plot_collection: Create a 'ggplot' object associated with a time series...

In vctsfr: Visualizing Collections of Time Series Forecasts

Create a ggplot object associated with a time series belonging to a collection.

Description

Apart from the time series, future values and forecasts for the future values form part of the ggplot object.

Usage

plot_collection(collection, number, methods = NULL, level = NULL, sdp = TRUE)

Arguments

collection	a list with the collection of time series. Each component of the list must have been built with the ts_info() function.
number	an integer. The number of the time series. It should be a value between 1 and length(collection).
methods	NULL (default) or a character vector indicating the names of the forecasting methods to be displayed.
level	NULL (default) or a number in the interval (0, 100) indicating the level of the prediction interval to be shown. This parameter in considered only when just one forecasting method is plotted and the forecasting method has a prediction interval with the specified level.
sdp	logical. Should data points be shown in the plot? (default value TRUE)

Details

The collection parameter must be a list. Each component of the list stores a time series and, optionally, its future values, forecasts for the future values and prediction intervals for the forecasts. Each component should have been created using the ts_info() function.

In the example section you can see an example of a collection of time series. If the collection parameter is not specified correctly, a proper message is shown.

Value

The ggplot object representing the time series and its forecast.

See Also

ts_info() function to see how to build the components of the collection parameter.

Examples

# create a collection of two time series and plot both time series
c <- list(ts_info(USAccDeaths), ts_info(ldeaths))
plot_collection(c, number = 1)
plot_collection(c, number = 2, sdp = FALSE)

# create a collection of one time series with future values and forecasts
if (require(forecast)) {
  c <- vector(2, mode = "list")
  timeS <- window(USAccDeaths, end = c(1977, 12))
  f <- window(USAccDeaths, start = c(1978, 1))
  ets_fit   <- ets(timeS)
  ets_pred  <- forecast(ets_fit, h = length(f), level = 90)
  mean_pred <- meanf(timeS, h = length(f), level = 90)
  c[[1]] <- ts_info(timeS, future = f,
            prediction_info("ES", ets_pred$mean,
                            pi_info(90, ets_pred$lower, ets_pred$upper)),
            prediction_info("Mean", mean_pred$mean,
                            pi_info(90, mean_pred$lower, mean_pred$upper))
  )
  timeS <- ts(rnorm(30, sd = 3))
  f <- rnorm(5, sd = 3)
  rw <- rwf(timeS, h = length(f), level = 80)
  mean <- meanf(timeS, h = length(f), level = 90)
  c[[2]] <- ts_info(timeS, future = f,
            prediction_info("Random Walk", rw$mean,
                            pi_info(80, rw$lower, rw$upper)),
            prediction_info("Mean", mean$mean,
                            pi_info(90, mean$lower, mean$upper))
  )
  plot_collection(c, number = 1)
}
if (require("forecast"))
  plot_collection(c, number = 2)
if (require("forecast"))
  plot_collection(c, number = 2, methods = "Mean") # just plot a forecasting method
if (require("forecast"))
  plot_collection(c, number = 2, methods = "Random Walk", level = 80)

vctsfr documentation built on May 29, 2024, 9:47 a.m.