# pyFTS.models.seasonal.msfts — pyFTS 1.4 documentation

# Source code for pyFTS.models.seasonal.msfts

    import numpy as np
    from pyFTS.common import FLR
    from pyFTS.models.seasonal import sfts

    class MultiSeasonalFTS(sfts.SeasonalFTS):
        """
        Multi-Seasonal Fuzzy Time Series
        """
        def __init__(self, name, indexer, **kwargs):
            super(MultiSeasonalFTS, self).__init__("MSFTS")
            self.name = "Multi Seasonal FTS"
            self.shortname = "MSFTS " + name
            self.detail = ""
            self.seasonality = 1
            self.has_seasonality = True
            self.has_point_forecasting = True
            self.is_high_order = False
            self.is_multivariate = True
            self.indexer = indexer
            self.flrgs = {}

        def generate_flrg(self, flrs):
            for flr in flrs:

                if str(flr.index) not in self.flrgs:
                    self.flrgs[str(flr.index)] = sfts.SeasonalFLRG(flr.index)

                self.flrgs[str(flr.index)].append_rhs(flr.RHS)

        def train(self, data,  **kwargs):
            if kwargs.get('sets', None) is not None:
                self.sets = kwargs.get('sets', None)
            if kwargs.get('parameters', None) is not None:
                self.seasonality = kwargs.get('parameters', None)
            #ndata = self.indexer.set_data(data,self.doTransformations(self.indexer.get_data(data)))
            flrs = FLR.generate_indexed_flrs(self.sets, self.indexer, data)
            self.generate_flrg(flrs)

        def forecast(self, data, **kwargs):

            ret = []

            index = self.indexer.get_season_of_data(data)
            ndata = self.indexer.get_data(data)

            for k in np.arange(0, len(index)):

                flrg = self.flrgs[str(index[k])]

                mp = self.getMidpoints(flrg)

                ret.append(sum(mp) / len(mp))

            return ret

        def forecast_ahead(self, data, steps, **kwargs):
            ret = []
            for i in steps:
                flrg = self.flrgs[str(i)]

                mp = self.getMidpoints(flrg)

                ret.append(sum(mp) / len(mp))

            return ret