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seasons

GlobalAdditiveSinusoidalSeasonGenerator

Bases: SeasonsGenerator

Add a sinusoidal season to the input time-series data

Source code in badgers/generators/time_series/seasons.py 
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class GlobalAdditiveSinusoidalSeasonGenerator(SeasonsGenerator):
    """
    Add a sinusoidal season to the input time-series data
    """

    def __init__(self, random_generator=default_rng(seed=0), period: int = 10):
        super().__init__(random_generator=random_generator)
        self.period = period

    def generate(self, X, y, **params) -> Tuple:
        """

        :param X:
        :param y:
        :param params:
        :return:
        """
        t = np.arange(len(X))
        season = np.sin(t*2*np.pi/self.period)
        Xt = X + season
        return Xt, y

generate(X, y, **params)

:param X: :param y: :param params: :return:

Source code in badgers/generators/time_series/seasons.py 
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def generate(self, X, y, **params) -> Tuple:
    """

    :param X:
    :param y:
    :param params:
    :return:
    """
    t = np.arange(len(X))
    season = np.sin(t*2*np.pi/self.period)
    Xt = X + season
    return Xt, y

SeasonsGenerator

Bases: GeneratorMixin

Base class for transformers that generate seasons in time-series data

Source code in badgers/generators/time_series/seasons.py 
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class SeasonsGenerator(GeneratorMixin):
    """
    Base class for transformers that generate seasons in time-series data
    """

    def __init__(self, random_generator=default_rng(seed=0)):
        """
        :param random_generator: a random number generator
        """
        self.random_generator = random_generator

    @abc.abstractmethod
    def generate(self, X, y, **params) -> Tuple:
        pass

__init__(random_generator=default_rng(seed=0))

:param random_generator: a random number generator

Source code in badgers/generators/time_series/seasons.py 
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def __init__(self, random_generator=default_rng(seed=0)):
    """
    :param random_generator: a random number generator
    """
    self.random_generator = random_generator