ts_auto_glmnet: Boilerplate Workflow

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

Boilerplate Workflow

Description

This is a boilerplate function to create automatically the following:

• recipe

• model specification

• workflow

• tuned model (grid ect)

• calibration tibble and plot

Usage

ts_auto_glmnet(
  .data,
  .date_col,
  .value_col,
  .formula,
  .rsamp_obj,
  .prefix = "ts_glmnet",
  .tune = TRUE,
  .grid_size = 10,
  .num_cores = 1,
  .cv_assess = 12,
  .cv_skip = 3,
  .cv_slice_limit = 6,
  .best_metric = "rmse",
  .bootstrap_final = FALSE
)

Arguments

.data	The data being passed to the function. The time-series object.
.date_col	The column that holds the datetime.
.value_col	The column that has the value
.formula	The formula that is passed to the recipe like value ~ .
.rsamp_obj	The rsample splits object
.prefix	Default is ts_glmnet
.tune	Defaults to TRUE, this creates a tuning grid and tuned model.
.grid_size	If .tune is TRUE then the .grid_size is the size of the tuning grid.
.num_cores	How many cores do you want to use. Default is 1
.cv_assess	How many observations for assess. See timetk::time_series_cv()
.cv_skip	How many observations to skip. See timetk::time_series_cv()
.cv_slice_limit	How many slices to return. See timetk::time_series_cv()
.best_metric	Default is "rmse". See modeltime::default_forecast_accuracy_metric_set()
.bootstrap_final	Not yet implemented.

Details

This uses parsnip::linear_reg() and sets the engine to glmnet

Value

A list

Author(s)

Steven P. Sanderson II, MPH

See Also

https://parsnip.tidymodels.org/reference/linear_reg.html

Other Boiler_Plate: ts_auto_arima(), ts_auto_arima_xgboost(), ts_auto_croston(), ts_auto_exp_smoothing(), ts_auto_lm(), ts_auto_mars(), ts_auto_nnetar(), ts_auto_prophet_boost(), ts_auto_prophet_reg(), ts_auto_smooth_es(), ts_auto_svm_poly(), ts_auto_svm_rbf(), ts_auto_theta(), ts_auto_xgboost()

Examples

library(dplyr)
library(timetk)
library(modeltime)
library(glmnet)

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

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

ts_glmnet <- ts_auto_glmnet(
  .data = data,
  .num_cores = 2,
  .date_col = date_col,
  .value_col = value,
  .rsamp_obj = splits,
  .formula = value ~ .,
  .grid_size = 5,
  .tune = FALSE
)

ts_glmnet$recipe_info

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