datasets (version 3.1.0)

AirPassengers: Monthly Airline Passenger Numbers 1949-1960

Description

The classic Box & Jenkins airline data. Monthly totals of international airline passengers, 1949 to 1960.

Usage

AirPassengers

Arguments

Format

A monthly time series, in thousands.

Source

Box, G. E. P., Jenkins, G. M. and Reinsel, G. C. (1976) Time Series Analysis, Forecasting and Control. Third Edition. Holden-Day. Series G.

Examples

Run this code
## Not run: 
# ## These are quite slow and so not run by example(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")
# ## End(Not run)

Run the code above in your browser using DataCamp Workspace