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
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
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, ...)
- Set of aesthetic mappings created by
aes_. If specified and
inherit.aes = TRUE(the default), it is combined with the default mapping at the top level of the plot. You must supply
mappingif there is no plot mapping.
- The data to be displayed in this layer. There are three
NULL, the default, the data is inherited from the plot data as specified in the call to
data.frame, or other object, will override the plot data. All objects will be fortified to produce a data frame. See
fortifyfor which variables will be created.
functionwill be called with a single argument, the plot data. The return value must be a
data.frame, and will be used as the layer data.
- The stat object to use calculate the data.
- Position adjustment, either as a string, or the result of a call to a position adjustment function.
FALSE(the default), removes missing values with a warning. If
TRUEsilently removes missing values.
- logical. Should this layer be included in the legends?
NA, the default, includes if any aesthetics are mapped.
FALSEnever includes, and
FALSE, overrides the default aesthetics, rather than combining with them. This is most useful for helper functions that define both data and aesthetics and shouldn't inherit behaviour from the default plot specification, e.g.
FALSE, confidence intervals will not be plotted, giving only the forecast line.
- Number of periods for forecasting
- Confidence level for prediction intervals.
- If TRUE,
levelis set to
seq(51,99,by=3). This is suitable for fan plots.
- If TRUE, the function is robust to missing values and outliers in
object. This argument is only valid when
objectis of class
- Box-Cox transformation parameter.
- If TRUE, the function determines the appropriate period, if the data is of unknown period.
- If TRUE, then ETS models with multiplicative trends are allowed. Otherwise, only additive or no trend ETS models are permitted.
- Matches an unidentified forecast layer with a coloured object on the plot.
- other arguments passed on to
layer. These are often aesthetics, used to set an aesthetic to a fixed value, like
color = "red"or
alpha = .5. They may also be parameters to the paired geom/stat.
## 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)