ssm_stan_fit_impl: Low-Level ARIMA function for translating modeltime to...
In AlbertoAlmuinha/bayesmodels: The 'Tidymodels' Extension for Bayesian Models

ssm_stan_fit_impl

R Documentation

Low-Level ARIMA function for translating modeltime to forecast

Description

Low-Level ARIMA function for translating modeltime to forecast

Usage

ssm_stan_fit_impl(
  x,
  y,
  trend = FALSE,
  damped = FALSE,
  seasonal = FALSE,
  period = 0,
  genT = FALSE,
  chains = 4,
  iter = 2000,
  warmup = iter/2,
  adapt.delta = 0.9,
  tree.depth = 10,
  seed = NULL,
  ...
)

Arguments

`x`	A dataframe of xreg (exogenous regressors)
`y`	A numeric vector of values to fit
`trend`	a boolean value to specify a trend local level model. By default is FALSE.
`damped`	a boolean value to specify a damped trend local level model. By default is FALSE.
`seasonal`	a boolean value to specify a seasonal local level model.
`period`	an integer specifying the periodicity of the time series.
`genT`	a boolean value to specify for a generalized t-student SSM model.
`chains`	An integer of the number of Markov Chains chains to be run, by default 4 chains are run.
`iter`	An integer of total iterations per chain including the warm-up, by default the number of iterations are 2000.
`warmup`	A positive integer specifying number of warm-up (aka burn-in) iterations. This also specifies the number of iterations used for step-size adaptation, so warm-up samples should not be used for inference. The number of warmup should not be larger than iter and the default is iter/2.
`adapt.delta`	An optional real value between 0 and 1, the thin of the jumps in a HMC method. By default is 0.9
`tree.depth`	An integer of the maximum depth of the trees evaluated during each iteration. By default is 10.
`seed`	An integer with the seed for using when predicting with the model.
`...`	Additional arguments passed to `forecast::Arima`