Not to mention the plethora of issues that arise from trying to fit an ARIMA onto an AR(1) process... It's weird that people just jump into using insanely complicated models right off the bat.
I've seen this in real time. I don't do statistics as part of my day job, but I've had enough experience and keep up with the field to know what I'm talking about. I've seen senior engineers try to ram in an overly specified ARIMA model just to claim that they've "improved" the system. It worked far worse than whatever model we were using before was (never got to take a look under the hood of that one unfortunately), was prone to wild swings in forecasting, and was eventually deprecated and we reverted to the old model.
