days_back
days_back: [int]
The maximal timeframe for which the test will collect data. This timeframe includes the training period and detection period.
- Default: 14
- Relevant tests: Anomaly detection tests with
timestamp_column

days_back change impact
models:
- name: this_is_a_model
tests:
- elementary.volume_anomalies:
days_back: 30
How it works?
The days_back
param only works for tests that have timestamp_column
configuration.
It works differently according to the table materialization:
- Regular tables and views - The values of the full
days_back
period is calculated on each run. - Incremental models and sources - The values of the full
days_back
period is calculated on the first test run, and on full refresh. The following test runs will only calculate the values of thebackfill_days
period.
Changes from default:
- Full time buckets - Elementary will increase the
days_back
automatically to insure full time buckets. For example if thetime_bucket
of the test isperiod: week
, and 14days_back
result in Tuesday, the test will collect 2 more days back to complete a week (starting on Sunday). - Seasonality training set - If seasonality is configured, Elementary will increase the
days_back
automatically to insure there are enough training set values to calculate an anomaly. For example if theseasonality
of the test isday_of_week
,days_back
will be increased to insure enough Sundays, Mondays, Tuesdays, etc. to calculate an anomaly for each.
The impact of changing days_back
If you increase days_back
your test training set will be larger. This means a larger sample size for calculating the expected range, which should make the test less sensitive to outliers. This means less chance of false positive anomalies, but also less sensitivity so anomalies have a higher threshold.
If you decrease days_back
your test training set will be smaller. This means a smaller sample size for calculating the expected range, which might make the test more sensitive to outliers. This means more chance of false positive anomalies, but also more sensitivity as anomalies have a lower threshold.
models:
- name: this_is_a_model
tests:
- elementary.volume_anomalies:
days_back: 30