Forecasts `h`

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

```
# 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, ...)

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.

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)

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.

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