auto_arima_xgboost_fit_impl: Bridge ARIMA-XGBoost Modeling function

View source: R/parsnip-arima_boost.R

auto_arima_xgboost_fit_implR Documentation

Bridge ARIMA-XGBoost Modeling function

Description

Bridge ARIMA-XGBoost Modeling function

Usage

auto_arima_xgboost_fit_impl(
  x,
  y,
  period = "auto",
  max.p = 5,
  max.d = 2,
  max.q = 5,
  max.P = 2,
  max.D = 1,
  max.Q = 2,
  max.order = 5,
  d = NA,
  D = NA,
  start.p = 2,
  start.q = 2,
  start.P = 1,
  start.Q = 1,
  stationary = FALSE,
  seasonal = TRUE,
  ic = c("aicc", "aic", "bic"),
  stepwise = TRUE,
  nmodels = 94,
  trace = FALSE,
  approximation = (length(x) > 150 | frequency(x) > 12),
  method = NULL,
  truncate = NULL,
  test = c("kpss", "adf", "pp"),
  test.args = list(),
  seasonal.test = c("seas", "ocsb", "hegy", "ch"),
  seasonal.test.args = list(),
  allowdrift = TRUE,
  allowmean = TRUE,
  lambda = NULL,
  biasadj = FALSE,
  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.

max.p

The maximum order of the non-seasonal auto-regressive (AR) terms.

max.d

The maximum order of integration for non-seasonal differencing.

max.q

The maximum order of the non-seasonal moving average (MA) terms.

max.P

The maximum order of the seasonal auto-regressive (SAR) terms.

max.D

The maximum order of integration for seasonal differencing.

max.Q

The maximum order of the seasonal moving average (SMA) terms.

max.order

Maximum value of p+q+P+Q if model selection is not stepwise.

d

Order of first-differencing. If missing, will choose a value based on test.

D

Order of seasonal-differencing. If missing, will choose a value based on season.test.

start.p

Starting value of p in stepwise procedure.

start.q

Starting value of q in stepwise procedure.

start.P

Starting value of P in stepwise procedure.

start.Q

Starting value of Q in stepwise procedure.

stationary

If TRUE, restricts search to stationary models.

seasonal

If FALSE, restricts search to non-seasonal models.

ic

Information criterion to be used in model selection.

stepwise

If TRUE, will do stepwise selection (faster). Otherwise, it searches over all models. Non-stepwise selection can be very slow, especially for seasonal models.

nmodels

Maximum number of models considered in the stepwise search.

trace

If TRUE, the list of ARIMA models considered will be reported.

approximation

If TRUE, estimation is via conditional sums of squares and the information criteria used for model selection are approximated. The final model is still computed using maximum likelihood estimation. Approximation should be used for long time series or a high seasonal period to avoid excessive computation times.

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. Can be abbreviated.

truncate

An integer value indicating how many observations to use in model selection. The last truncate values of the series are used to select a model when truncate is not NULL and approximation=TRUE. All observations are used if either truncate=NULL or approximation=FALSE.

test

Type of unit root test to use. See ndiffs for details.

test.args

Additional arguments to be passed to the unit root test.

seasonal.test

This determines which method is used to select the number of seasonal differences. The default method is to use a measure of seasonal strength computed from an STL decomposition. Other possibilities involve seasonal unit root tests.

seasonal.test.args

Additional arguments to be passed to the seasonal unit root test. See nsdiffs for details.

allowdrift

If TRUE, models with drift terms are considered.

allowmean

If TRUE, models with a non-zero mean are considered.

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.

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.