conformal: Conformal prediction

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

Conformal prediction

Description

This function allows you to specify the method used to perform conformal prediction.

Usage

conformal(object, ...)

## S3 method for class 'cvforecast'
conformal(object, method = c("scp", "acp", "pid", "acmcp"), ...)

Arguments

object	An object of class "cvforecast". It must have an argument x for the original univariate time series, an argument MEAN for the point forecasts and ERROR for the forecast errors on the validation set. See the results of a call to cvforecast.
...	Additional arguments to be passed to the selected conformal method.
method	A character string specifying the conformal method to be applied. Possible options include "scp" (scp), "acp" (acp), "pid" (pid), and "acmcp" (acmcp). Defaults to "scp".

Value

An object whose class depends on the method invoked.

See Also

cvforecast to produce object, and update.cpforecast to extend the results with new observations.

Other conformal prediction methods: acmcp(), acp(), pid(), scp()

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
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)

# Classical conformal prediction with equal weights
scpfc <- conformal(fc, method = "scp", ncal = 50, rolling = TRUE)
summary(scpfc)

# ACP with asymmetric nonconformity scores and rolling calibration sets
acpfc <- conformal(fc, method = "acp", gamma = 0.005,
                   ncal = 50, rolling = TRUE)
summary(acpfc)

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