Predict future behavior of a time series Generally available

POST /_ml/anomaly_detectors/{job_id}/_forecast
Api key auth

Forecasts are not supported for jobs that perform population analysis; an error occurs if you try to create a forecast for a job that has an over_field_name in its configuration. Forcasts predict future behavior based on historical data.

Required authorization

  • Cluster privileges: manage_ml

Path parameters

  • job_id string Required

    Identifier for the anomaly detection job. The job must be open when you create a forecast; otherwise, an error occurs.

Query parameters

  • duration string

    A period of time that indicates how far into the future to forecast. For example, 30d corresponds to 30 days. The forecast starts at the last record that was processed.

    Default value is 1d.

  • expires_in string

    The period of time that forecast results are retained. After a forecast expires, the results are deleted. If set to a value of 0, the forecast is never automatically deleted.

    Default value is 14d.

  • max_model_memory string

    The maximum memory the forecast can use. If the forecast needs to use more than the provided amount, it will spool to disk. Default is 20mb, maximum is 500mb and minimum is 1mb. If set to 40% or more of the job’s configured memory limit, it is automatically reduced to below that amount.

    Default value is 20mb.

application/json

Body

  • duration string

    Refer to the description for the duration query parameter.

    External documentation
  • expires_in string

    Refer to the description for the expires_in query parameter.

    External documentation
  • max_model_memory string

    Refer to the description for the max_model_memory query parameter.

    Default value is 20mb.

Responses

  • 200 application/json
    Hide response attributes Show response attributes object
    • acknowledged boolean Required
    • forecast_id string Required
POST /_ml/anomaly_detectors/{job_id}/_forecast 
POST _ml/anomaly_detectors/low_request_rate/_forecast
{
  "duration": "10d"
}
resp = client.ml.forecast(
    job_id="low_request_rate",
    duration="10d",
)
const response = await client.ml.forecast({
  job_id: "low_request_rate",
  duration: "10d",
});
response = client.ml.forecast(
  job_id: "low_request_rate",
  body: {
    "duration": "10d"
  }
)
$resp = $client->ml()->forecast([
    "job_id" => "low_request_rate",
    "body" => [
        "duration" => "10d",
    ],
]);
curl -X POST -H "Authorization: ApiKey $ELASTIC_API_KEY" -H "Content-Type: application/json" -d '{"duration":"10d"}' "$ELASTICSEARCH_URL/_ml/anomaly_detectors/low_request_rate/_forecast"
var response = await client.MachineLearning
    .ForecastAsync(jobId: "low_request_rate", d1 => d1
        .Duration("10d")
    );
client.ml().forecast(f -> f
    .duration(d -> d
        .time("10d")
    )
    .jobId("low_request_rate")
);
Request example
An example body for a `POST _ml/anomaly_detectors/low_request_rate/_forecast` request. 
{
  "duration": "10d"
}