def __init__(self, requiredParameters=None, valuesToForecast=1, hasToBeSorted=True, hasToBeNormalized=True):
        """Initializes the BaseForecastingMethod.

        :param list requiredParameters:    List of parameternames that have to be defined.
        :param integer valuesToForecast:    Number of entries that will be forecasted.
            This can be changed by using forecast_until().
        :param boolean hasToBeSorted:    Defines if the TimeSeries has to be sorted or not.
        :param boolean hasToBeNormalized:    Defines if the TimeSeries has to be normalized or not.

        :raise: Raises a :py:exc:`ValueError` when valuesToForecast is smaller than zero.
        """

        if requiredParameters is None:
            requiredParameters = []

        if "valuesToForecast" not in requiredParameters:
            requiredParameters.append("valuesToForecast")
        if valuesToForecast < 0:
            raise ValueError("valuesToForecast has to be larger than zero.")

        super(BaseForecastingMethod, self).__init__(requiredParameters, hasToBeSorted=hasToBeSorted, hasToBeNormalized=hasToBeNormalized)

        self.set_parameter("valuesToForecast", valuesToForecast)

        self._forecastUntil = None
def __init__ ( self , requiredParameters = None , valuesToForecast = 1 , hasToBeSorted = True , hasToBeNormalized = True ) : if requiredParameters is None : requiredParameters = [ ] if "valuesToForecast" not in requiredParameters : requiredParameters . append ( "valuesToForecast" ) if valuesToForecast < 0 : raise ValueError ( "valuesToForecast has to be larger than zero." ) super ( BaseForecastingMethod , self ) . __init__ ( requiredParameters , hasToBeSorted = hasToBeSorted , hasToBeNormalized = hasToBeNormalized ) self . set_parameter ( "valuesToForecast" , valuesToForecast ) self . _forecastUntil = None
Initializes the BaseForecastingMethod.

        :param list requiredParameters:    List of parameternames that have to be defined.
        :param integer valuesToForecast:    Number of entries that will be forecasted.
            This can be changed by using forecast_until().
        :param boolean hasToBeSorted:    Defines if the TimeSeries has to be sorted or not.
        :param boolean hasToBeNormalized:    Defines if the TimeSeries has to be normalized or not.

        :raise: Raises a :py:exc:`ValueError` when valuesToForecast is smaller than zero.