Skip to main content

Documentation Index

Fetch the complete documentation index at: https://elementary-devin-1774320697-fabric-cloud-docs.mintlify.app/llms.txt

Use this file to discover all available pages before exploring further.

Generate your anomaly test with Elementary AI

Let our Slack chatbot create the anomaly test you need.
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
models:
  - name: this_is_a_model
    data_tests:
      - elementary.volume_anomalies:
          arguments:
            ignore_small_changes:
              spike_failure_percent_threshold: 2
              drop_failure_percent_threshold: 50