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Improvements on EnsembleFTS.forecast_distribution_ahead

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This commit is contained in:
Petrônio Cândido 2019-05-28 17:06:24 -03:00
parent 60785ac89f
commit 839842d61f
2 changed files with 16 additions and 4 deletions
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14
pyFTS/models/ensemble/ensemble.py
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@ -13,6 +13,7 @@ from pyFTS.common import SortedCollection, fts, tree
from pyFTS.models import chen, cheng, hofts, hwang, ismailefendi, sadaei, song, yu from pyFTS.models import chen, cheng, hofts, hwang, ismailefendi, sadaei, song, yu
from pyFTS.probabilistic import ProbabilityDistribution from pyFTS.probabilistic import ProbabilityDistribution
import scipy.stats as st import scipy.stats as st
from itertools import product
def sampler(data, quantiles): def sampler(data, quantiles):
@ -69,6 +70,10 @@ class EnsembleFTS(fts.FTS):
elif model.original_max > self.original_max: elif model.original_max > self.original_max:
self.original_max = model.original_max self.original_max = model.original_max
def get_UoD(self):
return [self.original_min, self.original_max]
def train(self, data, **kwargs): def train(self, data, **kwargs):
pass pass
@ -252,10 +257,11 @@ class EnsembleFTS(fts.FTS):
uod = self.get_UoD() uod = self.get_UoD()
sample = data[start - self.order: start] sample = [[k] for k in data[start - self.order: start]]
for k in np.arange(self.order, steps+self.order): for k in np.arange(self.order, steps+self.order):
forecasts = [] forecasts = []
'''
lags = {} lags = {}
for i in np.arange(0, self.order): lags[i] = sample[k-self.order] for i in np.arange(0, self.order): lags[i] = sample[k-self.order]
@ -267,7 +273,13 @@ class EnsembleFTS(fts.FTS):
for p in root.paths(): for p in root.paths():
path = list(reversed(list(filter(None.__ne__, p)))) path = list(reversed(list(filter(None.__ne__, p))))
'''
lags = []
for i in np.arange(0, self.order):
lags.append(sample[k - self.order])
# Trace the possible paths
for path in product(*lags):
forecasts.extend(self.get_models_forecasts(path)) forecasts.extend(self.get_models_forecasts(path))
sample.append(sampler(forecasts, np.arange(0.1, 1, 0.1))) sample.append(sampler(forecasts, np.arange(0.1, 1, 0.1)))

6
pyFTS/tests/ensemble.py
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@ -21,15 +21,15 @@ data = TAIEX.get_data()
model = ensemble.EnsembleFTS() model = ensemble.EnsembleFTS()
for k in [15, 25, 35]: for k in [15, 25, 35]:
for order in [1, 2]: for order in [1, 2, 3]:
fs = Grid.GridPartitioner(data=data, npart=k) fs = Grid.GridPartitioner(data=data, npart=k)
tmp = hofts.WeightedHighOrderFTS(partitioner=fs) tmp = hofts.WeightedHighOrderFTS(partitioner=fs, order=order)
tmp.fit(data) tmp.fit(data)
model.append_model(tmp) model.append_model(tmp)
forecasts = model.predict(data, type='interval', method='quantile', alpha=.05) forecasts = model.predict(data, type='distribution', smooth='histogram', steps_ahead=10)
from pyFTS.benchmarks import benchmarks as bchmk from pyFTS.benchmarks import benchmarks as bchmk