Interval estimate accuracy measures — MSIS • conformalForecast

Accuracy measures for interval forecasts.

Usage

MSIS(
  lower,
  upper,
  actual,
  train,
  level = 95,
  period,
  d = period == 1,
  D = period > 1,
  na.rm = TRUE,
  ...
)

winkler_score(lower, upper, actual, level = 95, na.rm = TRUE, ...)

interval_measures

Format

An object of class list of length 2.

Arguments

lower: A numeric vector of lower bounds of interval forecasts.
upper: A numeric vector of upper bounds of interval forecasts.
actual: A numeric vector of realised values.
train: A numeric vector of responses used to train the model (for scaled scores).
level: The nominal level of the forecast interval (e.g., 95 or 0.95).
period: The seasonal period of the data.
d: Should the response model include a first difference?
D: Should the response model include a seasonal difference?
na.rm: If TRUE, remove missing values before calculating the measure.
...: Additional arguments for each measure.

Value

For winkler_score and MSIS, returns a single numeric scalar giving the average interval score (Winkler or mean scaled interval score).

For the exported object interval_measures, returns a named list of functions that can be supplied to higher-level accuracy routines.

Examples

set.seed(1)
actual <- rnorm(10)
lower  <- actual - runif(10, 0.5, 1)
upper  <- actual + runif(10, 0.5, 1)
train  <- rnorm(50)

# Winkler score at 95%
winkler_score(lower, upper, actual, level = 95)
#> [1] 1.473882

# Mean scaled interval score (needs training data and period)
MSIS(lower, upper, actual, train, level = 95, period = 1)
#> [1] 1.369701