exp_smoothing_params function

Tuning Parameters for Exponential Smoothing Models

error(values = c("additive", "multiplicative"))

trend(values = c("additive", "multiplicative", "none"))

trend_smooth(
  values = c("additive", "multiplicative", "none", "additive_damped",
    "multiplicative_damped")
)

season(values = c("additive", "multiplicative", "none"))

damping(values = c("none", "damped"))

damping_smooth(range = c(0, 2), trans = NULL)

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. If a transformation is specified, these values should be in the transformed units.
• trans: A trans object from the scales package, such as scales::transform_log10() or scales::transform_reciprocal(). 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()

trend()

season()