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:

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
(either `object`

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

).

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.

# NOT RUN { nile.fcast <- meanf(Nile, h=10) plot(nile.fcast) # }