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.
- Version0.2.1
- R versionunknown
- LicenseMIT
- LicenseLICENSE
- Needs compilation?No
- Last release08/24/2023
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Team
Rakshitha Godahewa
Christoph Bergmeir
Show author detailsRolesAuthorDaniel Schmidt
Show author detailsRolesAuthorGeoffrey Webb
Show author detailsRolesContributor
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