[DOCS] Add forecasting to ML tutorial (elastic/x-pack-elasticsearch#3489)

Original commit: elastic/x-pack-elasticsearch@ffb681ae96

docs/en/ml/getting-started-forecast.asciidoc

@@ -0,0 +1,76 @@
+[[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>>.

docs/en/ml/getting-started-next.asciidoc

@@ -5,7 +5,8 @@ By completing this tutorial, you've learned how you can detect anomalous
 behavior in a simple set of sample data. You created single and multi-metric
 jobs in {kib}, which creates and opens jobs and creates and starts {dfeeds} for
 you under the covers. You examined the results of the {ml} analysis in the
-**Single Metric Viewer** and **Anomaly Explorer** in {kib}.
+**Single Metric Viewer** and **Anomaly Explorer** in {kib}. You also
+extrapolated the future behavior of a job by creating a forecast.
 
 If you want to learn about advanced job options, you might be interested in
 the following video tutorial:
@@ -38,18 +39,17 @@ insights.
 that retrieves data in real time.  If your data is outside of {es}, you
 cannot use {kib} to create your jobs and you cannot use {dfeeds}. Machine
 learning analysis is still possible, however, by using APIs to create and manage
-jobs and to post data to them. 
+jobs and to post data to them.
 
 Once you have decided which data to analyze, you can start considering which
-analysis functions you want to use. For more information, see  <<ml-functions>>.
+analysis functions you want to use. For more information, see <<ml-functions>>.
 
 In general, it is a good idea to start with single metric jobs for your
 key performance indicators. After you examine these simple analysis results,
 you will have a better idea of what the influencers might be. You can create
 multi-metric jobs and split the data or create more complex analysis functions
-as necessary.
-//TO)DO: Add link to configuration section: For examples of
-//more complicated configuration options, see <<>>.
+as necessary. For examples of more complicated configuration options, see
+<<ml-configuring>>.
 
 If you encounter problems, we're here to help. See <<xpack-help>> and
 <<ml-troubleshooting>>.

docs/en/ml/getting-started.asciidoc

@@ -78,4 +78,5 @@ include::getting-started-data.asciidoc[]
 include::getting-started-wizards.asciidoc[]
 include::getting-started-single.asciidoc[]
 include::getting-started-multi.asciidoc[]
+include::getting-started-forecast.asciidoc[]
 include::getting-started-next.asciidoc[]