Add new alarm logics

[afraniomelo]

How we are today

BibMon monitors a specific metric called Squared Prediction Error (SPE). Various alarm logics can be used to detect specific variations in this metric. Traditionally, an alarm is triggered whenever a new SPE value exceeds a predefined limit, identifying it as an outlier SPE point. To reduce false alarms, a count of outliers within a specified window size can be implemented. These two functionalities are currently implemented in the detecOutlier.py method from the _alarms.py file.

Proposed enhancement

We propose adding new alarm logics to enhance BibMon's monitoring capabilities. These new logics can be implemented as functions in the _alarms.py file. For more details, please refer to the contributing guide.

Examples of new alarm logics

1. Other types of deviations: Alarms that monitor specific types of deviations, such as drift or bias.
2. Nelson Rules: Alarms inspired on Nelson rules, which are typically applied in univariate statistical process control. Some of these rules might be useful for scenarios where BibMon is applied.