def helper_test_prophet_model(self, period, freq, compare_all_models=False):
        """Test which includes adding custom seasonalities and future covariates. The tests compare the output of
        univariate and stochastic forecasting with the validation timeseries and Prophet's base model output.

        The underlying curve to forecast is a sine timeseries multiplied with another sine timeseries.
        The curve shape repeats every 2*period timesteps (i.e. for period=24 hours -> seasonal_periods=48).
        We take the second sine wave as a covariate for the model.
        With the added custom seasonality and covariate, the model should have a very accurate forecast.
        """
        repetitions = 8
        ts_sine1 = tg.sine_timeseries(
            value_frequency=1 / period, length=period * repetitions, freq=freq
        )
        ts_sine2 = tg.sine_timeseries(
            value_frequency=1 / (period * 2), length=period * repetitions, freq=freq
        )
        ts_sine = ts_sine1 * ts_sine2
        covariate = ts_sine2

        split = int(-period * repetitions / 2)
        train, val = ts_sine[:split], ts_sine[split:]
        train_cov, val_cov = covariate[:split], covariate[split:]

        supress_auto_seasonality = {
            "daily_seasonality": False,
            "weekly_seasonality": False,
            "yearly_seasonality": False,
        }
        custom_seasonality = {
            "name": "custom",
            "seasonal_periods": int(2 * period),
            "fourier_order": 4,
        }
        model = Prophet(
            add_seasonalities=custom_seasonality,
            seasonality_mode="additive",
            **supress_auto_seasonality,
        )

        model.fit(train, future_covariates=train_cov)

        # univariate, stochastic and Prophet's base model forecast
        pred_darts = model.predict(n=len(val), num_samples=1, future_covariates=val_cov)
        compare_preds = [pred_darts]

        if compare_all_models:
            pred_darts_stochastic = model.predict(
                n=len(val), num_samples=200, future_covariates=val_cov
            )
            pred_raw_df = model.predict_raw(n=len(val), future_covariates=val_cov)
            pred_raw = TimeSeries.from_dataframe(
                pred_raw_df[["ds", "yhat"]], time_col="ds"
            )
            compare_preds += [
                pred_darts_stochastic.quantile_timeseries(0.5),
                pred_raw,
            ]

        # all predictions should fit the underlying curve very well
        for pred in compare_preds:
            for val_i, pred_i in zip(val.univariate_values(), pred.univariate_values()):
                assert abs(val_i - pred_i) < 0.1
Test which includes adding custom seasonalities and future covariates. The tests compare the output of
        univariate and stochastic forecasting with the validation timeseries and Prophet's base model output.

        The underlying curve to forecast is a sine timeseries multiplied with another sine timeseries.
        The curve shape repeats every 2*period timesteps (i.e. for period=24 hours -> seasonal_periods=48).
        We take the second sine wave as a covariate for the model.
        With the added custom seasonality and covariate, the model should have a very accurate forecast.
        
darts/tests/models/forecasting/test_prophet.py