diff --git a/pyFTS/chen.py b/pyFTS/chen.py
index e295b2b..870cbe6 100644
--- a/pyFTS/chen.py
+++ b/pyFTS/chen.py
@@ -71,9 +71,9 @@ class ConventionalFTS(fts.FTS):
             if actual.name not in self.flrgs:
                 ret.append(actual.centroid)
             else:
-                flrg = self.flrgs[actual.name]
+                _flrg = self.flrgs[actual.name]
 
-                ret.append(flrg.get_midpoint())
+                ret.append(_flrg.get_midpoint())
 
         ret = self.doInverseTransformations(ret, params=[data[self.order - 1:]])
 
diff --git a/pyFTS/flrg.py b/pyFTS/flrg.py
index f6e116e..e90c1d2 100644
--- a/pyFTS/flrg.py
+++ b/pyFTS/flrg.py
@@ -20,7 +20,6 @@ class FLRG(object):
             ret = self.LHS.membership(data)
         return ret
 
-
     def get_midpoint(self):
         if self.midpoint is None:
             self.midpoint = sum(self.get_midpoints())/len(self.RHS)
diff --git a/pyFTS/fts.py b/pyFTS/fts.py
index 998c8ce..4ebac57 100644
--- a/pyFTS/fts.py
+++ b/pyFTS/fts.py
@@ -159,15 +159,17 @@ class FTS(object):
         return ndata
 
     def doInverseTransformations(self, data, params=None, **kwargs):
-        ndata = data
         if len(self.transformations) > 0:
             if params is None:
                 params = [None for k in self.transformations]
 
             for c, t in enumerate(reversed(self.transformations), start=0):
-                ndata = t.inverse(ndata, params[c], **kwargs)
+                print(c)
+                ndata = t.inverse(data, params[c], **kwargs)
 
-        return ndata
+            return ndata
+        else:
+            return data
 
     def __str__(self):
         tmp = self.name + ":\n"
diff --git a/pyFTS/hofts.py b/pyFTS/hofts.py
index 77c3119..1403ba4 100644
--- a/pyFTS/hofts.py
+++ b/pyFTS/hofts.py
@@ -27,7 +27,7 @@ class HighOrderFLRG(flrg.FLRG):
             for c in self.LHS:
                 if len(self.strlhs) > 0:
                     self.strlhs += ", "
-                self.strlhs = self.strlhs + c.name
+                self.strlhs = self.strlhs + str(c)
         return self.strlhs
 
     def appendLHS(self, c):
diff --git a/pyFTS/nonstationary/honsfts.py b/pyFTS/nonstationary/honsfts.py
index 7d9b7ef..575312a 100644
--- a/pyFTS/nonstationary/honsfts.py
+++ b/pyFTS/nonstationary/honsfts.py
@@ -196,8 +196,8 @@ class HighOrderNonStationaryFTS(hofts.HighOrderFTS):
             if len(affected_flrgs) == 0:
                 tmp.append(common.check_bounds(sample[-1], self.sets, tdisp))
             elif len(affected_flrgs) == 1:
-                if affected_flrgs[0].strLHS() in self.flrgs:
-                    flrg = affected_flrgs[0]
+                flrg = affected_flrgs[0]
+                if flrg.strLHS() in self.flrgs:
                     tmp.append(self.flrgs[flrg.strLHS()].get_midpoint(tdisp))
                 else:
                     tmp.append(flrg.LHS[-1].get_midpoint(tdisp))
@@ -250,13 +250,13 @@ class HighOrderNonStationaryFTS(hofts.HighOrderFTS):
                 lower.append(aset.get_lower(tdisp))
                 upper.append(aset.get_upper(tdisp))
             elif len(affected_flrgs) == 1:
-                if affected_flrgs[0].strLHS() in self.flrgs:
-                    flrg = affected_flrgs[0]
-                    lower.append(self.flrgs[flrg.strLHS()].get_lower(tdisp))
-                    upper.append(self.flrgs[flrg.strLHS()].get_upper(tdisp))
+                _flrg = affected_flrgs[0]
+                if _flrg.strLHS() in self.flrgs:
+                    lower.append(self.flrgs[_flrg.strLHS()].get_lower(tdisp))
+                    upper.append(self.flrgs[_flrg.strLHS()].get_upper(tdisp))
                 else:
-                    lower.append(flrg.LHS[-1].get_lower(tdisp))
-                    upper.append(flrg.LHS[-1].get_upper(tdisp))
+                    lower.append(_flrg.LHS[-1].get_lower(tdisp))
+                    upper.append(_flrg.LHS[-1].get_upper(tdisp))
             else:
                 for ct, aset in enumerate(affected_flrgs):
                     if aset.strLHS() in self.flrgs:
diff --git a/pyFTS/partitioners/Grid.py b/pyFTS/partitioners/Grid.py
index 0e7b5c2..6890aa2 100644
--- a/pyFTS/partitioners/Grid.py
+++ b/pyFTS/partitioners/Grid.py
@@ -18,7 +18,7 @@ class GridPartitioner(partitioner.Partitioner):
         partlen = dlen / self.partitions
 
