The last step in the ARIMA modeling process is model diagnostics. This involves evaluating whether the model assumptions are satisfied, whether the residuals are white noise, and whether the model is adequately fit to the data. You should learn various diagnostic plots to evaluate the model's performance, such as residuals plot, ACF plot, and QQ-plot.