conformalForecast (version 0.1.0)

ME: 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

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.

Format

An object of class list of length 8.

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?

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)