This is a simple forecasting model for epiprocess::epi_df data. It uses the most recent observation as the forecast for any future date, and produces intervals based on the quantiles of the residuals of such a "flatline" forecast over all available training data.
By default, the predictive intervals are computed separately for each
combination of key values (geo_value + any additional keys) in the
epi_data argument.
This forecaster is very similar to that used by the COVID19ForecastHub
Usage
flatline_forecaster(epi_data, outcome, args_list = flatline_args_list())Arguments
- epi_data
- outcome
A scalar character for the column name we wish to predict.
- args_list
A list of additional arguments as created by the
flatline_args_list()constructor function.
Value
A data frame of point (and optionally interval) forecasts at a single
ahead (unique horizon) for each unique combination of key_vars.
Details
Here is (roughly) the code for the flatline_forecaster() applied to the
case_rate for epidatasets::covid_case_death_rates.
jhu <- covid_case_death_rates %>%
filter(time_value > "2021-11-01", geo_value %in% c("ak", "ca", "ny"))
r <- epi_recipe(covid_case_death_rates) %>%
step_epi_ahead(case_rate, ahead = 7, skip = TRUE) %>%
recipes::update_role(case_rate, new_role = "predictor") %>%
recipes::add_role(all_of(key_colnames(jhu)), new_role = "predictor")
f <- frosting() %>%
layer_predict() %>%
layer_residual_quantiles() %>%
layer_add_forecast_date() %>%
layer_add_target_date() %>%
layer_threshold(starts_with(".pred"))
eng <- linear_reg() %>% set_engine("flatline")
wf <- epi_workflow(r, eng, f) %>% fit(jhu)
preds <- forecast(wf)