Sometimes there are periods when you expect unusual activity to take place, such as bank holidays, "Black Friday", or planned system outages. If you identify these events in advance, no anomalies are generated during that period. The machine learning model is not ill-affected and you do not receive spurious results.
You can create calendars and scheduled events in the Settings pane on the Machine Learning page in Kibana or by using Machine learning anomaly detection APIs.
A scheduled event must have a start time, end time, and description. In general, scheduled events are short in duration (typically lasting from a few hours to a day) and occur infrequently. If you have regularly occurring events, such as weekly maintenance periods, you do not need to create scheduled events for these circumstances; they are already handled by the machine learning analytics.
You can identify zero or more scheduled events in a calendar. Anomaly detection jobs can then subscribe to calendars and the machine learning analytics handle all subsequent scheduled events appropriately.
If you want to add multiple scheduled events at once, you can import an
.ics) file in Kibana or a JSON file in the
add events to calendar API.
- You must identify scheduled events before your anomaly detection job analyzes the data for that time period. Machine learning results are not updated retroactively.
- If your iCalendar file contains recurring events, only the first occurrence is imported.
- Bucket results are generated during scheduled events but they have an anomaly score of zero. For more information about bucket results, see Results resources.
- If you use long or frequent scheduled events, it might take longer for the machine learning analytics to learn to model your data and some anomalous behavior might be missed.