
    NSData* _connectionData = [NSURLConnection sendSynchronousRequest:_request returningResponse:&response error:&error];

    if (nil != error)
    {
        NSLog(@"Error: %@", error);
    }
    else
    {
        NSError* error = nil;
        NSMutableDictionary* json = nil;
        NSString* dataString = [[NSString alloc] initWithData:_connectionData encoding:NSUTF8StringEncoding];
        NSLog(@"%@", dataString);
        
        if (nil != _connectionData)
        {
            json = [NSJSONSerialization JSONObjectWithData:_connectionData options:NSJSONReadingMutableContainers error:&error];
        }
        
        if (error || !json)
        {
            NSLog(@"Could not parse loaded json with error:%@", error);
        }
        
        NSLog(@"%@", json);
        _connectionData = nil;
    }
    
    [pool drain];

    return 0;
}
<?php
// This sample uses the Apache HTTP client from HTTP Components (http://hc.apache.org/httpcomponents-client-ga/)
require_once 'HTTP/Request2.php';

$request = new Http_Request2('https://chinaeast2.api.cognitive.azure.cn/anomalydetector/v1.1-preview.1/multivariate/models/{modelId}/last/detect');
$url = $request->getUrl();

$headers = array(
    // Request headers
    'Content-Type' => 'application/json',
    'Ocp-Apim-Subscription-Key' => '{subscription key}',
);

$request->setHeader($headers);

$parameters = array(
    // Request parameters
);

$url->setQueryVariables($parameters);

$request->setMethod(HTTP_Request2::METHOD_POST);

// Request body
$request->setBody("{body}");

try
{
    $response = $request->send();
    echo $response->getBody();
}
catch (HttpException $ex)
{
    echo $ex;
}

?>
########### Python 2.7 #############
import httplib, urllib, base64

headers = {
    # Request headers
    'Content-Type': 'application/json',
    'Ocp-Apim-Subscription-Key': '{subscription key}',
}

params = urllib.urlencode({
})

try:
    conn = httplib.HTTPSConnection('chinaeast2.api.cognitive.azure.cn')
    conn.request("POST", "/anomalydetector/v1.1-preview.1/multivariate/models/{modelId}/last/detect?%s" % params, "{body}", headers)
    response = conn.getresponse()
    data = response.read()
    print(data)
    conn.close()
except Exception as e:
    print("[Errno {0}] {1}".format(e.errno, e.strerror))

####################################

########### Python 3.2 #############
import http.client, urllib.request, urllib.parse, urllib.error, base64

headers = {
    # Request headers
    'Content-Type': 'application/json',
    'Ocp-Apim-Subscription-Key': '{subscription key}',
}

params = urllib.parse.urlencode({
})

try:
    conn = http.client.HTTPSConnection('chinaeast2.api.cognitive.azure.cn')
    conn.request("POST", "/anomalydetector/v1.1-preview.1/multivariate/models/{modelId}/last/detect?%s" % params, "{body}", headers)
    response = conn.getresponse()
    data = response.read()
    print(data)
    conn.close()
except Exception as e:
    print("[Errno {0}] {1}".format(e.errno, e.strerror))

####################################
require 'net/http'

uri = URI('https://chinaeast2.api.cognitive.azure.cn/anomalydetector/v1.1-preview.1/multivariate/models/{modelId}/last/detect')
uri.query = URI.encode_www_form({
})

request = Net::HTTP::Post.new(uri.request_uri)
# Request headers
request['Content-Type'] = 'application/json'
# Request headers
request['Ocp-Apim-Subscription-Key'] = '{subscription key}'
# Request body
request.body = "{body}"

response = Net::HTTP.start(uri.host, uri.port, :use_ssl => uri.scheme == 'https') do |http|
    http.request(request)
end

puts response.body