A stochastic process is composed of a sequence of random variables ordered by time,
and a time series is just a realization of such a process.
The book that I'm reading says: "if we assume stationarity, then we can get expectation and variance from time series data".
I don't understand this. Doesn't stationarity mean that every random variable in the stochastic process has same distribution? If not, every time series data comes from different distributions how they can calculate expectation and variance from them?