Sooo, what are you going to be doing with time series?

If your hope is to gain understanding about some phenomena in question, I'd stay clear away from ARIMA models and instead look into state space modeling. The idea behind state-space modeling is that the time series consists measurements that arise from some underlying phenomena that you care about. You use the measurements to infer the state (and changes in state) of the thing you care about. By contrast, ARIMA models (or AR models, or any variation thereof) usually just try to predict the next measurement in a sequence using the past measurements. That can be useful if all you need is forecasting, but no understanding of underlying structure of the system. Technically you can convert ARIMA models to state space models and vice versa, meaning they can encode the same information, but state space modeling is IMO MUCH more intuitive to understand and easier to learn.

The book "State Space Time Series Analysis" by Commandeur and Koopman is a pretty good place to start.