forecast: Produce forecasts from a vital model

In vital: Tidy Analysis Tools for Mortality, Fertility, Migration and Population Data

Produce forecasts from a vital model

Description

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.

Usage

## 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.

Value

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.

Author(s)

Rob J Hyndman and Mitchell O'Hara-Wild

Examples

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

vital documentation built on June 22, 2024, 9:56 a.m.