interval_measures: Interval estimate accuracy measures

In conformalForecast: Conformal Prediction Methods for Multistep-Ahead Time Series Forecasting

Interval estimate accuracy measures

Description

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

Arguments

lower	A numeric vector of lower bounds of the interval forecasts.
upper	A numeric vector of upper bounds of the interval forecasts.
actual	A numeric vector of realised values.
train	A numeric vector of responses used to train the model. Required by the scaled scores.
level	The nominal level of the forecast interval (e.g., 95 or 0.95). Defaults to 95.
period	The seasonal period of the data. Required by the scaled scores.
d	Should the response model include a first difference? Defaults to period == 1.
D	Should the response model include a seasonal difference? Defaults to period > 1.
na.rm	If TRUE, remove missing values before calculating the measure. Defaults to TRUE.
...	Additional arguments for each measure.

Format

An object of class list of length 2.

Value

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

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

See Also

point_measures for the point counterparts, and accuracy.cvforecast, which applies these measures to a cross-validation or a conformal prediction object.

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)

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

conformalForecast documentation built on Oct. 15, 2026, 9:07 a.m.