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: int

dimension_anomalies, column_anomalies, all_columns_anomalies tests: — dimensions: sql expression

volume_anomalies test: — fail_on_zero: [true | false]

all_columns_anomalies test: — column_anomalies: column monitors listexclude_prefix: stringexclude_regexp: regex

dimension_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

  - name: <model_name>
        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>