forecast.mean_model: Mean Forecast

In forecast: Forecasting Functions for Time Series and Linear Models

Mean Forecast

Description

Returns forecasts and prediction intervals for a Gaussian iid model. meanf() is a convenience function that combines mean_model() and forecast().

Usage

## S3 method for class 'mean_model'
forecast(
  object,
  h = 10,
  level = c(80, 95),
  fan = FALSE,
  lambda = object$lambda,
  biasadj = attr(object$lambda, "biasadj"),
  bootstrap = FALSE,
  npaths = 5000,
  ...
)

meanf(
  y,
  h = 10,
  level = c(80, 95),
  fan = FALSE,
  lambda = NULL,
  biasadj = FALSE,
  bootstrap = FALSE,
  npaths = 5000,
  x = y
)

Arguments

object	An object of class mean_model as returned by mean_model().
h	Number of periods for forecasting. Default value is twice the largest seasonal period (for seasonal data) or ten (for non-seasonal data).
level	Confidence levels for prediction intervals.
fan	If TRUE, level is set to seq(51, 99, by = 3). This is suitable for fan plots.
lambda	Box-Cox transformation parameter. If lambda = "auto", then a transformation is automatically selected using BoxCox.lambda. The transformation is ignored if NULL. Otherwise, data transformed before model is estimated.
biasadj	Use adjusted back-transformed mean for Box-Cox transformations. If transformed data is used to produce forecasts and fitted values, a regular back transformation will result in median forecasts. If biasadj is TRUE, an adjustment will be made to produce mean forecasts and fitted values.
bootstrap	If TRUE, then prediction intervals are produced by simulation using resampled errors (rather than normally distributed errors). Ignored if innov is not NULL.
npaths	Number of sample paths used in computing simulated prediction intervals.
...	Additional arguments not used.
y	a numeric vector or univariate time series of class ts
x	Deprecated. Included for backwards compatibility.

Details

The model assumes that the data are independent and identically distributed

Y_t \sim N(\mu,\sigma^2)

Forecasts are given by

Y_{n+h|n}=\mu

where \mu is estimated by the sample mean.

The function summary() is used to obtain and print a summary of the results, while the function plot() produces a plot of the forecasts and prediction intervals. The generic accessor functions stats::fitted() and stats::residuals() extract useful features of the object returned by mean_model().

forecast class

An object of class forecast is a list usually containing at least the following elements:

model

A list containing information about the fitted model

method

The name of the forecasting method as a character string

mean

Point forecasts as a time series

lower

Lower limits for prediction intervals

upper

Upper limits for prediction intervals

level

The confidence values associated with the prediction intervals

x

The original time series.

residuals

Residuals from the fitted model. For models with additive errors, the residuals will be x minus the fitted values.

fitted

Fitted values (one-step forecasts)

The function summary can be used to obtain and print a summary of the results, while the functions plot and autoplot produce plots of the forecasts and prediction intervals. The generic accessor functions fitted.values and residuals extract various useful features from the underlying model.

Author(s)

Rob J Hyndman

See Also

mean_model()

Examples

fit_nile <- mean_model(Nile)
fit_nile |> forecast(h = 10) |> autoplot()
nile.fcast <- meanf(Nile, h = 10)

forecast documentation built on March 18, 2026, 9:07 a.m.