R: Multivariate forecast plot

plot.mforecast {forecast}

R Documentation

Multivariate forecast plot

Description

Plots historical data with multivariate forecasts and prediction intervals.

autoplot will produce an equivelant plot as a ggplot object.

Usage

## S3 method for class 'mforecast'
plot(x, main=paste("Forecasts from",unique(x$method)),xlab="time",...)
## S3 method for class 'mforecast'
autoplot(object, PI = TRUE, facets = TRUE, colour = FALSE, ...)
## S3 method for class 'mforecast'
autolayer(object, series = NULL, PI = TRUE, ...)

Arguments

`x`	Multivariate forecast object of class `mforecast`.
`object`	Multivariate forecast object of class `mforecast`. Used for ggplot graphics (S3 method consistency).
`main`	Main title. Default is the forecast method. For autoplot, specify a vector of titles for each plot.
`xlab`	X-axis label. For autoplot, specify a vector of labels for each plot.
`PI`	If `FALSE`, confidence intervals will not be plotted, giving only the forecast line.
`facets`	If TRUE, multiple time series will be faceted. If FALSE, each series will be assigned a colour.
`colour`	If TRUE, the time series will be assigned a colour aesthetic
`series`	Matches an unidentified forecast layer with a coloured object on the plot.
`...`	additional arguments to each individual `plot`.

Author(s)

Mitchell O'Hara-Wild

References

Hyndman and Athanasopoulos (2014) Forecasting: principles and practice, OTexts: Melbourne, Australia. http://www.otexts.org/fpp/

Examples

library(ggplot2)

lungDeaths <- cbind(mdeaths, fdeaths)
fit <- tslm(lungDeaths ~ trend + season)
fcast <- forecast(fit, h=10)
plot(fcast)
autoplot(fcast)

carPower <- as.matrix(mtcars[,c("qsec","hp")])
carmpg <- mtcars[,"mpg"]
fit <- lm(carPower ~ carmpg)
fcast <- forecast(fit, newdata=data.frame(carmpg=30))
plot(fcast, xlab="Year")
autoplot(fcast, xlab=rep("Year",2))

[Package forecast version 8.0 Index]