Returns forecasts and prediction intervals for an iid model applied to y.
meanf( y, h = 10, level = c(80, 95), fan = FALSE, lambda = NULL, biasadj = FALSE, bootstrap = FALSE, npaths = 5000, x = y )
a numeric vector or time series of class
Number of periods for forecasting
Confidence levels for prediction intervals.
If TRUE, level is set to seq(51,99,by=3). This is suitable for fan plots.
Box-Cox transformation parameter. If
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.
If TRUE, use a bootstrap method to compute prediction intervals. Otherwise, assume a normal distribution.
Number of bootstrapped sample paths to use if
Deprecated. Included for backwards compatibility.
An object of class "
summary is used to obtain and print a summary of the
results, while the function
plot produces a plot of the forecasts and
The generic accessor functions
extract useful features of the value returned by
An object of class
"forecast" is a list containing at least the
A list containing information about the fitted model
The name of the forecasting method as a character string
Point forecasts as a time series
Lower limits for prediction intervals
Upper limits for prediction intervals
The confidence values associated with the prediction intervals
The original time series
object itself or the time series used to create the model
Residuals from the fitted model. That is x minus fitted values.
Fitted values (one-step forecasts)
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.
Rob J Hyndman
nile.fcast <- meanf(Nile, h=10) plot(nile.fcast)