Update and reperform cross-validation forecasting and conformal prediction

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)
#> SCP 
#> 
#> Call:
#>  scp(object = object, alpha = 0.0499999999999999, symmetric = FALSE,  
#>      ncal = 50, rolling = TRUE, quantiletype = 1, weightfun = ..6,  
#>      kess = FALSE, update = TRUE, na.rm = TRUE) 
#> 
#>  cp_times (the forward step included): 104 (h=1), 103 (h=2), 102 (h=3)
#> 
#> Forecasts of the forward step:
#>     Point Forecast      Lo 95    Hi 95
#> 204      1.3171165 -0.3335623 3.753786
#> 205     -0.1244955 -2.2766551 3.079426
#> 206     -0.5533392 -2.7220088 2.644333
summary(scpfc_update)
#> SCP 
#> 
#> Call:
#>  scp(object = object, alpha = 0.0499999999999999, symmetric = FALSE,  
#>      ncal = 50, rolling = TRUE, quantiletype = 1, weightfun = ..6,  
#>      kess = FALSE, update = TRUE, na.rm = TRUE) 
#> 
#>  cp_times (the forward step included): 104 (h=1), 103 (h=2), 102 (h=3)
#> 
#> Forecasts of the forward step:
#>     Point Forecast      Lo 95    Hi 95
#> 204      1.3171165 -0.3335623 3.753786
#> 205     -0.1244955 -2.2766551 3.079426
#> 206     -0.5533392 -2.7220088 2.644333
#> 
#> Cross-validation error measures:
#>       ME   MAE   MSE  RMSE    MPE    MAPE  MASE RMSSE Winkler_95 MSIS_95
#> CV 0.037 0.957 1.448 1.072 -3.518 265.271 1.002  0.89      6.104   6.536