Bayesian Exponential Smoothing Models with Trend Modifications
Description
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.