         count = 0
-        for c in np.arange(self.min, self.max, partlen):
+        for c in np.linspace(self.min, self.max, self.partitions):
             if self.membership_function == Membership.trimf:
                 sets.append(
                     FuzzySet.FuzzySet(self.prefix + str(count), Membership.trimf, [c - partlen, c, c + partlen],c))
diff --git a/pyFTS/song.py b/pyFTS/song.py
index f4dd896..28fa330 100644
--- a/pyFTS/song.py
+++ b/pyFTS/song.py
@@ -68,7 +68,6 @@ class ConventionalFTS(fts.FTS):
 
                 ret.append( sum(mp)/len(mp))
 
-
         ret = self.doInverseTransformations(ret, params=[data[self.order - 1:]])
 
         return ret
diff --git a/pyFTS/tests/general.py b/pyFTS/tests/general.py
index 5318fb2..f8c89fb 100644
--- a/pyFTS/tests/general.py
+++ b/pyFTS/tests/general.py
@@ -9,17 +9,18 @@ import matplotlib as plt
 #from mpl_toolkits.mplot3d import Axes3D
 
 import pandas as pd
-from pyF import Grid, Entropy, FCM, Huarng
+from pyFTS.partitioners import Grid, Entropy, FCM, Huarng
 from pyFTS.common import FLR,FuzzySet,Membership,Transformations, Util as cUtil
 from pyFTS import fts,hofts,ifts,pwfts,tree, chen
 #from pyFTS.benchmarks import benchmarks as bchmk
 from pyFTS.benchmarks import naive, arima
 from pyFTS.benchmarks import Measures
 from numpy import random
-from pyFTS.models.seasonal import SeasonalIndexer
+from pyFTS.seasonal import SeasonalIndexer
 
 os.chdir("/home/petronio/dados/Dropbox/Doutorado/Codigos/")
 
+bc = Transformations.BoxCox(0)
 diff = Transformations.Differential(1)
 #ix = SeasonalIndexer.LinearSeasonalIndexer([12, 24], [720, 1],[False, False])
 
@@ -30,8 +31,8 @@ DATASETS
 #enrollments = pd.read_csv("DataSets/Enrollments.csv", sep=";")
 #enrollments = np.array(enrollments["Enrollments"])
 
-#passengers = pd.read_csv("DataSets/AirPassengers.csv", sep=",")
-#passengers = np.array(passengers["Passengers"])
+passengers = pd.read_csv("DataSets/AirPassengers.csv", sep=",")
+passengers = np.array(passengers["Passengers"])
 
 #sunspots = pd.read_csv("DataSets/sunspots.csv", sep=",")
 #sunspots = np.array(sunspots["SUNACTIVITY"])
@@ -63,16 +64,42 @@ DATASETS
 #print(lag)
 #print(a)
 #'''
+'''
 sonda = pd.read_csv("DataSets/SONDA_BSB_15MIN_AVG.csv", sep=";")
 
 sonda['data'] = pd.to_datetime(sonda['data'])
 
