Tuning Parameters for Prophet Models
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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())
A character string of possible values.
A two-element vector holding the defaults for the smallest and largest possible values, respectively.
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:
seasonality_yearly - Useful when seasonal patterns appear year-over-year
seasonality_weekly - Useful when seasonal patterns appear week-over-week (e.g. daily data)
seasonality_daily - Useful when seasonal patterns appear day-over-day (e.g. hourly data)
The form of the seasonal term: "additive" or "multiplicative".
"Prior Scale": Controls flexibility of
log10_trans() converts priors to a scale from 0.001 to 100,
which effectively weights lower values more heavily than larger values.
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