Prediction Object for Forecasting

This object wraps the predictions returned by a forecast learner (LearnerFcst, RecursiveForecaster, DirectForecaster). It subclasses mlr3::PredictionRegr, so forecasting is treated as regression: the response, se, quantiles and distr fields and all regression measures continue to work.

In addition, the prediction carries the time index (and any key columns) of the forecast horizon in its $data$extra slot. These are exposed via the $order and $key fields, lead the as.data.table() output, and are used by autoplot.PredictionFcst() to draw a forecast plot.

The task_type is kept as "regr" so that regression measures remain compatible: forecasting is scored as regression.

See also

Package mlr3viz for some generic visualizations.

Super classes

mlr3::Prediction -> mlr3::PredictionRegr -> PredictionFcst

Active bindings

order

(data.table::data.table() | NULL)
The forecast time index, recovered from $data$extra. A table with two columns:

• row_id (integer()), and

• order (Date() | POSIXct() | integer() | numeric()).

Returns NULL if no extra data is stored.

key

(data.table::data.table() | NULL)
The series identity columns of the forecast horizon, recovered from $data$extra. A table with two or more columns:

• row_id (integer()), and

• key variable(s) (factor() | ordered()).

If there is only one key column, it is named key. Returns NULL if there are no key columns.

Methods

Public methods

• PredictionFcst$new()

• PredictionFcst$clone()

Inherited methods

• mlr3::Prediction$filter()
• mlr3::Prediction$format()
• mlr3::Prediction$help()
• mlr3::Prediction$obs_loss()
• mlr3::Prediction$print()
• mlr3::Prediction$score()

---

PredictionFcst$new()

Creates a new instance of this R6 class.

Usage

PredictionFcst$new(
  task = NULL,
  row_ids = task$row_ids,
  truth = task$truth(),
  response = NULL,
  se = NULL,
  quantiles = NULL,
  distr = NULL,
  weights = NULL,
  check = TRUE,
  extra = NULL,
  raw = NULL
)

Arguments

task

(TaskFcst)
Task, used to extract defaults for row_ids and truth.

row_ids

(integer())
Row ids of the predicted observations, i.e. the row ids of the test set.

truth

(numeric())
True (observed) response.

response

(numeric())
Vector of numeric response values. One element for each observation in the test set.

se

(numeric())
Numeric vector of predicted standard errors. One element for each observation in the test set.

quantiles

(matrix())
Numeric matrix of predicted quantiles. One row per observation, one column per quantile.

distr

(VectorDistribution)
VectorDistribution from package distr6 (in repository https://raphaels1.r-universe.dev).

weights

(numeric())
Vector of measure weights for each observation.

check

(logical(1))
If TRUE, performs some argument checks and predict type conversions.

extra

(list())
Named list carrying the order (time) column and any key columns of the forecast horizon. The list names are the original task column names.

raw

(any)
Raw prediction object from the upstream model. Stored as-is without validation.

---

PredictionFcst$clone()

The objects of this class are cloneable with this method.

Usage

PredictionFcst$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.

Examples

task = tsk("airpassengers")
learner = lrn("fcst.auto_arima")$train(task)
p = forecast(learner, task, h = 12)
p$predict_types
#> [1] "response"
head(as.data.table(p))
#>         month row_ids truth response
#>        <Date>   <int> <num>    <num>
#> 1: 1961-01-01       1    NA 445.6349
#> 2: 1961-02-01       2    NA 420.3950
#> 3: 1961-03-01       3    NA 449.1983
#> 4: 1961-04-01       4    NA 491.8399
#> 5: 1961-05-01       5    NA 503.3945
#> 6: 1961-06-01       6    NA 566.8625