forecast function

Produce forecasts from a vital model

The forecast function allows you to produce future predictions of a vital model, where the response is a function of age. The forecasts returned contain both point forecasts and their distribution.

## S3 method for class 'FDM'
forecast(
  object,
  new_data = NULL,
  h = NULL,
  point_forecast = list(.mean = mean),
  simulate = FALSE,
  bootstrap = FALSE,
  times = 5000,
  ...
)

## S3 method for class 'LC'
forecast(
  object,
  new_data = NULL,
  h = NULL,
  point_forecast = list(.mean = mean),
  simulate = FALSE,
  bootstrap = FALSE,
  times = 5000,
  ...
)

## S3 method for class 'FMEAN'
forecast(
  object,
  new_data = NULL,
  h = NULL,
  point_forecast = list(.mean = mean),
  simulate = FALSE,
  bootstrap = FALSE,
  times = 5000,
  ...
)

## S3 method for class 'FNAIVE'
forecast(
  object,
  new_data = NULL,
  h = NULL,
  point_forecast = list(.mean = mean),
  simulate = FALSE,
  bootstrap = FALSE,
  times = 5000,
  ...
)

## S3 method for class 'mdl_vtl_df'
forecast(
  object,
  new_data = NULL,
  h = NULL,
  point_forecast = list(.mean = mean),
  simulate = FALSE,
  bootstrap = FALSE,
  times = 5000,
  ...
)

Arguments

• object: A mable containing one or more models.

• new_data: A tsibble containing future information used to forecast.

• h: Number of time steps ahead to forecast. This can be used instead of new_data

  when there are no covariates in the model. It is ignored if new_data is provided.

• point_forecast: A list of functions used to compute point forecasts from the forecast distribution.

• simulate: If TRUE, then forecast distributions are computed using simulation from a parametric model.

• bootstrap: If TRUE, then forecast distributions are computed using simulation with resampling.

• times: The number of sample paths to use in estimating the forecast distribution when bootstrap = TRUE.

• ...: Additional arguments passed to the specific model method.

Returns

A fable containing the following columns:

• .model: The name of the model used to obtain the forecast. Taken from the column names of models in the provided mable.
• The forecast distribution. The name of this column will be the same as the dependent variable in the model(s). If multiple dependent variables exist, it will be named .distribution.
• Point forecasts computed from the distribution using the functions in the point_forecast argument.
• All columns in new_data, excluding those whose names conflict with the above.

Examples

aus_mortality |>
 dplyr::filter(State == "Victoria", Sex == "female") |>
 model(naive = FNAIVE(Mortality)) |>
 forecast(h = 10)

Author(s)

Rob J Hyndman and Mitchell O'Hara-Wild