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* Remove `es-test-dir` book-scoped variable * Remove `plugins-examples-dir` book-scoped variable * Remove `:dependencies-dir:` and `:xes-repo-dir:` book-scoped variables - In `index.asciidoc`, two variables (`:dependencies-dir:` and `:xes-repo-dir:`) were removed. - In `sql/index.asciidoc`, the `:sql-tests:` path was updated to fuller path - In `esql/index.asciidoc`, the `:esql-tests:` path was updated idem * Replace `es-repo-dir` with `es-ref-dir` * Move `:include-xpack: true` to few files that use it, remove from index.asciidoc
100 lines
2.9 KiB
Text
100 lines
2.9 KiB
Text
[role="xpack"]
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[[ml-forecast]]
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= Forecast jobs API
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++++
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<titleabbrev>Forecast jobs</titleabbrev>
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++++
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Predicts the future behavior of a time series by using its historical behavior.
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[[ml-forecast-request]]
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== {api-request-title}
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`POST _ml/anomaly_detectors/<job_id>/_forecast`
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[[ml-forecast-prereqs]]
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== {api-prereq-title}
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Requires the `manage_ml` cluster privilege. This privilege is included in the
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`machine_learning_admin` built-in role.
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[[ml-forecast-desc]]
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== {api-description-title}
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You can create a forecast job based on an {anomaly-job} to extrapolate future
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behavior. Refer to
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{ml-docs}/ml-ad-forecast.html[Forecasting the future]
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and
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{ml-docs}/ml-limitations.html#ml-forecast-limitations[Forecast limitations] to
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learn more.
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You can delete a forecast by using the
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<<ml-delete-forecast,Delete forecast API>>.
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[NOTE]
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===============================
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* Forecasts are not supported for jobs that perform population analysis; an
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error occurs if you try to create a forecast for a job that has an
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`over_field_name` property in its configuration.
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* The job must be open when you create a forecast. Otherwise, an error occurs.
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===============================
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[[ml-forecast-path-parms]]
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== {api-path-parms-title}
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`<job_id>`::
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(Required, string)
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include::{es-ref-dir}/ml/ml-shared.asciidoc[tag=job-id-anomaly-detection]
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[[ml-forecast-query-parms]]
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== {api-query-parms-title}
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`duration`::
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(Optional, <<time-units, time units>>) A period of time that indicates how far
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into the future to forecast. For example, `30d` corresponds to 30 days. The
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default value is 1 day. The forecast starts at the last record that was
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processed.
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`expires_in`::
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(Optional, <<time-units, time units>>) The period of time that forecast
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results are retained. After a forecast expires, the results are deleted. The
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default value is 14 days. If set to a value of `0`, the forecast is never
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automatically deleted.
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`max_model_memory`::
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(Optional, <<byte-units,byte value>>) The maximum memory the forecast can use.
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If the forecast needs to use more than the provided amount, it will spool to
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disk. Default is 20mb, maximum is 500mb and minimum is 1mb. If set to 40% or
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more of the job's configured memory limit, it is automatically reduced to
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below that amount.
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[[ml-forecast-request-body]]
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== {api-request-body-title}
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You can also specify the query parameters (such as `duration` and
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`expires_in`) in the request body.
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[[ml-forecast-example]]
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== {api-examples-title}
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[source,console]
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--------------------------------------------------
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POST _ml/anomaly_detectors/low_request_rate/_forecast
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{
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"duration": "10d"
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}
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--------------------------------------------------
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// TEST[skip:requires delay]
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When the forecast is created, you receive the following results:
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[source,js]
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----
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{
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"acknowledged": true,
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"forecast_id": "wkCWa2IB2lF8nSE_TzZo"
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}
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----
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// NOTCONSOLE
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You can subsequently see the forecast in the *Single Metric Viewer* in {kib}.
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