> ## Documentation Index > Fetch the complete documentation index at: https://docs.elementary-data.com/llms.txt > Use this file to discover all available pages before exploring further. # freshness_anomalies Let our Slack chatbot create the anomaly test you need. `elementary.freshness_anomalies` Monitors the freshness of your table over time, as the expected time between data updates. Upon running the test, your data is split into time buckets (daily by default, configurable with the `time bucket` field), and then we compute the maximum time without updates (in seconds) as the freshness value per bucket for the last `training_period` (by default 14 days). The test then compares the freshness of each bucket within the detection period (last 2 days by default, controlled by the `detection_period` var), and compares it to the freshness of the previous time buckets. If there were any anomalies during the detection period, the test will fail. ### Test configuration *Required configuration: `timestamp_column`* *Default configuration: `anomaly_direction: spike` to alert only on delays.*

  
    data\_tests:
      -- elementary.freshness\_anomalies:
        arguments:
          timestamp\_column: column name
          where\_expression: sql expression
          anomaly\_sensitivity: int
          detection\_period:
            period: \[hour | day | week | month]
            count: int
          training\_period:
            period: \[hour | day | week | month]
            count: int
          time\_bucket:
            period: \[hour | day | week | month]
            count: int
          detection\_delay:
            period: \[hour | day | week | month]
            count: int
          ignore\_small\_changes:
            spike\_failure\_percent\_threshold: int
            drop\_failure\_percent\_threshold: int
          anomaly\_exclude\_metrics: \[SQL expression]

```yml Models theme={null} models: - name: < model name > data_tests: - elementary.freshness_anomalies: arguments: timestamp_column: < timestamp column > # Mandatory where_expression: < sql expression > time_bucket: # Daily by default period: < time period > count: < number of periods > ``` ```yml Models example theme={null} models: - name: login_events data_tests: - elementary.freshness_anomalies: arguments: timestamp_column: "updated_at" config: # optional - use tags to run elementary tests on a dedicated run tags: ["elementary"] # optional - change severity severity: warn ```