xgboost_multistep_fit_impl: Bridge XGBOOST Multistep Modeling function

In microsoft/finnts: Microsoft Finance Time Series Forecasting Framework

Bridge XGBOOST Multistep Modeling function

Description

Bridge XGBOOST Multistep Modeling function

Usage

xgboost_multistep_fit_impl(
  x,
  y,
  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,
  lag_periods = NULL,
  external_regressors = NULL,
  forecast_horizon = NULL,
  selected_features = NULL,
  ...
)

Arguments

x	A dataframe of xreg (exogenous regressors)
y	A numeric vector of values to fit
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.
lag_periods	lag periods
external_regressors	external regressors
forecast_horizon	forecast horizon
selected_features	selected features
...	Additional arguments passed to xgboost::xgb.train

microsoft/finnts documentation built on Oct. 30, 2024, 9:34 p.m.