I have time series dataset and as a requirement I prepare a 10 fold cross validation dataset. Briefly, I split data into 10 and for each training set I put a gap between last, first day of validation set and training set. That is, suppose that I take first 100 rows as validation. Then I take training set between the row range (150 - 5000). So 100-150 rows are removed. And repeated the same logic for other 9 folds. In the end I collected folds into dictionary like train, test for fold_0, train, test for fold_1 and so on...
My question is that can I introduce this specific cross val set into ensemble model in h2o. The example code in h2o's web site offers to use
fold_column. But I could not figure it out how to use it in this case.
One another thing I have tried is that for each fold I have trained the data and get the predictions. And in the end I put 10 predictions into one list as below:
preds =  preds.append([pred1,pred2,pred3,pred4,pred5,pred6,pred7,pred8,pred9,pred10])
preds list has the same pattern as returned by
But this time I get 10 different base models for each base model.
This is the link I am following and I need to ensemble regression base models.
So how should I proceed?