`forecast.lm`

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

## Arguments

- object
Object of class "lm", usually the result of a call to

`lm`

or`tslm`

.- 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

`predict.lm()`

.

## 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 `forecast.lm`

.

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 historical data for the response variable.

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

- fitted
Fitted values

## Details

`forecast.lm`

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

except that it allows variables "trend"
and "season" which are created on the fly from the time series
characteristics of the data. Also, the output is reformatted into a
`forecast`

object.