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'