I've read about How does h2o.r cross validation work?. However, for a time series dataset, does H2o support the type of CV described here Using k-fold cross-validation for time-series model selection?
In particular, something like this:
fold 1 : training [1], test [2] fold 2 : training [1 2], test [3] fold 3 : training [1 2 3], test [4] fold 4 : training [1 2 3 4], test [5] fold 5 : training [1 2 3 4 5], test [6]
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Best Answer
H2O algorithms can optionally use k-fold cross-validation. H2O does not yet support time-series (aka "walk-forward" or "rolling") cross-validation, however there is an open ticket to implement it here.
There is an example of how you can manually implement time-series CV using the h2o R package referenced here, if you want to give that a try.
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