# Getting Started with Modeltime Ensemble

> 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:

- Weighted Averaging
- Stacking using an Elastic Net regression model (meta-learning)

## Libraries

Load libraries to complete this short tutorial.

    # Time Series ML
    library(tidymodels)
    library(modeltime)
    library(modeltime.ensemble)

    # Core
    library(tidyverse)
    library(timetk)

    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)

# Modeling

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

## Recipe

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()

    #> # A tibble: 282 × 42
    #>    date       value date_index.num date_year date_half date_quarter date_month
    #>    <date>     <dbl>          <dbl>     <dbl>     <int>        <int>      <int>
    #>  1 1990-01-01  6370          -1.72     -1.66         1            1          1
    #>  2 1990-02-01  6430          -1.71     -1.66         1            1          2
    #>  3 1990-03-01  6520          -1.70     -1.66         1            1          3
    #>  4 1990-04-01  6580          -1.69     -1.66         1            2          4
    #>  5 1990-05-01  6620          -1.67     -1.66         1            2          5
    #>  6 1990-06-01  6690          -1.66     -1.66         1            2          6
    #>  7 1990-07-01  6000          -1.65     -1.66         2            3          7
    #>  8 1990-08-01  5450          -1.64     -1.66         2            3          8
    #>  9 1990-09-01  6480          -1.62     -1.66         2            3          9
    #> 10 1990-10-01  6820          -1.61     -1.66         2            4         10
    #> # ℹ 272 more rows
    #> # ℹ 35 more variables: date_day <int>, date_hour <int>, date_minute <int>,
    #> #   date_second <int>, date_hour12 <int>, date_am.pm <int>, date_wday <int>,
    #> #   date_mday <int>, date_qday <int>, date_yday <int>, date_mweek <int>,
    #> #   date_week <int>, date_week2 <int>, date_week3 <int>, date_week4 <int>,
    #> #   date_mday7 <int>, date_month.lbl_01 <dbl>, date_month.lbl_02 <dbl>,
    #> #   date_month.lbl_03 <dbl>, date_month.lbl_04 <dbl>, …

## Model 1 - Auto ARIMA

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

    model_spec_arima <- arima_reg() %>%
        set_engine("auto_arima")

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

## Model 2 - Prophet

Next, we’ll make a Prophet Model.

    model_spec_prophet <- prophet_reg() %>%
        set_engine("prophet")

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

## 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
    ) %>%
        set_engine("glmnet")

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

# 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(
        wflw_fit_arima,
        wflw_fit_prophet,
        wflw_fit_glmnet
    )

    m750_models

    #> # Modeltime Table
    #> # A tibble: 3 × 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 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")

    ensemble_fit

    #> ── Modeltime Ensemble ───────────────────────────────────────────
    #> Ensemble of 3 Models (MEAN)
    #>
    #> # Modeltime Table
    #> # A tibble: 3 × 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! (the Test Data)

To forecast, just follow the Modeltime Workflow.

    # Calibration
    calibration_tbl <- modeltime_table(
        ensemble_fit
    ) %>%
        modeltime_calibrate(testing(m750_splits))

    # Forecast vs Test Set
    calibration_tbl %>%
        modeltime_forecast(
            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 %>%
        modeltime_refit(m750)

    refit_tbl %>%
        modeltime_forecast(
            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.

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