geom_forecast {forecast} | R Documentation |
Generates forecasts from forecast.ts
and adds them to the plot. Forecasts can be modified via sending forecast specific arguments above.
Multivariate forecasting is supported by having each time series on a different group.
You can also pass geom_forecast
a forecast
object to add it to the plot.
The aesthetics required for the forecasting to work includes forecast observations on the y axis, and the time
of the observations on the x axis. Refer to the examples below. To automatically set up aesthetics, use autoplot
.
geom_forecast(mapping = NULL, data = NULL, stat = "forecast", position = "identity", na.rm = FALSE, show.legend = NA, inherit.aes = TRUE, plot.conf=TRUE, h=NULL, level=c(80,95), fan=FALSE, robust=FALSE, lambda=NULL, find.frequency=FALSE, allow.multiplicative.trend=FALSE, series, ...)
mapping |
Set of aesthetic mappings created by |
data |
The data to be displayed in this layer. There are three options: If A A |
stat |
The stat object to use calculate the data. |
position |
Position adjustment, either as a string, or the result of a call to a position adjustment function. |
na.rm |
If |
show.legend |
logical. Should this layer be included in the legends?
|
inherit.aes |
If |
plot.conf |
If |
h |
Number of periods for forecasting |
level |
Confidence level for prediction intervals. |
fan |
If TRUE, |
robust |
If TRUE, the function is robust to missing values and outliers in |
lambda |
Box-Cox transformation parameter. |
find.frequency |
If TRUE, the function determines the appropriate period, if the data is of unknown period. |
allow.multiplicative.trend |
If TRUE, then ETS models with multiplicative trends are allowed. Otherwise, only additive or no trend ETS models are permitted. |
series |
Matches an unidentified forecast layer with a coloured object on the plot. |
... |
other arguments passed on to |
A layer for a ggplot graph.
Mitchell O'Hara-Wild
## Not run: library(ggplot2) autoplot(USAccDeaths) + geom_forecast() lungDeaths <- cbind(mdeaths, fdeaths) autoplot(lungDeaths) + geom_forecast() # Using fortify.ts p <- ggplot(aes(x=x, y=y), data=USAccDeaths) p <- p + geom_line() p + geom_forecast() # Without fortify.ts data <- data.frame(USAccDeaths=as.numeric(USAccDeaths), time=as.numeric(time(USAccDeaths))) p <- ggplot(aes(x=time, y=USAccDeaths), data=data) p <- p + geom_line() p + geom_forecast() p + geom_forecast(h=60) p <- ggplot(aes(x=time, y=USAccDeaths), data=data) p + geom_forecast(level=c(70,98)) p + geom_forecast(level=c(70,98),colour="lightblue") #Add forecasts to multivariate series with colour groups lungDeaths <- cbind(mdeaths, fdeaths) autoplot(lungDeaths) + geom_forecast(forecast(mdeaths), series="mdeaths") ## End(Not run)