Variable Time Windows
Inventory is done twice a month, but that data is not collocated and made available till ~10 days (8-12 days) after each closing period. The store is only open Monday through Friday and closed on some holidays. During each inventory period, prices fluctuate with some regularity coming on and off sales promotions and waxing and waning demand from the public. We also know how unsatisfied customers come in every day to return or exchange the products across the country, but we don’t know which. Only after the two week inventory periods do we know the return exchange percentage. Returns/exchanges are significant and in poor economic times returns can be extreme as customers want their cash back to buy other items they need, instead of want. The total of sales and returning/exchanging (but not the mix) customers is known each day. We can rule of thumb that higher than normal returning/exchanging percentages favor returns over exchanges. Exchanges seem to be more a linearly correlated with sales numbers.
In what ways can machine learning forecast from previous twice monthly inventory checks, how many returns or exchanges there were with the daily data. Returns or exchanges only come back in sellable condition, so we don’t need to account for inventory level changes from loss. There are several hundred products like this and each needs to be calculated separately as the some are more aspirational than necessary for consumers.Is there a way to handle this in Rapid Miner?