Creates a Weighted Ensemble Model

Makes an ensemble by applying loadings to weight sub-model predictions

Usage

ensemble_weighted(object, loadings, scale_loadings = TRUE)

Arguments

object

A Modeltime Table

loadings

A vector of weights corresponding to the loadings

scale_loadings

If TRUE, divides by the sum of the loadings to proportionally weight the submodels.

Value

A mdl_time_ensemble object.

Details

The input to an ensemble_weighted() model is always a Modeltime Table, which contains the models that you will ensemble.

Weighting Method

The weighted method uses uses loadings by applying a loading x model prediction for each submodel.

Examples

# \donttest{
library(tidymodels)
library(modeltime)
library(modeltime.ensemble)
library(dplyr)
library(timetk)

# Make an ensemble from a Modeltime Table
ensemble_fit <- m750_models %>%
    ensemble_weighted(
        loadings = c(3, 3, 1),
        scale_loadings = TRUE
    )

ensemble_fit
#> ── Modeltime Ensemble ───────────────────────────────────────────
#> Ensemble of 3 Models (WEIGHTED) 
#> 
#> # Modeltime Table
#> # A tibble: 3 × 4
#>   .model_id .model     .model_desc             .loadings
#>       <int> <list>     <chr>                       <dbl>
#> 1         1 <workflow> ARIMA(0,1,1)(0,1,1)[12]     0.429
#> 2         2 <workflow> PROPHET                     0.429
#> 3         3 <workflow> GLMNET                      0.143

# Forecast with the Ensemble
modeltime_table(
    ensemble_fit
) %>%
    modeltime_forecast(
        new_data    = testing(m750_splits),
        actual_data = m750
    ) %>%
    plot_modeltime_forecast(
        .interactive = FALSE,
        .conf_interval_show = FALSE
    )
#> Warning: There were 2 warnings in `dplyr::mutate()`.
#> The first warning was:
#> ℹ In argument: `.nested.col = purrr::map2(...)`.
#> Caused by warning:
#> ! There was 1 warning in `dplyr::mutate()`.
#> ℹ In argument: `.nested.col = purrr::map2(...)`.
#> Caused by warning:
#> ! `keep_original_cols` was added to `step_dummy()` after this recipe was created.
#> ℹ Regenerate your recipe to avoid this warning.
#> ℹ Run `dplyr::last_dplyr_warnings()` to see the 1 remaining warning.

# }