modeltime (version 1.2.8)

prophet_params: Tuning Parameters for Prophet Models

Description

Tuning Parameters for Prophet Models

Usage

growth(values = c("linear", "logistic"))
changepoint_num(range = c(0L, 50L), trans = NULL)
changepoint_range(range = c(0.6, 0.9), trans = NULL)
seasonality_yearly(values = c(TRUE, FALSE))
seasonality_weekly(values = c(TRUE, FALSE))
seasonality_daily(values = c(TRUE, FALSE))
prior_scale_changepoints(range = c(-3, 2), trans = log10_trans())
prior_scale_seasonality(range = c(-3, 2), trans = log10_trans())
prior_scale_holidays(range = c(-3, 2), trans = log10_trans())

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::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 Prophet models are:

• growth: The form of the trend: "linear", or "logistic".

• changepoint_num: The maximum number of trend changepoints allowed when modeling the trend

• changepoint_range: The range affects how close the changepoints can go to the end of the time series. The larger the value, the more flexible the trend.

• Yearly, Weekly, and Daily Seasonality:

  • Yearly: seasonality_yearly - Useful when seasonal patterns appear year-over-year

  • Weekly: seasonality_weekly - Useful when seasonal patterns appear week-over-week (e.g. daily data)

  • Daily: seasonality_daily - Useful when seasonal patterns appear day-over-day (e.g. hourly data)

• season:

  • The form of the seasonal term: "additive" or "multiplicative".

  • See season().

• "Prior Scale": Controls flexibility of

  • Changepoints: prior_scale_changepoints

  • Seasonality: prior_scale_seasonality

  • Holidays: prior_scale_holidays

  • The log10_trans() converts priors to a scale from 0.001 to 100, which effectively weights lower values more heavily than larger values.

Examples

growth()

changepoint_num()

season()

prior_scale_changepoints()