View source: R/parsniparima_boost.R
arima_xgboost_fit_impl  R Documentation 
Bridge ARIMAXGBoost Modeling function
arima_xgboost_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("CSSML", "ML", "CSS"),
model = NULL,
max_depth = 6,
nrounds = 15,
eta = 0.3,
colsample_bytree = NULL,
colsample_bynode = NULL,
min_child_weight = 1,
gamma = 0,
subsample = 1,
validation = 0,
early_stop = NULL,
...
)
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 timebased phrase of "2 weeks" can be used if a date or datetime variable is provided. 
p 
The order of the nonseasonal autoregressive (AR) terms. 
d 
The order of integration for nonseasonal differencing. 
q 
The order of the nonseasonal moving average (MA) terms. 
P 
The order of the seasonal autoregressive (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 
include.drift 
Should the ARIMA model include a linear drift term?
(i.e., a linear regression with ARIMA errors is fitted.) The default is

include.constant 
If 
lambda 
BoxCox transformation parameter. If 
biasadj 
Use adjusted backtransformed mean for BoxCox 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 sumofsquares. The default (unless there are missing values) is to use conditionalsumofsquares to find starting values, then maximum likelihood. 
model 
Output from a previous call to 
max_depth 
An integer for the maximum depth of the tree. 
nrounds 
An integer for the number of boosting iterations. 
eta 
A numeric value between zero and one to control the learning rate. 
colsample_bytree 
Subsampling proportion of columns. 
colsample_bynode 
Subsampling proportion of columns for each node
within each tree. See the 
min_child_weight 
A numeric value for the minimum sum of instance weights needed in a child to continue to split. 
gamma 
A number for the minimum loss reduction required to make a further partition on a leaf node of the tree 
subsample 
Subsampling proportion of rows. 
validation 
A positive number. If on 
early_stop 
An integer or 
... 
Additional arguments passed to 
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