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: 0.2-3 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, Mcomp, RODBC, dplyr, ggplot2 Published: 2025-04-30 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 resultsRetroSearch is an open source project built by @garambo | Open a GitHub Issue
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