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Bugfixes in FTS.forecast_ahead

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This commit is contained in:
Petrônio Cândido 2018-09-10 10:33:47 -03:00
parent 033dc2807a
commit 9e0a4e312f
6 changed files with 25 additions and 13 deletions
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10
pyFTS/common/fts.py
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@ -189,15 +189,19 @@ class FTS(object):
:param kwargs: model specific parameters :param kwargs: model specific parameters
:return: a list with the forecasted values :return: a list with the forecasted values
""" """
if isinstance(data, np.ndarray):
data = data.tolist()
ret = [] ret = []
for k in np.arange(0,steps): for k in np.arange(0,steps):
tmp = self.forecast(data[-self.order:], **kwargs) tmp = self.forecast(data[-self.max_lag:], **kwargs)
if isinstance(tmp,(list, np.ndarray)): if isinstance(tmp,(list, np.ndarray)):
tmp = tmp[0] tmp = tmp[-1]
ret.append(tmp) ret.append(tmp)
data.append_rhs(tmp) data.append(tmp)
return ret return ret

2
pyFTS/data/Bitcoin.py
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@ -12,7 +12,7 @@ import pandas as pd
import numpy as np import numpy as np
def get_data(field='avg'): def get_data(field='AVG'):
""" """
Get the univariate time series data. Get the univariate time series data.

2
pyFTS/data/DowJones.py
View File

@ -12,7 +12,7 @@ import pandas as pd
import numpy as np import numpy as np
def get_data(field='avg'): def get_data(field='AVG'):
""" """
Get the univariate time series data. Get the univariate time series data.

2
pyFTS/data/Ethereum.py
View File

@ -12,7 +12,7 @@ import pandas as pd
import numpy as np import numpy as np
def get_data(field='avg'): def get_data(field='AVG'):
""" """
Get the univariate time series data. Get the univariate time series data.

2
pyFTS/models/hofts.py
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@ -117,7 +117,7 @@ class HighOrderFTS(fts.FTS):
l = len(ndata) l = len(ndata)
if l <= self.max_lag: if l < self.max_lag:
return ndata return ndata
for k in np.arange(self.max_lag, l+1): for k in np.arange(self.max_lag, l+1):

20
pyFTS/tests/general.py
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@ -19,7 +19,7 @@ dataset = TAIEX.get_data()
#print(len(dataset)) #print(len(dataset))
from pyFTS.partitioners import Grid, Util as pUtil from pyFTS.partitioners import Grid, Util as pUtil
partitioner = Grid.GridPartitioner(data=dataset[:800], npart=10, transformation=tdiff) partitioner = Grid.GridPartitioner(data=dataset[:2000], npart=20) #, transformation=tdiff)
from pyFTS.common import Util as cUtil from pyFTS.common import Util as cUtil
@ -28,15 +28,23 @@ from pyFTS.benchmarks import benchmarks as bchmk, Util as bUtil, Measures, knn,
from pyFTS.models import pwfts, song, chen, ifts, hofts from pyFTS.models import pwfts, song, chen, ifts, hofts
from pyFTS.models.ensemble import ensemble from pyFTS.models.ensemble import ensemble
model = chen.ConventionalFTS(partitioner=partitioner) print(partitioner)
#model = hofts.HighOrderFTS(partitioner=partitioner,order=2)
model.append_transformation(tdiff)
model.fit(dataset[:800])
cUtil.plot_rules(model, size=[20,20], rules_by_axis=5, columns=1) #model = chen.ConventionalFTS(partitioner=partitioner)
model = hofts.HighOrderFTS(partitioner=partitioner,order=2)
#model.append_transformation(tdiff)
model.fit(dataset[:2000])
print(model) print(model)
print(model.predict([3500, 7000], steps_ahead=5))
#cUtil.plot_rules(model, size=[20,20], rules_by_axis=5, columns=1)
#
print("fim") print("fim")