Bridge ARIMA-LightGBM Modeling function

sarima_lightgbm_fit_impl(
  x,
  y,
  period = "auto",
  p = 0,
  d = 0,
  q = 0,
  P = 0,
  D = 0,
  Q = 0,
  include.mean = TRUE,
  include.drift = FALSE,
  include.constant,
  lambda = model$lambda,
  biasadj = FALSE,
  method = c("CSS-ML", "ML", "CSS"),
  model = NULL,
  max_depth = 17,
  learning_rate = 0.1,
  num_iterations = 10,
  min_data_in_leaf = 20,
  min_gain_to_split = 0,
  bagging_fraction = 1,
  feature_fraction = 1,
  ...
)

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.
d	The order of integration for non-seasonal differencing.
q	The order of the non-seasonal moving average (MA) terms.
P	The order of the seasonal auto-regressive (SAR) terms.
D	The order of integration for seasonal differencing.
Q	The order of the seasonal moving average (SMA) terms.
include.mean	Should the ARIMA model include a mean term? The default is `TRUE` for undifferenced series, `FALSE` for differenced ones (where a mean would not affect the fit nor predictions).
include.drift	Should the ARIMA model include a linear drift term? (i.e., a linear regression with ARIMA errors is fitted.) The default is `FALSE`.
include.constant	If `TRUE`, then `include.mean` is set to be `TRUE` for undifferenced series and `include.drift` is set to be `TRUE` for differenced series. Note that if there is more than one difference taken, no constant is included regardless of the value of this argument. This is deliberate as otherwise quadratic and higher order polynomial trends would be induced.
lambda	Box-Cox transformation parameter. If `lambda="auto"`, then a transformation is automatically selected using `BoxCox.lambda`. The transformation is ignored if NULL. Otherwise, data transformed before model is estimated.
biasadj	Use adjusted back-transformed mean for Box-Cox transformations. If transformed data is used to produce forecasts and fitted values, a regular back transformation will result in median forecasts. If biasadj is TRUE, an adjustment will be made to produce mean forecasts and fitted values.
method	Fitting method: maximum likelihood or minimize conditional sum-of-squares. The default (unless there are missing values) is to use conditional-sum-of-squares to find starting values, then maximum likelihood.
model	Output from a previous call to `Arima`. If model is passed, this same model is fitted to `y` without re-estimating any parameters.
max_depth	The maximum depth of the tree (i.e. number of splits).
learning_rate	The rate at which the boosting algorithm adapts from iteration-to-iteration.
num_iterations	The number of trees contained in the ensemble.
min_data_in_leaf	The minimum number of data points in a node that is required for the node to be split further.
min_gain_to_split	The reduction in the loss function required to split further.
bagging_fraction	The amount of data exposed to the fitting routine.
feature_fraction	The number of predictors that will be randomly sampled at each split when creating the tree models.
...	Additional arguments passed to `lightgbm::lgb.train`