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Rlgt (version 0.1-3)

Bayesian Exponential Smoothing Models with Trend Modifications

Description

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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Install

install.packages('Rlgt')

Monthly Downloads

625

Version

0.1-3

License

GPL-3

Issues

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Stars

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Maintainer

Christoph Bergmeir

Last Published

June 14th, 2019

Functions in Rlgt (0.1-3)

umcsent.example

University of Michigan Monthly Survey of Consumer Sentiment & Google Trends Queries
posterior_interval.rlgtfit

rlgtfit posterior interval
print.rlgtfit

Generic print function for rlgtfit models
Rlgt-package

Getting started with the Rlgt package
rlgt

Fit an Rlgt model
iclaims.example

Weekly Initial Claims of US Unemployment Benefits & Google Trends Queries
rlgtfit

rlgtfit class
rlgt.control

Sets and initializes the control parameters
forecast.rlgtfit

Rlgt forecast
initModel

Initialize a model from the Rlgt family