I am trying to analyse time trend data across a 10 year period (monthly) using SPSS, to do an interrupted time series analysis. I am not sure however, when a seasonal ARIMA model is "good enough". For example, I am using the Stationary R-squared as a guide, because the data is seasonal, and the highest I can get for one data series is 0.702 (3,0,0) x (1,2,1)12 The goodness of fit line with observed values is OK, but wondering when I should stop? Is it a bad thing if d in the seasonal component is 2? The SPSS expert modeller (ARIMA only seasonal box ticked) comes up with something completely different and a low stationary R-squared at 0.420 but the goodness of fit line seem to reflect the observed data better.
Is it also possible to conduct a seasonal decomposition and then use the seasonal adjusted data in a simple ARIMA model instead?
Any guidance would be appreciated, I seem to be going round in circles!