Forecasting time series
mforecast is a class of objects for forecasting from multivariate
time series or multivariate time series models. The function invokes
particular methods which depend on the class of the first argument.
# S3 method for mts forecast(object, h = ifelse(frequency(object) > 1, 2 * frequency(object), 10), level = c(80, 95), fan = FALSE, robust = FALSE, lambda = NULL, biasadj = FALSE, find.frequency = FALSE, allow.multiplicative.trend = FALSE, ...)
a multivariate time series or multivariate time series model for which forecasts are required
Number of periods for forecasting
Confidence level for prediction intervals.
levelis set to
seq(51,99,by=3). This is suitable for fan plots.
If TRUE, the function is robust to missing values and outliers in
object. This argument is only valid when
objectis of class
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.
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.
If TRUE, the function determines the appropriate period, if the data is of unknown period.
If TRUE, then ETS models with multiplicative trends are allowed. Otherwise, only additive or no trend ETS models are permitted.
Additional arguments affecting the forecasts produced.
An object of class "
summary is used to obtain and print a summary of the
results, while the function
plot produces a plot of the multivariate
forecasts and prediction intervals.
The generic accessors functions
extract various useful features of the value returned by
An object of class
"mforecast" is a list usually containing at least
the following elements:
A list containing information about the fitted model
The name of the forecasting method as a character string
Point forecasts as a time series
Lower limits for prediction intervals
Upper limits for prediction intervals
The confidence values associated with the prediction intervals
The original time series
object itself or the time series used to create the model
Residuals from the fitted model. For models with additive errors, the residuals will be x minus the fitted values.
Fitted values (one-step forecasts)
Other functions which return objects of class