Tuning Parameters for Exponential Smoothing Models

error(values = c("additive", "multiplicative"))
trend(values = c("additive", "multiplicative", "none"))
season(values = c("additive", "multiplicative", "none"))
damping(values = c("damped", "none"))
smooth_level(range = c(0, 1), trans = NULL)
smooth_trend(range = c(0, 1), trans = NULL)
smooth_seasonal(range = c(0, 1), trans = NULL)

## Arguments

values |
A character string of possible values. |

range |
A two-element vector holding the *defaults* for the smallest and
largest possible values, respectively. |

trans |
A `trans` object from the `scales` package, such as
`scales::log10_trans()` or `scales::reciprocal_trans()` . If not provided,
the default is used which matches the units used in `range` . If no
transformation, `NULL` . |

## Details

The main parameters for Exponential Smoothing models are:

`error`

: The form of the error term: additive", or "multiplicative".
If the error is multiplicative, the data must be non-negative.

`trend`

: The form of the trend term: "additive", "multiplicative" or "none".

`season`

: The form of the seasonal term: "additive", "multiplicative" or "none"..

`damping`

: Apply damping to a trend: "damped", or "none".

`smooth_level`

: This is often called the "alpha" parameter used as the base level smoothing factor for exponential smoothing models.

`smooth_trend`

: This is often called the "beta" parameter used as the trend smoothing factor for exponential smoothing models.

`smooth_seasonal`

: This is often called the "gamma" parameter used as the seasonal smoothing factor for exponential smoothing models.

## Examples

error()

#> Error Term (qualitative)
#> 2 possible value include:
#> 'additive' and 'multiplicative'

trend()

#> Trend Term (qualitative)
#> 3 possible value include:
#> 'additive', 'multiplicative' and 'none'

season()

#> Season Term (qualitative)
#> 3 possible value include:
#> 'additive', 'multiplicative' and 'none'