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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?

I cannot comment because of low reputation, but how about forecast package in R ARIMA models ? – user4959 – 2017-04-15T05:53:36.213

I fail to understand how the number of items sold as a group will impact the probability of individual items being returned? The product features won't change, perhaps your forecast will contain the time and place of purchase, but what about the customer information? – Valentin Calomme – 2017-11-13T21:20:26.140

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An entire branch of statistics is devoted to this kind of problem: survival analysis (https://en.wikipedia.org/wiki/Survival_analysis).

– Elias Strehle – 2018-02-09T11:05:27.637