modeltime: default_forecast_accuracy_metric_set – R documentation

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default_forecast_accuracy_metric_set

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(...)

Arguments

...

Add additional yardstick metrics

Details

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 ....

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)

modeltime

The Tidymodels Extension for Time Series Modeling

v0.5.1

MIT + file LICENSE

Authors

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

Initial release