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.

Package details

AuthorSlawek 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)
MaintainerChristoph Bergmeir <[email protected]>
URL https://github.com/cbergmeir/Rlgt
Package repositoryView on CRAN
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Rlgt documentation built on June 14, 2019, 9:03 a.m.