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Direct autoregressive forecaster with covariates

Source: R/arx_forecaster.R
arx_forecaster.Rd

This is an autoregressive forecasting model for epiprocess::epi_df data. It does "direct" forecasting, meaning that it estimates a model for a particular target horizon of outcome based on the lags of the predictors. See the Get started vignette for some worked examples and Custom epi_workflows vignette for a recreation using a custom epi_workflow().

Usage

arx_forecaster(
  epi_data,
  outcome,
  predictors = outcome,
  trainer = linear_reg(),
  args_list = arx_args_list()
)

Arguments

epi_data

An epi_df object

outcome

A character (scalar) specifying the outcome (in the epi_df).

predictors

A character vector giving column(s) of predictor variables. This defaults to the outcome. However, if manually specified, only those variables specifically mentioned will be used. (The outcome will not be added.) By default, equals the outcome. If manually specified, does not add the outcome variable, so make sure to specify it.

trainer

A {parsnip} model describing the type of estimation. For now, we enforce mode = "regression".

args_list

A list of customization arguments to determine the type of forecasting model. See arx_args_list().

Value

An arx_fcast, with the fields predictions and epi_workflow. predictions is an epi_df of predicted values while epi_workflow() is the fit workflow used to make those predictions

See also

arx_fcast_epi_workflow(), arx_args_list()

Examples

jhu <- covid_case_death_rates %>%
  dplyr::filter(time_value >= as.Date("2021-12-01"))

out <- arx_forecaster(
  jhu,
  "death_rate",
  c("case_rate", "death_rate")
)

out <- arx_forecaster(jhu,
  "death_rate",
  c("case_rate", "death_rate"),
  trainer = quantile_reg(),
  args_list = arx_args_list(quantile_levels = 1:9 / 10)
)
out
#> ══ A basic forecaster of type ARX Forecaster ═══════════════════════════════════
#> 
#> This forecaster was fit on 2025-03-26 21:54:45.
#> 
#> Training data was an <epi_df> with:
#> • Geography: state,
#> • Time type: day,
#> • Using data up-to-date as of: 2023-03-10.
#> • With the last data available on 2021-12-31
#> 
#> ── Predictions ─────────────────────────────────────────────────────────────────
#> 
#> A total of 56 predictions are available for
#> • 56 unique geographic regions,
#> • At forecast date: 2021-12-31,
#> • For target date: 2022-01-07,
#>