Most of the prior posts and papers I've read on this subject deal with autoregressive models but mine is currently of the form:
$$y(t+h) = X(t) + e$$
where $X$ represents the set of predictor variables that does not include $y(t)$.
I've also tried machine learning techniques with the same outcome and predictor variables.
I'd like to use k-fold cross-validation or something similar rather than just having a single training sample and holdout test sample; however, both $y$ and $X$ exhibit some degree of autocorrelation (for instance, for both variables $t$ and $t+1$ have a significant correlation). Thus, I'm wondering if it's problematic to have months randomly assigned to the folds, and if so, what the possible remedies are?