Low-Level ARIMA function for translating modeltime to forecast

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`

Value

A modeltime model