More or less, with the approach above, I am getting decent outputs, but still not very close.
The best I was able to get using the SARIMA model, but I couldn't really find any decent explanation on how to seek for the parameters.
So the logic I used (whether correct or wrong, please tell me) is to use the p parameter as usual, but move the q to Q. Then, there is the s parameter, which more or less I pick in relation of the data. With daily data, I normally look at the peaks and then use a multiple of 7 according to the best performance.
But what for the weekly aggregated data? I had an instance of a dataset where the resulting RSME was getting better only with parameter like p=46 that doesn't sound normal.
Can you please feed me in with some explanation or guide(s) I can use to fill the gaps?
Below there are the initial ACF and PACF plots, for which a p=46 and q=8 demonstrate better prediction than a p=1 and q=8.