The machine learning features use the concept of a bucket to divide the time series into batches for processing.
The bucket span is part of the configuration information for a job. It defines the time interval that is used to summarize and model the data. This is typically between 5 minutes to 1 hour and it depends on your data characteristics. When you set the bucket span, take into account the granularity at which you want to analyze, the frequency of the input data, the typical duration of the anomalies, and the frequency at which alerting is required.
When you view your machine learning results, each bucket has an anomaly score. This score is a statistically aggregated and normalized view of the combined anomalousness of all the record results in the bucket. If you have more than one job, you can also obtain overall bucket results, which combine and correlate anomalies from multiple jobs into an overall score. When you view the results for jobs groups in Kibana, it provides the overall bucket scores.