def __init__(self, K: int = 1):
        """Naive Seasonal Model

        This model always predicts the value of `K` time steps ago.
        When `K=1`, this model predicts the last value of the training set.
        When `K>1`, it repeats the last `K` values of the training set.

        Parameters
        ----------
        K
            the number of last time steps of the training set to repeat

        Examples
        --------
        >>> from darts.datasets import AirPassengersDataset
        >>> from darts.models import NaiveSeasonal
        >>> series = AirPassengersDataset().load()
        # prior analysis suggested seasonality of 12
        >>> model = NaiveSeasonal(K=12)
        >>> model.fit(series)
        >>> pred = model.predict(6)
        >>> pred.values()
        array([[417.],
               [391.],
               [419.],
               [461.],
               [472.],
               [535.]])
        """
        super().__init__()
        self.last_k_vals = None
        self.K = K
Naive Seasonal Model

        This model always predicts the value of `K` time steps ago.
        When `K=1`, this model predicts the last value of the training set.
        When `K>1`, it repeats the last `K` values of the training set.

        Parameters
        ----------
        K
            the number of last time steps of the training set to repeat

        Examples
        --------
        >>> from darts.datasets import AirPassengersDataset
        >>> from darts.models import NaiveSeasonal
        >>> series = AirPassengersDataset().load()
        # prior analysis suggested seasonality of 12
        >>> model = NaiveSeasonal(K=12)
        >>> model.fit(series)
        >>> pred = model.predict(6)
        >>> pred.values()
        array([[417.],
               [391.],
               [419.],
               [461.],
               [472.],
               [535.]])
        
darts/models/forecasting/baselines.py