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