Getting Started with Modeltime Ensemble

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Ensemble Algorithms for Time Series Forecasting with Modeltime

A modeltime extension that that implements ensemble forecasting methods including model averaging, weighted averaging, and stacking. Let's go through a guided tour to kick the tires on modeltime.ensemble.


Time Series Ensemble Forecasting Example

We'll perform the simplest type of forecasting: Using a simple average of the forecasted models.

Note that modeltime.ensemble has capabilities for more sophisticated model ensembling using:


Load libraries to complete this short tutorial.

# Time Series ML

# Core

interactive <- FALSE

Collect the Data

We'll use the m750 dataset that comes with modeltime.ensemble. We can visualize the dataset.

m750 %>%
    plot_time_series(date, value, .color_var = id, .interactive = interactive)

Perform Train / Test Splitting

We'll split into a training and testing set.

splits <- time_series_split(m750, assess = "2 years", cumulative = TRUE)

splits %>%
    tk_time_series_cv_plan() %>%
    plot_time_series_cv_plan(date, value, .interactive = interactive)


Once the data has been collected, we can move into modeling.


We'll create a Feature Engineering Recipe that can be applied to the data to create features that machine learning models can key in on. This will be most useful for the Elastic Net (Model 3).

recipe_spec <- recipe(value ~ date, training(splits)) %>%
    step_timeseries_signature(date) %>%
    step_rm(matches("(.iso$)|(.xts$)")) %>%
    step_normalize(matches("(index.num$)|(_year$)")) %>%
    step_dummy(all_nominal()) %>%
    step_fourier(date, K = 1, period = 12)

recipe_spec %>% prep() %>% juice()

Model 1 - Auto ARIMA

First, we'll make an ARIMA model using Auto ARIMA.

model_spec_arima <- arima_reg() %>%

wflw_fit_arima <- workflow() %>%
    add_model(model_spec_arima) %>%
    add_recipe(recipe_spec %>% step_rm(all_predictors(), -date)) %>%

Model 2 - Prophet

Next, we'll make a Prophet Model.

model_spec_prophet <- prophet_reg() %>%

wflw_fit_prophet <- workflow() %>%
    add_model(model_spec_prophet) %>%
    add_recipe(recipe_spec %>% step_rm(all_predictors(), -date)) %>%

Model 3 - Elastic Net

Third, we'll make an Elastic Net Model using glmnet.

model_spec_glmnet <- linear_reg(
    mixture = 0.9,
    penalty = 4.36e-6
) %>%

wflw_fit_glmnet <- workflow() %>%
    add_model(model_spec_glmnet) %>%
    add_recipe(recipe_spec %>% step_rm(date)) %>%

Modeltime Workflow for Ensemble Forecasting

With the models created, we can can create an Ensemble Average Model using a simple Mean Average.

Step 1 - Create a Modeltime Table

Create a Modeltime Table using the modeltime package.

m750_models <- modeltime_table(


Step 2 - Make an Ensemble

Then use ensemble_average() to turn that Modeltime Table into a Modeltime Ensemble. This is a fitted ensemble specification containing the ingredients to forecast future data and be refitted on data sets using the 3 submodels.

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


Step 3 - Forecast! (the Test Data)

To forecast, just follow the Modeltime Workflow.

# Calibration
calibration_tbl <- modeltime_table(
) %>%

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

Step 4 - Refit on Full Data & Forecast Future

Once satisfied with our ensemble model, we can modeltime_refit() on the full data set and forecast forward gaining the confidence intervals in the process.

refit_tbl <- calibration_tbl %>%

refit_tbl %>%
        h = "2 years",
        actual_data = m750
    ) %>%
    plot_modeltime_forecast(.interactive = interactive)

This was a very short tutorial on the simplest type of forecasting, but there's a lot more to learn.

Take the High-Performance Forecasting Course

Become the forecasting expert for your organization

High-Performance Time Series Forecasting Course

High-Performance Time Series Course

Time Series is Changing

Time series is changing. Businesses now need 10,000+ time series forecasts every day. This is what I call a High-Performance Time Series Forecasting System (HPTSF) - Accurate, Robust, and Scalable Forecasting.

High-Performance Forecasting Systems will save companies by improving accuracy and scalability. Imagine what will happen to your career if you can provide your organization a "High-Performance Time Series Forecasting System" (HPTSF System).

How to Learn High-Performance Time Series Forecasting

I teach how to build a HPTFS System in my High-Performance Time Series Forecasting Course. You will learn:

Become the Time Series Expert for your organization.

Take the High-Performance Time Series Forecasting Course

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modeltime.ensemble documentation built on Oct. 20, 2021, 1:06 a.m.