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 (≥, RcppParallel (≥ 5.0.2)
Suggests: knitr, rmarkdown
Published: 2023-09-15
DOI: 10.32614/CRAN.package.Rlgt
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


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


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


Please use the canonical form https://CRAN.R-project.org/package=Rlgt to link to this page.