forecast.bats: Forecasting using BATS and TBATS models

In ttnsdcn/forecast-package: Forecasting functions for time series

Description

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

Usage

## S3 method for class 'bats'
forecast(object, h=10, level=c(80,95), fan=FALSE, ...)
## S3 method for class 'tbats'
forecast(object, h=10, level=c(80,95), fan=FALSE, ...)

Arguments

object	An object of class "bats". Usually the result of a call to bats.
h	Number of periods for forecasting
level	Confidence level for prediction intervals.
fan	If TRUE, level is set to seq(50,99,by=1). This is suitable for fan plots.
...	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. That is x minus fitted values.
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. (2012), Forecasting time series with complex seasonal patterns using exponential smoothing, Journal of the American Statistical Association, to appear.

See Also

bats, tbats,forecast.ets.

Examples

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

## End(Not run)

ttnsdcn/forecast-package documentation built on June 1, 2019, 2:49 a.m.