Package: Rlgt 0.2-2

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.

Authors: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]

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Rlgt/json (API)

Peer review:

Bug tracker:https://github.com/cbergmeir/rlgt/issues

On CRAN:

4 exports 20 stars 2.93 score 75 dependencies 31 scripts 357 downloads

Last updated 3 months agofrom:20197605e7. Checks:OK: 1 WARNING: 8. Indexed: yes.

Exports:blgt.multi.forecastinitModelrlgtrlgt.control

Dependencies:abindbackportsBHcallrcheckmateclicolorspacecurldescdistributionalfansifarverforecastfracdiffgenericsggplot2gluegridExtragtableinlineisobandjsonlitelabelinglatticelifecyclelmtestloomagrittrMASSMatrixMatrixModelsmatrixStatsmgcvmnormtmunsellnlmennetnumDerivpillarpkgbuildpkgconfigposteriorprocessxpsquadprogquantmodquantregQuickJSRR6RColorBrewerRcppRcppArmadilloRcppEigenRcppParallelrlangrstanrstantoolsscalessnSparseMStanHeaderssurvivaltensorAtibbletimeDatetruncnormtseriesTTRurcautf8vctrsviridisLitewithrxtszoo