update_base_models: Update the base models of an ensemble
In tsensembler: Dynamic Ensembles for Time Series Forecasting

Description Usage Arguments Details See Also Examples

This is a generic function for updating the base models comprising an ensemble.

update_base_models(object, newdata, num_cores = 1)

## S4 method for signature 'ADE'
update_base_models(object, newdata, num_cores = 1)

## S4 method for signature 'DETS'
update_base_models(object, newdata, num_cores = 1)

`object`	an ensemble object, of class `DETS-class` or `ADE-class`;
`newdata`	new data used to update the models. Each base model is retrained, so `newdata` should be the past data used for initially training the models along with any further available observations.
`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.

update_base_models function receives a model object and a new dataset for retraining the base models. This new data should have the same structure as the one used to build the ensemble.

ADE-class for the ADE model information, and DETS-class for the DETS model information; update_ade_meta for updating the meta models of an ADE ensemble. See update_weights for the method used to update the weights of the ensemble. Updating the weights only changes the information about the recent observations for computing the weights of the base models, while updating the model uses that information to retrain the models.

data("water_consumption")
dataset <- embed_timeseries(water_consumption, 5)
# toy size for checks execution time
train <- dataset[1:300,]
test <- dataset[301:305, ]

specs <- model_specs(c("bm_ppr","bm_glm","bm_mars"), NULL)

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

predictions <- numeric(nrow(test))
for (i in seq_along(predictions)) {
  predictions[i] <- predict(model, test[i, ])@y_hat
  model <-
    update_base_models(model,
                       rbind.data.frame(train, test[seq_len(i), ]))
}

####

specs2 <- model_specs(c("bm_ppr","bm_randomforest","bm_svr"), NULL)

modeldets <- DETS(target ~., train, specs2)

predictions <- numeric(nrow(test))
# predict new data and update models every three points
# in the remaining points, the only the weights are updated
for (i in seq_along(predictions)) {
  predictions[i] <- predict(modeldets, test[i, ])@y_hat

  if (i %% 3 == 0)
    modeldets <-
      update_base_models(modeldets,
                         rbind.data.frame(train, test[seq_len(i), ]))
  else
    modeldets <- update_weights(modeldets, test[seq_len(i), ])
}