Rlgt (Q108502)
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Bayesian Exponential Smoothing Models with Trend Modifications
| Language | Label | Description | Also known as |
|---|---|---|---|
| English | Rlgt |
Bayesian Exponential Smoothing Models with Trend Modifications |
Statements
15 September 2023
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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.
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