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
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, ... )
The time series to be forecast. Can be
The number of parallel processes to be used if using
parallel processing. If
The lower limit (inclusive) for the Box-Cox transformation.
The upper limit (inclusive) for the Box-Cox transformation.
Use adjusted back-transformed mean for Box-Cox transformations. If TRUE, point forecasts and fitted values are mean forecast. Otherwise, these points can be considered the median of the forecast densities.
Output from a previous call to
Additional arguments to be passed to
An object of class "
bats". The generic accessor functions
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 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.
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.
Slava Razbash and Rob J Hyndman