Point estimate accuracy measures

Accuracy measures for point forecast residuals.

Usage

ME(resid, na.rm = TRUE)

MAE(resid, na.rm = TRUE, ...)

MSE(resid, na.rm = TRUE, ...)

RMSE(resid, na.rm = TRUE, ...)

MPE(resid, actual, na.rm = TRUE, ...)

MAPE(resid, actual, na.rm = TRUE, ...)

MASE(
  resid,
  train,
  demean = FALSE,
  na.rm = TRUE,
  period,
  d = period == 1,
  D = period > 1,
  ...
)

RMSSE(
  resid,
  train,
  demean = FALSE,
  na.rm = TRUE,
  period,
  d = period == 1,
  D = period > 1,
  ...
)

point_measures

Format

An object of class list of length 8.

Arguments

resid

A numeric vector of residuals from either the validation or the test data.

na.rm

If TRUE, remove missing values before calculating the measure. Defaults to TRUE.

...

Additional arguments for each measure.

actual

A numeric vector of the realised values matching the forecasts. Required by the percentage measures.

train

A numeric vector of responses used to train the model. Required by the scaled measures.

demean

Should the response be demeaned (for MASE and RMSSE)? Defaults to FALSE.

period

The seasonal period of the data. Required by the scaled measures.

d

Should the response model include a first difference? Defaults to period == 1.

D

Should the response model include a seasonal difference? Defaults to period > 1.

Value

For the individual functions (ME, MAE, MSE, RMSE, MPE, MAPE, MASE, RMSSE), a single numeric scalar giving the requested accuracy measure.

For the exported object point_measures, a named list of functions that can be supplied to higher-level accuracy routines.

See also

interval_measures for the interval counterparts, and accuracy.cvforecast, which applies these measures to a cross-validation or a conformal prediction object.

Examples

# Toy residuals and data
set.seed(1)
y_train <- rnorm(50)
y_test  <- rnorm(10)
fcast   <- y_test + rnorm(10, sd = 0.2)
resid   <- y_test - fcast

# Basic measures
ME(resid)
#> [1] -0.09024199
MAE(resid)
#> [1] 0.1937409
RMSE(resid)
#> [1] 0.2606406

# Percentage measures require 'actual'
MPE(resid, actual = y_test)
#> [1] 9.271545
MAPE(resid, actual = y_test)
#> [1] 62.86441

# Scaled measures require training data (and seasonal period if applicable)
MASE(resid, train = y_train, period = 1)
#> [1] 0.2124482
RMSSE(resid, train = y_train, period = 1)
#> [1] 0.2271102