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]
```