# bats

0th

Percentile

##### 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 computatons.

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=TRUE,
num.cores=2, bc.lower=0, bc.upper=1, ...)
##### 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.
...
Additional parameters to be passed to auto.arima when choose an ARMA(p, q) model for the errors.
##### 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.

##### 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.

• bats
##### Examples
fit <- bats(USAccDeaths, use.parallel=FALSE)
plot(forecast(fit))

taylor.fit <- bats(taylor)
plot(forecast(taylor.fit))
Documentation reproduced from package forecast, version 5.3, License: GPL (>= 2)

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