Hello! I'd be happy to help you with configuring column anomalies in Elementary Data. Based on the knowledge sources, there are several aspects to consider when setting up column anomalies tests. Let me provide some guidance: ## Basic Configuration For a basic column anomalies test, you can use this structure:

yaml - name: your_column_name   tests:     - elementary.column_anomalies:         column_anomalies:           - sum  # or other metrics like average, min, max, etc.         timestamp_column: your_timestamp_column

## Common Configuration Options 1. Timestamp Column: This is crucial for time-based anomaly detection - Can be set at model level:

yaml    config:      elementary:        timestamp_column: 'your_timestamp_column'

- Or at test level as shown above 2. Time Bucket: Define how to group your data

yaml    time_bucket:      period: day  # can be hour, day, month, etc.      count: 1

3. Training and Detection Periods: Control how much historical data to use

yaml    detection_period:      period: month      count: 1    training_period:      period: month      count: 6

4. Anomaly Sensitivity: Adjust how sensitive the test is (higher = less sensitive)

yaml    anomaly_sensitivity: 3  # default is 3

5. Test Severity: Make tests warn instead of fail

yaml    config:      severity: warn

## Common Issues 1. "Not enough data": Ensure you have sufficient historical data for the configured periods 2. Incorrect data types: Make sure your timestamp column is actually a timestamp type 3. Version compatibility: Some parameters require newer versions of Elementary If you could share more details about your specific issue or configuration, I could provide more targeted help! Is there a specific aspect of column anomalies configuration you're struggling with? [Slack forum discussion on column anomalies configuration](https://slack.com/archives/C02CTC89LAX/p1706039576189589) [Slack forum discussion on column anomalies test parameters](https://slack.com/archives/C02CTC89LAX/p1737832587221769)