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.


Reference manual

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0.1-3 by Christoph Bergmeir, 2 years ago

Browse source code at

Authors: 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)

Documentation:   PDF Manual  

Task views: Time Series Analysis

GPL-3 license

Imports rstan, sn

Depends on Rcpp, methods, rstantools, forecast

Suggests knitr, rmarkdown

Linking to StanHeaders, rstan, BH, Rcpp, RcppEigen

System requirements: GNU make

See at CRAN