Getting Started with Modeltime

Step 1 - Collect data and split into training and test sets.

# Data
m750 <- m4_monthly %>% filter(id == "M750")

We can visualize the dataset.

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

Let’s split the data into training and test sets using initial_time_split()

# Split Data 80/20
splits <- initial_time_split(m750, prop = 0.9)

Step 2 - Create & Fit Multiple Models

We can easily create dozens of forecasting models by combining modeltime and parsnip. We can also use the workflows interface for adding preprocessing! Your forecasting possibilities are endless. Let’s get a few basic models developed:

• ARIMA
• Exponential Smoothing
• Linear Regression
• MARS (Multivariate Adaptive Regression Splines)

Important note: Handling Date Features

Modeltime models (e.g. arima_reg()) are created with a date or date time feature in the model. You will see that most models include a formula like fit(value ~ date, data).

Parsnip models (e.g. linear_reg()) typically should not have date features, but may contain derivatives of dates (e.g. month, year, etc). You will often see formulas like fit(value ~ as.numeric(date) + month(date), data).

# Model 1: auto_arima ----
model_fit_arima_no_boost <- arima_reg() %>%
    set_engine(engine = "auto_arima") %>%
    fit(value ~ date, data = training(splits))
#> frequency = 12 observations per 1 year

# Model 2: arima_boost ----
model_fit_arima_boosted <- arima_boost(
    min_n = 2,
    learn_rate = 0.015
) %>%
    set_engine(engine = "auto_arima_xgboost") %>%
    fit(value ~ date + as.numeric(date) + factor(month(date, label = TRUE), ordered = F),
        data = training(splits))
#> frequency = 12 observations per 1 year

# Model 3: ets ----
model_fit_ets <- exp_smoothing() %>%
    set_engine(engine = "ets") %>%
    fit(value ~ date, data = training(splits))
#> frequency = 12 observations per 1 year

# Model 4: prophet ----
model_fit_prophet <- prophet_reg() %>%
    set_engine(engine = "prophet") %>%
    fit(value ~ date, data = training(splits))
#> Disabling weekly seasonality. Run prophet with weekly.seasonality=TRUE to override this.
#> Disabling daily seasonality. Run prophet with daily.seasonality=TRUE to override this.

# Model 5: lm ----
model_fit_lm <- linear_reg() %>%
    set_engine("lm") %>%
    fit(value ~ as.numeric(date) + factor(month(date, label = TRUE), ordered = FALSE),
        data = training(splits))

# Model 6: earth ----
model_spec_mars <- mars(mode = "regression") %>%
    set_engine("earth") 

recipe_spec <- recipe(value ~ date, data = training(splits)) %>%
    step_date(date, features = "month", ordinal = FALSE) %>%
    step_mutate(date_num = as.numeric(date)) %>%
    step_normalize(date_num) %>%
    step_rm(date)
  
wflw_fit_mars <- workflow() %>%
    add_recipe(recipe_spec) %>%
    add_model(model_spec_mars) %>%
    fit(training(splits))

Step 3 - Add fitted models to a Model Table.

The next step is to add each of the models to a Modeltime Table using modeltime_table(). This step does some basic checking to make sure each of the models are fitted and that organizes into a scalable structure called a “Modeltime Table” that is used as part of our forecasting workflow.

We have 6 models to add. A couple of notes before moving on:

• Note that some of the models have tunable parameters.
• It’s expected that tuning and parameter selection is performed prior to incorporating into a Modeltime Table.
• If you try to add an unfitted model, the modeltime_table() will complain (throw an informative error) saying you need to fit() the model.

models_tbl <- modeltime_table(
    model_fit_arima_no_boost,
    model_fit_arima_boosted,
    model_fit_ets,
    model_fit_prophet,
    model_fit_lm,
    wflw_fit_mars
)

models_tbl
#> # Modeltime Table
#> # A tibble: 6 x 3
#>   .model_id .model     .model_desc                              
#>       <int> <list>     <chr>                                    
#> 1         1 <fit[+]>   ARIMA(0,1,1)(0,1,1)[12]                  
#> 2         2 <fit[+]>   ARIMA(0,1,1)(0,1,1)[12] W/ XGBOOST ERRORS
#> 3         3 <fit[+]>   ETS(M,A,A)                               
#> 4         4 <fit[+]>   PROPHET                                  
#> 5         5 <fit[+]>   LM                                       
#> 6         6 <workflow> EARTH

Step 4 - Calibrate the model to a testing set.

Calibrating adds a new column, .calibration_data, with the test predictions and residuals inside. A few notes on Calibration:

• Calibration is how confidence intervals and accuracy metrics are determined
• Calibration Data is simply forecasting predictions and residuals that are calculated from out-of-sample data.
• After calibrating, the calibration data follows the data through the forecasting workflow.

calibration_tbl <- models_tbl %>%
    modeltime_calibrate(new_data = testing(splits))

calibration_tbl
#> # Modeltime Table
#> # A tibble: 6 x 5
#>   .model_id .model     .model_desc                               .type .calibr~1
#>       <int> <list>     <chr>                                     <chr> <list>   
#> 1         1 <fit[+]>   ARIMA(0,1,1)(0,1,1)[12]                   Test  <tibble> 
#> 2         2 <fit[+]>   ARIMA(0,1,1)(0,1,1)[12] W/ XGBOOST ERRORS Test  <tibble> 
#> 3         3 <fit[+]>   ETS(M,A,A)                                Test  <tibble> 
#> 4         4 <fit[+]>   PROPHET                                   Test  <tibble> 
#> 5         5 <fit[+]>   LM                                        Test  <tibble> 
#> 6         6 <workflow> EARTH                                     Test  <tibble> 
#> # ... with abbreviated variable name 1: .calibration_data

Step 5 - Testing Set Forecast & Accuracy Evaluation

There are 2 critical parts to an evaluation.

• Visualizing the Forecast vs Test Data Set
• Evaluating the Test (Out of Sample) Accuracy

calibration_tbl %>%
    modeltime_forecast(
        new_data    = testing(splits),
        actual_data = m750
    ) %>%
    plot_modeltime_forecast(
      .legend_max_width = 25, # For mobile screens
      .interactive      = interactive
    )

calibration_tbl %>%
    modeltime_accuracy() %>%
    table_modeltime_accuracy(
        .interactive = interactive
    )

Step 6 - Refit to Full Dataset & Forecast Forward

The final step is to refit the models to the full dataset using modeltime_refit() and forecast them forward.

refit_tbl <- calibration_tbl %>%
    modeltime_refit(data = m750)

refit_tbl %>%
    modeltime_forecast(h = "3 years", actual_data = m750) %>%
    plot_modeltime_forecast(
      .legend_max_width = 25, # For mobile screens
      .interactive      = interactive
    )

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