% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/forecast.R
\name{forecast.ts}
\alias{forecast.ts}
\alias{print.forecast}
\alias{summary.forecast}
\alias{as.data.frame.forecast}
\alias{as.ts.forecast}
\alias{forecast.default}
\title{Forecasting time series}
\usage{
\method{forecast}{ts}(
  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,
  model = NULL,
  ...
)

\method{forecast}{default}(object, ...)

\method{print}{forecast}(x, ...)
}
\arguments{
\item{object}{a time series or 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{ts}.}

\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{model}{An object describing a time series model; e.g., one of of class
\code{ets}, \code{Arima}, \code{bats}, \code{tbats}, or \code{nnetar}.}

\item{...}{Additional arguments affecting the forecasts produced. If
\code{model=NULL}, \code{forecast.ts} passes these to \code{\link{ets}} or
\code{\link{stlf}} depending on the frequency of the time series. If
\code{model} is not \code{NULL}, the arguments are passed to the relevant
modelling function.}

\item{x}{a numeric vector or time series of class \code{ts}.}
}
\value{
An object of class "\code{forecast}".

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 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{"forecast"} 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{forecast} is a generic function for forecasting from time series or
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.Arima}} makes forecasts based
on the results produced by \code{\link[stats]{arima}}.

If \code{model=NULL},the function \code{\link{forecast.ts}} makes forecasts
using \code{\link{ets}} models (if the data are non-seasonal or the seasonal
period is 12 or less) or \code{\link{stlf}} (if the seasonal period is 13 or
more).

If \code{model} is not \code{NULL}, \code{forecast.ts} will apply the
\code{model} to the \code{object} time series, and then generate forecasts
accordingly.
}
\examples{

WWWusage \%>\% forecast \%>\% plot
fit <- ets(window(WWWusage, end=60))
fc <- forecast(WWWusage, model=fit)
}
\seealso{
Other functions which return objects of class \code{"forecast"} are
\code{\link{forecast.ets}}, \code{\link{forecast.Arima}},
\code{\link{forecast.HoltWinters}}, \code{\link{forecast.StructTS}},
\code{\link{meanf}}, \code{\link{rwf}}, \code{\link{splinef}},
\code{\link{thetaf}}, \code{\link{croston}}, \code{\link{ses}},
\code{\link{holt}}, \code{\link{hw}}.
}
\author{
Rob J Hyndman
}
\keyword{ts}