models:
  - name: this_is_a_model
    tests:
      - elementary.volume_anomalies:
          time_bucket:
            period: day
            count: 2

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time_bucket:
  period: < time period > # supported periods: hour, day, week, month
  count: < number of periods >

This configuration controls the duration of the time buckets.

To calculate how data changes over time and detect issues, we split the data into consistent time buckets. For example, if we use daily (period=day, count=1) time bucket and monitor for row count anomalies, we will count new rows per day.

Depending on the nature of your data, it may make sense to modify this parameter. For example, if you want to detect volume anomalies in an hourly resolution, you should set the time bucket to period=hour and count=1.

  • Default: daily buckets. time_bucket: {period: day, count: 1}
  • Relevant tests: Anomaly detection tests with timestamp_column

time_bucket change impact

models:
  - name: this_is_a_model
    tests:
      - elementary.volume_anomalies:
          time_bucket:
            period: day
            count: 2

​
How it works?

  • The training_period and detection_period of the test might be extended to ensure full time buckets (for example, full week Sunday-Saturday).
  • Weekly buckets start at the day that is configured as week start on the data warehouse.
models:
  - name: this_is_a_model
    tests:
      - elementary.volume_anomalies:
          time_bucket:
            period: day
            count: 2

Generate your anomaly test with Elementary AI

Let our Slack chatbot create the anomaly test you need.

 
time_bucket:
  period: < time period > # supported periods: hour, day, week, month
  count: < number of periods >

This configuration controls the duration of the time buckets.

To calculate how data changes over time and detect issues, we split the data into consistent time buckets. For example, if we use daily (period=day, count=1) time bucket and monitor for row count anomalies, we will count new rows per day.

Depending on the nature of your data, it may make sense to modify this parameter. For example, if you want to detect volume anomalies in an hourly resolution, you should set the time bucket to period=hour and count=1.

  • Default: daily buckets. time_bucket: {period: day, count: 1}
  • Relevant tests: Anomaly detection tests with timestamp_column

time_bucket change impact

models:
  - name: this_is_a_model
    tests:
      - elementary.volume_anomalies:
          time_bucket:
            period: day
            count: 2

​
How it works?

  • The training_period and detection_period of the test might be extended to ensure full time buckets (for example, full week Sunday-Saturday).
  • Weekly buckets start at the day that is configured as week start on the data warehouse.