Rlgt: Bayesian Exponential Smoothing Models with Trend Modifications

An implementation of a number of Global Trend models for time series forecasting that are Bayesian generalizations and extensions of some Exponential Smoothing models. The main differences/additions include 1) nonlinear global trend, 2) Student-t error distribution, and 3) a function for the error size, so heteroscedasticity. The methods are particularly useful for short time series. When tested on the well-known M3 dataset, they are able to outperform all classical time series algorithms. The models are fitted with MCMC using the 'rstan' package.

Version:	0.2-1
Depends:	R (≥ 3.4.0), Rcpp (≥ 0.12.0), methods, rstantools, forecast, truncnorm
Imports:	rstan (≥ 2.26.0), sn
LinkingTo:	StanHeaders (≥ 2.26.0), rstan (≥ 2.26.0), BH (≥ 1.66.0), Rcpp (≥ 0.12.0), RcppEigen (≥ 0.3.3.3.0), RcppParallel (≥ 5.0.2)
Suggests:	knitr, rmarkdown
Published:	2023-09-15
Author:	Slawek Smyl [aut], Christoph Bergmeir [aut, cre], Erwin Wibowo [aut], To Wang Ng [aut], Xueying Long [aut], Alexander Dokumentov [aut], Daniel Schmidt [aut], Trustees of Columbia University [cph] (tools/make_cpp.R, R/stanmodels.R)
Maintainer:	Christoph Bergmeir <christoph.bergmeir at monash.edu>
License:	GPL-3
URL:	https://github.com/cbergmeir/Rlgt
NeedsCompilation:	yes
SystemRequirements:	GNU make
Materials:	ChangeLog
In views:	TimeSeries
CRAN checks:	Rlgt results

Documentation:

Reference manual:	Rlgt.pdf
Vignettes:	Global Trend Models - LGT, SGT, and S2GT Getting Started with Global Trend Models

Downloads:

Package source:	Rlgt_0.2-1.tar.gz
Windows binaries:	r-devel: Rlgt_0.2-1.zip, r-release: Rlgt_0.2-1.zip, r-oldrel: Rlgt_0.2-1.zip
macOS binaries:	r-release (arm64): Rlgt_0.2-1.tgz, r-oldrel (arm64): Rlgt_0.2-1.tgz, r-release (x86_64): Rlgt_0.2-1.tgz, r-oldrel (x86_64): Rlgt_0.2-1.tgz
Old sources:	Rlgt archive

Linking:

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