R: BATS model (Exponential smoothing state space model with...

bats {forecast} | R Documentation

## BATS model (Exponential smoothing state space model with Box-Cox transformation, ARMA errors, Trend and Seasonal components)

### Description

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 computations.

### Usage

    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, ...)

### 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 a 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 and all available cores are used.
`bc.lower` | The lower limit (inclusive) for the Box-Cox transformation.
`bc.upper` | The upper limit (inclusive) for the Box-Cox transformation.
`biasadj` | 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.
`model` | Output from a previous call to `bats`. If model is passed, this same model is fitted to `y` without re-estimating any parameters.
`...` | Additional arguments to be passed to `auto.arima` when choose an ARMA(p, q) model for the errors. (Note that xreg will be ignored, as will any arguments concerning seasonality and differencing, but arguments controlling the values of p and q will be used.)

### Value

An object of class "`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 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.

### Author(s)

Slava Razbash and Rob J Hyndman

### 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.

### Examples

    ## Not run:
    fit <- bats(USAccDeaths)
    plot(forecast(fit))

    taylor.fit <- bats(taylor)
    plot(forecast(taylor.fit))
    ## End(Not run)

[Package forecast version 8.0 Index]