forecast (version 5.0)

tbats: TBATS model (Exponential smoothing state space model with Box-Cox transformation, ARMA errors, Trend and Seasonal components)

Description

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.

Usage

tbats(y, use.box.cox=NULL, use.trend=NULL, use.damped.trend=NULL, 
    seasonal.periods=NULL, use.arma.errors=TRUE, use.parallel=TRUE, 
    num.cores=NULL, 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 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.

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 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.

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.

See Also

tbats.components.

Examples

fit <- tbats(USAccDeaths, use.parallel=FALSE)
plot(forecast(fit))

taylor.fit <- tbats(taylor)
plot(forecast(taylor.fit))