I am studying ARIMA models from this tutorial time-series-forecasting-codes-python, but I got confused on many points:
- We do many transformations to get stationarity data and every transformation we get data with good stationarity and on the example, he got the best stationary after applying the Decomposing, then why he did use the ts_log_diff and ts_log data with ACF, PACF and ARIMA instead of using the Decomposing data !?
- I did see many styles for ACF and PACF one like continuous graph and another one like pins, which one I should go for it?
- What is the best and easiest way to detect AR and MA by ACF and PACF? Some tutorials mention about every ARIMA model has a special ACF and PACF pattern and others mention about the intersection between the lags and the confidence upper line!
- Is there any way to automate the step of getting the AR and MA instead of trying to investigate the ACF and PACF plots?