This function is used to quickly create a workflowsets object.

## Usage

```
ts_wfs_prophet_reg(
.model_type = "all_engines",
.recipe_list,
.growth = NULL,
.changepoint_num = 25,
.changepoint_range = 0.8,
.seasonality_yearly = "auto",
.seasonality_weekly = "auto",
.seasonality_daily = "auto",
.season = "additive",
.prior_scale_changepoints = 25,
.prior_scale_seasonality = 1,
.prior_scale_holidays = 1,
.logistic_cap = NULL,
.logistic_floor = NULL,
.trees = 50,
.min_n = 10,
.tree_depth = 5,
.learn_rate = 0.01,
.loss_reduction = NULL,
.stop_iter = NULL
)
```

## Arguments

- .model_type
This is where you will set your engine. It uses

`modeltime::prophet_reg()`

under the hood and can take one of the following:"prophet" Or

`modeltime::prophet_boost()`

under the hood and can take one of the following:"prophet_xgboost" You can also choose:

"all_engines" - This will make a model spec for all available engines.

- .recipe_list
You must supply a list of recipes. list(rec_1, rec_2, ...)

- .growth
String 'linear' or 'logistic' to specify a linear or logistic trend.

- .changepoint_num
Number of potential changepoints to include for modeling trend.

- .changepoint_range
Adjusts the flexibility of the trend component by limiting to a percentage of data before the end of the time series. 0.80 means that a changepoint cannot exist after the first 80% of the data.

- .seasonality_yearly
One of "auto", TRUE or FALSE. Set to FALSE for

`prophet_xgboost`

. Toggles on/off a seasonal component that models year-over-year seasonality.- .seasonality_weekly
One of "auto", TRUE or FALSE. Toggles on/off a seasonal component that models week-over-week seasonality. Set to FALSE for

`prophet_xgboost`

- .seasonality_daily
One of "auto", TRUE or FALSE. Toggles on/off a seasonal componet that models day-over-day seasonality. Set to FALSE for

`prophet_xgboost`

- .season
'additive' (default) or 'multiplicative'.

- .prior_scale_changepoints
Parameter modulating the flexibility of the automatic changepoint selection. Large values will allow many changepoints, small values will allow few changepoints.

- .prior_scale_seasonality
Parameter modulating the strength of the seasonality model. Larger values allow the model to fit larger seasonal fluctuations, smaller values dampen the seasonality.

- .prior_scale_holidays
Parameter modulating the strength of the holiday components model, unless overridden in the holidays input.

- .logistic_cap
When growth is logistic, the upper-bound for "saturation".

- .logistic_floor
When growth is logistic, the lower-bound for "saturation"

- .trees
An integer for the number of trees contained in the ensemble.

- .min_n
An integer for the minimum number of data points in a node that is required for the node to be split further.

- .tree_depth
An integer for the maximum depth of the tree (i.e. number of splits) (specific engines only).

- .learn_rate
A number for the rate at which the boosting algorithm adapts from iteration-to-iteration (specific engines only).

- .loss_reduction
A number for the reduction in the loss function required to split further (specific engines only).

- .stop_iter
The number of iterations without improvement before stopping (xgboost only).

## Details

This function expects to take in the recipes that you want to use in
the modeling process. This is an automated workflow process. There are sensible
defaults set for the `prophet`

and `prophet_xgboost`

model specification,
but if you choose you can set them yourself if you have a good understanding
of what they should be.

## See also

https://workflowsets.tidymodels.org/(workflowsets)

https://business-science.github.io/modeltime/reference/prophet_reg.html

https://business-science.github.io/modeltime/reference/prophet_boost.html

Other Auto Workflowsets:
`ts_wfs_arima_boost()`

,
`ts_wfs_auto_arima()`

,
`ts_wfs_ets_reg()`

,
`ts_wfs_lin_reg()`

,
`ts_wfs_mars()`

,
`ts_wfs_nnetar_reg()`

,
`ts_wfs_svm_poly()`

,
`ts_wfs_svm_rbf()`

## Examples

```
suppressPackageStartupMessages(library(modeltime))
suppressPackageStartupMessages(library(timetk))
suppressPackageStartupMessages(library(dplyr))
suppressPackageStartupMessages(library(rsample))
data <- AirPassengers %>%
ts_to_tbl() %>%
select(-index)
splits <- time_series_split(
data
, date_col
, assess = 12
, skip = 3
, cumulative = TRUE
)
rec_objs <- ts_auto_recipe(
.data = training(splits)
, .date_col = date_col
, .pred_col = value
)
wf_sets <- ts_wfs_prophet_reg("all_engines", rec_objs)
wf_sets
#> # A workflow set/tibble: 8 × 4
#> wflow_id info option result
#> <chr> <list> <list> <list>
#> 1 rec_base_prophet_reg <tibble [1 × 4]> <opts[0]> <list [0]>
#> 2 rec_base_prophet_boost <tibble [1 × 4]> <opts[0]> <list [0]>
#> 3 rec_date_prophet_reg <tibble [1 × 4]> <opts[0]> <list [0]>
#> 4 rec_date_prophet_boost <tibble [1 × 4]> <opts[0]> <list [0]>
#> 5 rec_date_fourier_prophet_reg <tibble [1 × 4]> <opts[0]> <list [0]>
#> 6 rec_date_fourier_prophet_boost <tibble [1 × 4]> <opts[0]> <list [0]>
#> 7 rec_date_fourier_nzv_prophet_reg <tibble [1 × 4]> <opts[0]> <list [0]>
#> 8 rec_date_fourier_nzv_prophet_boost <tibble [1 × 4]> <opts[0]> <list [0]>
```