modeltime (version 0.4.0)

modeltime_accuracy: Calculate Accuracy Metrics

Description

This is a wrapper for yardstick that simplifies time series regression accuracy metric calculations from a fitted workflow (trained workflow) or model_fit (trained parsnip model).

Usage

modeltime_accuracy(
  object,
  new_data = NULL,
  metric_set = default_forecast_accuracy_metric_set(),
  quiet = TRUE,
  ...
)

Arguments

object

A Modeltime Table

new_data

A tibble to predict and calculate residuals on. If provided, overrides any calibration data.

metric_set

A yardstick::metric_set() that is used to summarize one or more forecast accuracy (regression) metrics.

quiet

Hide errors (TRUE, the default), or display them as they occur?

...

Not currently used

Value

A tibble with accuracy estimates.

Details

The following accuracy metrics are included by default via default_forecast_accuracy_metric_set():

• MAE - Mean absolute error, mae()

• MAPE - Mean absolute percentage error, mape()

• MASE - Mean absolute scaled error, mase()

• SMAPE - Symmetric mean absolute percentage error, smape()

• RMSE - Root mean squared error, rmse()

• RSQ - R-squared, rsq()

Examples

# NOT RUN {
library(tidymodels)
library(tidyverse)
library(lubridate)
library(timetk)

# 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
)

# ---- ACCURACY ----

models_tbl %>%
    modeltime_calibrate(new_data = testing(splits)) %>%
    modeltime_accuracy(
        metric_set = metric_set(mae, rmse, rsq)
    )


# }