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Weak Stationarity


A stochastic process {X_t} with finite second moments is weakly stationary, or covariance stationary, if its population mean is constant and its covariance depends only on the lag between observations. Thus there are functions mu and gamma such that

 E[X_t]=mu

and

 Cov(X_t,X_(t+h))=gamma(h)

for all t and h.

Weak stationarity does not in general imply strict stationarity. Conversely, strict stationarity implies weak stationarity when the necessary second moments exist.


See also

Autocorrelation, Nonstationary Time Series, Stationary Time Series, Strict Stationarity

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Cite this as:

Weisstein, Eric W. "Weak Stationarity." From MathWorld--A Wolfram Resource. https://mathworld.wolfram.com/WeakStationarity.html

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