ts_wfs_arima_boost: Auto Arima XGBoost Workflowset Function

In spsanderson/healthyR.ts: The Time Series Modeling Companion to 'healthyR'

Auto Arima XGBoost Workflowset Function

Description

This function is used to quickly create a workflowsets object.

Usage

ts_wfs_arima_boost(
  .model_type = "all_engines",
  .recipe_list,
  .trees = 10,
  .min_node = 2,
  .tree_depth = 6,
  .learn_rate = 0.015,
  .stop_iter = NULL,
  .seasonal_period = 0,
  .non_seasonal_ar = 0,
  .non_seasonal_differences = 0,
  .non_seasonal_ma = 0,
  .seasonal_ar = 0,
  .seasonal_differences = 0,
  .seasonal_ma = 0
)

Arguments

.model_type	This is where you will set your engine. It uses modeltime::arima_boost() under the hood and can take one of the following: "arima_xgboost" "auto_arima_xgboost "all_engines" - This will make a model spec for all available engines.
.recipe_list	You must supply a list of recipes. list(rec_1, rec_2, ...)
.trees	An integer for the number of trees contained in the ensemble.
.min_node	An integer for the minimum number of data points in a node that is required for the node to be split further.
.tree_depth	An integer for the maximum depth of the tree (i.e. number of splits) (specific engines only).
.learn_rate	A number for the rate at which the boosting algorithm adapts from iteration-to-iteration (specific engines only).
.stop_iter	The number of iterations without improvement before stopping (xgboost only).
.seasonal_period	Set to 0,
.non_seasonal_ar	Set to 0,
.non_seasonal_differences	Set to 0,
.non_seasonal_ma	Set to 0,
.seasonal_ar	Set to 0,
.seasonal_differences	Set to 0,
.seasonal_ma	Set to 0,

Details

This function expects to take in the recipes that you want to use in the modeling process. This is an automated workflow process. There are sensible defaults set for the model specification, but if you choose you can set them yourself if you have a good understanding of what they should be. The mode is set to "regression".

This uses the option set_engine("auto_arima_xgboost") or set_engine("arima_xgboost")

modeltime::arima_boost() arima_boost() is a way to generate a specification of a time series model that uses boosting to improve modeling errors (residuals) on Exogenous Regressors. It works with both "automated" ARIMA (auto.arima) and standard ARIMA (arima). The main algorithms are:

• Auto ARIMA + XGBoost Errors (engine = auto_arima_xgboost, default)

• ARIMA + XGBoost Errors (engine = arima_xgboost)

Value

Returns a workflowsets object.

Author(s)

Steven P. Sanderson II, MPH

See Also

https://workflowsets.tidymodels.org/

https://business-science.github.io/modeltime/reference/arima_boost.html

Other Auto Workflowsets: ts_wfs_auto_arima(), ts_wfs_ets_reg(), ts_wfs_lin_reg(), ts_wfs_mars(), ts_wfs_nnetar_reg(), ts_wfs_prophet_reg(), ts_wfs_svm_poly(), ts_wfs_svm_rbf(), ts_wfs_xgboost()

Examples

suppressPackageStartupMessages(library(modeltime))
suppressPackageStartupMessages(library(timetk))
suppressPackageStartupMessages(library(dplyr))
suppressPackageStartupMessages(library(rsample))

data <- AirPassengers %>%
  ts_to_tbl() %>%
  select(-index)

splits <- time_series_split(
   data
  , date_col
  , assess = 12
  , skip = 3
  , cumulative = TRUE
)

rec_objs <- ts_auto_recipe(
 .data = training(splits)
 , .date_col = date_col
 , .pred_col = value
)

wf_sets <- ts_wfs_arima_boost("all_engines", rec_objs)
wf_sets

spsanderson/healthyR.ts documentation built on Oct. 18, 2024, 5:51 p.m.