arima_xgboost_fit_impl: Bridge ARIMA-XGBoost Modeling function

View source: R/parsnip-arima_boost.R

arima_xgboost_fit_implR Documentation

Bridge ARIMA-XGBoost Modeling function

Description

Bridge ARIMA-XGBoost Modeling function

Usage

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("CSS-ML", "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,
  ...
)

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

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 counts argument below. The default uses all columns.

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 [0, 1) the value, validation is a random proportion of data in x and y that are used for performance assessment and potential early stopping. If 1 or greater, it is the number of training set samples use for these purposes.

early_stop

An integer or NULL. If not NULL, it is the number of training iterations without improvement before stopping. If validation is used, performance is base on the validation set; otherwise the training set is used.

...

Additional arguments passed to xgboost::xgb.train


modeltime documentation built on June 8, 2022, 1:07 a.m.