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This is an earlier version of the OpenSearch documentation. For the latest version, see the current documentation. For information about OpenSearch version maintenance, see Release Schedule and Maintenance Policy.

Anomaly result mapping

If you enabled custom result index, the anomaly detection plugin stores the results in your own index.

If the anomaly detector doesn’t detect an anomaly, the result has the following format:

{
  "detector_id": "kzcZ43wBgEQAbjDnhzGF",
  "schema_version": 5,
  "data_start_time": 1635898161367,
  "data_end_time": 1635898221367,
  "feature_data": [
    {
      "feature_id": "processing_bytes_max",
      "feature_name": "processing bytes max",
      "data": 2322
    },
    {
      "feature_id": "processing_bytes_avg",
      "feature_name": "processing bytes avg",
      "data": 1718.6666666666667
    },
    {
      "feature_id": "processing_bytes_min",
      "feature_name": "processing bytes min",
      "data": 1375
    },
    {
      "feature_id": "processing_bytes_sum",
      "feature_name": "processing bytes sum",
      "data": 5156
    },
    {
      "feature_id": "processing_time_max",
      "feature_name": "processing time max",
      "data": 31198
    }
  ],
  "execution_start_time": 1635898231577,
  "execution_end_time": 1635898231622,
  "anomaly_score": 1.8124904404395776,
  "anomaly_grade": 0,
  "confidence": 0.9802940756605277,
  "entity": [
    {
      "name": "process_name",
      "value": "process_3"
    }
  ],
  "model_id": "kzcZ43wBgEQAbjDnhzGF_entity_process_3",
  "threshold": 1.2368549346675202
}

Response body fields

Field Description
detector_id A unique ID for identifying a detector.
schema_version The mapping version of the result index.
data_start_time The start of the detection range of the aggregated data.
data_end_time The end of the detection range of the aggregated data.
feature_data An array of the aggregated data points between the data_start_time and data_end_time.
execution_start_time The actual start time of the detector for a specific run that produces the anomaly result. This start time includes the window delay parameter that you can set to delay data collection. Window delay is the difference between the execution_start_time and data_start_time.
execution_end_time The actual end time of the detector for a specific run that produces the anomaly result.
anomaly_score Indicates relative severity of an anomaly. The higher the score, the more anomalous a data point is.
anomaly_grade A normalized version of the anomaly_score on a scale between 0 and 1.
confidence The probability of the accuracy of the anomaly_score. The closer this number is to 1, the higher the accuracy. During the probation period of a running detector, the confidence is low (< 0.9) because of its exposure to limited data.
entity An entity is a combination of specific category fields’ values. It includes the name and value of the category field. In the previous example, process_name is the category field and one of the processes such as process_3 is the field’s value. The entity field is only present for a high-cardinality detector (where you’ve selected a category field).
model_id A unique ID that identifies a model. If a detector is a single-stream detector (with no category field), it has only one model. If a detector is a high-cardinality detector (with one or more category fields), it might have multiple models, one for each entity.
threshold One of the criteria for a detector to classify a data point as an anomaly is that its anomaly_score must surpass a dynamic threshold. This field records the current threshold.

If an anomaly detector detects an anomaly, the result has the following format:

{
  "detector_id": "fylE53wBc9MCt6q12tKp",
  "schema_version": 0,
  "data_start_time": 1635927900000,
  "data_end_time": 1635927960000,
  "feature_data": [
    {
      "feature_id": "processing_bytes_max",
      "feature_name": "processing bytes max",
      "data": 2291
    },
    {
      "feature_id": "processing_bytes_avg",
      "feature_name": "processing bytes avg",
      "data": 1677.3333333333333
    },
    {
      "feature_id": "processing_bytes_min",
      "feature_name": "processing bytes min",
      "data": 1054
    },
    {
      "feature_id": "processing_bytes_sum",
      "feature_name": "processing bytes sum",
      "data": 5032
    },
    {
      "feature_id": "processing_time_max",
      "feature_name": "processing time max",
      "data": 11422
    }
  ],
  "anomaly_score": 1.1986675882872033,
  "anomaly_grade": 0.26806225550178464,
  "confidence": 0.9607519742565531,
  "entity": [
    {
      "name": "process_name",
      "value": "process_3"
    }
  ],
  "approx_anomaly_start_time": 1635927900000,
  "relevant_attribution": [
    {
      "feature_id": "processing_bytes_max",
      "data": 0.03628638020431366
    },
    {
      "feature_id": "processing_bytes_avg",
      "data": 0.03384479053991436
    },
    {
      "feature_id": "processing_bytes_min",
      "data": 0.058812549572819096
    },
    {
      "feature_id": "processing_bytes_sum",
      "data": 0.10154576265526988
    },
    {
      "feature_id": "processing_time_max",
      "data": 0.7695105170276828
    }
  ],
  "expected_values": [
    {
      "likelihood": 1,
      "value_list": [
        {
          "feature_id": "processing_bytes_max",
          "data": 2291
        },
        {
          "feature_id": "processing_bytes_avg",
          "data": 1677.3333333333333
        },
        {
          "feature_id": "processing_bytes_min",
          "data": 1054
        },
        {
          "feature_id": "processing_bytes_sum",
          "data": 6062
        },
        {
          "feature_id": "processing_time_max",
          "data": 23379
        }
      ]
    }
  ],
  "threshold": 1.0993584705913992,
  "execution_end_time": 1635898427895,
  "execution_start_time": 1635898427803
}

