Nested Ensemble Average

Creates an Ensemble Model using Mean/Median Averaging in the Modeltime Nested Forecasting Workflow.

Usage

ensemble_nested_average(
  object,
  type = c("mean", "median"),
  keep_submodels = TRUE,
  model_ids = NULL,
  control = control_nested_fit()
)

Arguments

object

A nested modeltime object (inherits class nested_mdl_time)

type

One of "mean" for mean averaging or "median" for median averaging

keep_submodels

Whether or not to keep the submodels in the nested modeltime table results

model_ids

A vector of id's (.model_id) identifying which submodels to use in the ensemble.

control

Controls various aspects of the ensembling process. See control_nested_fit().

Value

The nested modeltime table with an ensemble model added.

Details

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]>