# bats

##### BATS model (Exponential smoothing state space model with Box-Cox transformation, ARMA errors, Trend and Seasonal components)

Fits a BATS model applied to `y`

, as described in De Livera, Hyndman &
Snyder (2011). Parallel processing is used by default to speed up the
computations.

- Keywords
- ts

##### Usage

```
bats(
y,
use.box.cox = NULL,
use.trend = NULL,
use.damped.trend = NULL,
seasonal.periods = NULL,
use.arma.errors = TRUE,
use.parallel = length(y) > 1000,
num.cores = 2,
bc.lower = 0,
bc.upper = 1,
biasadj = FALSE,
model = NULL,
...
)
```

##### Arguments

- y
The time series to be forecast. Can be

`numeric`

,`msts`

or`ts`

. Only univariate time series are supported.- use.box.cox
`TRUE/FALSE`

indicates whether to use the Box-Cox transformation or not. If`NULL`

then both are tried and the best fit is selected by AIC.- use.trend
`TRUE/FALSE`

indicates whether to include a trend or not. If`NULL`

then both are tried and the best fit is selected by AIC.- use.damped.trend
`TRUE/FALSE`

indicates whether to include a damping parameter in the trend or not. If`NULL`

then both are tried and the best fit is selected by AIC.- seasonal.periods
If

`y`

is a numeric then seasonal periods can be specified with this parameter.- use.arma.errors
`TRUE/FALSE`

indicates whether to include ARMA errors or not. If`TRUE`

the best fit is selected by AIC. If`FALSE`

then the selection algorithm does not consider ARMA errors.- use.parallel
`TRUE/FALSE`

indicates whether or not to use parallel processing.- num.cores
The number of parallel processes to be used if using parallel processing. If

`NULL`

then the number of logical cores is detected and all available cores are used.- bc.lower
The lower limit (inclusive) for the Box-Cox transformation.

- bc.upper
The upper limit (inclusive) for the Box-Cox transformation.

- 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.

- model
Output from a previous call to

`bats`

. If model is passed, this same model is fitted to`y`

without re-estimating any parameters.- ...
Additional arguments to be passed to

`auto.arima`

when choose an ARMA(p, q) model for the errors. (Note that xreg will be ignored, as will any arguments concerning seasonality and differencing, but arguments controlling the values of p and q will be used.)

##### Value

An object of class "`bats`

". The generic accessor functions
`fitted.values`

and `residuals`

extract useful features of the
value returned by `bats`

and associated functions. The fitted model is
designated BATS(omega, p,q, phi, m1,...mJ) where omega is the Box-Cox
parameter and phi is the damping parameter; the error is modelled as an
ARMA(p,q) process and m1,...,mJ list the seasonal periods used in the model.

##### 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.

##### 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 8.10, License: GPL-3*