You can see the following additional fields:

Field Description
relevant_attribution Represents the contribution of each input variable. The sum of the attributions is normalized to 1.
expected_values The expected value for each feature.

At times, the detector might detect an anomaly late. Let’s say the detector sees a random mix of the triples {1, 2, 3} and {2, 4, 5} that correspond to slow weeks and busy weeks, respectively. For example 1, 2, 3, 1, 2, 3, 2, 4, 5, 1, 2, 3, 2, 4, 5, … and so on. If the detector comes across a pattern {2, 2, X} and it’s yet to see X, the detector infers that the pattern is anomalous, but it can’t determine at this point which of the 2’s is the cause. If X = 3, then the detector knows it’s the first 2 in that unfinished triple, and if X = 5, then it’s the second 2. If it’s the first 2, then the detector detects the anomaly late.

If a detector detects an anomaly late, the result has the following additional fields:

Field Description
past_values The actual input that triggered an anomaly. If past_values is null, the attributions or expected values are from the current input. If past_values is not null, the attributions or expected values are from a past input (for example, the previous two steps of the data [1,2,3]).
approx_anomaly_start_time The approximate time of the actual input that triggers an anomaly. This field helps you understand when a detector flags an anomaly. Both single-stream and high-cardinality detectors don’t query previous anomaly results because these queries are expensive operations. The cost is especially high for high-cardinality detectors that might have a lot of entities. If the data is not continuous, the accuracy of this field is low and the actual time that the detector detects an anomaly can be earlier.
{
  "detector_id": "kzcZ43wBgEQAbjDnhzGF",
  "confidence": 0.9746820962328963,
  "relevant_attribution": [
    {
      "feature_id": "deny_max1",
      "data": 0.07339452532666227
    },
    {
      "feature_id": "deny_avg",
      "data": 0.04934972719948845
    },
    {
      "feature_id": "deny_min",
      "data": 0.01803003656061806
    },
    {
      "feature_id": "deny_sum",
      "data": 0.14804918212089874
    },
    {
      "feature_id": "accept_max5",
      "data": 0.7111765287923325
    }
  ],
  "task_id": "9Dck43wBgEQAbjDn4zEe",
  "threshold": 1,
  "model_id": "kzcZ43wBgEQAbjDnhzGF_entity_app_0",
  "schema_version": 5,
  "anomaly_score": 1.141419389056506,
  "execution_start_time": 1635898427803,
  "past_values": [
    {
      "feature_id": "processing_bytes_max",
      "data": 905
    },
    {
      "feature_id": "processing_bytes_avg",
      "data": 479
    },
    {
      "feature_id": "processing_bytes_min",
      "data": 128
    },
    {
      "feature_id": "processing_bytes_sum",
      "data": 1437
    },
    {
      "feature_id": "processing_time_max",
      "data": 8440
    }
  ],
  "data_end_time": 1635883920000,
  "data_start_time": 1635883860000,
  "feature_data": [
    {
      "feature_id": "processing_bytes_max",
      "feature_name": "processing bytes max",
      "data": 1360
    },
    {
      "feature_id": "processing_bytes_avg",
      "feature_name": "processing bytes avg",
      "data": 990
    },
    {
      "feature_id": "processing_bytes_min",
      "feature_name": "processing bytes min",
      "data": 608
    },
    {
      "feature_id": "processing_bytes_sum",
      "feature_name": "processing bytes sum",
      "data": 2970
    },
    {
      "feature_id": "processing_time_max",
      "feature_name": "processing time max",
      "data": 9670
    }
  ],
  "expected_values": [
    {
      "likelihood": 1,
      "value_list": [
        {
          "feature_id": "processing_bytes_max",
          "data": 905
        },
        {
          "feature_id": "processing_bytes_avg",
          "data": 479
        },
        {
          "feature_id": "processing_bytes_min",
          "data": 128
        },
        {
          "feature_id": "processing_bytes_sum",
          "data": 4847
        },
        {
          "feature_id": "processing_time_max",
          "data": 15713
        }
      ]
    }
  ],
  "execution_end_time": 1635898427895,
  "anomaly_grade": 0.5514172746375128,
  "entity": [
    {
      "name": "process_name",
      "value": "process_3"
    }
  ],
  "approx_anomaly_start_time": 1635883620000
}

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