exponential_smoothing_params: Tuning Parameters for Exponential Smoothing Models

In AlbertoAlmuinha/bayesmodels: The 'Tidymodels' Extension for Bayesian Models

Tuning Parameters for Exponential Smoothing Models

Description

Tuning Parameters for Exponential Smoothing Models

Usage

seasonality_type()

method()

error_method()

Details

The main parameters for Exponential Smoothing models are:

• garch_order: Integer with the garch order.

• arch_order: Integer with the arch_order.

• mgarch_order: Integer with the mgarch order.

• garch_t_student: A boolean value to specify for a generalized t-student garch model.

• asymmetry: a string value for the asymmetric function for an asymmetric GARCH process. By default the value "none" for standard GARCH process. If "logit" a logistic function is used for asymmetry, and if "exp" an exponential function is used.

• non_seasonal_ar: The order of the non-seasonal auto-regressive (AR) terms.

• non_seasonal_ma: The order of the non-seasonal moving average (MA) terms.

• markov_chains: The number of markov chains.

• adapt_delta: The thin of the jumps in a HMC method

• tree_depth: Maximum depth of the trees

Value

A parameter

A parameter

A parameter

Examples

non_seasonal_ar()

non_seasonal_differences()

non_seasonal_ma()

AlbertoAlmuinha/bayesmodels documentation built on Aug. 13, 2022, 1:45 p.m.