# ARIMA vs SARIMA

I am a self learner, and I am studying time series analysis.

I came through the fact that ARIMA can be used to model a time series which is not stationary (Integrated ARMA model). The non stationarity taken in to account in an ARIMA model is the nonstationarity that can be removed by differencing.

I am wondering whether a seasonality is the type of nonstationarity that can be removed by differencing?

If yes, so for a non seasonal data, we may use ARIMA modeling, so why we have SARIMA modeling ?

If no, how seasonality can be removed in order to make data stationary?

• Just a warning: differencing a trend-stationary series will produce an integrated moving-average process, which is why deterministic trends are not handled by differencing but rather by including a variable representing the deterministic time trend ($t$, $t^2$ and such). If you want to learn more about the problem, check the keyword overdifferencing. @Nizar – Richard Hardy Jun 16 at 7:35