Iteratively Forecast

Usage

iterative_rt_forecast(
  rts,
  model = NULL,
  horizon = 7,
  samples = 1000,
  timeout = 30,
  bound_rt = TRUE,
  min_points = 3
)

Arguments

rts: A dataframe of containing two variables rt and date with rt being numeric and date being a date.
model: A model object in the format of bsts_model or fable_model. See the corresponding help files for details.
horizon: Numeric, the time horizon over which to predict.
samples: Numeric, number of samples to take.
timeout: Numeric, timeout of model fitting in seconds. Defaults to 30 seconds.
bound_rt: Logical, defaults to TRUE. Should Rt values be bounded to be greater than or equal to 0.
min_points: Numeric, defaults to 3. The minimum number of time points at which to begin iteratively evaluating the forecast.

Value

A tibble of iterative forecasts

Examples

if (FALSE) {
iterative_rt_forecast(EpiSoon::example_obs_rts,
  model = function(...) {
    EpiSoon::bsts_model(
      model =
        function(ss, y) {
          bsts::AddSemilocalLinearTrend(ss, y = y)
        }, ...
    )
  },
  horizon = 7, samples = 10, min_points = 4
) -> tmp
}