forecast.mlm: Forecast a multiple linear model with possible time series components

Description

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

Usage

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

Value

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:

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 multivariate time series
lower: Lower limits for prediction intervals of each series
upper: Upper limits for prediction intervals of each series
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

Arguments

object: Object of class "mlm", 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 forecast.lm().

Author

Mitchell O'Hara-Wild

Details

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.

Examples

Run this code


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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