forecast.bats: Forecasting using BATS and TBATS models

In forecast: Forecasting Functions for Time Series and Linear Models

Forecasting using BATS and TBATS models

Description

Forecasts h steps ahead with a BATS model. Prediction intervals are also produced.

Usage

## S3 method for class 'bats'
forecast(object, h, level = c(80, 95), fan = FALSE, biasadj = NULL, ...)

## S3 method for class 'tbats'
forecast(object, h, level = c(80, 95), fan = FALSE, biasadj = NULL, ...)

Arguments

object	An object of class "bats". Usually the result of a call to bats.
h	Number of periods for forecasting. Default value is twice the largest seasonal period (for seasonal data) or ten (for non-seasonal data).
level	Confidence level for prediction intervals.
fan	If TRUE, level is set to seq(51,99,by=3). This is suitable for fan plots.
biasadj	Use adjusted back-transformed mean for Box-Cox transformations. If TRUE, point forecasts and fitted values are mean forecast. Otherwise, these points can be considered the median of the forecast densities.
...	Other arguments, currently ignored.

Value

An object of class "forecast".

The function summary is used to obtain and print a summary of the results, while the function plot produces a plot of the forecasts and prediction intervals.

The generic accessor functions fitted.values and residuals extract useful features of the value returned by forecast.bats.

An object of class "forecast" is a list containing at least the following elements:

model	A copy of the bats object
method	The name of the forecasting method as a character string
mean	Point forecasts as a time series
lower	Lower limits for prediction intervals
upper	Upper limits for prediction intervals
level	The confidence values associated with the prediction intervals
x	The original time series (either object itself or the time series used to create the model stored as object).
residuals	Residuals from the fitted model.
fitted	Fitted values (one-step forecasts)

Author(s)

Slava Razbash and Rob J Hyndman

References

De Livera, A.M., Hyndman, R.J., & Snyder, R. D. (2011), Forecasting time series with complex seasonal patterns using exponential smoothing, Journal of the American Statistical Association, 106(496), 1513-1527.

See Also

bats, tbats,forecast.ets.

Examples

## Not run: 
fit <- bats(USAccDeaths)
plot(forecast(fit))

taylor.fit <- bats(taylor)
plot(forecast(taylor.fit))

## End(Not run)

forecast documentation built on June 22, 2024, 9:20 a.m.