Plots historical data with multivariate forecasts and prediction intervals.

# 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", ...)

object | Multivariate forecast object of class |
---|---|

PI | If |

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 |

series | Matches an unidentified forecast layer with a coloured object on the plot. |

x | Multivariate forecast object of class |

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. |

`autoplot`

will produce an equivalent plot as a ggplot object.

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

Mitchell O'Hara-Wild

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")