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 exclude_detection_period_from_training: true | false When the detection period spans multiple values, there can be overlap between the training period and the detection period. By default, values in the detection period are included in the training calculation, which can lead to false negatives because the detection period values influence the expected range used to evaluate those same values. Setting exclude_detection_period_from_training: true ensures that no values from the detection period are used in the training calculation, preventing this overlap and improving anomaly detection accuracy. Example use case: When detection_period is set to more than 1 time bucket (e.g., detection_period: 7 days), the detection period overlaps with the training period. Without excluding the detection period from training, values being evaluated for anomalies are also contributing to the expected range calculation, which can mask actual anomalies and result in false negatives.
  • Default: false
  • Supported values: true, false
  • Relevant tests: Anomaly detection tests with timestamp_column and detection_period greater than 1 time bucket

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How it works?

  • When exclude_detection_period_from_training: false (default), all values within both the training period and detection period are used to calculate the expected range.
  • When exclude_detection_period_from_training: true, values within the detection period are excluded from the training calculation, ensuring the expected range is based solely on historical data that is not being evaluated.
models:
  - name: this_is_a_model
    data_tests:
      - elementary.volume_anomalies:
          arguments:
            exclude_detection_period_from_training: true