Hi,
The slight difference in scores between the timeline and the anomalies table in the Anomaly Explorer is down to the components using different result types.
For each machine learning job, different types of results are generated - bucket results, influencer results and record results. The 'Overall' anomaly timeline uses the bucket-level anomaly scores. The 'view by' swimlane below, and the list of Top Influencers on the left use the influencer-level anomaly scores. The table of anomalies at the bottom uses the record-level anomaly scores.
The bucket level results provide the top-level view of the job, the influencer results show which entities were anomalous, whilst the record results provide details on the individual anaomlies.
More information on the Anomaly Explorer can be found in the docs here, whilst this page explains the different types of machine learning results.
Hope this helps.
Pete