Perform rolling-origin calibration with recursive forecasting — calibrate_horizon

This function computes horizon-specific nonconformity scores by performing rolling-origin evaluation with recursive multi-step forecasting. This ensures proper out-of-sample calibration that respects the exchangeability assumption required for valid conformal prediction intervals.

Usage

calibrate_horizon_scores(
  y,
  y_modified,
  max_lag,
  caret_method,
  seasonal,
  K,
  lambda,
  pre_process,
  tune_grid,
  xreg = NULL,
  calibration_horizon,
  n_windows = NULL,
  initial_window = NULL,
  verbose = FALSE
)

Arguments

y: Original time series (untransformed)
y_modified: Transformed time series (Box-Cox if applicable)
max_lag: Maximum lag used in the model
caret_method: The caret method name
seasonal: Logical, whether seasonal terms are used
K: Fourier order for seasonality
lambda: Box-Cox transformation parameter
pre_process: Pre-processing specification
tune_grid: Tuning grid (uses best parameters from initial fit)
xreg: External regressors matrix (optional)
calibration_horizon: Maximum forecast horizon for calibration
n_windows: Number of rolling windows for calibration
initial_window: Initial training window size for calibration
verbose: Logical, print progress

Value

A list with horizon-indexed vectors of sorted absolute errors