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https://github.com/mlr-org/mlr3temporal
Forecasting for mlr3
https://github.com/mlr-org/mlr3temporal
Last synced: about 2 months ago
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Forecasting for mlr3
- Host: GitHub
- URL: https://github.com/mlr-org/mlr3temporal
- Owner: mlr-org
- License: lgpl-3.0
- Created: 2018-11-16T10:39:54.000Z (about 6 years ago)
- Default Branch: main
- Last Pushed: 2024-08-16T18:47:58.000Z (4 months ago)
- Last Synced: 2024-08-16T20:09:29.839Z (4 months ago)
- Language: HTML
- Homepage: https://mlr3temporal.mlr-org.com
- Size: 5.28 MB
- Stars: 19
- Watchers: 12
- Forks: 2
- Open Issues: 9
-
Metadata Files:
- Readme: README.Rmd
- License: LICENSE
Awesome Lists containing this project
README
---
output: github_document
---
```{r setup, include=FALSE}
set.seed(123)
knitr::opts_chunk$set(fig.path = "man/figures/")
```
# mlr3temporal
Temporal prediction / forecasting for mlr3
[](https://github.com/mlr-org/mlr3temporal/actions/workflows/r-cmd-check.yml)
[](https://cran.r-project.org/package=mlr3temporal)
[](https://codecov.io/gh/mlr-org/mlr3temporal)
[](https://lifecycle.r-lib.org/articles/stages.html#experimental)
[](https://stackoverflow.com/questions/tagged/mlr3)
Time series analysis accounts for the fact that data points taken over time may have an internal structure (such as autocorrelation, trend or seasonal variation) that should be accounted for.
This package extends the [mlr3](https://github.com/mlr-org/mlr3) package framework by time-series prediction and resampling methods.
```{r, echo=FALSE}
knitr::include_graphics("man/figures/multi_timeseries.png")
```
## Installation
Install the development version from GitHub:
``` r
remotes::install_github("mlr-org/mlr3temporal")
```
## Forecasting
Currently the following methods are implemented:
### Tasks
| Id | Code | Type |
| ------------------------------------------------------------------------------------------- | -------------------- | ----------------------- |
| [airpassengers](https://mlr3temporal.mlr-org.com/reference/mlr_tasks_airpassengers.html) | `tsk("airpassengers")` | Univariate Timeseries |
| [petrol](https://mlr3temporal.mlr-org.com/reference/mlr_tasks_petrol.html) | `tsk("petrol")` | Multivariate Timeseries |
### Learners
| Id | Learner | Package |
| ------------------------------------------------------------------------------------------------ | --------------------- | ------------------------------------------------------- |
| [forecast.auto_arima](https://mlr3temporal.mlr-org.com/reference/mlr_learners_regr.auto_arima.html) | Auto Arima | [forecast](https://cran.r-project.org/package=forecast) |
| [forecast.arima](https://mlr3temporal.mlr-org.com/reference/mlr_learners_regr.arima.html) | Arima | [forecast](https://cran.r-project.org/package=forecast) |
| [forecast.average](https://mlr3temporal.mlr-org.com/reference/mlr_learners_regr.average.html) | Average | base |
| [forecast.VAR](https://mlr3temporal.mlr-org.com/reference/mlr_learners_regr.VAR.html) | Vector Autoregression | [vars](https://cran.r-project.org/package=vars) |
### Measures
| Id | Measure | Package |
|----------------------------------------------------------------------------------------------|---------------------|---------|
| [forecast.mae](https://mlr3temporal.mlr-org.com/reference/mlr_measures_forecast.mae.html) | Mean Absolute Error | base |
| [forecast.mape](https://mlr3temporal.mlr-org.com/reference/mlr_measures_forecast.mape.html) | Mean Absolute Percentage Error | base |
| [forecast.mse](https://mlr3temporal.mlr-org.com/reference/mlr_measures_forecast.mse.html) | Mean Squared Error | base |
| [forecast.rmse](https://mlr3temporal.mlr-org.com/reference/mlr_measures_forecast.rmse.html) | Root Mean Squared Error | base |
### Resampling Methods
| Id | Resampling | Package |
|-------------------------------------------------------------------------------------------------------|--------------------------------|---------|
| [forecast_holdout](https://mlr3temporal.mlr-org.com/reference/mlr_resamplings_forecast_holdout.html) | Holdout | base |
| [forecast_cv](https://mlr3temporal.mlr-org.com/reference/mlr_resamplings_forecast_cv.html) | Rolling Window CrossValidation | base |
## Code Example
```r
library(mlr3temporal)
task = tsk("airpassengers")
learner = lrn("forecast.auto_arima")
learner$train(task, row_ids = 1:20)
predictions = learner$predict(task, row_ids = 21:55)
measure = msr("forecast.mae")
predictions$score(measure)
autoplot(task)
```
## Resampling
#### Holdout
Split data into a training set and a test set.
Parameter `ratio` determines the ratio of observation going into the training set (default: 2/3).
```r
task = tsk("petrol")
learner = lrn("forecast.VAR")
resampling = rsmp("forecast_holdout", ratio = 0.8)
rr = resample(task, learner, resampling, store_models = TRUE)
rr$aggregate(msr("forecast.mae"))
```
#### Rolling Window CV
Splits data using a `folds`-folds (default: 10 folds) rolling window cross-validation.
```r
task = tsk("petrol")
learner = lrn("forecast.VAR")
resampling = rsmp("forecast_cv", folds = 5, fixed_window = FALSE)
rr = resample(task, learner, resampling, store_models = TRUE)
rr$aggregate(msr("forecast.mae"))
```
## More resources
For detailed information on how to get started with `mlr3` please read the [mlr3 book](https://mlr3book.mlr-org.com/) and consult the [Vignette](https://mlr3temporal.mlr-org.com/articles/vignettes.html) for more examples of mlr3temporal.
## Contributing to mlr3temporal
Please consult the [wiki](https://github.com/mlr-org/mlr3/wiki/) for a [style guide](https://github.com/mlr-org/mlr3/wiki/Style-Guide), a [roxygen guide](https://github.com/mlr-org/mlr3/wiki/Roxygen-Guide) and a [pull request guide](https://github.com/mlr-org/mlr3/wiki/PR-Guidelines).