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Rlgt  

Bayesian Exponential Smoothing Models with Trend Modifications
View on CRAN: Click here


Download and install Rlgt package within the R console
Install from CRAN:
install.packages("Rlgt")

Install from Github:
library("remotes")
install_github("cran/Rlgt")

Install by package version:
library("remotes")
install_version("Rlgt", "0.2-3")



Attach the package and use:
library("Rlgt")
Maintained by
Christoph Bergmeir
[Scholar Profile | Author Map]
All associated links for this package
First Published: 2019-02-22
Latest Update: 2025-04-30
Description:
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.
How to cite:
Christoph Bergmeir (2019). Rlgt: Bayesian Exponential Smoothing Models with Trend Modifications. R package version 0.2-3, https://cran.r-project.org/web/packages/Rlgt. Accessed 25 Jun. 2026.
Previous versions and publish date:
0.1-2 (2019-02-22 10:40), 0.1-3 (2019-06-14 06:20), 0.1-4 (2022-05-17 09:50), 0.2-0 (2023-08-30 19:20), 0.2-1 (2023-09-16 00:12), 0.2-2 (2024-07-16 23:40)
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Complete documentation for Rlgt
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