{ "_id": "6ae338a08d250e7ad09f0603", "Package": "Rlgt", "Type": "Package", "Title": "Bayesian Exponential Smoothing Models with Trend Modifications", "Version": "0.2-3", "URL": "https://github.com/cbergmeir/Rlgt", "Date": "2025-04-17", "Authors@R": "c(\nperson(\"Slawek\", \"Smyl\", email = \"slaweks@hotmail.co.uk\", role = \"aut\"),\nperson(\"Christoph\", \"Bergmeir\", email = \"christoph.bergmeir@monash.edu\", role = c(\"aut\", \"cre\")),\nperson(\"Erwin\", \"Wibowo\", email = \"rwinwibowo@gmail.com\", role = \"aut\"),\nperson(\"To Wang\", \"Ng\", email = \"edwinnglabs@gmail.com\", role = \"aut\"),\nperson(\"Xueying\", \"Long\", email = \"xueying.long@monash.edu\", role = \"aut\"),\nperson(\"Alexander\", \"Dokumentov\", email = \"alexander.dokumentov@gmail.com\", role = \"aut\"),\nperson(\"Daniel\", \"Schmidt\", email = \"daniel.schmidt@monash.edu\", role = \"aut\"),\nperson(\"Trustees of Columbia University\", \"\", role = c(\"cph\"), comment= \"tools/make_cpp.R, R/stanmodels.R\"))", "Description": "An implementation of a number of Global Trend models for\ntime series forecasting that are Bayesian generalizations and\nextensions of some Exponential Smoothing models. The main\ndifferences/additions include 1) nonlinear global trend, 2)\nStudent-t error distribution, and 3) a function for the error\nsize, so heteroscedasticity. The methods are particularly\nuseful for short time series. When tested on the well-known M3\ndataset, they are able to outperform all classical time series\nalgorithms. 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