94 lines
2.5 KiB
Python
94 lines
2.5 KiB
Python
"""
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Simple First Order Seasonal Fuzzy Time Series implementation of Song (1999) based of Conventional FTS by Chen (1996)
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Q. Song, “Seasonal forecasting in fuzzy time series,” Fuzzy sets Syst., vol. 107, pp. 235–236, 1999.
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S.-M. Chen, “Forecasting enrollments based on fuzzy time series,” Fuzzy Sets Syst., vol. 81, no. 3, pp. 311–319, 1996.
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"""
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import numpy as np
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from pyFTS.common import FuzzySet, FLR, flrg, fts
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class SeasonalFLRG(flrg.FLRG):
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"""First Order Seasonal Fuzzy Logical Relationship Group"""
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def __init__(self, seasonality):
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super(SeasonalFLRG, self).__init__(1)
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self.LHS = seasonality
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self.RHS = []
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def get_key(self):
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return self.LHS
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def append_rhs(self, c, **kwargs):
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self.RHS.append(c)
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def __str__(self):
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tmp = str(self.LHS) + " -> "
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tmp2 = ""
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for c in sorted(self.RHS, key=lambda s: str(s)):
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if len(tmp2) > 0:
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tmp2 = tmp2 + ","
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tmp2 = tmp2 + str(c)
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return tmp + tmp2
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def __len__(self):
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return len(self.RHS)
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class SeasonalFTS(fts.FTS):
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"""First Order Seasonal Fuzzy Time Series"""
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def __init__(self, **kwargs):
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super(SeasonalFTS, self).__init__(**kwargs)
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self.name = "Seasonal FTS"
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self.shortname = "SFTS"
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self.order = 1
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self.seasonality = 1
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self.has_seasonality = True
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self.has_point_forecasting = True
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self.is_high_order = False
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self.flrgs = {}
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def generate_flrg(self, flrs):
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for ct, flr in enumerate(flrs, start=1):
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season = self.indexer.get_season_by_index(ct)[0]
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ss = str(season)
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if ss not in self.flrgs:
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self.flrgs[ss] = SeasonalFLRG(season)
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#print(season)
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self.flrgs[ss].append_rhs(flr.RHS)
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def get_midpoints(self, flrg):
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ret = np.array([self.sets[s].centroid for s in flrg.RHS])
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return ret
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def train(self, data, **kwargs):
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if kwargs.get('sets', None) is not None:
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self.sets = kwargs.get('sets', None)
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tmpdata = FuzzySet.fuzzyfy_series_old(data, self.sets)
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flrs = FLR.generate_non_recurrent_flrs(tmpdata)
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self.generate_flrg(flrs)
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def forecast(self, data, **kwargs):
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l = len(data)
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ret = []
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for k in np.arange(0, l):
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season = self.indexer.get_season_by_index(k)[0]
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flrg = self.flrgs[str(season)]
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mp = self.get_midpoints(flrg)
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ret.append(np.percentile(mp, 50))
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return ret
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