In the original implementation another threshold is used to detect unusually low errors.
The intuition is that unusually low errors are anomalous as well, as the prediction is more accurate than on average. This is done by 'flipping' the errors 'around the mean' and applying the existing threshold function again.
However, this improves the score just slightly, so we could make it a boolean parameter for the find_anomalies primitive whether to use the low-error threshold or not and therefore making it optional to use.
In the original implementation another threshold is used to detect unusually low errors.
The intuition is that unusually low errors are anomalous as well, as the prediction is more accurate than on average. This is done by 'flipping' the errors 'around the mean' and applying the existing threshold function again.
However, this improves the score just slightly, so we could make it a boolean parameter for the find_anomalies primitive whether to use the low-error threshold or not and therefore making it optional to use.