Get Final Forecast Data
get_forecast_data(run_info, return_type = "df")run info using the set_run_info() function
return type
table of final forecast results
For non-agentic standard or grouped hierarchical runs, returns every
successfully saved per-model reconciled forecast together with Best-Model.
Filter Best_Model == "Yes" to retain only the reconciled selected forecast.
Best-Model can combine different winning models or averages across series;
it does not require one model family to win everywhere. Models that did not
successfully produce a reconciled artifact are not added to the output.
Use get_agent_forecast() for final Agent results. Hierarchical Agent output
contains only the reconciled selected forecast, because iterate_forecast()
can run different model and recipe sets for different series. It is not an
all-model reconciled comparison table.
# \donttest{
data_tbl <- timetk::m4_monthly %>%
dplyr::rename(Date = date) %>%
dplyr::mutate(id = as.character(id)) %>%
dplyr::filter(
id == "M2",
Date >= "2012-01-01",
Date <= "2015-06-01"
)
run_info <- set_run_info()
#> Finn Submission Info
#> • Project Name: finn_project
#> • Run Name: finn_fcst-20261002T154347Z
#>
prep_data(run_info,
input_data = data_tbl,
combo_variables = c("id"),
target_variable = "value",
date_type = "month",
forecast_horizon = 3,
recipes_to_run = "R1"
)
#> ℹ Prepping Data
#> ✔ Prepping Data [796ms]
#>
prep_models(run_info,
models_to_run = c("arima", "ets"),
num_hyperparameters = 1
)
#> ℹ Creating Model Workflows
#> ✔ Creating Model Workflows [88ms]
#>
#> ℹ Creating Model Hyperparameters
#> ✔ Creating Model Hyperparameters [84ms]
#>
#> ℹ Creating Train Test Splits
#> ℹ Turning ensemble models off since no multivariate models were chosen to run.
#> ℹ Creating Train Test Splits
#> ✔ Creating Train Test Splits [402ms]
#>
train_models(run_info,
run_local_models = TRUE
)
#> ℹ Training Individual Models
#> ✔ Training Individual Models [15.9s]
#>
final_models(run_info,
average_models = FALSE
)
#> ℹ Selecting Best Models
#> ✔ Selecting Best Models [359ms]
#>
fcst_tbl <- get_forecast_data(run_info)
# }