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
)
```

- 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.

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)

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.

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