learning_base_models: Training the base models of an ensemble
In tsensembler: Dynamic Ensembles for Time Series Forecasting

Description Usage Arguments Value See Also Examples

This function uses train to build a set of predictive models, according to specs

1	learning_base_models(train, form, specs, num_cores)

`train`	training set to build the predictive models;
`form`	formula;
`specs`	object of class `model_specs-class`
`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.

A series of predictive models (base_model), and the weights of the models computed in the training data (preweights).

build_base_ensemble.

data("water_consumption")
dataset <- embed_timeseries(water_consumption, 5)
specs <- model_specs(c("bm_ppr","bm_svr"), NULL)
M <- build_base_ensemble(target ~., dataset, specs, 1)