setartree: SETAR-Tree - A Novel and Accurate Tree Algorithm for Global Time Series Forecasting

The implementation of a forecasting-specific tree-based model that is in particular suitable for global time series forecasting, as proposed in Godahewa et al. (2022) <doi:10.48550/arXiv.2211.08661>. The model uses the concept of Self Exciting Threshold Autoregressive (SETAR) models to define the node splits and thus, the model is named SETAR-Tree. The SETAR-Tree uses some time-series-specific splitting and stopping procedures. It trains global pooled regression models in the leaves allowing the models to learn cross-series information. The depth of the tree is controlled by conducting a statistical linearity test as well as measuring the error reduction percentage at each node split. Thus, the SETAR-Tree requires minimal external hyperparameter tuning and provides competitive results under its default configuration. A forest is developed by extending the SETAR-Tree. The SETAR-Forest combines the forecasts provided by a collection of diverse SETAR-Trees during the forecasting process.

Version: 0.2.1
Depends: R (≥ 3.5.0)
Imports: stats, utils, methods, parallel, generics (≥ 0.1.2)
Suggests: forecast
Published: 2023-08-24
Author: Rakshitha Godahewa [cre, aut, cph], Christoph Bergmeir [aut], Daniel Schmidt [aut], Geoffrey Webb [ctb]
Maintainer: Rakshitha Godahewa <rakshithagw at>
License: MIT + file LICENSE
NeedsCompilation: no
Materials: ChangeLog
In views: TimeSeries
CRAN checks: setartree results


Reference manual: setartree.pdf


Package source: setartree_0.2.1.tar.gz
Windows binaries: r-prerel:, r-release:, r-oldrel:
macOS binaries: r-prerel (arm64): setartree_0.2.1.tgz, r-release (arm64): setartree_0.2.1.tgz, r-oldrel (arm64): setartree_0.2.1.tgz, r-prerel (x86_64): setartree_0.2.1.tgz, r-release (x86_64): setartree_0.2.1.tgz
Old sources: setartree archive


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