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PWFTS bugfix on Naïve forecast

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This commit is contained in:
Petrônio Cândido 2019-11-07 16:03:52 -03:00
parent 4f693ea9bb
commit 517a715a1d
2 changed files with 34 additions and 5 deletions
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2
pyFTS/models/pwfts.py
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@ -247,7 +247,7 @@ class ProbabilisticWeightedFTS(ifts.IntervalFTS):
if flrg.get_key() in self.flrgs:
return self.flrgs[flrg.get_key()].frequency_count / self.global_frequency_count
else:
return 0.0
return 1.0
#self.add_new_PWFLGR(flrg)
#return self.flrg_lhs_unconditional_probability(flrg)

37
pyFTS/tests/general.py
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@ -16,13 +16,43 @@ from pyFTS.models.ensemble import ensemble
from pyFTS.common import Transformations, Membership, Util
from pyFTS.benchmarks import arima, quantreg, BSTS, gaussianproc, knn
from pyFTS.fcm import fts, common, GA
from pyFTS.common import Transformations
tdiff = Transformations.Differential(1)
boxcox = Transformations.BoxCox(0)
df = pd.read_csv('https://query.data.world/s/l3u4gqbrbm5ymo6ghxl7jmxed7sgyk')
dados = df.iloc[2710:2960 , 0:1].values # somente a 1 coluna sera usada
dados = dados.flatten().tolist()
qtde_dt_tr = 150
dados_treino = dados[:qtde_dt_tr]
#print(dados_treino)
ttr = list(range(len(dados_treino)))
ordem = 1 # ordem do modelo, indica quantos ultimos valores serao usados
dados_teste = dados[qtde_dt_tr - ordem:250]
tts = list(range(len(dados_treino) - ordem, len(dados_treino) + len(dados_teste) - ordem))
particionador = Grid.GridPartitioner(data = dados_treino, npart = 30, func = Membership.trimf)
modelo = pwfts.ProbabilisticWeightedFTS(partitioner = particionador, order = ordem)
modelo.fit(dados_treino)
print(modelo)
# Todo o procedimento de inferência é feito pelo método predict
predicoes = modelo.predict(dados_teste[38:40])
print(predicoes)
'''
from pyFTS.data import TAIEX, NASDAQ, SP500
from pyFTS.common import Util
@ -45,18 +75,17 @@ from pyFTS.benchmarks import ResidualAnalysis as ra
ra.plot_residuals_by_model(test, [model])
horizon = 10
'''
forecasts = model.predict(test[9:20], type='point')
intervals = model.predict(test[9:20], type='interval')
distributions = model.predict(test[9:20], type='distribution')
forecasts = model.predict(test[9:20], type='point', steps_ahead=horizon)
intervals = model.predict(test[9:20], type='interval', steps_ahead=horizon)
'''
#distributions = model.predict(test[9:20], type='distribution', steps_ahead=horizon)
'''
train = TAIEX.get_data()[:800]
test = TAIEX.get_data()[800:1000]