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.
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, ...)
y  The time series to be forecast. Can be 

use.box.cox 

use.trend 

use.damped.trend 

seasonal.periods  If 
use.arma.errors 

use.parallel 

num.cores  The number of parallel processes to be used if using
parallel processing. If 
bc.lower  The lower limit (inclusive) for the BoxCox transformation. 
bc.upper  The upper limit (inclusive) for the BoxCox transformation. 
biasadj  Use adjusted backtransformed mean for BoxCox 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 
...  Additional arguments to be passed to 
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 BoxCox
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.
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), 15131527.
# NOT RUN { fit < bats(USAccDeaths) plot(forecast(fit)) taylor.fit < bats(taylor) plot(forecast(taylor.fit)) # }