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)

## 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. Ignored if NULL. Otherwise,
forecasts back-transformed via an inverse Box-Cox transformation. |

biasadj |
Use adjusted back-transformed mean for Box-Cox
transformations. If TRUE, point forecasts and fitted values are mean
forecast. Otherwise, these points can be considered the median of the
forecast densities. |

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:

modelA list containing information about the
fitted model

methodThe name of the forecasting method as a
character string

meanPoint forecasts as a time series

lowerLower limits for prediction intervals

upperUpper
limits for prediction intervals

levelThe confidence values
associated with the prediction intervals

xThe original time series
(either `object`

itself or the time series used to create the model
stored as `object`

).

residualsResiduals from the fitted model.
That is x minus fitted values.

fittedFitted 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.

## See also

`rwf`

## Examples

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