Multivariate forecast plot

Plots historical data with multivariate forecasts and prediction intervals.

Usage

# S3 method for class 'mforecast'
autoplot(object, PI = TRUE, facets = TRUE, colour = FALSE, ...)

# S3 method for class 'mforecast'
autolayer(object, series = NULL, PI = TRUE, ...)

# S3 method for class 'mforecast'
plot(x, main = paste("Forecasts from", unique(x$method)), xlab = "time", ...)

Arguments

object: Multivariate forecast object of class mforecast. Used for ggplot graphics (S3 method consistency).
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
...: additional arguments to each individual plot.
series: Matches an unidentified forecast layer with a coloured object on the plot.
x: Multivariate forecast object of class mforecast.
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.

Details

autoplot will produce an equivalent plot as a ggplot object.

References

Hyndman and Athanasopoulos (2018) Forecasting: principles and practice, 2nd edition, OTexts: Melbourne, Australia. https://otexts.com/fpp2/

Author

Mitchell O'Hara-Wild

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))