# geom_forecast

0th

Percentile

##### Forecast plot

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.

##### Usage
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, ...)
##### Arguments
mapping
Set of aesthetic mappings created by aes or 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 mapping if there is no plot mapping.
data
The data to be displayed in this layer. There are three options:

If NULL, the default, the data is inherited from the plot data as specified in the call to ggplot.

A data.frame, or other object, will override the plot data. All objects will be fortified to produce a data frame. See fortify for which variables will be created.

A function will 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.

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 FALSE (the default), removes missing values with a warning. If TRUE silently removes missing values.
show.legend
logical. Should this layer be included in the legends? NA, the default, includes if any aesthetics are mapped. FALSE never includes, and TRUE always includes.
inherit.aes
If 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. borders.
plot.conf
If FALSE, confidence intervals will not be plotted, giving only the forecast line.
h
Number of periods for forecasting
level
Confidence level for prediction intervals.
fan
If TRUE, level is set to seq(51,99,by=3). This is suitable for fan plots.
robust
If TRUE, the function is robust to missing values and outliers in object. This argument is only valid when object is of class ts.
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 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.
##### Value

forecast, ggproto

##### Aliases
• geom_forecast
• StatForecast
• GeomForecast
##### Examples
## 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)

Documentation reproduced from package forecast, version 7.2, License: GPL (>= 2)

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