forecast.epi_workflow predicts by restricting the training data to the
latest available data, and predicting on that. It binds together
get_test_data() and predict().
Usage
# S3 method for class 'epi_workflow'
forecast(object, ...)Examples
jhu <- covid_case_death_rates %>%
filter(time_value > "2021-08-01")
r <- epi_recipe(jhu) %>%
step_epi_lag(death_rate, lag = c(0, 7, 14)) %>%
step_epi_ahead(death_rate, ahead = 7) %>%
step_epi_naomit()
epi_workflow(r, parsnip::linear_reg()) %>%
fit(jhu) %>%
forecast()
#> An `epi_df` object, 56 x 3 with metadata:
#> * geo_type = state
#> * time_type = day
#> * as_of = 2023-03-10
#>
#> # A tibble: 56 × 3
#> geo_value time_value .pred
#> <chr> <date> <dbl>
#> 1 ak 2021-12-31 0.550
#> 2 al 2021-12-31 0.290
#> 3 ar 2021-12-31 0.504
#> 4 as 2021-12-31 0.148
#> 5 az 2021-12-31 0.724
#> 6 ca 2021-12-31 0.258
#> 7 co 2021-12-31 0.454
#> 8 ct 2021-12-31 0.333
#> 9 dc 2021-12-31 0.204
#> 10 de 2021-12-31 0.402
#> # ℹ 46 more rows