Tuning thresholds

As described in the overview, adjusting sensitivity and training data are the two ways for users to tune thresholds.

Sensitivity

The most common way of tuning a threshold is to change a monitor's sensitivity. All automated thresholds support low, medium (default), and high levels of sensitivity. Low sensitivity will widen thresholds, resulting in fewer anomalies. High sensitivity will narrow ("tighten") thresholds, resulting in fewer anomalies.

Training data

By adjusting training data, users can indicate which periods of time represent normal behavior for a particular metric (for example, the row count of a particular table). The anomaly detection models will then train on this data and produce thresholds, which can then be further widened or narrowed using sensitivity.

There are two key ways to manage which data is included in training models:

• Mark as normal: anomalies are automatically excluded from the set of data that trains models. When users review an alert, they can "mark as normal" for that anomaly to be added back into the set of training data. This will cause the threshold will then widen, and similar anomalies will not be alerted to in the future.
• Select training data: by interacting with the chart of a monitor, users can exclude periods of data from training models. They can also use exclusion windows to define periods of time that should be ignored for an entire warehouse, database, schema, or table. These can be one-off or set for recurring holidays.

After taking either of these actions, it can take several hours before the new thresholds are visible.