[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:
@@ -41,15 +42,14 @@ learning analysis is still possible, however, by using APIs to create and manage
 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[]