AI engineering glossary · Time series recsys
What is weak stationarity?
Weak stationarity (also called Covariance stationarity): The practical version of stationarity that requires only a constant mean, a constant variance, and covariance between two values that depends solely on the lag between them. It is what most classical time-series methods actually assume.
Weak stationarity: an example
A series with mean 50, variance 4 and lag-1 covariance 2 throughout is weakly stationary.
Related terms
More Time series recsys terms
- Weighted absolute percentage error
- White noise
- Wide and Deep
- Additive decomposition
- Alternating least squares
- Anomaly detection (time series)
- ARCH model
- ARIMA
Where you learn it
Weak 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.