84 lines
2.3 KiB
Python
84 lines
2.3 KiB
Python
"""
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First Order Conventional Fuzzy Time Series by Chen (1996)
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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 ConventionalFLRG(object):
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"""First Order Conventional Fuzzy Logical Relationship Group"""
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def __init__(self, LHS):
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self.LHS = LHS
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self.RHS = set()
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def append(self, c):
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self.RHS.add(c)
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def __str__(self):
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tmp = self.LHS.name + " -> "
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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 ConventionalFTS(fts.FTS):
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"""Conventional Fuzzy Time Series"""
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def __init__(self, name, **kwargs):
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super(ConventionalFTS, self).__init__(1, "CFTS " + name)
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self.name = "Conventional FTS"
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self.detail = "Chen"
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self.flrgs = {}
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def generateFLRG(self, flrs):
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flrgs = {}
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for flr in flrs:
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if flr.LHS.name in flrgs:
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flrgs[flr.LHS.name].append(flr.RHS)
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else:
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flrgs[flr.LHS.name] = ConventionalFLRG(flr.LHS)
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flrgs[flr.LHS.name].append(flr.RHS)
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return (flrgs)
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def train(self, data, sets,order=1,parameters=None):
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self.sets = sets
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ndata = self.doTransformations(data)
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tmpdata = FuzzySet.fuzzySeries(ndata, sets)
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flrs = FLR.generateNonRecurrentFLRs(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(0, l):
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mv = FuzzySet.fuzzyInstance(ndata[k], self.sets)
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actual = self.sets[np.argwhere(mv == max(mv))[0, 0]]
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if actual.name not in self.flrgs:
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ret.append(actual.centroid)
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else:
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flrg = self.flrgs[actual.name]
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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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