Hi everyone,    I am working to understand some of the differences between SAS and R (using the fable, forecasting, or basic stats packages) to be able to run the same model in SAS and R. After being unable to replicate a more complex model, I tried to run a basic ARIMA (1,1,1) and work my way up. I know that this is not the right model for my data, the point of this is not to fit a model, rather to get the same forecasted values using SAS and R.   However, I am not understanding the SAS outcome - although the data has seasonality, I have not indicated that to SAS. But the forecasted values make me think that something is happening in SAS that I don't know about. Interestingly, when I add an intercept to my R model, I get similar values as the SAS model where I specify no intercept.    I would appreciate any insight anyone has into this! I have read through a lot of R and SAS documentation, but have not found anything that sheds light on why this would happen. I have attached a screenshot of the forecasted values in SAS, R without an intercept, and R with an intercept. Thanks!   SAS Code:  proc arima data=cld; identify var=lgs1(1) nlag=13 minic scan esacf stationarity=(adf=5) crosscorr=(sspben)noprint; estimate p=(1)q=(1) input=(sspben) noint method=ml plot; forecast lead=61 interval=month id=month alpha=0.05 out=s1out; run;   R (fable) code without an intercept (with an intercept, replace ~0 with ~1): arima111<-ow_input%>%   model(arima = ARIMA(lgs1 ~ 0+ sspben +pdq(1,1,1)                         +PDQ(0,0,0), method="ML")) s1_forecast<-forecast(arima111, fc_data)%>% rename(forecast=.mean)%>% mutate(forecast=exp(forecast))%>% as.data.frame()%>% select(month, lgs1, forecast) ... View more