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

In modeltime: The Tidymodels Extension for Time Series Modeling

Description

Low-Level ADAM function for translating modeltime to forecast

Usage

auto_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::auto.adam

modeltime documentation built on Oct. 18, 2021, 5:08 p.m.