plot.mforecast: Multivariate forecast plot

In forecast: Forecasting Functions for Time Series and Linear Models

Multivariate forecast plot

Description

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.

Author(s)

Mitchell O'Hara-Wild

References

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

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))

forecast documentation built on June 22, 2024, 9:20 a.m.