Ensemble Algorithms for Time Series Forecasting with Modeltime

A modeltime extension that implements ensemble forecasting methods including model averaging, weighted averaging, and stacking.

Installation

Install the CRAN version:

install.packages("modeltime.ensemble")

Or, install the development version:

remotes::install_github("business-science/modeltime.ensemble")

Getting Started

  1. Getting Started with Modeltime: Learn the basics of forecasting with Modeltime.
  2. Getting Started with Modeltime Ensemble: Learn the basics of forecasting with Modeltime ensemble models.

Make Your First Ensemble in Minutes

Load the following libraries.

Step 1 - Create a Modeltime Table

Create a Modeltime Table using the modeltime package.

m750_models
#> # Modeltime Table
#> # A tibble: 3 x 3
#>   .model_id .model     .model_desc            
#>       <int> <list>     <chr>                  
#> 1         1 <workflow> ARIMA(0,1,1)(0,1,1)[12]
#> 2         2 <workflow> PROPHET                
#> 3         3 <workflow> GLMNET

Step 2 - Make a Modeltime Ensemble

Then turn that Modeltime Table into a Modeltime Ensemble.

ensemble_fit <- m750_models %>%
    ensemble_average(type = "mean")

ensemble_fit
#> ── Modeltime Ensemble ───────────────────────────────────────────
#> Ensemble of 3 Models (MEAN)
#> 
#> # Modeltime Table
#> # A tibble: 3 x 3
#>   .model_id .model     .model_desc            
#>       <int> <list>     <chr>                  
#> 1         1 <workflow> ARIMA(0,1,1)(0,1,1)[12]
#> 2         2 <workflow> PROPHET                
#> 3         3 <workflow> GLMNET

Step 3 - Forecast!

To forecast, just follow the Modeltime Workflow.

# Calibration
calibration_tbl <- modeltime_table(
    ensemble_fit
) %>%
    modeltime_calibrate(testing(m750_splits), quiet = FALSE)

# Forecast vs Test Set
calibration_tbl %>%
    modeltime_forecast(
        new_data    = testing(m750_splits),
        actual_data = m750
    ) %>%
    plot_modeltime_forecast(.interactive = FALSE)

Learning More

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