setartree
SETAR-Tree - A Novel and Accurate Tree Algorithm for Global Time Series Forecasting
Description
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) <arXiv:2211.08661v1>. 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.
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Show all 14 flavors
| Flavor | Status |
|---|---|
| r-devel-linux-x86_64-debian-clang | NOTE |
| r-devel-linux-x86_64-debian-gcc | NOTE |
| r-devel-linux-x86_64-fedora-clang | OK |
| r-devel-linux-x86_64-fedora-gcc | OK |
| r-devel-macos-arm64 | OK |
| r-devel-windows-x86_64 | OK |
| r-oldrel-macos-arm64 | OK |
| r-oldrel-macos-x86_64 | OK |
| r-oldrel-windows-x86_64 | OK |
| r-patched-linux-x86_64 | OK |
| r-release-linux-x86_64 | OK |
| r-release-macos-arm64 | OK |
| r-release-macos-x86_64 | OK |
| r-release-windows-x86_64 | OK |
Check details (14 non-OK)
CRAN incoming feasibility
Maintainer: ‘Rakshitha Godahewa <rakshithagw@gmail.com>’ The Description field contains in Godahewa et al. (2022) <arXiv:2211.08661v1>. The model uses the Please refer to arXiv e-prints via their arXiv DOI <doi:10.48550/arXiv.YYMM.NNNNN>.
CRAN incoming feasibility
Maintainer: ‘Rakshitha Godahewa <rakshithagw@gmail.com>’ The Description field contains in Godahewa et al. (2022) <arXiv:2211.08661v1>. The model uses the Please refer to arXiv e-prints via their arXiv DOI <doi:10.48550/arXiv.YYMM.NNNNN>.
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NOTE 12 OK · 2 NOTE · 0 WARNING · 0 ERROR · 0 FAILURE Mar 9, 2026
CRAN incoming feasibility
Maintainer: ‘Rakshitha Godahewa <rakshithagw@gmail.com>’ The Description field contains in Godahewa et al. (2022) <arXiv:2211.08661v1>. The model uses the Please refer to arXiv e-prints via their arXiv DOI <doi:10.48550/arXiv.YYMM.NNNNN>.
CRAN incoming feasibility
Maintainer: ‘Rakshitha Godahewa <rakshithagw@gmail.com>’ The Description field contains in Godahewa et al. (2022) <arXiv:2211.08661v1>. The model uses the Please refer to arXiv e-prints via their arXiv DOI <doi:10.48550/arXiv.YYMM.NNNNN>.