avgWidth: Calculate interval forecast width

In smimodel: Sparse Multiple Index Models for Nonparametric Forecasting

Calculate interval forecast width

Description

This is a wrapper for the function conformalForecast::width. Calculates the mean width of prediction intervals on the validation set. If window is not NULL, a matrix of the rolling means of interval width is also returned. If includemedian is TRUE, the information of the median interval width will be returned.

Usage

avgWidth(
  object,
  level = 95,
  includemedian = FALSE,
  window = NULL,
  na.rm = FALSE
)

Arguments

object	An object of class bb_cvforecast or cb_cvforecast.
level	Target confidence level for prediction intervals.
includemedian	If TRUE, the median interval width will also be returned.
window	If not NULL, the rolling mean (and rolling median if applicable) matrix for interval width will also be returned.
na.rm	A logical indicating whether NA values should be stripped before the rolling mean and rolling median computation proceeds.

Value

A list of class width with the following components:

width	Forecast interval width as a multivariate time series, where the hth column holds the interval width for the forecast horizon h. The time index corresponds to the period for which the forecast is produced.
mean	Mean interval width across the validation set.
rollmean	If window is not NULL, a matrix of the rolling means of interval width will be returned.
median	Median interval width across the validation set.
rollmedian	If window is not NULL, a matrix of the rolling medians of interval width will be returned.

Examples

library(dplyr)
library(tibble)
library(tidyr)
library(tsibble)

# Simulate data
n = 1055
set.seed(123)
sim_data <- tibble(x_lag_000 = runif(n)) |>
  mutate(
    # Add x_lags
    x_lag = lag_matrix(x_lag_000, 5)) |>
  unpack(x_lag, names_sep = "_") |>
  mutate(
    # Response variable
    y = (0.9*x_lag_000 + 0.6*x_lag_001 + 0.45*x_lag_003)^3 + rnorm(n, sd = 0.1),
    # Add an index to the data set
    inddd = seq(1, n)) |>
  drop_na() |>
  select(inddd, y, starts_with("x_lag")) |>
  # Make the data set a `tsibble`
  as_tsibble(index = inddd)

# Training set
sim_train <- sim_data[1:1000, ]
# Test set
sim_test <- sim_data[1001:1050, ]

# Index variables
index.vars <- colnames(sim_data)[3:8]

# Model fitting
pprModel <- model_ppr(data = sim_train,
                      yvar = "y",
                      index.vars = index.vars)
                      
# Conformal bootstrap prediction intervals (2-steps-ahead interval forecasts)
set.seed(12345)
pprModel_cb <- cb_cvforecast(object = pprModel,
                             data = sim_data,
                             yvar = "y",
                             predictor.vars = index.vars,
                             h = 2,
                             ncal = 30,
                             num.futures = 100,
                             window = 1000)
                                 
# Mean width of generated 95% conformal bootstrap prediction intervals
width_data <- avgWidth(object = pprModel_cb)
width_data$mean

                                 

smimodel documentation built on April 8, 2026, 5:06 p.m.