Low-Level ARIMA function for translating modeltime to forecast

Sarima_stan_fit_impl(
  x,
  y,
  period = "auto",
  p = 0,
  d = 0,
  q = 0,
  P = 0,
  D = 0,
  Q = 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
period	A seasonal frequency. Uses "auto" by default. A character phrase of "auto" or time-based phrase of "2 weeks" can be used if a date or date-time variable is provided.
p	The order of the non-seasonal auto-regressive (AR) terms. Often denoted "p" in pdq-notation.
d	The order of integration for non-seasonal differencing. Often denoted "d" in pdq-notation.
q	The order of the non-seasonal moving average (MA) terms. Often denoted "q" in pdq-notation.
P	The order of the seasonal auto-regressive (SAR) terms. Often denoted "P" in PDQ-notation.
D	The order of integration for seasonal differencing. Often denoted "D" in PDQ-notation.
Q	The order of the seasonal moving average (SMA) terms. Often denoted "Q" in PDQ-notation.
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