Produce coherent forecasts using the information combination method

The forecast function allows you to generate coherent future predictions for hierarchical or grouped time series based on fitted models.

Usage

# S3 method for class 'mdl_icomb_lst'
forecast(
  object,
  new_data = NULL,
  h = NULL,
  point_forecast = list(.mean = mean),
  ...
)

Arguments

object

The time series models used to produce the forecasts.

new_data

A tsibble containing future information used to forecast.

h

The forecast horizon (can be used instead of new_data for regular time series with no exogenous regressors).

point_forecast

The point forecast measure(s) which should be returned in the resulting fable. Specify as a named list of functions which accept a distribution and return a vector.

...

Additional arguments for forecast model methods.

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. If bootstrap = TRUE, point forecasts are generated using bootstrapped sample paths.

Details

The forecasts returned contain both point forecasts and their distribution. A specific forecast interval can be extracted from the distribution using the distributional::hilo() function. These intervals are stored in a single column using the hilo class, to extract the numerical upper and lower bounds you can use fabletools::unpack_hilo().

Examples

# \donttest{
library(fable)
#> Loading required package: fabletools
library(fabletools)
library(tsibble)
#> 
#> Attaching package: ‘tsibble’
#> The following objects are masked from ‘package:base’:
#> 
#>     intersect, setdiff, union
library(distributional)

tourism_gts <- tourism |>
  aggregate_key(State * Purpose,
                Trips = sum(Trips))

fit <- tourism_gts |>
  model(base = ETS(Trips)) |>
  reconcile(ols = min_trace(base, method = "ols"),
            icomb = icomb(base, train_size = 75))
fit |>
  forecast(bootstrap = TRUE, times = 1000) |>
  hilo(level = c(80, 95))
#> # A tsibble: 1,080 x 8 [1Q]
#> # Key:       State, Purpose, .model [135]
#> Loading required namespace: crayon
#>    State  Purpose  .model Quarter        Trips .mean
#>    <chr*> <chr*>   <chr>    <qtr>       <dist> <dbl>
#>  1 ACT    Business base   2018 Q1 sample[1000]  145.
#>  2 ACT    Business base   2018 Q2 sample[1000]  203.
#>  3 ACT    Business base   2018 Q3 sample[1000]  197.
#>  4 ACT    Business base   2018 Q4 sample[1000]  191.
#>  5 ACT    Business base   2019 Q1 sample[1000]  142.
#>  6 ACT    Business base   2019 Q2 sample[1000]  204.
#>  7 ACT    Business base   2019 Q3 sample[1000]  197.
#>  8 ACT    Business base   2019 Q4 sample[1000]  189.
#>  9 ACT    Business ols    2018 Q1 sample[1000]  173.
#> 10 ACT    Business ols    2018 Q2 sample[1000]  237.
#> # ℹ 1,070 more rows
#> # ℹ 2 more variables: `80%` <hilo>, `95%` <hilo>
# }