Postprocessing step to add the forecast date

Usage

layer_add_forecast_date(
  frosting,
  forecast_date = NULL,
  id = rand_id("add_forecast_date")
)

Arguments

frosting: a frosting postprocessor
forecast_date: The forecast date to add as a column to the epi_df. For most cases, this should be specified in the form "yyyy-mm-dd". Note that when the forecast date is left unspecified, it is set to the maximum time value from the data used in pre-processing, fitting the model, and postprocessing.
id: a random id string

Value

an updated frosting postprocessor

Details

To use this function, either specify a forecast date or leave the forecast date unspecifed here. In the latter case, the forecast date will be set as the maximum time value from the data used in pre-processing, fitting the model, and postprocessing. In any case, when the forecast date is less than the maximum as_of value (from the data used pre-processing, model fitting, and postprocessing), an appropriate warning will be thrown.

Examples

library(dplyr)
jhu <- case_death_rate_subset %>%
  filter(time_value > "2021-11-01", geo_value %in% c("ak", "ca", "ny"))
r <- epi_recipe(jhu) %>%
  step_epi_lag(death_rate, lag = c(0, 7, 14)) %>%
  step_epi_ahead(death_rate, ahead = 7) %>%
  step_epi_naomit()
wf <- epi_workflow(r, linear_reg()) %>% fit(jhu)
latest <- jhu %>%
  filter(time_value >= max(time_value) - 14)

# Don't specify `forecast_date` (by default, this should be last date in latest)
f <- frosting() %>%
  layer_predict() %>%
  layer_naomit(.pred)
wf0 <- wf %>% add_frosting(f)
p0 <- predict(wf0, latest)
p0
#> An `epi_df` object, 3 x 3 with metadata:
#> * geo_type  = state
#> * time_type = day
#> * as_of     = 2022-05-31 19:08:25.791826
#> 
#> # A tibble: 3 × 3
#>   geo_value time_value .pred
#> * <chr>     <date>     <dbl>
#> 1 ak        2021-12-31 0.245
#> 2 ca        2021-12-31 0.313
#> 3 ny        2021-12-31 0.295

# Specify a `forecast_date` that is greater than or equal to `as_of` date
f <- frosting() %>%
  layer_predict() %>%
  layer_add_forecast_date(forecast_date = "2022-05-31") %>%
  layer_naomit(.pred)
wf1 <- wf %>% add_frosting(f)

p1 <- predict(wf1, latest)
p1
#> An `epi_df` object, 3 x 4 with metadata:
#> * geo_type  = state
#> * time_type = day
#> * as_of     = 2022-05-31 19:08:25.791826
#> 
#> # A tibble: 3 × 4
#>   geo_value time_value .pred forecast_date
#> * <chr>     <date>     <dbl> <date>       
#> 1 ak        2021-12-31 0.245 2022-05-31   
#> 2 ca        2021-12-31 0.313 2022-05-31   
#> 3 ny        2021-12-31 0.295 2022-05-31   

# Specify a `forecast_date` that is less than `as_of` date
f2 <- frosting() %>%
  layer_predict() %>%
  layer_add_forecast_date(forecast_date = "2021-12-31") %>%
  layer_naomit(.pred)
wf2 <- wf %>% add_frosting(f2)

p2 <- predict(wf2, latest)
p2
#> An `epi_df` object, 3 x 4 with metadata:
#> * geo_type  = state
#> * time_type = day
#> * as_of     = 2022-05-31 19:08:25.791826
#> 
#> # A tibble: 3 × 4
#>   geo_value time_value .pred forecast_date
#> * <chr>     <date>     <dbl> <date>       
#> 1 ak        2021-12-31 0.245 2021-12-31   
#> 2 ca        2021-12-31 0.313 2021-12-31   
#> 3 ny        2021-12-31 0.295 2021-12-31   

# Do not specify a forecast_date
f3 <- frosting() %>%
  layer_predict() %>%
  layer_add_forecast_date() %>%
  layer_naomit(.pred)
wf3 <- wf %>% add_frosting(f3)

p3 <- predict(wf3, latest)
p3
#> An `epi_df` object, 3 x 4 with metadata:
#> * geo_type  = state
#> * time_type = day
#> * as_of     = 2022-05-31 19:08:25.791826
#> 
#> # A tibble: 3 × 4
#>   geo_value time_value .pred forecast_date
#> * <chr>     <date>     <dbl> <date>       
#> 1 ak        2021-12-31 0.245 2021-12-31   
#> 2 ca        2021-12-31 0.313 2021-12-31   
#> 3 ny        2021-12-31 0.295 2021-12-31