How do you monitor drift when data has natural seasonality?

hard

Answer

$(1) Baseline per time period (hour-of-day, day-of-week, month) not one static.$
$(2) Detrend via time-series decomposition (STL, Prophet) before comparing.$
$(3) Compare same-period-last-year rather than yesterday.$
$(4) Rolling window baselines auto-adapt.$
$(5) Distinguish 'expected variation' from 'true drift' via multi-window ensemble.$
$Anti-pattern: alert every Monday morning because Monday differs from Sunday.$

Check yourself — multiple choice

Ignore
Baseline per time period (hour/day/month) + detrend via STL/Prophet + same-period-last-year compare + rolling window + multi-window ensemble to distinguish expected from true drift
Random
Not real

Seasonal drift: period-baseline + detrend + same-period-last-year.

#monitoring

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