https://github.com/mlr-org/mlr3forecast

Time series forecasting for mlr3
https://github.com/mlr-org/mlr3forecast

mlr3 r r-package time-series

Last synced: 6 days ago
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Time series forecasting for mlr3

• Host: GitHub
• URL: https://github.com/mlr-org/mlr3forecast
• Owner: mlr-org
• License: lgpl-3.0
• Created: 2024-07-16T16:33:00.000Z (4 months ago)
• Default Branch: main
• Last Pushed: 2024-09-10T17:49:52.000Z (2 months ago)
• Last Synced: 2024-09-10T19:58:53.594Z (2 months ago)
• Topics: mlr3, r, r-package, time-series
• Language: R
• Homepage: http://mlr3forecast.mlr-org.com/
• Size: 1.26 MB
• Stars: 3
• Watchers: 5
• Forks: 0
• Open Issues: 6
• Metadata Files:
  • Readme: README.Rmd
  • Changelog: NEWS.md
  • License: LICENSE

Awesome Lists containing this project

README

        
---

output: github_document

---

```{r, include = FALSE}

knitr::opts_chunk$set(

  collapse = TRUE,

  comment = "#>",

  fig.path = "man/figures/README-",

  out.width = "100%"

)

lgr::get_logger("mlr3")$set_threshold("warn")

options(datatable.print.class = FALSE, datatable.print.keys = FALSE)

```

# mlr3forecast

Extending mlr3 to time series forecasting.

[![Lifecycle: experimental](https://img.shields.io/badge/lifecycle-experimental-orange.svg)](https://lifecycle.r-lib.org/articles/stages.html#experimental)

[![RCMD Check](https://github.com/mlr-org/mlr3forecast/actions/workflows/rcmdcheck.yaml/badge.svg)](https://github.com/mlr-org/mlr3forecast/actions/workflows/rcmdcheck.yaml)

[![CRAN status](https://www.r-pkg.org/badges/version/mlr3forecast)](https://CRAN.R-project.org/package=mlr3forecast)

[![StackOverflow](https://img.shields.io/badge/stackoverflow-mlr3-orange.svg)](https://stackoverflow.com/questions/tagged/mlr3)

[![Mattermost](https://img.shields.io/badge/chat-mattermost-orange.svg)](https://lmmisld-lmu-stats-slds.srv.mwn.de/mlr_invite/)

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

## Installation

Install the development version from [GitHub](https://github.com/):

```{r, eval = FALSE}

# install.packages("pak")

pak::pak("mlr-org/mlr3forecast")

```

## Usage

```{r}

library(mlr3forecast)

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

prediction = ff$predict_newdata(task, 3L)

prediction

prediction = ff$predict(task, 142:144)

prediction

prediction$score(msr("regr.rmse"))

```