SETAR-Tree - A Novel and Accurate Tree Algorithm for Global Time Series Forecasting R package setartree version 0.2.1 CRAN Task View: TimeSeries (https://CRAN.R-project.org/view=TimeSeries) 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. Software Depends: R (>= 3.5.0) Imports: stats, utils, methods, parallel, generics (>= 0.1.2) Suggests: forecast Rakshitha Godahewa <rakshithagw@gmail.com> Comprehensive R Archive Network (CRAN) Rakshitha Godahewa [cre, aut, cph], Christoph Bergmeir [aut], Daniel Schmidt [aut], Geoffrey Webb [ctb] MIT + file LICENSE (https://CRAN.R-project.org/package=setartree/LICENSE) 2023-08-24 application/tgz https://CRAN.R-project.org/package=setartree doi:10.32614/CRAN.package.setartree