anchor.diff:  indicated in blue. An interval with a high anomaly score is significant and
   requires investigation.
   
  -Slide the time selector to a section of the time series that contains a red data
  +Slide the time selector to a section of the time series that contains a red anomaly data
   point. If you hover over the point, you can see more information about that
   data point. You can also see details in the **Anomalies** section of the viewer.
   For example:
  
positive.code: TIP: The sample data sets include summarized data. For example, the `total`
  value is a sum of the requests that were received by a specific service at a
  particular time. If your data is stored in Elasticsearch, you can generate
  this type of sum or average by using search queries. One of the benefits of
  summarizing data this way is that Elasticsearch automatically distributes
  these calculations across your cluster. You can then feed this summarized data
  into the {xpack} {ml} features instead of raw results, which reduces the volume
  of data that must be considered while detecting anomalies. For the purposes of
  this tutorial, however, these summary values are provided directly in the JSON
  source files. They are not generated by Elasticsearch queries.
  
  Before you load the data set, you need to set up {ref}/mapping.html[_mappings_]
  for the fields. Mappings divide the documents in the index into logical groups
  
negative.code: * Create a {ml} job
  * Use the results to identify possible anomalies in the data
  
  At the end of this tutorial, you should have a good idea of what {ml} is and
  will hopefully be inspired to use it to detect anomalies in your own data.
  
  