pyFTS/yu.py

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"""
First Order Weighted Fuzzy Time Series by Yu(2005)
H.-K. Yu, Weighted fuzzy time series models for TAIEX forecasting,
Phys. A Stat. Mech. its Appl., vol. 349, no. 3, pp. 609624, 2005.
"""
import numpy as np
from pyFTS.common import FuzzySet,FLR
from pyFTS import fts
class WeightedFLRG(object):
"""First Order Weighted Fuzzy Logical Relationship Group"""
def __init__(self, LHS, **kwargs):
self.LHS = LHS
self.RHS = []
self.count = 1.0
def append(self, c):
self.RHS.append(c)
self.count = self.count + 1.0
def weights(self):
tot = sum(np.arange(1.0, self.count, 1.0))
return np.array([k / tot for k in np.arange(1.0, self.count, 1.0)])
def __str__(self):
tmp = self.LHS.name + " -> "
tmp2 = ""
cc = 1.0
tot = sum(np.arange(1.0, self.count, 1.0))
for c in sorted(self.RHS, key=lambda s: s.name):
if len(tmp2) > 0:
tmp2 = tmp2 + ","
tmp2 = tmp2 + c.name + "(" + str(round(cc / tot, 3)) + ")"
cc = cc + 1.0
return tmp + tmp2
class WeightedFTS(fts.FTS):
"""First Order Weighted Fuzzy Time Series"""
def __init__(self, name, **kwargs):
super(WeightedFTS, self).__init__(1, "WFTS " + name)
self.name = "Weighted FTS"
self.detail = "Yu"
def generateFLRG(self, flrs):
flrgs = {}
for flr in flrs:
if flr.LHS.name in flrgs:
flrgs[flr.LHS.name].append(flr.RHS)
else:
flrgs[flr.LHS.name] = WeightedFLRG(flr.LHS);
flrgs[flr.LHS.name].append(flr.RHS)
return (flrgs)
def train(self, data, sets,order=1,parameters=None):
self.sets = sets
ndata = self.doTransformations(data)
tmpdata = FuzzySet.fuzzySeries(ndata, sets)
flrs = FLR.generateRecurrentFLRs(tmpdata)
self.flrgs = self.generateFLRG(flrs)
def forecast(self, data, **kwargs):
l = 1
data = np.array(data)
ndata = self.doTransformations(data)
l = len(ndata)
ret = []
for k in np.arange(0, l):
mv = FuzzySet.fuzzyInstance(ndata[k], self.sets)
actual = self.sets[np.argwhere(mv == max(mv))[0, 0]]
if actual.name not in self.flrgs:
ret.append(actual.centroid)
else:
flrg = self.flrgs[actual.name]
mp = self.getMidpoints(flrg)
ret.append(mp.dot(flrg.weights()))
ret = self.doInverseTransformations(ret, params=[data[self.order - 1:]])
return ret