Forecast a multiple linear model with possible time series components

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

"forecast"(object, newdata, h = 10, level = c(80, 95), fan = FALSE, lambda = object$lambda, biasadj = FALSE, ts = TRUE, ...)
Object of class "mlm", usually the result of a call to lm or tslm.
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.
Confidence level for prediction intervals.
If TRUE, level is set to seq(51,99,by=3). This is suitable for fan plots.
Number of periods for forecasting. Ignored if newdata present.
Box-Cox transformation parameter. Ignored if NULL. Otherwise, forecasts back-transformed via an inverse Box-Cox transformation.
Use adjusted back-transformed mean for Box-Cox transformations. If TRUE, point forecasts and fitted values are mean forecast. Otherwise, these points can be considered the median of the forecast densities.
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().

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.


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: is a list containing at least the following elements:

See Also

tslm, forecast.lm, lm.

  • forecast.mlm
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))
Documentation reproduced from package forecast, version 7.3, License: GPL (>= 2)

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