I have a dataset consisting of many product sales over time on a daily basis. I do know when promotions and discounts happen with four months in advance.
When there are no promotions sales are 0 or very very low. However, when there is a promotion or (several promotions for a single product) sales really increase, so it makes the series to explode for the promotion period.
I am trying to forecast sales for each product the following month.
The problem I am facing is that I have tried to model the data as a time series, however I believe there is not a clear time series structure, because:
1.- I know when promotions are going to occurr and then when sales are going to peak.
2.- Time series tries to forecast demand based on past values, however we know when and why this happened, so there is not a "hidden" pattern/structure. Time itself doesn't seem to influence.
I have read about intermittent time series, hurdle model.. but I am not sure if this is the right way to proceed. Maybe instead of using a time series model a causal model may perform better e.g. linear regression, Zero Inflated Negative Binomial, ZIP... Someone has suggested me to use a Quantile regression but not sure if this may work.
I have applied linear and poisson models and seem to have quite a good fitness.
Any suggestion on what is the best way to proceed with such a problem: time series or causal modelling?