# forecast.lm

##### Forecast a linear model with possible time series components

`forecast.lm`

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

- Keywords
- stats

##### Usage

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

##### 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()`

.

##### 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.

##### 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:

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 historical data for the response variable.

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

Fitted values

##### See Also

##### Examples

```
# NOT RUN {
y <- ts(rnorm(120,0,3) + 1:120 + 20*sin(2*pi*(1:120)/12), frequency=12)
fit <- tslm(y ~ trend + season)
plot(forecast(fit, h=20))
# }
```

*Documentation reproduced from package forecast, version 8.13, License: GPL-3*