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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.
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Package details |
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| Author | Slawek Smyl [aut], Christoph Bergmeir [aut, cre], Erwin Wibowo [aut], To Wang Ng [aut], Xueying Long [aut], Alexander Dokumentov [aut], Daniel Schmidt [aut], Trustees of Columbia University [cph] (tools/make_cpp.R, R/stanmodels.R) |
| Maintainer | Christoph Bergmeir <christoph.bergmeir@monash.edu> |
| License | GPL-3 |
| Version | 0.2-2 |
| URL | https://github.com/cbergmeir/Rlgt |
| Package repository | View on CRAN |
| Installation |
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