AI engineering glossary · Time series recsys
What is strict stationarity?
Strict stationarity: A stronger condition requiring that the joint probability distribution of any group of values stays the same when the whole group is shifted in time. It is hard to verify in practice, so weak stationarity is used instead.
Strict stationarity: an example
Independent, identically distributed noise is strictly stationary.
Related terms
More Time series recsys terms
- Structural break
- Temporal fusion transformer
- Time series
- Time series cross-validation
- Trend
- Unit root
- User-based collaborative filtering
- User-item matrix
Where you learn it
Strict stationarity is part of the AI Engineering Program, which teaches it with notes, a hands-on lab and an exam. The first lesson is free.