I'm new to time series and used the monthly ozone concentration data from Rob Hyndman's websiteto do some forecasting.After doing a log transformation and differencing by lags 1... moreI'm new to time series and used the monthly ozone concentration data from Rob Hyndman's websiteto do some forecasting.After doing a log transformation and differencing by lags 1 and 12 to get rid of the trend and seasonality respectively, I plotted the ACF and PACF shown . Am I on the right track and how would I interpret this as a SARIMA?There seems to be a pattern every 11 lags in the PACF plot, which makes me think I should do more differencing (at 11 lags), but doing so gives me a worse plot. I'd really appreciate any of your help!EDIT: I got rid of the differencing at lag 1 and just used lag 12 instead, and this is what I got for the ACF and PACF.From there, I deduced that: SARIMA(1,0,1)x(1,1,1) (AIC: 520.098) or SARIMA(1,0,1)x(2,1,1) (AIC: 521.250) would be a good fit, but auto.arima gave me (3,1,1)x(2,0,0) (AIC: 560.7) normally and (1,1,1)x(2,0,0) (AIC: 558.09) without stepwise and approximation.I am confused on which model to use, but based on the lowest AIC, SAR(1,0,1)x(1,1,1) would be the... less