mlr3forecast

Extending mlr3 to time series forecasting.

Important

This package is in an early stage of development and should be considered experimental. If you are interested in experimenting with it, we welcome your feedback!

Installation

Install the development version from GitHub:

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

Usage

Univariate

library(mlr3forecast)
library(mlr3learners)

task = tsk("airpassengers")
task$select(setdiff(task$feature_names, "date"))
measure = msr("regr.rmse")
ff = Forecaster$new(lrn("regr.ranger"), 1:3)$train(task)
newdata = data.frame(passengers = rep(NA_real_, 3L))
prediction = ff$predict_newdata(newdata, task)
prediction
#> <PredictionRegr> for 3 observations:
#>  row_ids truth response
#>        1    NA 445.6966
#>        2    NA 474.1113
#>        3    NA 483.3287
prediction = ff$predict(task, 142:144)
prediction
#> <PredictionRegr> for 3 observations:
#>  row_ids truth response
#>        1   461 458.3815
#>        2   390 410.9329
#>        3   432 395.2375
prediction$score(measure)
#> regr.rmse 
#>  24.47126

ff = Forecaster$new(lrn("regr.ranger"), 1:3)
resampling = rsmp("forecast_holdout", ratio = 0.8)
rr = resample(task, ff, resampling)
rr$aggregate(measure)
#> regr.rmse 
#>  111.1243

resampling = rsmp("forecast_cv")
rr = resample(task, ff, resampling)
rr$aggregate(measure)
#> regr.rmse 
#>  53.55455

Multivariate

library(mlr3learners)
library(mlr3pipelines)

task = tsk("airpassengers")
# datefeatures currently requires POSIXct
graph = ppl("convert_types", "Date", "POSIXct") %>>%
  po("datefeatures",
    param_vals = list(is_day = FALSE, cyclic = FALSE, hour = FALSE, minute = FALSE, second = FALSE)
  )
new_task = graph$train(task)[[1L]]
ff = Forecaster$new(lrn("regr.ranger"), 1:3)$train(new_task)
prediction = ff$predict(new_task, 142:144)
prediction$score(measure)
#> regr.rmse 
#>  19.19258

row_ids = new_task$nrow - 0:2
ff$predict_newdata(new_task$data(rows = row_ids), new_task)
#> <PredictionRegr> for 3 observations:
#>  row_ids truth response
#>        1   432 406.4261
#>        2   390 390.4117
#>        3   461 395.0697
newdata = new_task$data(rows = row_ids, cols = new_task$feature_names)
ff$predict_newdata(newdata, new_task)
#> <PredictionRegr> for 3 observations:
#>  row_ids truth response
#>        1    NA 406.4261
#>        2    NA 390.4117
#>        3    NA 395.0697

resampling = rsmp("forecast_holdout", ratio = 0.8)
rr = resample(new_task, ff, resampling)
rr$aggregate(measure)
#> regr.rmse 
#>  79.89178

resampling = rsmp("forecast_cv")
rr = resample(new_task, ff, resampling)
rr$aggregate(measure)
#> regr.rmse 
#>  45.18738

mlr3pipelines integration

ff = Forecaster$new(lrn("regr.ranger"), 1:3)
glrn = as_learner(graph %>>% ff)$train(task)
prediction = glrn$predict(task, 142:144)
prediction$score(measure)
#> regr.rmse 
#>  34.23849

Example: Forecasting electricity demand

library(data.table)
library(mlr3learners)
library(mlr3pipelines)

task = tsibbledata::vic_elec |>
  as.data.table() |>
  setnames(tolower) |>
  _[
    year(time) == 2014L,
    .(demand = sum(demand) / 1e3, temperature = max(temperature), holiday = any(holiday)),
    by = date
  ] |>
  as_task_regr(target = "demand")

graph = ppl("convert_types", "Date", "POSIXct") %>>%
  po("datefeatures",
    param_vals = list(is_day = FALSE, cyclic = FALSE, hour = FALSE, minute = FALSE, second = FALSE)
  )
ff = Forecaster$new(lrn("regr.ranger"), 1:3)
glrn = as_learner(graph %>>% ff)$train(task)

max_date = task$data()[.N, date]
newdata = data.frame(
  date = max_date + 1:14,
  demand = rep(NA_real_, 14L),
  temperature = 26,
  holiday = c(TRUE, rep(FALSE, 13L))
)
prediction = glrn$predict_newdata(newdata, task)
prediction
#> <PredictionRegr> for 14 observations:
#>  row_ids truth response
#>        1    NA 187.1200
#>        2    NA 191.2271
#>        3    NA 184.5125
#>      ---   ---      ---
#>       12    NA 214.8611
#>       13    NA 217.7954
#>       14    NA 219.8105