update.cpforecast: Update and reperform cross-validation forecasting and...

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

Update and reperform cross-validation forecasting and conformal prediction

Description

Update conformal prediction intervals and other information by applying the cvforecast and conformal functions. Only the newly added time steps are computed: the prediction intervals produced earlier are carried over unchanged.

Usage

## S3 method for class 'cpforecast'
update(object, new_data, forecastfun, new_xreg = NULL, ...)

Arguments

object	An object of class "cpforecast", as returned by one of the conformal prediction methods.
new_data	A non-empty numeric vector of newly available data.
forecastfun	Function to return an object of class "forecast". Its first argument must be a univariate time series, and it must have an argument h for the forecast horizon and an argument level for the confidence level for prediction intervals. If exogenous predictors are used, then it must also have xreg and newxreg arguments corresponding to the training and the test periods, respectively.
new_xreg	Newly available exogenous predictor variables passed to forecastfun if required. The number of rows should match the length of new_data, and the number of columns should match the dimensions of the xreg argument in object. These rows extend object$xreg and correspond to the periods immediately following it. Defaults to NULL.
...	Other arguments are passed to forecastfun.

Value

A refreshed object of class "cpforecast" with updated fields (e.g., x, MEAN, ERROR, LOWER, UPPER, and any method-specific components), reflecting the newly appended data and the re-computed cross-validation forecasts and conformal prediction intervals.

See Also

cvforecast and conformal, which produce the object to be updated.

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)

# Update conformal prediction using newly available data
scpfc_update <- update(scpfc, forecastfun = far2,
                       new_data = c(1.5, 0.8, 2.3))
print(scpfc_update)
summary(scpfc_update)

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