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)
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
#> # A workflow set/tibble: 8 × 4
#> wflow_id info option result
#> <chr> <list> <list> <list>
#> 1 rec_base_arima_boost_1 <tibble [1 × 4]> <opts[0]> <list [0]>
#> 2 rec_base_arima_boost_2 <tibble [1 × 4]> <opts[0]> <list [0]>
#> 3 rec_date_arima_boost_1 <tibble [1 × 4]> <opts[0]> <list [0]>
#> 4 rec_date_arima_boost_2 <tibble [1 × 4]> <opts[0]> <list [0]>
#> 5 rec_date_fourier_arima_boost_1 <tibble [1 × 4]> <opts[0]> <list [0]>
#> 6 rec_date_fourier_arima_boost_2 <tibble [1 × 4]> <opts[0]> <list [0]>
#> 7 rec_date_fourier_nzv_arima_boost_1 <tibble [1 × 4]> <opts[0]> <list [0]>
#> 8 rec_date_fourier_nzv_arima_boost_2 <tibble [1 × 4]> <opts[0]> <list [0]>