A stochastic process (a collection of random variables ordered in time, e.g. GDP(t)) is said to be (weakly) stationary if its mean and variance are constant over time, i.e. time invariant (along with its autocovariance). Such a time series will tend to return to its mean (mean reversion) and fluctuations around this mean will have a broadly constant amplitude. Alternatively, a stationary process will not drift too far away from its mean value because of the nite variance. By contrast, a nonstationary time series will have a time-varying mean or a time-varying variance or both. … Non-stationary Stochastic Processes