Anomaly tests params
ignore_small_changes
ignore_small_changes:
spike_failure_percent_threshold: [int]
drop_failure_percent_threshold: [int]
If defined, an anomaly test will fail only if all the following conditions hold:
- The z-score of the metric within the detection period is anomoulous
- One of the following holds:
- The metric within the detection period is higher than
spike_failure_percent_threshold
percentages of the mean value in the training period, if defined. - The metric within the detection period is lower than
drop_failure_percent_threshold
percentages of the mean value in the training period, if defined
- The metric within the detection period is higher than
Those settings can help to deal with situations where your metrics are stable and small changes causes to high z-scores, and therefore to anomaly.
If undefined, default is null for both spike and drop.
- Default: none
- Relevant tests: All anomaly detection tests