forecast (version 9.0.0)

autoplot.mforecast: Multivariate forecast plot

Description

Plots historical data with multivariate forecasts and prediction intervals.

Usage

# S3 method for mforecast
autoplot(object, PI = TRUE, facets = TRUE, colour = FALSE, ...)
# S3 method for mforecast
autolayer(object, series = NULL, PI = TRUE, ...)
# S3 method for 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.

Author

Mitchell O'Hara-Wild

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/

See Also

plot.forecast(), stats::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))