# meanf

##### Mean Forecast

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

- Keywords
- ts

##### Usage

```
meanf(y, h = 10, level = c(80, 95), fan = FALSE, lambda = NULL,
biasadj = FALSE, 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.

- x
Deprecated. Included for backwards compatibility.

##### 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.

##### 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:

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)

##### See Also

##### Examples

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

*Documentation reproduced from package forecast, version 8.2, License: GPL-3*