adam_fit_impl: Low-Level ADAM function for translating modeltime to forecast

Description

Low-Level ADAM function for translating modeltime to forecast

Usage

adam_fit_impl(
  x,
  y,
  period = "auto",
  p = 0,
  d = 0,
  q = 0,
  P = 0,
  D = 0,
  Q = 0,
  model = "ZXZ",
  constant = FALSE,
  regressors = c("use", "select", "adapt"),
  outliers = c("ignore", "use", "select"),
  level = 0.99,
  occurrence = c("none", "auto", "fixed", "general", "odds-ratio", "inverse-odds-ratio",
    "direct"),
  distribution = c("default", "dnorm", "dlaplace", "ds", "dgnorm", "dlnorm", "dinvgauss",
    "dgamma"),
  loss = c("likelihood", "MSE", "MAE", "HAM", "LASSO", "RIDGE", "MSEh", "TMSE", "GTMSE",
    "MSCE"),
  ic = c("AICc", "AIC", "BIC", "BICc"),
  select_order = FALSE,
  ...
)

Arguments

x: A data.frame of predictors
y: A vector with outcome
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.
model: The type of ETS model.
constant: Logical, determining, whether the constant is needed in the model or not.
regressors: The variable defines what to do with the provided explanatory variables.
outliers: Defines what to do with outliers.
level: What confidence level to use for detection of outliers.
occurrence: The type of model used in probability estimation.
distribution: what density function to assume for the error term.
loss: The type of Loss Function used in optimization.
ic: The information criterion to use in the model selection / combination procedure.
select_order: If TRUE, then the function will select the most appropriate order using a mechanism similar to auto.msarima(), but implemented in auto.adam(). The values list(ar=...,i=...,ma=...) specify the maximum orders to check in this case
...: Additional arguments passed to smooth::adam