plot.mforecast: Multivariate forecast plot

In pli2016/forecast: Forecasting Functions for Time Series and Linear Models

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/

See Also

plot.forecast, plot.ts

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))

pli2016/forecast documentation built on May 25, 2019, 8:22 a.m.