This is a wrapper for fit() that takes a Modeltime Table and retrains each model on new data re-using the parameters and preprocessing steps used during the training process.

modeltime_refit(object, data, ..., control = control_refit())

Arguments

object

A Modeltime Table

data

A tibble that contains data to retrain the model(s) using.

...

Additional arguments to control refitting.

Ensemble Model Spec (modeltime.ensemble):

When making a meta-learner with modeltime.ensemble::ensemble_model_spec(), used to pass resamples argument containing results from modeltime.resample::modeltime_fit_resamples().

control

Used to control verbosity and parallel processing. See control_refit().

Value

A Modeltime Table containing one or more re-trained models.

Details

Refitting is an important step prior to forecasting time series models. The modeltime_refit() function makes it easy to recycle models, retraining on new data.

Recycling Parameters

Parameters are recycled during retraining using the following criteria:

  • Automated models (e.g. "auto arima") will have parameters recalculated.

  • Non-automated models (e.g. "arima") will have parameters preserved.

  • All preprocessing steps will be reused on the data

Refit

The modeltime_refit() function is used to retrain models trained with fit().

Refit XY

The XY format is not supported at this time.

See also

Examples

library(tidyverse) library(lubridate) library(timetk) library(parsnip) library(rsample) library(modeltime) # Data m750 <- m4_monthly %>% filter(id == "M750") # Split Data 80/20 splits <- initial_time_split(m750, prop = 0.9) # --- MODELS --- model_fit_prophet <- prophet_reg() %>% set_engine(engine = "prophet") %>% fit(value ~ date, data = training(splits))
#> Disabling weekly seasonality. Run prophet with weekly.seasonality=TRUE to override this.
#> Disabling daily seasonality. Run prophet with daily.seasonality=TRUE to override this.
# ---- MODELTIME TABLE ---- models_tbl <- modeltime_table( model_fit_prophet ) # ---- CALIBRATE ---- # - Calibrate on training data set calibration_tbl <- models_tbl %>% modeltime_calibrate(new_data = testing(splits)) # ---- REFIT ---- # - Refit on full data set refit_tbl <- calibration_tbl %>% modeltime_refit(m750)
#> Disabling weekly seasonality. Run prophet with weekly.seasonality=TRUE to override this.
#> Disabling daily seasonality. Run prophet with daily.seasonality=TRUE to override this.