How can one generate future forecasts from probabilistic events? I have an event "whether an item sold will be returned or not" which I can predict with a certain probability based on information gathered at the time that the purchase occurs (product features, customer information, time and place, etc). So: 

P(Return | transaction information) = x% for a specific unit sold


I also have a historical time series of total units sold for that item, and a future forecast of sales of that item over the next few weeks. Assuming I gave the relevant transaction data for each historical sale that occurred, is there away to generate a future forecast from the return probability, so that I can state with some confidence that I will get 15% total returns on the item next week, 10% the week after etc?
If you have historical data on percentage of return for few weeks after the sales, I believe you can do it. It will be a multiple-output regression problem e.g. you will have to formulate the target values (i.e. output matrix) in a way so that %-return in first week will be in column one, %-return in second week will be in columns two and so on. After this, plug in all your input variables and run a multivariate multiple output regression algorithm. Such an algorithm on scikit-learn can be found (sklearn.multioutput.MultiOutputRegressor: here)