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