pyFTS/sfts.py
Petrônio Cândido de Lima e Silva 26656153f4 Contextual Multi Season FTS
2017-02-09 11:04:48 -02:00

84 lines
2.1 KiB
Python

import numpy as np
from pyFTS.common import FuzzySet,FLR
from pyFTS import fts
class SeasonalFLRG(FLR.FLR):
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
class SeasonalFTS(fts.FTS):
def __init__(self, name):
super(SeasonalFTS, self).__init__(1, "SFTS")
self.name = "Seasonal FTS"
self.detail = "Chen"
self.seasonality = 1
self.hasSeasonality = True
self.hasPointForecasting = True
self.isHighOrder = 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):
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