maybe it is a bit trivial, but I would like to ask how can I do weekly demand forecasting when I have got daily demand? the thing is that every year has 12 months, but does not have exactly 52 weeks. Therefore, I do not know how to handle it.
If you have daily data you can detect daily effects . weekly effects , monthly effects , lead and lag holiday effects , long-weekend effects , week-of-the-month effects , day-of-the-month effects, level shifts , local time trends , pulse effects, changes in day-of-the-week effects , end-of-month-effects..et al . You can then aggregate your daily forecasts to higher aggregates and even compute probabilities of making weekly/monthly/quarterly totals as you go through the month. Weekly data analysis/models should be studiously avoided because weekly totals are effected by a number of factors thus leading to distortion .
Daily data is less "smooth" than higher aggregates but that is not a problem as anomaly detection can be used to render the affect/distortion of "unusual observations" providing robustification.
By resampling the residuals from a model using monte carlo methods one can obtain a family of forecasts for each forecast period. Then one can create a distribution of the aggregate of these forecasts. This easily extends to allowing for identified pulses to play a role in the family/distribution of forecasts which were necessary to get robust model parameter estimates . If one doesn't allow for pulses to be possible in the forecasts one obtains naive tight limits due to the smaller variance of the errors. Finally the idea of MC is applicable to any level of temporal forecasting .