modeltime (version 0.1.0)

modeltime_table: Scale forecast analysis with a Modeltime Table

Description

Designed to perform forecasts at scale using models created with modeltime, parsnip, workflows, and regression modeling extensions in the tidymodels ecosystem.

Usage

modeltime_table(...)

Arguments

...

Fitted parsnip model or workflow objects

Details

This function:

1. Creates a table of models

2. Validates that all objects are models (parsnip or workflows objects) and all models have been fitted (trained)

3. Provides an ID and Description of the models

Examples

# NOT RUN {
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 1: auto_arima ----
model_fit_arima <- arima_reg() %>%
    set_engine(engine = "auto_arima") %>%
    fit(value ~ date, data = training(splits))


# ---- MODELTIME TABLE ----

models_tbl <- modeltime_table(
    model_fit_arima
)

# ---- CALIBRATE ----

calibration_tbl <- models_tbl %>%
    modeltime_calibrate(new_data = testing(splits))

# ---- ACCURACY ----

calibration_tbl %>%
    modeltime_accuracy()

# ---- FORECAST ----

calibration_tbl %>%
    modeltime_forecast(
        new_data    = testing(splits),
        actual_data = m750
    )

# }