forecast: bats – R documentation

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bats

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)

forecast

Forecasting Functions for Time Series and Linear Models

v8.14

GPL-3

Authors

Rob Hyndman [aut, cre, cph] (<https://orcid.org/0000-0002-2140-5352>), George Athanasopoulos [aut], Christoph Bergmeir [aut] (<https://orcid.org/0000-0002-3665-9021>), Gabriel Caceres [aut], Leanne Chhay [aut], Mitchell O'Hara-Wild [aut] (<https://orcid.org/0000-0001-6729-7695>), Fotios Petropoulos [aut] (<https://orcid.org/0000-0003-3039-4955>), Slava Razbash [aut], Earo Wang [aut], Farah Yasmeen [aut] (<https://orcid.org/0000-0002-1479-5401>), R Core Team [ctb, cph], Ross Ihaka [ctb, cph], Daniel Reid [ctb], David Shaub [ctb], Yuan Tang [ctb] (<https://orcid.org/0000-0001-5243-233X>), Zhenyu Zhou [ctb]

Initial release