Package: setartree
Title: SETAR-Tree - A Novel and Accurate Tree Algorithm for Global Time
        Series Forecasting
Version: 0.2.1
Authors@R: c(
    person("Rakshitha", "Godahewa", email = "rakshithagw@gmail.com", role = c("cre", "aut", "cph")),
    person("Christoph", "Bergmeir", email = "christoph.bergmeir@monash.edu", role = "aut"),
    person("Daniel", "Schmidt", email = "daniel.schmidt@monash.edu", role = "aut"),
	person("Geoffrey", "Webb", email = "geoff.webb@monash.edu", role = "ctb"))
Maintainer: Rakshitha Godahewa <rakshithagw@gmail.com>
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. 
License: MIT + file LICENSE
URL: https://github.com/rakshitha123/setartree
BugReports: https://github.com/rakshitha123/setartree/issues
Depends: R (>= 3.5.0)
Imports: stats, utils, methods, parallel, generics (>= 0.1.2)
Suggests: forecast
Encoding: UTF-8
LazyData: true
RoxygenNote: 7.2.3
NeedsCompilation: no
Packaged: 2023-08-24 07:21:20 UTC; raks0002
Author: Rakshitha Godahewa [cre, aut, cph],
  Christoph Bergmeir [aut],
  Daniel Schmidt [aut],
  Geoffrey Webb [ctb]
Repository: CRAN
Date/Publication: 2023-08-24 11:00:02 UTC
Built: R 4.6.0; ; 2026-04-27 03:07:55 UTC; unix
