diff --git a/pyFTS/models/pwfts.py b/pyFTS/models/pwfts.py
index b3d381d..95880a1 100644
--- a/pyFTS/models/pwfts.py
+++ b/pyFTS/models/pwfts.py
@@ -517,7 +517,10 @@ class ProbabilisticWeightedFTS(ifts.IntervalFTS):
 
         start = kwargs.get('start_at', 0)
 
-        ret = data[start: start+self.max_lag].tolist()
+        if isinstance(data, np.ndarray):
+            data = data.tolist()
+
+        ret = data[start: start+self.max_lag]
 
         for k in np.arange(self.max_lag, steps+self.max_lag):
 
diff --git a/pyFTS/tests/general.py b/pyFTS/tests/general.py
index 918f410..45c8521 100644
--- a/pyFTS/tests/general.py
+++ b/pyFTS/tests/general.py
@@ -8,40 +8,44 @@ import matplotlib.pylab as plt
 
 import pandas as pd
 
-from pyFTS.common import Util as cUtil, FuzzySet
-from pyFTS.partitioners import Grid, Entropy, Util as pUtil, Simple
-from pyFTS.benchmarks import benchmarks as bchmk, Measures
-from pyFTS.models import chen, yu, cheng, ismailefendi, hofts, pwfts, tsaur, song, sadaei, ifts
-from pyFTS.models.ensemble import ensemble
+#from pyFTS.common import Util as cUtil, FuzzySet
+from pyFTS.partitioners import Grid #, Entropy, Util as pUtil, Simple
+#from pyFTS.benchmarks import benchmarks as bchmk, Measures
+#from pyFTS.models import chen, yu, cheng, ismailefendi, hofts, pwfts, tsaur, song, sadaei, ifts
+from pyFTS.models import pwfts
+#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
+#from pyFTS.benchmarks import arima, quantreg #BSTS, gaussianproc, knn
+#from pyFTS.fcm import fts, common, GA
+#from pyFTS.common import Transformations
+from pyFTS.data import Enrollments
 
-tdiff = Transformations.Differential(1)
+#tdiff = Transformations.Differential(1)
 
-boxcox = Transformations.BoxCox(0)
+#boxcox = Transformations.BoxCox(0)
 
-df = pd.read_csv('https://query.data.world/s/z2xo3t32pkl4mdzp63x6lyne53obmi')
+#df = pd.read_csv('https://query.data.world/s/z2xo3t32pkl4mdzp63x6lyne53obmi')
 #dados = df.iloc[2710:2960 , 0:1].values # somente a 1 coluna sera usada
-dados = df['temperature'].values
+#dados = df['temperature'].values
 #dados = dados.flatten().tolist()
 
+dados = Enrollments.get_data()
+
 l = len(dados)
 
-dados_treino = dados[:int(l*.7)]
-dados_teste = dados[int(l*.7):]
+#dados_treino = dados[:int(l*.7)]
+#dados_teste = dados[int(l*.7):]
 
-particionador = Grid.GridPartitioner(data = dados_treino, npart = 15, func = Membership.trimf)
+particionador = Grid.GridPartitioner(data = dados, npart = 5, func = Membership.trimf)
 
-modelo = pwfts.ProbabilisticWeightedFTS(partitioner = particionador, order = 2)
+modelo = pwfts.ProbabilisticWeightedFTS(partitioner = particionador, order = 1)
 
-modelo.fit(dados_treino)
+modelo.fit(dados)
 
 # print(modelo)
 
 # Todo o procedimento de inferência é feito pelo método predict
-predicoes = modelo.predict(dados_teste, step_to=30)
+predicoes = modelo.predict(dados)
 
 print(predicoes)