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ResidualAnalysis bugfix

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Petrônio Cândido 2019-09-30 19:28:04 -03:00
parent aa4598f79f
commit 039dca9d5d
2 changed files with 17 additions and 5 deletions
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2
pyFTS/benchmarks/ResidualAnalysis.py
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@ -69,7 +69,7 @@ def plot_residuals_by_model(targets, models, tam=[8, 8], save=False, file=None):
else: else:
ax = axes ax = axes
forecasts = mfts.predict(targets) forecasts = mfts.predict(targets)
res = residuals(targets, forecasts, mfts.order+1) res = residuals(targets, forecasts, mfts.order)
mu = np.mean(res) mu = np.mean(res)
sig = np.std(res) sig = np.std(res)

20
pyFTS/tests/general.py
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@ -17,7 +17,11 @@ from pyFTS.common import Transformations, Membership, Util
from pyFTS.benchmarks import arima, quantreg, BSTS, gaussianproc, knn from pyFTS.benchmarks import arima, quantreg, BSTS, gaussianproc, knn
from pyFTS.fcm import fts, common, GA from pyFTS.fcm import fts, common, GA
from pyFTS.data import TAIEX, NASDAQ, SP500 from pyFTS.common import Transformations
tdiff = Transformations.Differential(1)
boxcox = Transformations.BoxCox(0)
from pyFTS.data import TAIEX, NASDAQ, SP500 from pyFTS.data import TAIEX, NASDAQ, SP500
from pyFTS.common import Util from pyFTS.common import Util
@ -28,10 +32,18 @@ test = TAIEX.get_data()[1800:2000]
from pyFTS.models import pwfts from pyFTS.models import pwfts
from pyFTS.partitioners import Grid from pyFTS.partitioners import Grid
fs = Grid.GridPartitioner(data=train, npart=45) fs = Grid.GridPartitioner(data=train, npart=15, transformation=tdiff)
model = pwfts.ProbabilisticWeightedFTS(partitioner=fs, order=1) #model = pwfts.ProbabilisticWeightedFTS(partitioner=fs, order=1)
model = chen.ConventionalFTS(partitioner=fs)
model.append_transformation(tdiff)
model.fit(train) model.fit(train)
from pyFTS.benchmarks import ResidualAnalysis as ra
ra.plot_residuals_by_model(test, [model])
horizon = 10 horizon = 10
''' '''
forecasts = model.predict(test[9:20], type='point') forecasts = model.predict(test[9:20], type='point')
@ -41,7 +53,7 @@ distributions = model.predict(test[9:20], type='distribution')
forecasts = model.predict(test[9:20], type='point', steps_ahead=horizon) forecasts = model.predict(test[9:20], type='point', steps_ahead=horizon)
intervals = model.predict(test[9:20], type='interval', 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) #distributions = model.predict(test[9:20], type='distribution', steps_ahead=horizon)
''' '''