accuracy.cvforecast: Accuracy measures for a cross-validation model and a...

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

Accuracy measures for a cross-validation model and a conformal prediction model

Description

Return a range of summary measures of the out-of-sample forecast accuracy. If x is given, the function also measures the test set forecast accuracy. If x is not given, the function only produces accuracy measures on the validation set.

Usage

## S3 method for class 'cvforecast'
accuracy(
  object,
  x,
  CV = TRUE,
  period = NULL,
  measures = interval_measures,
  byhorizon = FALSE,
  ...
)

## S3 method for class 'cpforecast'
accuracy(object, ...)

Arguments

object	An object of class "cvforecast" or "cpforecast".
x	An optional numerical vector containing the actual values of the same length as mean in object.
CV	If TRUE, the cross-validation forecast accuracy will be returned. Defaults to TRUE.
period	The seasonal period of the data. Defaults to NULL, in which case it is taken from the frequency of the series.
measures	A list of accuracy measure functions to compute (such as point_measures or interval_measures). Defaults to interval_measures.
byhorizon	If TRUE, accuracy measures will be calculated for each individual forecast horizon h separately. Defaults to FALSE.
...	Additional arguments depending on the specific measure.

Details

The measures calculated are:

• ME: Mean Error

• MAE: Mean Absolute Error

• MSE: Mean Squared Error

• RMSE: Root Mean Squared Error

• MPE: Mean Percentage Error

• MAPE: Mean Absolute Percentage Error

• MASE: Mean Absolute Scaled Error

• RMSSE: Root Mean Squared Scaled Error

• Winkler: Winkler Score

• MSIS: Mean Scaled Interval Score

Value

A matrix giving mean out-of-sample forecast accuracy measures.

See Also

point_measures and interval_measures for the measures that can be supplied through measures.

Other evaluation functions: coverage(), width()

Examples

# Simulate time series from an AR(2) model
library(forecast)
set.seed(1)
series <- arima.sim(n = 200, list(ar = c(0.8, -0.5)), sd = sqrt(1))

# Cross-validation forecasting with a rolling window
far2 <- function(x, h, level) {
  Arima(x, order = c(2, 0, 0)) |>
    forecast(h = h, level)
}
fc <- cvforecast(series, forecastfun = far2, h = 3, level = 95, window = 50)

# Out-of-sample forecast accuracy on validation set
accuracy(fc, measures = point_measures, byhorizon = TRUE)
accuracy(fc, measures = interval_measures, level = 95, byhorizon = TRUE)

# Accuracy of conformal prediction intervals
scpfc <- conformal(fc, method = "scp", ncal = 50, rolling = TRUE)
accuracy(scpfc, measures = interval_measures, level = 95,
         byhorizon = TRUE)

# Out-of-sample forecast accuracy on test set
accuracy(fc, x = c(1, 0.5, 0), measures = interval_measures,
         CV = FALSE, level = 95, byhorizon = TRUE)

conformalForecast documentation built on Oct. 15, 2026, 9:07 a.m.