modeltime: modeltime_accuracy – R documentation

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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

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()

Value

A tibble with accuracy estimates.

Examples

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)
    )

modeltime

The Tidymodels Extension for Time Series Modeling

v0.5.1

MIT + file LICENSE

Authors

Matt Dancho [aut, cre], Business Science [cph]

Initial release