Forecast a linear model with possible time series components

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

Usage

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

Arguments

object: Object of class "lm", usually the result of a call to stats::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 levels 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 stats::predict.lm().

Value

An object of class forecast.

Details

forecast.lm is largely a wrapper for stats::predict.lm() except that it allows variables "trend" and "season" which are created on the fly from the time series characteristics of the data. Also, the output is reformatted into a forecast object.

forecast class

An object of class forecast is a list usually 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 time series
lower: Lower limits for prediction intervals
upper: Upper limits for prediction intervals
level: The confidence values associated with the prediction intervals
x: The original time series.
residuals: Residuals from the fitted model. For models with additive errors, the residuals will be x minus the fitted values.
fitted: Fitted values (one-step forecasts)

The function summary can be used to obtain and print a summary of the results, while the functions plot and autoplot produce plots of the forecasts and prediction intervals. The generic accessors functions fitted.values and residuals extract various useful features from the underlying model.

Author

Rob J Hyndman

Examples


y <- ts(rnorm(120, 0, 3) + 1:120 + 20 * sin(2 * pi * (1:120) / 12), frequency = 12)
fit <- tslm(y ~ trend + season)
plot(forecast(fit, h = 20))