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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 gmail.com> |
| BugReports: | https://github.com/rakshitha123/setartree/issues |
| License: | MIT + file LICENSE |
| URL: | https://github.com/rakshitha123/setartree |
| 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-devel: setartree_0.2.1.zip, r-release: setartree_0.2.1.zip, r-oldrel: setartree_0.2.1.zip |
| macOS binaries: | r-release (arm64): setartree_0.2.1.tgz, r-oldrel (arm64): setartree_0.2.1.tgz, r-release (x86_64): setartree_0.2.1.tgz, r-oldrel (x86_64): setartree_0.2.1.tgz |
| Old sources: | setartree archive |
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