random_walk_stan_fit_impl: Low-Level ARIMA function for translating modeltime to...

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

Low-Level ARIMA function for translating modeltime to forecast

Description

Low-Level ARIMA function for translating modeltime to forecast

Usage

random_walk_stan_fit_impl(
  x,
  y,
  seasonal = FALSE,
  m = 0,
  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
seasonal	a Boolean value for select a seasonal random walk instead
m	an optional integer value for the seasonal period.
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

Value

A modeltime model

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