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: 0.1-2
Depends: R (≥ 3.4.0), Rcpp (≥ 0.12.0), methods, rstantools, forecast
Imports: rstan (≥ 2.18.1), sn
LinkingTo: StanHeaders (≥ 2.18.0), rstan (≥ 2.18.1), BH (≥ 1.66.0), Rcpp (≥ 0.12.0), RcppEigen (≥ 0.3.3.3.0)
Suggests: knitr, rmarkdown
Published: 2019-02-22
Author: Slawek Smyl [aut], Christoph Bergmeir [aut, cre], Erwin Wibowo [aut], To Wang Ng [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

Downloads:

Reference manual: Rlgt.pdf
Vignettes: Global Trend Models - LGT, SGT, and S2GT
Getting Started with Global Trend Models
Package source: Rlgt_0.1-2.tar.gz
Windows binaries: r-devel: Rlgt_0.1-2.zip, r-release: Rlgt_0.1-2.zip, r-oldrel: Rlgt_0.1-2.zip
OS X binaries: r-release: Rlgt_0.1-2.tgz, r-oldrel: Rlgt_0.1-2.tgz

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