forecast (version 8.23.0)

StatForecast: Forecast plot

Description

Generates forecasts from forecast.ts and adds them to the plot. Forecasts can be modified via sending forecast specific arguments above.

Usage

StatForecast
GeomForecast
geom_forecast(
  mapping = NULL,
  data = NULL,
  stat = "forecast",
  position = "identity",
  na.rm = FALSE,
  show.legend = NA,
  inherit.aes = TRUE,
  PI = TRUE,
  showgap = TRUE,
  series = NULL,
  ...
)

Value

A layer for a ggplot graph.

Format

An object of class StatForecast (inherits from Stat, ggproto, gg) of length 3.

An object of class GeomForecast (inherits from Geom, ggproto, gg) of length 7.

Arguments

mapping

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 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.

PI

If FALSE, confidence intervals will not be plotted, giving only the forecast line.

showgap

If showgap=FALSE, the gap between the historical observations and the forecasts is removed.

series

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

...

Additional arguments for forecast.ts, other arguments are 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.

Author

Mitchell O'Hara-Wild

Details

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.

See Also

forecast, ggproto

Examples

if (FALSE) {
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")
}