Refit one or more trained models to new data
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 = NULL)
object |
A Modeltime Table |
data |
A |
... |
Under construction. Additional arguments to control refitting. |
control |
Under construction. Will be used to control refitting. |
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.
A Modeltime Table containing one or more re-trained models.
library(tidyverse)
library(lubridate)
library(timetk)
library(parsnip)
library(rsample)
# Data
m750 <- m4_monthly %>% filter(id == "M750")
# Split Data 80/20
splits <- initial_time_split(m750, prop = 0.9)
# --- MODELS ---
model_fit_auto_arima <- arima_reg() %>%
set_engine(engine = "auto_arima") %>%
fit(value ~ date, data = training(splits))
# ---- MODELTIME TABLE ----
models_tbl <- modeltime_table(
model_fit_auto_arima
)
# ---- 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)Please choose more modern alternatives, such as Google Chrome or Mozilla Firefox.