In many countries around the world, a time shift related to Daylight Saving Time (DST) causes users to change their behavior with respect to UTC. While we continue to improve the machine learning algorithms associated with detecting these shifts, it still takes some time to gather evidence that supports the time shift hypothesis. The anomaly detection job may take a few days to adjust to the change. During this time, anomaly detection may produce false positive or false negative alerts.
Enhancement
We introduce a new detection rule that enforces a time shift in the anomaly model, directly overriding the training and hypothesis testing functionality.
Problem
In many countries around the world, a time shift related to Daylight Saving Time (DST) causes users to change their behavior with respect to UTC. While we continue to improve the machine learning algorithms associated with detecting these shifts, it still takes some time to gather evidence that supports the time shift hypothesis. The anomaly detection job may take a few days to adjust to the change. During this time, anomaly detection may produce false positive or false negative alerts.
Enhancement
We introduce a new detection rule that enforces a time shift in the anomaly model, directly overriding the training and hypothesis testing functionality.