modeltime_wfs_fit: Modeltime workflowsets fit
In sknifedatar: Swiss Knife of Data

Description Usage Arguments Details Value See Also Examples

allows working with workflow sets and modeltime. Combination of recipes and models are trained and evaluation metrics are returned.

1	modeltime_wfs_fit(.wfsets, .split_prop, .serie)

`.wfsets`	workflow_set object, generated with the `workflow_set()` function from the 'workflowsets' package.
`.split_prop`	time series split proportion.
`.serie`	time series dataframe.

Given a workflow_set containing multiple time series recipes and models, adjusts all the possible combinations on a time series. It uses a split proportion in order to train on a time series partition and evaluate metrics on the testing partition.

tbl_df containing the model id (based on workflow_set), model description and metrics on the time series testing dataframe. Also, a .fit_model column is included, which contains each fitted model.

sknifedatar website

library(dplyr)
library(earth)

data <- sknifedatar::data_avellaneda %>% mutate(date=as.Date(date)) %>% filter(date<'2012-06-01')

recipe_date <- recipes::recipe(value ~ ., data = data) %>% 
  recipes::step_date(date, features = c('dow','doy','week','month','year')) 

mars <- parsnip::mars(mode = 'regression') %>% parsnip::set_engine('earth')

wfsets <- workflowsets::workflow_set(
  preproc = list(
    R_date = recipe_date),
  models  = list(M_mars = mars),
  cross   = TRUE)

sknifedatar::modeltime_wfs_fit(.wfsets = wfsets, 
                               .split_prop = 0.8, 
                               .serie = data)