forecast.mean_model: 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 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.

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 accessors functions fitted.values and residuals extract various useful features from the underlying model.

Author

Rob J Hyndman

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().

Examples

Run this code

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

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