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  "Title": "SETAR-Tree - A Novel and Accurate Tree Algorithm for Global Time\nSeries Forecasting",
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  "Authors@R": "c(\nperson(\"Rakshitha\", \"Godahewa\", email = \"rakshithagw@gmail.com\", role = c(\"cre\", \"aut\", \"cph\")),\nperson(\"Christoph\", \"Bergmeir\", email = \"christoph.bergmeir@monash.edu\", role = \"aut\"),\nperson(\"Daniel\", \"Schmidt\", email = \"daniel.schmidt@monash.edu\", role = \"aut\"),\nperson(\"Geoffrey\", \"Webb\", email = \"geoff.webb@monash.edu\", role = \"ctb\"))",
  "Maintainer": "Rakshitha Godahewa <rakshithagw@gmail.com>",
  "Description": "The implementation of a forecasting-specific tree-based\nmodel that is in particular suitable for global time series\nforecasting, as proposed in Godahewa et al. (2022)\n<arXiv:2211.08661v1>. The model uses the concept of Self\nExciting Threshold Autoregressive (SETAR) models to define the\nnode splits and thus, the model is named SETAR-Tree. The\nSETAR-Tree uses some time-series-specific splitting and\nstopping procedures. It trains global pooled regression models\nin the leaves allowing the models to learn cross-series\ninformation. The depth of the tree is controlled by conducting\na statistical linearity test as well as measuring the error\nreduction percentage at each node split. Thus, the SETAR-Tree\nrequires minimal external hyperparameter tuning and provides\ncompetitive results under its default configuration. A forest\nis developed by extending the SETAR-Tree. The SETAR-Forest\ncombines the forecasts provided by a collection of diverse\nSETAR-Trees during the forecasting process.",
  "License": "MIT + file LICENSE",
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