Predict the future with the most recent value

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 by shuffling the quantiles of the residuals of such a "flatline" forecast and incrementing these forward over all available training data.

Usage

cdc_baseline_forecaster(
  epi_data,
  outcome,
  args_list = cdc_baseline_args_list()
)

Arguments

epi_data

An epiprocess::epi_df

outcome

A scalar character for the column name we wish to predict.

args_list

A list of additional arguments as created by the cdc_baseline_args_list() constructor function.

Value

A data frame of point and interval forecasts for all aheads (unique horizons) for each unique combination of key_vars.

Details

By default, the predictive intervals are computed separately for each combination of geo_value in the epi_data argument.

This forecaster is meant to produce exactly the CDC Baseline used for COVID19ForecastHub

Examples

library(dplyr)
weekly_deaths <- covid_case_death_rates %>%
  select(geo_value, time_value, death_rate) %>%
  left_join(state_census %>% select(pop, abbr), by = c("geo_value" = "abbr")) %>%
  mutate(deaths = pmax(death_rate / 1e5 * pop * 7, 0)) %>%
  select(-pop, -death_rate) %>%
  group_by(geo_value) %>%
  epi_slide(~ sum(.$deaths), .window_size = 7, .new_col_name = "deaths_7dsum") %>%
  ungroup() %>%
  filter(weekdays(time_value) == "Saturday")

cdc <- cdc_baseline_forecaster(weekly_deaths, "deaths_7dsum")
preds <- pivot_quantiles_wider(cdc$predictions, .pred_distn)

if (require(ggplot2)) {
  forecast_date <- unique(preds$forecast_date)
  four_states <- c("ca", "pa", "wa", "ny")
  preds %>%
    filter(geo_value %in% four_states) %>%
    ggplot(aes(target_date)) +
    geom_ribbon(aes(ymin = `0.1`, ymax = `0.9`), fill = blues9[3]) +
    geom_ribbon(aes(ymin = `0.25`, ymax = `0.75`), fill = blues9[6]) +
    geom_line(aes(y = .pred), color = "orange") +
    geom_line(
      data = weekly_deaths %>% filter(geo_value %in% four_states),
      aes(x = time_value, y = deaths_7dsum)
    ) +
    scale_x_date(limits = c(forecast_date - 90, forecast_date + 30)) +
    labs(x = "Date", y = "Weekly deaths") +
    facet_wrap(~geo_value, scales = "free_y") +
    theme_bw() +
    geom_vline(xintercept = forecast_date)
}
#> Loading required package: ggplot2
#> 
#> Attaching package: ‘ggplot2’
#> The following object is masked from ‘package:epipredict’:
#> 
#>     layer
#> Warning: Removed 1 row containing missing values or values outside the scale range
#> (`geom_line()`).
#> Warning: Removed 39 rows containing missing values or values outside the scale range
#> (`geom_line()`).