Using Individual finnts Forecast Components

The easiest way to use the finnts package is through the function forecast_time_series(), but instead of calling that function you can also call the sub components of the Finn forecast process. This could enable you to break out your time series forecast process into separate steps in a production pipeline, or even give you more control over how you use Finn.

Below is an example workflow of using the sub components of Finn.

Get Data and Set Run Info

Let’s get some example data and then set our Finn run info.

library(finnts)

hist_data <- timetk::m4_monthly %>%
  dplyr::filter(
    date >= "2013-01-01",
    id == "M2"
  ) %>%
  dplyr::rename(Date = date) %>%
  dplyr::mutate(id = as.character(id))

run_info <- set_run_info(
  experiment_name = "finnts_fcst",
  run_name = "finn_sub_component_run"
)

Prep the Data

Clean and prepare our data before training models. We can even pull out our prepped data to see the features and transformations applied before models are trained.

prep_data(
  run_info = run_info,
  input_data = hist_data,
  combo_variables = c("id"),
  target_variable = "value",
  date_type = "month",
  forecast_horizon = 6
)

R1_prepped_data_tbl <- get_prepped_data(
  run_info = run_info,
  recipe = "R1"
)

print(R1_prepped_data_tbl)
#> # A tibble: 36 × 66
#>    Date       Combo id    Target Date_index.num Date_diff Date_year Date_half
#>    <date>     <chr> <chr>  <dbl>          <dbl>     <dbl>     <dbl>     <dbl>
#>  1 2013-01-01 M2    M2       260     1356998400         0      2013         1
#>  2 2013-02-01 M2    M2       260     1359676800   2678400      2013         1
#>  3 2013-03-01 M2    M2      -100     1362096000   2419200      2013         1
#>  4 2013-04-01 M2    M2       -10     1364774400   2678400      2013         1
#>  5 2013-05-01 M2    M2        50     1367366400   2592000      2013         1
#>  6 2013-06-01 M2    M2       110     1370044800   2678400      2013         1
#>  7 2013-07-01 M2    M2       130     1372636800   2592000      2013         2
#>  8 2013-08-01 M2    M2      -250     1375315200   2678400      2013         2
#>  9 2013-09-01 M2    M2      -260     1377993600   2678400      2013         2
#> 10 2013-10-01 M2    M2       160     1380585600   2592000      2013         2
#> # ℹ 26 more rows
#> # ℹ 58 more variables: Date_quarter <dbl>, Date_month <dbl>,
#> #   Date_month.lbl <chr>, Target_lag6 <dbl>, Target_lag9 <dbl>,
#> #   Target_lag12 <dbl>, Target_lag6_roll3_Avg <dbl>,
#> #   Target_lag9_roll3_Avg <dbl>, Target_lag12_roll3_Avg <dbl>,
#> #   Target_lag6_roll6_Avg <dbl>, Target_lag9_roll6_Avg <dbl>,
#> #   Target_lag12_roll6_Avg <dbl>, Target_lag6_roll9_Avg <dbl>, …

R2_prepped_data_tbl <- get_prepped_data(
  run_info = run_info,
  recipe = "R2"
)

print(R2_prepped_data_tbl)
#> # A tibble: 216 × 133
#>    Date       Combo id    Target Date_index.num Date_diff Date_year Date_half
#>    <date>     <chr> <chr>  <dbl>          <dbl>     <dbl>     <dbl>     <dbl>
#>  1 2013-01-01 M2    M2       260     1356998400         0      2013         1
#>  2 2013-02-01 M2    M2       260     1359676800   2678400      2013         1
#>  3 2013-03-01 M2    M2      -100     1362096000   2419200      2013         1
#>  4 2013-04-01 M2    M2       -10     1364774400   2678400      2013         1
#>  5 2013-05-01 M2    M2        50     1367366400   2592000      2013         1
#>  6 2013-06-01 M2    M2       110     1370044800   2678400      2013         1
#>  7 2013-07-01 M2    M2       130     1372636800   2592000      2013         2
#>  8 2013-08-01 M2    M2      -250     1375315200   2678400      2013         2
#>  9 2013-09-01 M2    M2      -260     1377993600   2678400      2013         2
#> 10 2013-10-01 M2    M2       160     1380585600   2592000      2013         2
#> # ℹ 206 more rows
#> # ℹ 125 more variables: Date_quarter <dbl>, Date_month <dbl>,
#> #   Date_month.lbl <chr>, Horizon <dbl>, Origin <dbl>, Target_lag1 <dbl>,
#> #   Target_lag2 <dbl>, Target_lag3 <dbl>, Target_lag4 <dbl>, Target_lag5 <dbl>,
#> #   Target_lag6 <dbl>, Target_lag9 <dbl>, Target_lag12 <dbl>,
#> #   Target_lag1_roll3_Avg <dbl>, Target_lag2_roll3_Avg <dbl>,
#> #   Target_lag3_roll3_Avg <dbl>, Target_lag4_roll3_Avg <dbl>, …

Train Individual Models

Now that our data is prepared for modeling, let’s now train some models. First we need to create the model workflows, determine our back testing process, and how many hyperparameter combinations to try during the validation process.

Then we can kick off training each model on our data.

prep_models(
  run_info = run_info,
  models_to_run = c("arima", "ets", "glmnet"),
  num_hyperparameters = 2
)

train_models(
  run_info = run_info,
  run_global_models = FALSE
)

Train Ensemble Models

After each individual model is trained, we can feed those predictions into ensemble models.

ensemble_models(run_info = run_info)

Final Models

The last step is to create the final simple model averages and select the best models.

final_models(run_info = run_info)

Get Forecast Results

Finally we can now retrieve the forecast results from this Finn run.

finn_output_tbl <- get_forecast_data(run_info = run_info)

print(finn_output_tbl)
#> # A tibble: 390 × 17
#>    Combo id    Model_ID   Model_Name Model_Type Recipe_ID Run_Type Train_Test_ID
#>    <chr> <chr> <chr>      <chr>      <chr>      <chr>     <chr>            <dbl>
#>  1 M2    M2    arima--lo… <NA>       local      simple_a… Future_…             1
#>  2 M2    M2    arima--lo… <NA>       local      simple_a… Future_…             1
#>  3 M2    M2    arima--lo… <NA>       local      simple_a… Future_…             1
#>  4 M2    M2    arima--lo… <NA>       local      simple_a… Future_…             1
#>  5 M2    M2    arima--lo… <NA>       local      simple_a… Future_…             1
#>  6 M2    M2    arima--lo… <NA>       local      simple_a… Future_…             1
#>  7 M2    M2    arima--lo… <NA>       local      simple_a… Back_Te…             2
#>  8 M2    M2    arima--lo… <NA>       local      simple_a… Back_Te…             3
#>  9 M2    M2    arima--lo… <NA>       local      simple_a… Back_Te…             3
#> 10 M2    M2    arima--lo… <NA>       local      simple_a… Back_Te…             4
#> # ℹ 380 more rows
#> # ℹ 9 more variables: Best_Model <chr>, Horizon <dbl>, Date <date>,
#> #   Target <dbl>, Forecast <dbl>, lo_95 <dbl>, lo_80 <dbl>, hi_80 <dbl>,
#> #   hi_95 <dbl>