# autoplot.mforecast

##### Multivariate forecast plot

Plots historical data with multivariate forecasts and prediction intervals.

- Keywords
- ts

##### 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.

##### 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.org/fpp2/

##### See Also

##### Examples

```
# NOT RUN {
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))
# }
```

*Documentation reproduced from package forecast, version 8.9, License: GPL-3*