Here's the scenario, slightly altered to a common one.
Credit card fraud, payments for the last 12 months (a rolling window). Train with the data from the first 10 months, validate with data from the 11th and test with the data from the 12th month.
My rationale for this is that when used for real, we'll always use the history (be it of the same card or everything in the past, like fraud patterns).
Are there any methodological problems with this approach?