Bayesian Exponential Smoothing Models with Trend Modifications
Getting started with the Rlgt package
Sets and initializes the control parameters
Rlgt LSGT Gibbs run in parallel
Rlgt forecast
Initialize a model from the Rlgt family
Generic print function for rlgtfit models
rlgtfit posterior interval
Fit an Rlgt model
rlgtfit class
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.