tbats  R Documentation 
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.
Slava Razbash and Rob J Hyndman
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.
tbats.components
.
## Not run: fit < tbats(USAccDeaths) plot(forecast(fit)) taylor.fit < tbats(taylor) plot(forecast(taylor.fit)) ## End(Not run)
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