\name{AirPassengers}
\docType{data}
\alias{AirPassengers}
\title{Monthly Airline Passenger Numbers 1949-1960}
\description{
 The classic Box \& Jenkins airline data.  Monthly totals of
 international airline passengers, 1949 to 1960.
}
\usage{data(AirPassengers)}
\format{
  A monthly time series, in thousands.
}
\source{
  Box, G. E. P., Jenkins, G. M. and Reinsel, G. C. (1976)
  \emph{Time Series Analysis, Forecasting and Control.}
  Third Edition. Holden-Day. Series G.
}
 \examples{
\dontrun{## These are quite slow and so not run by example(AirPassengers)

data(AirPassengers)
## The classic `airline model', by full ML
(fit <- arima(log10(AirPassengers), c(0, 1, 1),
              seasonal = list(order=c(0, 1 ,1), period=12)))
update(fit, method = "CSS")
update(fit, x=window(log10(AirPassengers), start = 1954))
pred <- predict(fit, n.ahead = 24)
tl <- pred$pred - 1.96 * pred$se
tu <- pred$pred + 1.96 * pred$se
ts.plot(AirPassengers, 10^tl, 10^tu, log = "y", lty = c(1,2,2))

## full ML fit is the same if the series is reversed, CSS fit is not
ap0 <- rev(log10(AirPassengers))
attributes(ap0) <- attributes(AirPassengers)
arima(ap0, c(0, 1, 1), seasonal = list(order=c(0, 1 ,1), period=12))
arima(ap0, c(0, 1, 1), seasonal = list(order=c(0, 1 ,1), period=12),
      method = "CSS")

## Structural Time Series
ap <- log10(AirPassengers) - 2
(fit <- StructTS(ap, type= "BSM"))
par(mfrow=c(1,2))
plot(cbind(ap, fitted(fit)), plot.type = "single")
plot(cbind(ap, tsSmooth(fit)), plot.type = "single")
}}
\keyword{datasets}
