R/tbats.R
tbats.Rd
Fits a TBATS model applied to y
, as described in De Livera, Hyndman &
Snyder (2011). Parallel processing is used by default to speed up the
computations.
tbats(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 with class c("tbats", "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 TBATS(omega, p,q, phi, <m1,k1>,...,<mJ,kJ>) 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 and k1,...,kJ are the corresponding number of Fourier
terms used for each seasonality.
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 < tbats(USAccDeaths) plot(forecast(fit)) taylor.fit < tbats(taylor) plot(forecast(taylor.fit)) # }