modeltime_table: Scale forecast analysis with a Modeltime Table

In modeltime: The Tidymodels Extension for Time Series Modeling

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(...)

as_modeltime_table(.l)

Arguments

...	Fitted parsnip model or workflow objects
.l	A list containing fitted parsnip model or workflow objects

Details

modeltime_table():

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

as_modeltime_table():

Converts a list of models to a modeltime table. Useful if programatically creating Modeltime Tables from models stored in a list.

Examples

library(dplyr)
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: prophet ----
model_fit_prophet <- prophet_reg() %>%
    set_engine(engine = "prophet") %>%
    fit(value ~ date, data = training(splits))


# ---- MODELTIME TABLE ----

# Make a Modeltime Table
models_tbl <- modeltime_table(
    model_fit_prophet
)

# Can also convert a list of models
list(model_fit_prophet) %>%
    as_modeltime_table()

# ---- 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
    )

modeltime documentation built on Oct. 23, 2024, 1:07 a.m.