97 lines
2.6 KiB
Python
97 lines
2.6 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
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from pyFTS import fts
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class SeasonalFLRG(FLR.FLR):
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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__(None,None)
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self.LHS = seasonality
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self.RHS = []
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def append(self, c):
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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: s.name):
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if len(tmp2) > 0:
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tmp2 = tmp2 + ","
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tmp2 = tmp2 + c.name
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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, name, **kwargs):
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super(SeasonalFTS, self).__init__(1, "SFTS")
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self.name = "Seasonal FTS"
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self.detail = "Chen"
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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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def generateFLRG(self, flrs):
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flrgs = []
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season = 1
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for flr in flrs:
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if len(flrgs) < self.seasonality:
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flrgs.append(SeasonalFLRG(season))
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#print(season)
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flrgs[season-1].append(flr.RHS)
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season = (season + 1) % (self.seasonality + 1)
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if season == 0: season = 1
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return (flrgs)
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def train(self, data, sets, order=1,parameters=12):
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self.sets = sets
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self.seasonality = parameters
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ndata = self.doTransformations(data)
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tmpdata = FuzzySet.fuzzySeries(ndata, sets)
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flrs = FLR.generateRecurrentFLRs(tmpdata)
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self.flrgs = self.generateFLRG(flrs)
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def forecast(self, data, **kwargs):
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ndata = np.array(self.doTransformations(data))
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l = len(ndata)
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ret = []
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for k in np.arange(1, l):
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#flrg = self.flrgs[ndata[k]]
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season = (k + 1) % (self.seasonality + 1)
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#print(season)
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flrg = self.flrgs[season-1]
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mp = self.getMidpoints(flrg)
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ret.append(sum(mp) / len(mp))
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ret = self.doInverseTransformations(ret, params=[data[self.order - 1:]])
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return ret
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