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forecast.bats

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

Examples

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

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

## 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

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