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


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_hts <- tourism |>
  aggregate_key(State,
                Trips = sum(Trips))

fit <- tourism_hts |>
  model(base = ETS(Trips)) |>
  reconcile(ols = min_trace(base, method = "ols"),
            icomb = icomb(base, train_size = 75))
#> Warning: There were 2 warnings in `mutate()`.
#> The first warning was:
#> ℹ In argument: `icomb = icomb(base, train_size = 75)`.
#> Caused by warning in `.resolve_control()`:
#> ! Passing 'thresh' to glmnet() is deprecated. Use control = list(thresh = ...) instead.
#> ℹ Run `dplyr::last_dplyr_warnings()` to see the 1 remaining warning.

# \donttest{
fit |>
  forecast(bootstrap = TRUE, times = 1000) |>
  hilo(level = c(80, 95))
#> # A tsibble: 216 x 7 [1Q]
#> # Key:       State, .model [27]
#> Loading required namespace: crayon
#>    State  .model Quarter        Trips .mean                  `80%`
#>    <chr*> <chr>    <qtr>       <dist> <dbl>                 <hilo>
#>  1 ACT    base   2018 Q1 sample[1000]  703. [599.5647, 813.1174]80
#>  2 ACT    base   2018 Q2 sample[1000]  720. [609.7173, 835.1879]80
#>  3 ACT    base   2018 Q3 sample[1000]  737. [621.1969, 859.2365]80
#>  4 ACT    base   2018 Q4 sample[1000]  753. [638.0372, 869.9488]80
#>  5 ACT    base   2019 Q1 sample[1000]  765. [642.7770, 890.6511]80
#>  6 ACT    base   2019 Q2 sample[1000]  789. [667.6355, 908.2969]80
#>  7 ACT    base   2019 Q3 sample[1000]  802. [675.2354, 934.7294]80
#>  8 ACT    base   2019 Q4 sample[1000]  813. [679.5546, 943.1796]80
#>  9 ACT    ols    2018 Q1 sample[1000]  714. [537.2034, 887.3422]80
#> 10 ACT    ols    2018 Q2 sample[1000]  814. [629.9566, 991.8888]80
#> # ℹ 206 more rows
#> # ℹ 1 more variable: `95%` <hilo>
# }