bayesmodels (version 0.1.1)

sarima_params: Tuning Parameters for SARIMA Models

Description

Tuning Parameters for SARIMA Models

Usage

non_seasonal_ar(range = c(0L, 5L), trans = NULL)
non_seasonal_differences(range = c(0L, 2L), trans = NULL)
non_seasonal_ma(range = c(0L, 5L), trans = NULL)
seasonal_ar(range = c(0L, 2L), trans = NULL)
seasonal_differences(range = c(0L, 1L), trans = NULL)
seasonal_ma(range = c(0L, 2L), trans = NULL)
markov_chains(range = c(0L, 8L), trans = NULL)
adapt_delta(range = c(0, 1), trans = NULL)
tree_depth(range = c(0L, 100L), trans = NULL)

Arguments

range

A two-element vector holding the defaults for the smallest and largest possible values, respectively.

trans

A trans object from the scales package, such as scales::log10_trans() or scales::reciprocal_trans(). If not provided, the default is used which matches the units used in range. If no transformation, NULL.

Value

A parameter

A parameter

A parameter

A parameter

A parameter

A parameter

A parameter

A parameter

A parameter

Details

The main parameters for SARIMA models are:

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

• non_seasonal_differences: The order of integration for non-seasonal differencing.

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

• seasonal_ar: The order of the seasonal auto-regressive (SAR) terms.

• seasonal_differences: The order of integration for seasonal differencing.

• seasonal_ma: The order of the seasonal moving average (SMA) 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

Examples

# NOT RUN {
non_seasonal_ar()

non_seasonal_differences()

non_seasonal_ma()


# }