pyFTS/sfts.py
2017-05-07 11:41:31 -03:00

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"""
Simple First Order Seasonal Fuzzy Time Series implementation of Song (1999) based of Conventional FTS by Chen (1996)
Q. Song, “Seasonal forecasting in fuzzy time series,” Fuzzy sets Syst., vol. 107, pp. 235236, 1999.
S.-M. Chen, “Forecasting enrollments based on fuzzy time series,” Fuzzy Sets Syst., vol. 81, no. 3, pp. 311319, 1996.
"""
import numpy as np
from pyFTS.common import FuzzySet,FLR
from pyFTS import fts
class SeasonalFLRG(FLR.FLR):
"""First Order Seasonal Fuzzy Logical Relationship Group"""
def __init__(self, seasonality):
super(SeasonalFLRG, self).__init__(None,None)
self.LHS = seasonality
self.RHS = []
def append(self, c):
self.RHS.append(c)
def __str__(self):
tmp = str(self.LHS) + " -> "
tmp2 = ""
for c in sorted(self.RHS, key=lambda s: s.name):
if len(tmp2) > 0:
tmp2 = tmp2 + ","
tmp2 = tmp2 + c.name
return tmp + tmp2
def __len__(self):
return len(self.RHS)
class SeasonalFTS(fts.FTS):
"""First Order Seasonal Fuzzy Time Series"""
def __init__(self, name, **kwargs):
super(SeasonalFTS, self).__init__(1, "SFTS")
self.name = "Seasonal FTS"
self.detail = "Chen"
self.seasonality = 1
self.has_seasonality = True
self.has_point_forecasting = True
self.is_high_order = False
def generateFLRG(self, flrs):
flrgs = []
season = 1
for flr in flrs:
if len(flrgs) < self.seasonality:
flrgs.append(SeasonalFLRG(season))
#print(season)
flrgs[season-1].append(flr.RHS)
season = (season + 1) % (self.seasonality + 1)
if season == 0: season = 1
return (flrgs)
def train(self, data, sets, order=1,parameters=12):
self.sets = sets
self.seasonality = parameters
ndata = self.doTransformations(data)
tmpdata = FuzzySet.fuzzySeries(ndata, sets)
flrs = FLR.generateRecurrentFLRs(tmpdata)
self.flrgs = self.generateFLRG(flrs)
def forecast(self, data, **kwargs):
ndata = np.array(self.doTransformations(data))
l = len(ndata)
ret = []
for k in np.arange(1, l):
#flrg = self.flrgs[ndata[k]]
season = (k + 1) % (self.seasonality + 1)
#print(season)
flrg = self.flrgs[season-1]
mp = self.getMidpoints(flrg)
ret.append(sum(mp) / len(mp))
ret = self.doInverseTransformations(ret, params=[data[self.order - 1:]])
return ret