Generate your anomaly test with Elementary AI

Let our Slack chatbot create the anomaly test you need.
If your data set has a timestamp column that represents the creation time of a field, it is highly recommended configuring it as a timestamp_column.
All anomaly detection tests:
— timestamp_column: column namewhere_expression: sql expressionanomaly_sensitivity: intanomaly_direction: [both | spike | drop]ignore_small_changes:
    spike_failure_percent_threshold: int
    drop_failure_percent_threshold: intanomaly_exclude_metrics: [SQL expression]Anomaly detection tests with timestamp_column:
— training_period: int
    period: [hour | day | week | month]
    count: intdetection_period: int
    period: [hour | day | week | month]
    count: inttime_bucket:
    period: [hour | day | week | month]
    count: intseasonality: day_of_weekdetection_delay:
    period: [hour | day | week | month]
    count: intdimension_anomalies, column_anomalies, all_columns_anomalies tests:
— dimensions: sql expressionvolume_anomalies test:
— fail_on_zero: [true | false]all_columns_anomalies test:
— column_anomalies: column monitors listexclude_prefix: stringexclude_regexp: regexdimension_anomalies test:
— exclude_final_results: [SQL where expression on fields value / average]event_freshness_anomalies:
— event_timestamp_column: column nameupdate_timestamp_column: column name

Example configurations

version: 2

models:
  - name: <model_name>
    config:
      elementary:
        timestamp_column: < model timestamp column >
    tests: < here you will add elementary monitors as tests >

  - name: <your model with no timestamp>
    ## if no timestamp is configured, elementary will monitor without time filtering
    tests: <here you will add elementary monitors as tests>