Rlgt (0.1-3)

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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.

Maintainer: Christoph Bergmeir
Author(s): 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)

License: GPL-3

Uses: forecast, Rcpp, rstan, rstantools, sn, knitr, rmarkdown

Released 5 months ago.

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