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,
...)
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.
Number of periods for forecasting. Ignored if newdata
present.
Confidence level for prediction intervals.
If TRUE
, level is set to seq(51,99,by=3). This is suitable
for fan plots.
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.
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.
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))
# }
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