# forecast.bats

##### Forecasting using BATS and TBATS models

Forecasts `h`

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

- Keywords
- ts

##### Usage

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

A copy of the `bats`

object

The name of the forecasting method as a character string

Point forecasts as a time series

Lower limits for prediction intervals

Upper limits for prediction intervals

The confidence values associated with the prediction intervals

The original time series (either `object`

itself or the time
series used to create the model stored as `object`

).

Residuals from the fitted model.

Fitted values (one-step forecasts)

##### 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 {
# }
# NOT RUN {
fit <- bats(USAccDeaths)
plot(forecast(fit))
taylor.fit <- bats(taylor)
plot(forecast(taylor.fit))
# }
# NOT RUN {
# }
```

*Documentation reproduced from package forecast, version 8.9, License: GPL-3*