Conformal prediction

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)
#> SCP 
#> 
#> Call:
#>  scp(object = object, ncal = 50, rolling = TRUE) 
#> 
#>  cp_times (the forward step included): 101 (h=1), 100 (h=2), 99 (h=3)
#> 
#> Forecasts of the forward step:
#>     Point Forecast     Lo 95    Hi 95
#> 201      0.6271538 -1.023525 3.234253
#> 202      0.8607034 -1.291456 4.064625
#> 203      0.4935805 -1.675089 3.691253
#> 
#> Cross-validation error measures:
#>       ME   MAE   MSE RMSE    MPE    MAPE  MASE RMSSE Winkler_95 MSIS_95
#> CV 0.007 0.946 1.415 1.06 -3.933 269.763 0.992 0.882      6.123   6.568

# ACP with asymmetric nonconformity scores and rolling calibration sets
acpfc <- conformal(fc, method = "acp", gamma = 0.005,
                   ncal = 50, rolling = TRUE)
summary(acpfc)
#> ACP 
#> 
#> Call:
#>  acp(object = object, gamma = 0.005, ncal = 50, rolling = TRUE) 
#> 
#>  cp_times (the forward step included): 101 (h=1), 100 (h=2), 99 (h=3)
#> 
#> Forecasts of the forward step:
#>     Point Forecast     Lo 95    Hi 95
#> 201      0.6271538 -1.706076 3.234253
#> 202      0.8607034 -1.825564      Inf
#> 203      0.4935805 -2.027163 3.691253
#> 
#> Cross-validation error measures:
#>       ME   MAE   MSE RMSE    MPE    MAPE  MASE RMSSE Winkler_95 MSIS_95
#> CV 0.007 0.946 1.415 1.06 -3.933 269.763 0.992 0.882        Inf     Inf