I have a data set that is transformed to stationarity and I'm trying to fit it to an ARIMA model. I found that variance is lowest when the transformed set is differenced to 1, and here are my ACF and PCF plots for that:
Running the auto.arima() function on R using the transformed but non-differenced data says I should use an ARIMA(1,1,2) model. I know the difference value of 1 is correct, but I don't understand where the AR(1) and MA(2) models come from. How do you read the ACF and PCF to interpret p=1 and q=2?