Empirical Interval Method

Creates prediction intervals by bootstrapping from historical forecast errors. This is a non-parametric method that doesn't assume any particular distribution.

EmpiricalInterval(
  n_trajectories = 1000L,
  min_observation = 1L,
  bootstrap_distribution = NULL,
  seed = NULL,
  positivity_correction = "none",
  symmetry_correction = FALSE,
  stepwise = FALSE,
  return_trajectories = FALSE
)

Arguments

n_trajectories

Number of bootstrap samples to generate (default: 1000)

min_observation

Minimum number of observations required (default: 1)

bootstrap_distribution

Optional distribution to sample from (default: NULL)

seed

Random seed for reproducibility (default: NULL)

positivity_correction

Method to ensure positive forecasts: "none", "post_clip", "truncate", or "zero_floor" (default: "none")

symmetry_correction

Whether to apply symmetry correction (default: FALSE)

stepwise

Whether to use stepwise intervals (default: FALSE)

return_trajectories

Whether to return forecast trajectories (default: FALSE)

Value

An EmpiricalInterval object

Examples

if (FALSE) { # \dontrun{
# Basic empirical intervals
method <- EmpiricalInterval()

# With more trajectories and seed
method <- EmpiricalInterval(n_trajectories = 2000, seed = 123)

# With positivity correction for count data
method <- EmpiricalInterval(
  n_trajectories = 1000,
  positivity_correction = "post_clip"
)

# Return trajectories for visualization
method <- EmpiricalInterval(return_trajectories = TRUE)
} # }