Mean Forecast

Returns forecasts and prediction intervals for an iid model applied to y.

Usage

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

Arguments

y: a numeric vector or time series of class ts
h: Number of periods for forecasting
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, use a bootstrap method to compute prediction intervals. Otherwise, assume a normal distribution.
npaths: Number of bootstrapped sample paths to use if bootstrap==TRUE.
x: Deprecated. Included for backwards compatibility.

Value

An object of class "forecast".

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 fitted.values and residuals extract useful features of the value returned by meanf.

An object of class "forecast" is a list 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 (either object itself or the time series used to create the model stored as object).
residuals: Residuals from the fitted model. That is x minus fitted values.
fitted: Fitted values (one-step forecasts)

Details

The iid model is

$$Y_t=\mu + Z_t$$

where $Z_t$ is a normal iid error. Forecasts are given by

$$Y_n(h)=\mu$$

where $\mu$ is estimated by the sample mean.

Author

Rob J Hyndman

Examples

nile.fcast <- meanf(Nile, h=10)
plot(nile.fcast)