modeltime (version 1.2.8)

metric_sets: Forecast Accuracy Metrics Sets

Description

This is a wrapper for metric_set() with several common forecast / regression accuracy metrics included. These are the default time series accuracy metrics used with modeltime_accuracy().

Usage

default_forecast_accuracy_metric_set(...)
extended_forecast_accuracy_metric_set(...)

Arguments

...

Add additional yardstick metrics

Default Forecast Accuracy Metric Set

The primary purpose is to use the default accuracy metrics to calculate the following forecast accuracy metrics using modeltime_accuracy():

• 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()

Adding additional metrics is possible via ....

Extended Forecast Accuracy Metric Set

Extends the default metric set by adding:

• MAAPE - Mean Arctangent Absolute Percentage Error, maape(). MAAPE is designed for intermittent data where MAPE returns Inf.

See Also

• yardstick::metric_tweak() - For modifying yardstick metrics

Examples

library(tibble)
library(dplyr)
library(timetk)
library(yardstick)

fake_data <- tibble(
    y    = c(1:12, 2*1:12),
    yhat = c(1 + 1:12, 2*1:12 - 1)
)

# ---- HOW IT WORKS ----

# Default Forecast Accuracy Metric Specification
default_forecast_accuracy_metric_set()

# Create a metric summarizer function from the metric set
calc_default_metrics <- default_forecast_accuracy_metric_set()

# Apply the metric summarizer to new data
calc_default_metrics(fake_data, y, yhat)

# ---- ADD MORE PARAMETERS ----

# Can create a version of mase() with seasonality = 12 (monthly)
mase12 <- metric_tweak(.name = "mase12", .fn = mase, m = 12)

# Add it to the default metric set
my_metric_set <- default_forecast_accuracy_metric_set(mase12)
my_metric_set

# Apply the newly created metric set
my_metric_set(fake_data, y, yhat)