Hey. The library is built around the premise that you want to forecast 10 periods ahead for example and you want to estimate how good are your models doing that, so you run that procedure a couple of times (maybe 4 times 10 periods ahead). I don't think there's a way to do what you're asking here with the built-in cross_validation. However, that's just a convenience function, you could achieve this by iterating over the series, determining what you want your train size and number of windows to be and just use
StatsForecast.forecast
on that subset.