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
— time_bucket:
    period: [hour | day | week | month]
    count: int
— seasonality: day_of_week
— detection_delay:
    period: [hour | day | week | month]
    count: int
volume_anomalies test:
— fail_on_zero: [true | false]
all_columns_anomalies test:
— column_anomalies: column monitors list
— exclude_prefix: string
— exclude_regexp: regex
dimension_anomalies test:
— dimensions: sql expression
event_freshness_anomalies:
— event_timestamp_column: column name
— update_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>
Detection methodtimestamp_column