[[ml-gs-forecast]]
=== Creating Forecasts

In addition to detecting anomalous behavior in your data, you can use
{ml} to predict future behavior. For more information, see <<ml-forecasting>>.

To create a forecast in {kib}:

. Go to the **Single Metric Viewer** and select one of the jobs that you created
in this tutorial. For example, select the `total-requests` job. 

. Click **Forecast**. +
+
--
[role="screenshot"]
image::images/ml-gs-forecast.jpg["Create a forecast from the Single Metric Viewer"]
--

. Specify a duration for your forecast. This value indicates how far to
extrapolate beyond the last record that was processed. You must use time units,
such as `30d` for 30 days.  For more information, see
{ref}/common-options.html#time-units[Time Units]. In this example, we use a
duration of 1 week: +
+
--
[role="screenshot"]
image::images/ml-gs-duration.jpg["Specify a duration of 1w"]
--

. View the forecast in the **Single Metric Viewer**: +
+
--
[role="screenshot"]
image::images/ml-gs-forecast-results.jpg["View a forecast from the Single Metric Viewer"]

The yellow line in the chart represents the predicted data values. The shaded
yellow area represents the bounds for the predicted values, which also gives an
indication of the confidence of the predictions. Note that the bounds generally
increase with time (that is to say, the confidence levels decrease), since you
are forecasting further into the future. Eventually if the confidence levels are
too low, the forecast stops.
--

. Optional: Compare the forecast to actual data. +
+
--
You can try this with the sample data by choosing a subset of the data when you
create the job, as described in <<ml-gs-jobs>>. Create the forecast then process
the remaining data, as described in <<ml-gs-job1-datafeed>>.
--

.. After you restart the {dfeed}, re-open the forecast by selecting the job in
the **Single Metric Viewer**, clicking **Forecast**, and selecting your forecast
from the list. For example: +
+
--
[role="screenshot"]
image::images/ml-gs-forecast-open.jpg["Open a forecast in the Single Metric Viewer"]
--

.. View the forecast and actual data in the **Single Metric Viewer**: +
+
--
[role="screenshot"]
image::images/ml-gs-forecast-actual.jpg["View a forecast over actual data in the Single Metric Viewer"]

The chart contains the actual data values, the bounds for the expected values,
the anomalies, the forecast data values, and the bounds for the forecast. This
combination of actual and forecast data gives you an indication of how well the
{xpack} {ml} features can extrapolate the future behavior of the data.
--

Now that you have seen how easy it is to create forecasts with the sample data,
consider what type of events you might want to predict in your own data. For
more information and ideas, as well as a list of limitations related to
forecasts, see <<ml-forecasting>>.