86 lines
2.7 KiB
Python
86 lines
2.7 KiB
Python
"""
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First Order Traditional Fuzzy Time Series method by Song & Chissom (1993)
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Q. Song and B. S. Chissom, “Fuzzy time series and its models,” Fuzzy Sets Syst., vol. 54, no. 3, pp. 269–277, 1993.
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"""
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import numpy as np
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from pyFTS.common import FuzzySet, FLR, fts
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class ConventionalFTS(fts.FTS):
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"""Traditional Fuzzy Time Series"""
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def __init__(self, **kwargs):
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super(ConventionalFTS, self).__init__(order=1, name="FTS", **kwargs)
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self.name = "Traditional FTS"
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self.detail = "Song & Chissom"
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self.R = None
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if self.partitioner.sets is not None:
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l = len(self.partitioner.sets)
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self.R = np.zeros((l,l))
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def flr_membership_matrix(self, flr):
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ordered_set = FuzzySet.set_ordered(self.partitioner.sets)
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centroids = [self.partitioner.sets[k].centroid for k in ordered_set]
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lm = [self.partitioner.sets[flr.LHS].membership(k) for k in centroids]
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rm = [self.partitioner.sets[flr.RHS].membership(k) for k in centroids]
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l = len(ordered_set)
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r = np.zeros((l, l))
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for k in range(0,l):
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for l in range(0, l):
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r[k][l] = min(lm[k], rm[l])
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return r
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def operation_matrix(self, flrs):
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l = len(self.partitioner)
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if self.R is None or len(self.R) == 0 :
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self.R = np.zeros((l, l))
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for k in flrs:
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mm = self.flr_membership_matrix(k)
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for k in range(0, l):
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for l in range(0, l):
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self.R[k][l] = max(self.R[k][l], mm[k][l])
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def train(self, data, **kwargs):
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tmpdata = self.partitioner.fuzzyfy(data, method='maximum', mode='sets')
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flrs = FLR.generate_non_recurrent_flrs(tmpdata)
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self.operation_matrix(flrs)
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def forecast(self, ndata, **kwargs):
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if self.partitioner is not None:
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ordered_sets = self.partitioner.ordered_sets
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else:
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ordered_sets = FuzzySet.set_ordered(self.partitioner.sets)
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l = len(ndata)
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npart = len(self.partitioner)
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ret = []
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for k in np.arange(0, l):
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mv = FuzzySet.fuzzyfy_instance(ndata[k], self.partitioner.sets)
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r = [max([ min(self.R[i][j], mv[j]) for j in np.arange(0,npart) ]) for i in np.arange(0,npart)]
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fs = np.ravel(np.argwhere(r == max(r)))
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if len(fs) == 1:
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ret.append(self.partitioner.sets[ordered_sets[fs[0]]].centroid)
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else:
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mp = [self.partitioner.sets[ordered_sets[s]].centroid for s in fs]
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ret.append( sum(mp)/len(mp))
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return ret
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def __str__(self):
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tmp = self.name + ":\n"
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return tmp + str(self.R)
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