def __init__(
        self,
        season_length: int,
        alias: str = "SeasESOpt",
        prediction_intervals: Optional[ConformalIntervals] = None,
    ):
        r"""SeasonalExponentialSmoothingOptimized model.

        Uses a weighted average of all past observations where the weights decrease exponentially into the past.
        Suitable for data with no clear trend or seasonality.
        Assuming there are $t$ observations and season $s$, the one-step forecast is given by:
        $\hat{y}_{t+1,s} = \alpha y_t + (1-\alpha) \hat{y}_{t-1,s}$

        The smoothing parameter $\alpha^*$ is optimized by square error minimization.

        Notes
        -----
        This method is an extremely simplified of Holt-Winter's method where the trend and level are set to zero.
        And a single seasonal smoothing parameter $\alpha$ is shared across seasons.

        References
        ----------
        [Charles. C. Holt (1957). "Forecasting seasonals and trends by exponentially weighted moving averages", ONR Research Memorandum, Carnegie Institute of Technology 52.](https://www.sciencedirect.com/science/article/abs/pii/S0169207003001134).
        [Peter R. Winters (1960). "Forecasting sales by exponentially weighted moving averages". Management Science](https://pubsonline.informs.org/doi/abs/10.1287/mnsc.6.3.324).

        Parameters
        ----------
        season_length : int
            Number of observations per unit of time. Ex: 24 Hourly data.
        alias : str
            Custom name of the model.
        prediction_intervals : Optional[ConformalIntervals]
            Information to compute conformal prediction intervals.
            This is required for generating future prediction intervals.
        """
        self.season_length = season_length
        self.alias = alias
        self.prediction_intervals = prediction_intervals
        self.only_conformal_intervals = True
SeasonalExponentialSmoothingOptimized model.

        Uses a weighted average of all past observations where the weights decrease exponentially into the past.
        Suitable for data with no clear trend or seasonality.
        Assuming there are $t$ observations and season $s$, the one-step forecast is given by:
        $\hat{y}_{t+1,s} = \alpha y_t + (1-\alpha) \hat{y}_{t-1,s}$

        The smoothing parameter $\alpha^*$ is optimized by square error minimization.

        Notes
        -----
        This method is an extremely simplified of Holt-Winter's method where the trend and level are set to zero.
        And a single seasonal smoothing parameter $\alpha$ is shared across seasons.

        References
        ----------
        [Charles. C. Holt (1957). "Forecasting seasonals and trends by exponentially weighted moving averages", ONR Research Memorandum, Carnegie Institute of Technology 52.](https://www.sciencedirect.com/science/article/abs/pii/S0169207003001134).
        [Peter R. Winters (1960). "Forecasting sales by exponentially weighted moving averages". Management Science](https://pubsonline.informs.org/doi/abs/10.1287/mnsc.6.3.324).

        Parameters
        ----------
        season_length : int
            Number of observations per unit of time. Ex: 24 Hourly data.
        alias : str
            Custom name of the model.
        prediction_intervals : Optional[ConformalIntervals]
            Information to compute conformal prediction intervals.
            This is required for generating future prediction intervals.
        
python/statsforecast/models.py