Update and repeform cross-validation forecasting and conformal prediction

Update conformal prediction intervals and other information by applying the cvforecast and conformal functions.

Usage

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

Arguments

object: An object of class "cpforecast".
new_data: A 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 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.
...: 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 newly appended data and re-computed cross-validation forecasts and conformal prediction intervals.

Examples

# Simulate time series from an AR(2) model
library(forecast)
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,
                 forward = TRUE, initial = 1, window = 50)

# Classical conformal prediction with equal weights
scpfc <- conformal(fc, method = "scp", symmetric = FALSE, 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, symmetric = FALSE, ncal = 50, rolling = TRUE,  
#>      update = TRUE) 
#> 
#>  cp_times = 102 (the forward step included) 
#> 
#> Forecasts of the forward step:
#> Cross-validation
#> 
#> Call:
#>  scp(object = object, symmetric = FALSE, ncal = 50, rolling = TRUE,  
#>      update = TRUE) 
#> 
#>  fit_times =  (the forward step included) 
#> 
#> Forecasts of the forward step:
#>     Point Forecast     Lo 95    Hi 95
#> 204      1.2910976 -1.249629 5.592616
#> 205     -0.3496234 -3.679947 4.522049
#> 206     -0.9708415 -4.416003 3.579224
summary(scpfc_update)
#> SCP 
#> 
#> Call:
#>  scp(object = object, symmetric = FALSE, ncal = 50, rolling = TRUE,  
#>      update = TRUE) 
#> 
#>  cp_times = 102 (the forward step included) 
#> 
#> Forecasts of the forward step:
#> Cross-validation
#> 
#> Call:
#>  scp(object = object, symmetric = FALSE, ncal = 50, rolling = TRUE,  
#>      update = TRUE) 
#> 
#>  fit_times =  (the forward step included) 
#> 
#> Forecasts of the forward step:
#>     Point Forecast     Lo 95    Hi 95
#> 204      1.2910976 -1.249629 5.592616
#> 205     -0.3496234 -3.679947 4.522049
#> 206     -0.9708415 -4.416003 3.579224
#> 
#> Cross-validation error measures:
#>        ME   MAE   MSE  RMSE    MPE    MAPE MASE RMSSE Winkler_95 MSIS_95
#> CV -0.001 1.131 2.037 1.276 26.357 239.353 1.04 0.943      8.998   7.994