Scale forecast analysis with a Modeltime Table

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))
#> 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 ----

# 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()
#> # Modeltime Table
#> # A tibble: 1 × 3
#>   .model_id .model   .model_desc
#>       <int> <list>   <chr>      
#> 1         1 <fit[+]> PROPHET    

# ---- CALIBRATE ----

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

# ---- ACCURACY ----

calibration_tbl %>%
    modeltime_accuracy()
#> # A tibble: 1 × 9
#>   .model_id .model_desc .type   mae  mape  mase smape  rmse   rsq
#>       <int> <chr>       <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1         1 PROPHET     Test   177.  1.69 0.604  1.69  234. 0.880

# ---- FORECAST ----

calibration_tbl %>%
    modeltime_forecast(
        new_data    = testing(splits),
        actual_data = m750
    )
#> # Forecast Results
#>   
#> Conf Method: conformal_default | Conf Interval: 0.95 | Conf By ID: FALSE
#> (GLOBAL CONFIDENCE)
#> # A tibble: 337 × 7
#>    .model_id .model_desc .key   .index     .value .conf_lo .conf_hi
#>        <int> <chr>       <fct>  <date>      <dbl>    <dbl>    <dbl>
#>  1        NA ACTUAL      actual 1990-01-01   6370       NA       NA
#>  2        NA ACTUAL      actual 1990-02-01   6430       NA       NA
#>  3        NA ACTUAL      actual 1990-03-01   6520       NA       NA
#>  4        NA ACTUAL      actual 1990-04-01   6580       NA       NA
#>  5        NA ACTUAL      actual 1990-05-01   6620       NA       NA
#>  6        NA ACTUAL      actual 1990-06-01   6690       NA       NA
#>  7        NA ACTUAL      actual 1990-07-01   6000       NA       NA
#>  8        NA ACTUAL      actual 1990-08-01   5450       NA       NA
#>  9        NA ACTUAL      actual 1990-09-01   6480       NA       NA
#> 10        NA ACTUAL      actual 1990-10-01   6820       NA       NA
#> # ℹ 327 more rows