R/setartree-package.R

In setartree: SETAR-Tree - A Novel and Accurate Tree Algorithm for Global Time Series Forecasting

#' The setartree is a library containing the implementations of SETAR-Tree and SETAR-Forest
#' which are forecasting-specific tree-based models that are in particular suitable for global time series forecasting.
#'
#' If you have problems using setartree, find a bug, or have suggestions, please
#' file an issue on github (bugs/suggestions). If that fails, then you can contact the maintainer
#' directly by email.
#'
#' If you use the package, please cite the following work in your publications:
#'
#' Godahewa, R., Webb, G. I., Schmidt, D., & Bergmeir, C. (2023). SETAR-Tree: A novel and accurate tree algorithm for
#' global time series forecasting. Machine Learning, 112, 2555-2591. \doi{10.1007/s10994-023-06316-x}
#'
#' Demos for using SETAR-Tree and SETAR-Forest are available.
#' To get a list of them, type:
#'
#' \code{library(setartree)}
#'
#' \code{demo()}
#'
#' To execute the SETAR-Tree demo, type:
#'
#' \code{demo(tree_demo)}
#'
#' To execute the SETAR-Forest demo, type:
#'
#' \code{demo(forest_demo)}
#'
#' To fit a SETAR-Tree model either using a list of time series or an embedded input matrix and labels,
#' use the function \code{\link{setartree}}.
#' To fit a SETAR-Forest model either using a list of time series or an embedded input matrix and labels,
#' use the function \code{\link{setarforest}}.
#' To obtain forecasts from a SETAR-Tree or a SETAR-Forest, use the functions \code{\link{forecast.setartree}} and \code{\link{forecast.setarforest}}, respectively.
#'
#' The setartree package also contains three datasets that can be used to train/test the SETAR-Tree and SETAR-Forest models:
#' \code{\link{chaotic_logistic_series}}, \code{\link{web_traffic_train}} and \code{\link{web_traffic_test}}.
#'
#' See the setartree user manual for detailed explanations about the datasets and the parameters taken by each function.
#'
#' Another nice tool is the \code{forecast} package, that can be used to
#' plot the time series together with the forecasts generated by SETAR-Tree or SETAR-Forest.
#'
#' @title Getting started with the setartree package
#' @name setartree-package
# @aliases setartree
#' @docType package
# @encoding UTF-8
# @encoding Latin-1
#' @author Rakshitha Godahewa \email{rakshithagw@@gmail.com}
#'
#' Christoph Bergmeir \email{christoph.bergmeir@@monash.edu}
#'
#' Daniel Schmidt \email{daniel.schmidt@@monash.edu}
#'
#' and Geoffrey Webb \email{geoff.webb@@monash.edu}
#'
#' Department of Data Science and AI, Faculty of Information Technology, Monash University, Australia.
#'
#' \url{https://www.monash.edu/it/dsai}
#'
#' @references
#'
#' Godahewa, R., Webb, G. I., Schmidt, D., & Bergmeir, C. (2023). SETAR-Tree: A novel and accurate tree algorithm for
#' global time series forecasting. Machine Learning, 112, 2555-2591. \doi{10.1007/s10994-023-06316-x}
#'
#'
#' @keywords package setartree forecasting
#' @import methods
#' @import generics
#' @importFrom methods is
#' @importFrom utils tail
#' @importFrom stats as.formula embed glm pf predict predict.glm ts qnorm
#' @importFrom generics forecast
#' @importFrom parallel detectCores makeCluster clusterExport parLapply stopCluster
NULL

# Generics to re-export

#' @export
generics::forecast