`forecast.mlm`

is used to predict multiple linear models, especially
those involving trend and seasonality components.

```
# S3 method for mlm
forecast(
object,
newdata,
h = 10,
level = c(80, 95),
fan = FALSE,
lambda = object$lambda,
biasadj = NULL,
ts = TRUE,
...
)
```

newdata

An optional data frame in which to look for variables with
which to predict. If omitted, it is assumed that the only variables are
trend and season, and `h`

forecasts are produced.

h

Number of periods for forecasting. Ignored if `newdata`

present.

level

Confidence level 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.

ts

If `TRUE`

, the forecasts will be treated as time series
provided the original data is a time series; the `newdata`

will be
interpreted as related to the subsequent time periods. If `FALSE`

, any
time series attributes of the original data will be ignored.

...

Other arguments passed to `forecast.lm()`

.

An object of class "`mforecast`

".

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 `forecast.lm`

.

An object of class `"mforecast"`

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 multivariate time series

Lower limits for prediction intervals of each series

Upper limits for prediction intervals of each series

The confidence values associated with the prediction intervals

The historical data for the response variable.

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

Fitted values

`forecast.mlm`

is largely a wrapper for
`forecast.lm()`

except that it allows forecasts to be
generated on multiple series. Also, the output is reformatted into a
`mforecast`

object.

`tslm`

, `forecast.lm`

,
`lm`

.

# NOT RUN { lungDeaths <- cbind(mdeaths, fdeaths) fit <- tslm(lungDeaths ~ trend + season) fcast <- forecast(fit, h=10) carPower <- as.matrix(mtcars[,c("qsec","hp")]) carmpg <- mtcars[,"mpg"] fit <- lm(carPower ~ carmpg) fcast <- forecast(fit, newdata=data.frame(carmpg=30)) # }