pyFTS/models/msfts.py
Petrônio Cândido de Lima e Silva 728e8414b7 - General refactoring to include **kwargs on methods signatures
- Ensemble FTS
2017-04-14 19:57:39 -03:00

70 lines
1.9 KiB
Python

import numpy as np
from pyFTS.common import FuzzySet,FLR
from pyFTS import fts, sfts
class MultiSeasonalFTS(sfts.SeasonalFTS):
def __init__(self, order, name, **kwargs):
super(MultiSeasonalFTS, self).__init__("MSFTS")
self.name = "Multi Seasonal FTS"
self.shortname = "MSFTS " + name
self.detail = ""
self.seasonality = 1
self.hasSeasonality = True
self.hasPointForecasting = True
self.isHighOrder = True
self.isMultivariate = True
self.indexer = indexer
self.flrgs = {}
def generateFLRG(self, flrs):
flrgs = {}
for flr in flrs:
if str(flr.index) not in self.flrgs:
flrgs[str(flr.index)] = sfts.SeasonalFLRG(flr.index)
flrgs[str(flr.index)].append(flr.RHS)
return (flrgs)
def train(self, data, sets, order=1, parameters=None):
self.sets = sets
self.seasonality = parameters
#ndata = self.indexer.set_data(data,self.doTransformations(self.indexer.get_data(data)))
flrs = FLR.generateIndexedFLRs(self.sets, self.indexer, data)
self.flrgs = self.generateFLRG(flrs)
def forecast(self, data, **kwargs):
ret = []
index = self.indexer.get_season_of_data(data)
ndata = self.indexer.get_data(data)
for k in np.arange(1, len(data)):
flrg = self.flrgs[str(index[k])]
mp = self.getMidpoints(flrg)
ret.append(sum(mp) / len(mp))
ret = self.doInverseTransformations(ret, params=[ndata[self.order - 1:]])
return ret
def forecastAhead(self, data, steps, **kwargs):
ret = []
for i in steps:
flrg = self.flrgs[str(i)]
mp = self.getMidpoints(flrg)
ret.append(sum(mp) / len(mp))
ret = self.doInverseTransformations(ret, params=data)
return ret