# forecast.lm

From forecast v5.3
by Rob Hyndman

##### 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 class 'lm':
forecast(object, newdata, h=10, level=c(80,95), fan=FALSE,
lambda=object$lambda, 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. - level
- Confidence level for prediction intervals.
- fan
- If
`TRUE`

, level is set to seq(50,99,by=1). This is suitable for fan plots. - h
- Number of periods for forecasting. Ignored if
`newdata`

present. - lambda
- Box-Cox transformation parameter. Ignored if
`NULL`

. Otherwise, forecasts back-transformed via an inverse Box-Cox transformation. - 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 th - ...
- 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: 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

##### See Also

##### Examples

```
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 5.3, License: GPL (>= 2)*

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