mlr3forecast

Extending mlr3 to time series forecasting.

This package is in an early stage of development and should be considered experimental.

Installation

Install the development version from GitHub:

# install.packages("pak")
pak::pak("mlr-org/mlr3forecast")

Usage

library(mlr3forecast)
#> Loading required package: mlr3
library(mlr3learners)

dt = tsbox::ts_dt(AirPassengers)
dt[, time := NULL]
task = as_task_regr(dt, target = "value")

ff = Forecaster$new(
  learner = lrn("regr.ranger"),
  lag = 1:3
)
ff$train(task)
prediction = ff$predict(task)
prediction
#> <PredictionRegr> for 144 observations:
#>     row_ids    truth response
#>           1 432.0000 404.1487
#>           2 404.1487 450.5137
#>           3 450.5137 420.9816
#> ---                          
#>         142 452.6298 454.5250
#>         143 454.5250 454.5353
#>         144 454.5353 445.7902
prediction = ff$predict_newdata(task, 3L)
prediction
#> <PredictionRegr> for 3 observations:
#>  row_ids truth response
#>        1    NA 404.1487
#>        2    NA 450.5137
#>        3    NA 420.9816
prediction = ff$predict(task, 142:144)
prediction
#> <PredictionRegr> for 3 observations:
#>  row_ids    truth response
#>        1 508.0000 498.0064
#>        2 498.0064 460.8071
#>        3 460.8071 445.5276
prediction$score(msr("regr.rmse"))
#> regr.rmse 
#>  23.92435