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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()
#> Growth Trend  (qualitative)
#> 2 possible value include:
#> 'linear' and 'logistic' 

changepoint_num()
#> Number of Possible Trend Changepoints (quantitative)
#> Range: [0, 50]

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

prior_scale_changepoints()
#> Prior Scale Changepoints (quantitative)
#> Transformer: log-10 [1e-100, Inf]
#> Range (transformed scale): [-3, 2]