initModel

type of the forecasting model selected, a character object

model.type

binary parameter indicating whether additional regressors will be used for forecasting in multivariate settings.

use.regression

Seasonality method Id (0- HW, 1- generalized).

seasonalityMethodId

levelMethodId

if the non-standard function for error size should be used, one based on smoothed innovations or surprises

useSmoothingMethodForError

This is an internal function that usually won't be called by users directly. It validates the model type and generates the corresponding list of parameters for the model.

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.

initModel: Initialize a model from the Rlgt family

Description

Usage

Arguments

Value