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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: 0.2-2
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: doParallel, foreach, knitr, rmarkdown
Published: 2024-07-16
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

Documentation:

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

Downloads:

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

Linking:

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

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.