Forecasting is just trying to identify recurring and long-time trends in data, and you're absolutely right that with a narrow observation window it is more difficult to do so, however if you have enough historical data (i.e. several years), these models are remarkably good at picking up on patterns leading up to e.g. bank holidays, the information is contained in the data.
That being said, more information is of course better, assuming it doesn't introduce bias, like focusing on specific events like holidays too much, when the information is already contained in the historical data, without having to make assumptions about what an upcoming holiday means.
The biggest challenge last time I worked with time series forecasting (which is ~5 years ago) was learning long-term shifts in the baseline of the data (e.g., the general use of electricity always goes up, a lot more solar panels and EVs influence the baseline, etc.).
I can only assume (hope) that there has been some substantial progress in that area though.
(Edited the formatting for a nicer reading experience)
That being said, more information is of course better, assuming it doesn't introduce bias, like focusing on specific events like holidays too much, when the information is already contained in the historical data, without having to make assumptions about what an upcoming holiday means.
The biggest challenge last time I worked with time series forecasting (which is ~5 years ago) was learning long-term shifts in the baseline of the data (e.g., the general use of electricity always goes up, a lot more solar panels and EVs influence the baseline, etc.).
I can only assume (hope) that there has been some substantial progress in that area though.
(Edited the formatting for a nicer reading experience)