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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 name
— where_expression: sql expression
— anomaly_sensitivity: int
— anomaly_direction: [both | spike | drop]
— ignore_small_changes:
    spike_failure_percent_threshold: int
    drop_failure_percent_threshold: int
— anomaly_exclude_metrics: [SQL expression]Anomaly detection tests with timestamp_column:
— training_period: int
    period: [hour | day | week | month]
    count: int
— detection_period: int
    period: [hour | day | week | month]
    count: int
— exclude_detection_period_from_training: [true | false]
— time_bucket:
    period: [hour | day | week | month]
    count: int
— seasonality: day_of_week
— detection_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 list
— exclude_prefix: string
— exclude_regexp: regexdimension_anomalies test:
— exclude_final_results: [SQL where expression on fields value / average]event_freshness_anomalies:
— event_timestamp_column: column name
— update_timestamp_column: column name

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Example configurations