I have watched the examples for time windowing, but my data is not always equally spaced, Here is my problem:
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?
