train_ade_quick: ADE training poor version Train meta-models in the training...
In tsensembler: Dynamic Ensembles for Time Series Forecasting

Saves times by not computing oob predictions. Testing comp costs are the same.

1	train_ade_quick(form, train, specs, lambda, lfun, meta_model_type, num_cores)

`form`	formula
`train`	training data
`specs`	a `model_specs-class` object class. It contains the parameter setting specifications for training the ensemble;
`lambda`	window size. Number of observations to compute the recent performance of the base models, according to the committee ratio omega. Essentially, the top omega models are selected and weighted at each prediction instance, according to their performance in the last lambda observations. Defaults to 50 according to empirical experiments;
`lfun`	meta loss function - defaults to ae (absolute error)
`meta_model_type`	algorithm used to train meta models. Defaults to a random forest (using ranger package)
`num_cores`	A numeric value to specify the number of cores used to train base and meta models. num_cores = 1 leads to sequential training of models. num_cores > 1 splits the training of the base models across num_cores cores.