tsensembler (version 0.0.5)

update_ade_meta: Updating the metalearning layer of an ADE model

Description

The update_ade_meta function uses new information to update the meta models of an ADE-class ensemble. As input it receives a ADE-class model object class and a new dataset for updating the weights of the base models in the ensemble. This new data should have the same structure as the one used to build the ensemble. Updating the base models of the ensemble is done using the update_base_models function.

Usage

update_ade_meta(object, newdata, num_cores = 1)
# S4 method for ADE
update_ade_meta(object, newdata, num_cores = 1)

Arguments

object

a ADE-class object.

newdata

data used to update the meta models. This should be the data used to initially train the meta-models (training set), together with new observations (for example, validation set). Each meta model is retrained using newdata.

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.

See Also

ADE-class for building an ADE model; update_weights for updating the weights of the ensemble (without retraining the models); and update_base_models for updating the base models of an ensemble.

Other updating models: update_ade, update_weights

Examples

# NOT RUN {
specs <- model_specs(
 learner = c("bm_svr", "bm_glm", "bm_mars"),
 learner_pars = NULL
)

data("water_consumption")
dataset <- embed_timeseries(water_consumption, 5)
train <- dataset[1:1000, ]
validation <- dataset[1001:1200, ]
test <- dataset[1201:1500, ]

model <- ADE(target ~., train, specs)

preds_val <- predict(model, validation)
model <- update_ade_meta(model, rbind.data.frame(train, validation))

preds_test <- predict(model, test)
# }
# NOT RUN {
# }