CRAN/E | Rlgt

Rlgt

Bayesian Exponential Smoothing Models with Trend Modifications

Installation

About

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.

github.com/cbergmeir/Rlgt
System requirements GNU make

Key Metrics

Version 0.2-1
R ≥ 3.4.0
Published 2023-09-15 272 days ago
Needs compilation? yes
License GPL-3
CRAN checks Rlgt results

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Maintainer

Maintainer

Christoph Bergmeir

Authors

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)

Material

ChangeLog
Reference manual
Package source

In Views

TimeSeries

Vignettes

Global Trend Models - LGT, SGT, and S2GT
Getting Started with Global Trend Models

macOS

r-release

arm64

r-oldrel

arm64

r-release

x86_64

r-oldrel

x86_64

Windows

r-devel

x86_64

r-release

x86_64

r-oldrel

x86_64

Old Sources

Rlgt archive

Depends

R ≥ 3.4.0
Rcpp ≥ 0.12.0
methods
rstantools
forecast
truncnorm

Imports

rstan ≥ 2.26.0
sn

Suggests

knitr
rmarkdown

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