forecast: geom_forecast – R documentation

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geom_forecast

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,
  ...
)

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`.
`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.

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.

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.

Value

A layer for a ggplot graph.

Author(s)

Mitchell O'Hara-Wild

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)

forecast

Forecasting Functions for Time Series and Linear Models

v8.14

GPL-3

Authors

Rob Hyndman [aut, cre, cph] (<https://orcid.org/0000-0002-2140-5352>), George Athanasopoulos [aut], Christoph Bergmeir [aut] (<https://orcid.org/0000-0002-3665-9021>), Gabriel Caceres [aut], Leanne Chhay [aut], Mitchell O'Hara-Wild [aut] (<https://orcid.org/0000-0001-6729-7695>), Fotios Petropoulos [aut] (<https://orcid.org/0000-0003-3039-4955>), Slava Razbash [aut], Earo Wang [aut], Farah Yasmeen [aut] (<https://orcid.org/0000-0002-1479-5401>), R Core Team [ctb, cph], Ross Ihaka [ctb, cph], Daniel Reid [ctb], David Shaub [ctb], Yuan Tang [ctb] (<https://orcid.org/0000-0001-5243-233X>), Zhenyu Zhou [ctb]

Initial release