- name: this_is_a_model
      - elementary.volume_anomalies:
          anomaly_exclude_metrics: metric_value < 10

      - elementary.all_columns_anomalies:
            - null_count
            - missing_count
            - zero_count
          anomaly_exclude_metrics: metric_time_bucket >= '2023-10-01 06:00:00' and metric_time_bucket <= '2023-10-01 07:00:00'

anomaly_exclude_metrics: [SQL where expression on fields metric_date / metric_time_bucket / metric_value]

By default, data points are compared to the all data points in the training set. Using this param, you can exclude metrics from the training set, to improve the test accuracy.

The filter can be configured using an SQL where expression syntax, and the following fields:

  1. metric_date - The date of the relevant bucket (even if the bucket is not daily).
  2. metric_time_bucket - The exact time bucket.
  3. metric_value - The value of the metric.
  • Supported values: valid SQL where expression on the columns metric_date / metric_time_bucket / metric_value
  • Relevant tests: All anomaly detection tests