IMPORTANT: No additional bug fixes or documentation updates will be released for this version. For the latest information, see the current release documentation.
« Count functions Information Content Functions »
Elastic Docs ›Machine Learning in the Elastic Stack [7.4] ›Anomaly detection ›Function reference

Geographic functions

edit

Geographic functions

edit

The geographic functions detect anomalies in the geographic location of the input data.

The machine learning features include the following geographic function: lat_long.

You cannot create forecasts for anomaly detection jobs that contain geographic functions. You also cannot add rules with conditions to detectors that use geographic functions.

Lat_long

edit

The lat_long function detects anomalies in the geographic location of the input data.

This function supports the following properties:

  • field_name (required)
  • by_field_name (optional)
  • over_field_name (optional)
  • partition_field_name (optional)

For more information about those properties, see Detector configuration objects.

Example 1: Analyzing transactions with the lat_long function.

PUT _ml/anomaly_detectors/example1
{
  "analysis_config": {
    "detectors": [{
      "function" : "lat_long",
      "field_name" : "transactionCoordinates",
      "by_field_name" : "creditCardNumber"
    }]
  },
  "data_description": {
    "time_field":"timestamp",
    "time_format": "epoch_ms"
  }
}

If you use this lat_long function in a detector in your anomaly detection job, it detects anomalies where the geographic location of a credit card transaction is unusual for a particular customer’s credit card. An anomaly might indicate fraud.

The field_name that you supply must be a single string that contains two comma-separated numbers of the form latitude,longitude, a geo_point field, a geo_shape field that contains point values, or a geo_centroid aggregation. The latitude and longitude must be in the range -180 to 180 and represent a point on the surface of the Earth.

For example, JSON data might contain the following transaction coordinates:

{
  "time": 1460464275,
  "transactionCoordinates": "40.7,-74.0",
  "creditCardNumber": "1234123412341234"
}

In Elasticsearch, location data is likely to be stored in geo_point fields. For more information, see Geo-point datatype. This data type is supported natively in machine learning features. Specifically, datafeed when pulling data from a geo_point field, will transform the data into the appropriate lat,lon string format before sending to the anomaly detection job.

For more information, see Transforming data with script fields.

« Count functions Information Content Functions »