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Small bugfix in pwfts.forecast_ahead_distribution

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This commit is contained in:
Petrônio Cândido 2019-06-23 11:07:34 -03:00
parent 1168365a17
commit 61707f94a6
2 changed files with 21 additions and 26 deletions
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5
pyFTS/models/pwfts.py
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@ -541,7 +541,10 @@ class ProbabilisticWeightedFTS(ifts.IntervalFTS):
start = kwargs.get('start_at', 0)
fuzzyfied = kwargs.pop('fuzzyfied')
if 'fuzzyfied' in kwargs:
fuzzyfied = kwargs.pop('fuzzyfied')
else:
fuzzyfied = False
sample = data[start: start + self.max_lag]

42
pyFTS/tests/ensemble.py
View File

@ -18,39 +18,31 @@ from pyFTS.models.ensemble import ensemble
from pyFTS.models import hofts
from pyFTS.data import TAIEX
data = TAIEX.get_data()
from pyFTS.data import TAIEX, NASDAQ, SP500
from pyFTS.common import Util
train = TAIEX.get_data()[1000:1800]
test = TAIEX.get_data()[1800:2000]
from pyFTS.models import hofts
model1 = ensemble.SimpleEnsembleFTS()
model = ensemble.SimpleEnsembleFTS(
fts_method=hofts.WeightedHighOrderFTS,
orders=[1, 2, 3],
partitions=np.arange(10,50,5)
)
from pyFTS.benchmarks import arima, quantreg, BSTS
methods = [ensemble.SimpleEnsembleFTS, arima.ARIMA, quantreg.QuantileRegression, BSTS.ARIMA]
parameters = [{},{'order': (2,0,0)}, {'order': 1, 'dist': True}, {'order': (2,0,0)}]
from pyFTS.benchmarks import Measures
horizon = 5
for ct, train, test, in Util.sliding_window(data,1000,0.8,.5):
print('data window {}'.format(ct))
for ct, method in enumerate(methods):
model = method(**parameters[ct])
model.fit(train)
start = model.order + 1
end = start + horizon
intervals = model.predict(test[:10], type='interval', alpha=.25, steps_ahead=horizon)
distributions = model.predict(test[:10], type='distribution', smooth='histogram', steps_ahead=horizon, num_bins=100)
print(model.name, Measures.get_interval_ahead_statistics(test[start:end], intervals))
print(model.name, Measures.get_distribution_ahead_statistics(test[start:end], distributions))
print('end')
model.fit(train)
horizon=10
intervals05 = model.predict(test[:horizon], type='interval', alpha=.05)
print(intervals05)
intervals25 = model.predict(test[:horizon], type='interval', alpha=.25)
print(intervals25)
distributions = model.predict(test[:horizon], type='distribution', smooth='histogram', num_bins=100)