Rlgt
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.
Versions across snapshots
| Version | Repository | File | Size |
|---|---|---|---|
0.2-3 |
rolling linux/jammy R-4.5 | Rlgt_0.2-3.tar.gz |
4.0 MiB |
0.2-3 |
rolling linux/noble R-4.5 | Rlgt_0.2-3.tar.gz |
4.0 MiB |
0.2-3 |
rolling source/ R- | Rlgt_0.2-3.tar.gz |
370.7 KiB |
0.2-3 |
latest linux/jammy R-4.5 | Rlgt_0.2-3.tar.gz |
4.0 MiB |
0.2-3 |
latest linux/noble R-4.5 | Rlgt_0.2-3.tar.gz |
4.0 MiB |
0.2-3 |
latest source/ R- | Rlgt_0.2-3.tar.gz |
370.7 KiB |
0.2-3 |
2026-04-26 source/ R- | Rlgt_0.2-3.tar.gz |
370.7 KiB |
0.2-3 |
2026-04-23 source/ R- | Rlgt_0.2-3.tar.gz |
370.7 KiB |
0.2-3 |
2026-04-09 windows/windows R-4.5 | Rlgt_0.2-3.zip |
3.7 MiB |
0.2-2 |
2025-04-20 source/ R- | Rlgt_0.2-2.tar.gz |
428.1 KiB |