-#sonda = sonda[:][527041:].dropna()
+sonda = sonda[:][527041:].dropna()
 
 sonda.index = np.arange(0,len(sonda.index))
 
 sonda_treino = sonda[:105313].dropna()
 sonda_teste = sonda[105314:].dropna()
+'''
+
+from pyFTS.partitioners import Grid
+from pyFTS import song, chen, yu, sadaei, ismailefendi, cheng
+
+train = passengers[:100]
+test = passengers[100:]
+
+fs = Grid.GridPartitioner(train, 10, transformation=bc)
+
+methods = [song.ConventionalFTS, chen.ConventionalFTS, yu.WeightedFTS, sadaei.ExponentialyWeightedFTS,
+           ismailefendi.ImprovedWeightedFTS, cheng.TrendWeightedFTS]
+
+#fig, axes = plt.subplots(nrows=1, ncols=1, figsize=[15, 5])
+
+#axes.plot(test, label="Original")
+
+for method in methods:
+    model = method("")
+    model.appendTransformation(bc)
+    model.train(train, sets=fs.sets)
+
+    forecasts = model.forecast(test)
+
+    print(forecasts)
 
 #ix_m15 = SeasonalIndexer.DateTimeSeasonalIndexer('data',[SeasonalIndexer.DateTime.minute],[15],'glo_avg', name='m15')
 
@@ -92,11 +119,11 @@ sonda_teste = sonda[105314:].dropna()
 
 #cUtil.persist_obj(obj, "models/sonda_msfts_Entropy40_Mhm15.pkl")
 
-ftse = cUtil.load_obj("models/sonda_ensemble_msfts.pkl")
+#ftse = cUtil.load_obj("models/sonda_ensemble_msfts.pkl")
 
-tmp = ftse.forecastDistribution(sonda_teste[850:860],  h=0.5, method="gaussian")
+#tmp = ftse.forecastDistribution(sonda_teste[850:860],  h=0.5, method="gaussian")
 
-print(tmp[0])
+#print(tmp[0])
 
 #'''
 
diff --git a/pyFTS/tests/nonstationary.py b/pyFTS/tests/nonstationary.py
index 184e5f8..eeb692c 100644
--- a/pyFTS/tests/nonstationary.py
+++ b/pyFTS/tests/nonstationary.py
@@ -4,9 +4,22 @@ from pyFTS.common import Membership, Transformations
 from pyFTS.nonstationary import common,perturbation, partitioners, util,nsfts, honsfts, cvfts
 from pyFTS.partitioners import Grid
 import matplotlib.pyplot as plt
+from pyFTS.common import Util as cUtil
 import pandas as pd
 os.chdir("/home/petronio/Dropbox/Doutorado/Codigos/")
 
+data = pd.read_csv("DataSets/synthetic_nonstationary_dataset_A.csv", sep=";")
+data = np.array(data["0"][:])
+
+for ct, train, test in cUtil.sliding_window(data, 300):
+    for partition in np.arange(10,50):
+        print(partition)
+        tmp_fsp = Grid.GridPartitioner(train, partition)
+        print(len(tmp_fsp.sets))
+
+        fsp = partitioners.PolynomialNonStationaryPartitioner(train, tmp_fsp, window_size=35, degree=1)
+
+'''
 diff = Transformations.Differential(1)
 
 def generate_heteroskedastic_linear(mu_ini, sigma_ini, mu_inc, sigma_inc, it=10, num=35):
@@ -43,6 +56,7 @@ fs = partitioners.ConstantNonStationaryPartitioner(train, tmp_fs,
                                                    width=perturbation.polynomial,
                                                    width_params=[1,0],
                                                    width_roots=0)
+'''
 """
 perturb = [0.5, 0.25]
 for i in [0,1]:
@@ -51,6 +65,7 @@ for i in [0,1]:
 for i in [0,1]:
     print(fs.sets[i].perturbated_parameters[perturb[i]])
 """
+'''
 #nsfts1 = nsfts.NonStationaryFTS("", partitioner=fs)
 
 nsfts1 = cvfts.ConditionalVarianceFTS("", partitioner=fs)
@@ -113,4 +128,5 @@ axes.plot(testp, label="Original")
 
 handles0, labels0 = axes.get_legend_handles_labels()
 lgd = axes.legend(handles0, labels0, loc=2)
-"""
\ No newline at end of file
+"""
+'''
\ No newline at end of file