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

In forecast: Forecasting Functions for Time Series and Linear Models

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 class 'mlm'
forecast(
  object,
  newdata,
  h = 10,
  level = c(80, 95),
  fan = FALSE,
  lambda = object$lambda,
  biasadj = NULL,
  ts = TRUE,
  ...
)

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().

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.

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

Author(s)

Mitchell O'Hara-Wild

See Also

tslm, forecast.lm, lm.

Examples

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))

forecast documentation built on June 22, 2024, 9:20 a.m.