models:
  - name: this_is_a_model
    tests:
      ignore_small_changes:
        spike_failure_percent_threshold: 2
        drop_failure_percent_threshold: 50

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

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
update_timestamp_columnfail_on_zero