Creates an Ensemble Model using Mean/Median Averaging in the Modeltime Nested Forecasting Workflow.
ensemble_nested_average(
object,
type = c("mean", "median"),
keep_submodels = TRUE,
model_ids = NULL,
control = control_nested_fit()
)The nested modeltime table with an ensemble model added.
A nested modeltime object (inherits class nested_mdl_time)
One of "mean" for mean averaging or "median" for median averaging
Whether or not to keep the submodels in the nested modeltime table results
A vector of id's (.model_id) identifying which submodels to
use in the ensemble.
Controls various aspects of the ensembling process. See modeltime::control_nested_fit().
If we start with a nested modeltime table, we can add ensembles.
nested_modeltime_tbl# Nested Modeltime Table
Trained on: .splits | Model Errors: [0]
# A tibble: 2 x 5
id .actual_data .future_data .splits .modeltime_tables
<fct> <list> <list> <list> <list>
1 1_1 <tibble [104 x 2]> <tibble [52 x 2]> <split [52|52]> <mdl_time_tbl [2 x 5]>
2 1_3 <tibble [104 x 2]> <tibble [52 x 2]> <split [52|52]> <mdl_time_tbl [2 x 5]>
An ensemble can be added to a Nested modeltime table.
ensem <- nested_modeltime_tbl %>%
ensemble_nested_average(
type = "mean",
keep_submodels = TRUE,
control = control_nested_fit(allow_par = FALSE, verbose = TRUE)
)
We can then verify the model has been added.
ensem %>% extract_nested_modeltime_table()
This produces an ensemble .model_id 3, which is an ensemble of the first two models.
# A tibble: 4 x 6
id .model_id .model .model_desc .type .calibration_data
<fct> <dbl> <list> <chr> <chr> <list>
1 1_1 1 <workflow> PROPHET Test <tibble [52 x 4]>
2 1_1 2 <workflow> XGBOOST Test <tibble [52 x 4]>
3 1_1 3 <ensemble [2]> ENSEMBLE (MEAN): 2 MODELS Test <tibble [52 x 4]>
Additional ensembles can be added by simply adding onto the nested modeltime table.
Notice that we make use of model_ids to make sure it only uses model id's 1 and 2.
ensem_2 <- ensem %>%
ensemble_nested_average(
type = "median",
keep_submodels = TRUE,
model_ids = c(1,2),
control = control_nested_fit(allow_par = FALSE, verbose = TRUE)
)
This returns a 4th model that is a median ensemble of the first two models.
ensem_2 %>% extract_nested_modeltime_table()
# A tibble: 4 x 6
id .model_id .model .model_desc .type .calibration_data
<fct> <dbl> <list> <chr> <chr> <list>
1 1_1 1 <workflow> PROPHET Test <tibble [52 x 4]>
2 1_1 2 <workflow> XGBOOST Test <tibble [52 x 4]>
3 1_1 3 <ensemble [2]> ENSEMBLE (MEAN): 2 MODELS Test <tibble [52 x 4]>
4 1_1 4 <ensemble [2]> ENSEMBLE (MEDIAN): 2 MODELS Test <tibble [52 x 4]>