I have ~ 17000 hourly observations which seem to exhibit daily (frequency=24) and weekly (frequency=168) seasonalities, as well as general nonstationary unit root (d=1). But even after removing these three components I'm still seeing troubling autocorrelation at lag 24 and 168. I'm not sure if even may have overdifferenced. Below are the ACF plots:
Original Data ACF
After seasonal difference of 168
After seasonal difference of 24
The data is taken from Kaggle in the hour.csv file, using the first 8645 observations to model the
I'm at a loss at how to resolve this. I'm trying to make 8 hour ahead forecasts. Can anyone please help?