Chris Chatfield, whose many quality books and papers I enjoyed reading, in (1) gives the following advice:
For example, the choice between ARIMA time-series models with low and approximately equal values of the AIC should probably be made, not on which happens to give the minimum AIC, but on which gives the best forecasts of the most recent year's data.
What is the rationale for such advice? If it is sound, why does forecast::auto.arima and other forecasting routines do not follow it? Yet to be implemented? It has already been discussed here that to look for models that just happened to give the minimum AIC is probably not a good idea. Why is the option to have $n\ge1$ ARIMA models with low but approximately equal (e.g. within 1 or 2 values of the minimal AIC) is not a default in much of the time series forecasting software?
(1) Chatfield, C. (1991). Avoiding statistical pitfalls. Statistical Science, 6(3), 240–252. Available online, URL: https://projecteuclid.org/euclid.ss/1177011686.