Say I have a model that I'm using to forecast demand for some product. I can train it on some training data in order to get good predictions.
But, the predictions it outputs will only really match past demand. This seems to be a problem related to the data that we give the model because if the past demand data is only how much we sold, but that was our supply as well, then this wouldn't be able to help improve future predictions.
For example, if I had in supply 10 units of product x, and I always sell 10 units of that product, then based on this training data, my model will always say that I should always order 10 units for every day. But, really, it might be better to order 25 units because if I'm always selling everything, then there might be more demand than supply. The problem is that I don't know what the true demand is.
So my question is, how would I get my model to account for that and actually improve predictions rather than trying to fit to historical data.