Produce a forward forecast

Fit forecasting models to the full time series and generate forecasts for the next h reporting intervals.

Usage

get_fcast(
  x,
  models = default_models(),
  h = 4,
  top_n = 3,
  ensemble = c("linear_pool", "quantile_average")
)

Arguments

x

An incast_* object.

models

Named list of fable model specifications. Defaults to default_models. When x is an incast_cv object, leave unset to use the top-ranked models from cross-validation, or provide a custom set of models.

h

Integer giving the forecast horizon in reporting intervals. Defaults to 4. When x is an incast_cv object, the default is the cross-validation horizon.

top_n

Integer giving the number of top-ranked models to combine into the ensemble for each series. Used only when x is an incast_cv object and models is not provided. Defaults to 3.

ensemble

Method used to combine the models into the ENSEMBLE forecast. "linear_pool" (default) mixes the models' predictive distributions with equal weights. "quantile_average" takes, at each quantile level, the median of the models' quantiles using simple_ensemble

Value

An incast_fcast object containing:

hub

Hub-format forecasts containing model_out_tbl and oracle_output.

score

Cross-validation model performance scores, or NULL.

meta

Forecast settings including models, model selection, ensemble method, horizon, series keys, target, reporting interval, nowcast information, and evaluation date.

Forecast outputs can be exported with to_respilens.

Details

When provided with an incast_cv object, the function uses the cross-validation results to select the best-performing models for each series and combines them into an equal-weight ensemble. For incast_data or incast_ncast objects, all models in models are fitted and forecast.

If the input contains nowcast uncertainty from get_ncast, this uncertainty is incorporated into the forecast intervals.

Author

Cyril Geismar

Examples

if (FALSE) { # \dontrun{
ncast <- get_data("covid", "ny", revisions = TRUE) |> get_ncast()
cv <- ncast |> get_cv(eval_start_date = "2025-01-01", h = 4)

get_fcast(cv, top_n = 3) # use cross-validation rankings
get_fcast(cv, models = default_models()) # use custom models
get_fcast(ncast) # forecast directly from nowcast data
} # }