[role="xpack"] [[ml-forecast]] = Forecast jobs API ++++ Forecast jobs ++++ Predicts the future behavior of a time series by using its historical behavior. [[ml-forecast-request]] == {api-request-title} `POST _ml/anomaly_detectors//_forecast` [[ml-forecast-prereqs]] == {api-prereq-title} Requires the `manage_ml` cluster privilege. This privilege is included in the `machine_learning_admin` built-in role. [[ml-forecast-desc]] == {api-description-title} You can create a forecast job based on an {anomaly-job} to extrapolate future behavior. Refer to {ml-docs}/ml-ad-forecast.html[Forecasting the future] and {ml-docs}/ml-limitations.html#ml-forecast-limitations[Forecast limitations] to learn more. You can delete a forecast by using the <>. [NOTE] =============================== * 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` property in its configuration. * The job must be open when you create a forecast. Otherwise, an error occurs. =============================== [[ml-forecast-path-parms]] == {api-path-parms-title} ``:: (Required, string) include::{es-ref-dir}/ml/ml-shared.asciidoc[tag=job-id-anomaly-detection] [[ml-forecast-query-parms]] == {api-query-parms-title} `duration`:: (Optional, <>) A period of time that indicates how far into the future to forecast. For example, `30d` corresponds to 30 days. The default value is 1 day. The forecast starts at the last record that was processed. `expires_in`:: (Optional, <>) The period of time that forecast results are retained. After a forecast expires, the results are deleted. The default value is 14 days. If set to a value of `0`, the forecast is never automatically deleted. `max_model_memory`:: (Optional, <>) 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. [[ml-forecast-request-body]] == {api-request-body-title} You can also specify the query parameters (such as `duration` and `expires_in`) in the request body. [[ml-forecast-example]] == {api-examples-title} [source,console] -------------------------------------------------- POST _ml/anomaly_detectors/low_request_rate/_forecast { "duration": "10d" } -------------------------------------------------- // TEST[skip:requires delay] When the forecast is created, you receive the following results: [source,js] ---- { "acknowledged": true, "forecast_id": "wkCWa2IB2lF8nSE_TzZo" } ---- // NOTCONSOLE You can subsequently see the forecast in the *Single Metric Viewer* in {kib}.