tbats {forecast} | 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, 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 Box-Cox transformation. |
bc.upper |
The upper limit (inclusive) for the Box-Cox transformation. |
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 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 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), 1513-1527.
## Not run: fit <- tbats(USAccDeaths) plot(forecast(fit)) taylor.fit <- tbats(taylor) plot(forecast(taylor.fit)) ## End(Not run)