In mlr-org/mlr3forecasting: Forecasting for 'mlr3'

set.seed(123)
knitr::opts_chunk$set(fig.path = "man/figures/")

mlr3temporal

Temporal prediction / forecasting for 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 package framework by time-series prediction and resampling methods.

knitr::include_graphics("man/figures/multi_timeseries.png")

Installation

Install the development version from GitHub:

remotes::install_github("mlr-org/mlr3temporal")

Forecasting

Currently the following methods are implemented:

Tasks

| Id | Code | Type | | ------------------------------------------------------------------------------------------- | -------------------- | ----------------------- | | airpassengers | tsk("airpassengers") | Univariate Timeseries | | petrol | tsk("petrol") | Multivariate Timeseries |

Learners

| Id | Learner | Package | | ------------------------------------------------------------------------------------------------ | --------------------- | ------------------------------------------------------- | | forecast.auto_arima | Auto Arima | forecast | | forecast.arima | Arima | forecast | | forecast.average | Average | base | | forecast.VAR | Vector Autoregression | vars |

Measures

| Id | Measure | Package | |----------------------------------------------------------------------------------------------|---------------------|---------| | forecast.mae | Mean Absolute Error | base | | forecast.mape | Mean Absolute Percentage Error | base | | forecast.mse | Mean Squared Error | base | | forecast.rmse | Root Mean Squared Error | base |

Resampling Methods

| Id | Resampling | Package | |-------------------------------------------------------------------------------------------------------|--------------------------------|---------| | forecast_holdout | Holdout | base | | forecast_cv | Rolling Window CrossValidation | base |

Code Example

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).

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.

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"))