I am exploring the use of ARIMA and Seasonal ARIMA models (SARIMA). In some of my datasets, I can clearly observe seasonality in the ACF and PACF plots (the lines at seasonal lags clearly cutting the confidence interval region).
In order to make such data stationary, and to account for seasonality in my model, is seasonal differencing enough? For example, would a first order seasonal differencing for monthly data (as shown below) work?
ARIMA (0, 0, 0) × (0, 1, 0)12
Or should I try a more traditional method of finding the seasonal index by using a smoother (MA/Exponential Smoothing) and then using an additive or multiplicative method to calculate seasonal index? How would these methods affect my model?