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
- 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
hforecasts are produced.
- Confidence level for prediction intervals.
TRUE, level is set to seq(51,99,by=3). This is suitable for fan plots.
- Number of periods for forecasting. Ignored if
- 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.
TRUE, the forecasts will be treated as time series provided the original data is a time series; the
newdatawill 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.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
summaryis used to obtain and print a summary of the results, while the function
plotproduces a plot of the forecasts and prediction intervals.The generic accessor functions
residualsextract 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:
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))