% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/mforecast.R
\name{forecast.mts}
\alias{forecast.mts}
\alias{mforecast}
\alias{print.mforecast}
\alias{summary.mforecast}
\alias{as.data.frame.mforecast}
\title{Forecasting time series}
\usage{
\method{forecast}{mts}(
  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,
  ...
)
}
\arguments{
\item{object}{a multivariate time series or multivariate time series model
for which forecasts are required}

\item{h}{Number of periods for forecasting}

\item{level}{Confidence level for prediction intervals.}

\item{fan}{If TRUE, \code{level} is set to \code{seq(51,99,by=3)}. This is
suitable for fan plots.}

\item{robust}{If TRUE, the function is robust to missing values and outliers
in \code{object}. This argument is only valid when \code{object} is of class
\code{mts}.}

\item{lambda}{Box-Cox transformation parameter. If \code{lambda="auto"},
then a transformation is automatically selected using \code{BoxCox.lambda}.
The transformation is ignored if NULL. Otherwise,
data transformed before model is estimated.}

\item{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.}

\item{find.frequency}{If TRUE, the function determines the appropriate
period, if the data is of unknown period.}

\item{allow.multiplicative.trend}{If TRUE, then ETS models with
multiplicative trends are allowed. Otherwise, only additive or no trend ETS
models are permitted.}

\item{...}{Additional arguments affecting the forecasts produced.}
}
\value{
An object of class "\code{mforecast}".

The function \code{summary} is used to obtain and print a summary of the
results, while the function \code{plot} produces a plot of the multivariate
forecasts and prediction intervals.

The generic accessors functions \code{fitted.values} and \code{residuals}
extract various useful features of the value returned by
\code{forecast$model}.

An object of class \code{"mforecast"} is a list usually containing at least
the following elements: \item{model}{A list containing information about the
fitted model} \item{method}{The name of the forecasting method as a
character string} \item{mean}{Point forecasts as a time series}
\item{lower}{Lower limits for prediction intervals} \item{upper}{Upper
limits for prediction intervals} \item{level}{The confidence values
associated with the prediction intervals} \item{x}{The original time series
(either \code{object} itself or the time series used to create the model
stored as \code{object}).} \item{residuals}{Residuals from the fitted model.
For models with additive errors, the residuals will be x minus the fitted
values.} \item{fitted}{Fitted values (one-step forecasts)}
}
\description{
\code{mforecast} is a class of objects for forecasting from multivariate
time series or multivariate time series models. The function invokes
particular \emph{methods} which depend on the class of the first argument.
}
\details{
For example, the function \code{\link{forecast.mlm}} makes multivariate
forecasts based on the results produced by \code{\link{tslm}}.
}
\seealso{
Other functions which return objects of class \code{"mforecast"}
are \code{\link{forecast.mlm}}, \code{forecast.varest}.
}
\author{
Rob J Hyndman & Mitchell O'Hara-Wild
}
