forecast.lm  R Documentation 
forecast.lm
is used to predict linear models, especially those
involving trend and seasonality components.
## S3 method for class 'lm' forecast( object, newdata, h = 10, level = c(80, 95), fan = FALSE, lambda = object$lambda, biasadj = NULL, ts = TRUE, ... )
object 
Object of class "lm", usually the result of a call to

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 
Number of periods for forecasting. Ignored if 
level 
Confidence level for prediction intervals. 
fan 
If 
lambda 
BoxCox transformation parameter. If 
biasadj 
Use adjusted backtransformed mean for BoxCox 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 
... 
Other arguments passed to 
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.
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 
Rob J Hyndman
tslm
, lm
.
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))
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