A common assumption for a few of the time series models is that data has to be stationary. Let's look at what stationarity means regarding time series.
A stationary process is one for which the mean, variance, and autocorrelation structure doesn't change over time. What this means is that the data doesn't have a trend (increasing or decreasing).
We can describe this by using the following formulas:
E(xt)= μ, for all t
E(xt2)= σ2, for all t
cov(xt,xk)= cov(xt+s, xk+s), for all t, k, and s