auto_sarima_catboost_fit_impl: Bridge ARIMA-Catboost Modeling function

In AlbertoAlmuinha/boostime: The Tidymodels Extension for Time Series Boosting Models

Bridge ARIMA-Catboost Modeling function

Description

Bridge ARIMA-Catboost Modeling function

Usage

auto_sarima_catboost_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,
  depth = 6,
  eta = 0.3,
  rsm = 1,
  iterations = 1000,
  min_data_in_leaf = 1,
  subsample = 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.
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.
depth	The maximum depth of the tree (i.e. number of splits).
eta	The rate at which the boosting algorithm adapts from iteration-to-iteration.
rsm	The number of predictors that will be randomly sampled at each split when creating the tree models.
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.
subsample	The amount of data exposed to the fitting routine.
...	Additional arguments passed to catboost::catboost.train

AlbertoAlmuinha/boostime documentation built on Aug. 13, 2022, 1:46 p.m.