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As the documentation does not povide a an example for hyperparameter tuning a prophet model, I tired a simple snippet like follows:
modeltime::prophet_reg( growth = growth(values = c("linear", "logistic")), changepoint_num = changepoint_num(range = c(0L, 50L), trans = NULL) ) |> parsnip::set_engine( engine = "prophet", holidays = generated_holidays ) |> fit(target_var ~ ., data = split_train )
which fails with
growth must be 'linear' or 'logistic'. Defaulting to 'linear'. Disabling daily seasonality. Run prophet with daily.seasonality=TRUE to override this. Fehler in m$n.changepoints + 1 : nicht-numerisches Argument für binären Operator
Is this usage correc?
The text was updated successfully, but these errors were encountered:
In general you specify a hyperparameter for tuning by passing, e.g. growth = tune(). There is a full walkthrough of the process here.
growth = tune()
Sorry, something went wrong.
Alternatively, you can use create_model_grid to create a list of model that you need to train. Below the code:
create_model_grid
library(modeltime) library(tidymodels) m750 models <- grid_regular( changepoint_num(), growth(), levels = 3) |> create_model_grid( f_model_spec = prophet_reg, engine_name = "prophet", mode = "regression" ) |> select(.models) |> pull() # or pluck(1) preprocessing <- list(basic_preproc = recipe(value ~ date, data = m750)) wf <- workflow_set(preproc = preprocessing, models = models) modeltime_fit_workflowset(wf, data = m750)
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As the documentation does not povide a an example for hyperparameter tuning a prophet model, I tired a simple snippet like follows:
which fails with
Is this usage correc?
The text was updated successfully, but these errors were encountered: