point_measures: Point estimate accuracy measures

In conformalForecast: Conformal Prediction Methods for Multistep-Ahead Time Series Forecasting

Point estimate accuracy measures

Description

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

Arguments

resid	A numeric vector of residuals from either the validation or test data.
na.rm	If TRUE, remove missing values before calculating the measure.
...	Additional arguments for each measure.
actual	A numeric vector of responses matching the forecasts (for percentage measures).
train	A numeric vector of responses used to train the model (for scaled measures).
demean	Should the response be demeaned (for MASE and RMSSE)?
period	The seasonal period of the data.
d	Should the response model include a first difference?
D	Should the response model include a seasonal difference?

Format

An object of class list of length 8.

Value

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

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

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)
MAE(resid)
RMSE(resid)

# Percentage measures require 'actual'
MPE(resid, actual = y_test)
MAPE(resid, actual = y_test)

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

conformalForecast documentation built on Nov. 5, 2025, 6:01 p.m.