diff --git a/pyFTS/models/hofts.py b/pyFTS/models/hofts.py
index 3a3c732..8ebe84f 100644
--- a/pyFTS/models/hofts.py
+++ b/pyFTS/models/hofts.py
@@ -166,7 +166,7 @@ class HighOrderFTS(fts.FTS):
 
             sample = data[k - self.max_lag: k]
 
-            rhs = self.partitioner.fuzzyfy(data[k+_tmp_steps], mode="sets", alpha_cut=self.alpha_cut)
+            rhs = self.partitioner.fuzzyfy(data[k + _tmp_steps], mode="sets", alpha_cut=self.alpha_cut)
 
             flrgs = self.generate_lhs_flrg(sample)
 
@@ -181,12 +181,12 @@ class HighOrderFTS(fts.FTS):
     def generate_flrg_fuzzyfied(self, data):
         _tmp_steps = self.standard_horizon - 1
         l = len(data)
-        for k in np.arange(self.max_lag, l):
+        for k in np.arange(self.max_lag, l - _tmp_steps):
             if self.dump: print("FLR: " + str(k))
 
             sample = data[k - self.max_lag: k]
 
-            rhs = data[k+_tmp_steps]
+            rhs = data[k + _tmp_steps]
 
             flrgs = self.generate_lhs_flrg_fuzzyfied(sample)
 
diff --git a/pyFTS/models/pwfts.py b/pyFTS/models/pwfts.py
index 95880a1..26d68bd 100644
--- a/pyFTS/models/pwfts.py
+++ b/pyFTS/models/pwfts.py
@@ -137,8 +137,9 @@ class ProbabilisticWeightedFTS(ifts.IntervalFTS):
         self.generate_flrg_fuzzyfied(fuzz)
 
     def generate_flrg_fuzzyfied(self, data):
+        _tmp_steps = self.standard_horizon - 1
         l = len(data)
-        for k in np.arange(self.max_lag, l):
+        for k in np.arange(self.max_lag, l - _tmp_steps):
             sample = data[k - self.max_lag: k]
             set_sample = []
             for instance in sample:
@@ -154,8 +155,8 @@ class ProbabilisticWeightedFTS(ifts.IntervalFTS):
                 lhs_mv = self.pwflrg_lhs_memberhip_fuzzyfied(flrg, sample)
 
                 mvs = []
-                inst = data[k]
-                for set, mv in inst:
+                rhs = data[k + _tmp_steps]
+                for set, mv in rhs:
                     self.flrgs[flrg.get_key()].append_rhs(set, count=lhs_mv * mv)
                     mvs.append(mv)
 
@@ -203,8 +204,9 @@ class ProbabilisticWeightedFTS(ifts.IntervalFTS):
         return flrgs
 
     def generate_flrg(self, data):
+        _tmp_steps = self.standard_horizon - 1
         l = len(data)
-        for k in np.arange(self.max_lag, l):
+        for k in np.arange(self.max_lag, l - _tmp_steps):
             if self.dump: print("FLR: " + str(k))
 
             sample = data[k - self.max_lag: k]
@@ -218,7 +220,7 @@ class ProbabilisticWeightedFTS(ifts.IntervalFTS):
                 if flrg.get_key() not in self.flrgs:
                     self.flrgs[flrg.get_key()] = flrg;
 
-                fuzzyfied = self.partitioner.fuzzyfy(data[k], mode='both', method='fuzzy',
+                fuzzyfied = self.partitioner.fuzzyfy(data[k+_tmp_steps], mode='both', method='fuzzy',
                                                      alpha_cut=self.alpha_cut)
 
                 mvs = []
diff --git a/pyFTS/tests/general.py b/pyFTS/tests/general.py
index aea02a3..f3825bc 100644
--- a/pyFTS/tests/general.py
+++ b/pyFTS/tests/general.py
@@ -38,8 +38,8 @@ l = len(dados)
 
 particionador = Grid.GridPartitioner(data = dados, npart = 10, func = Membership.trimf)
 
-modelo = pwfts.ProbabilisticWeightedFTS(partitioner = particionador, order = 1)
-#modelo = hofts.WeightedHighOrderFTS(partitioner = particionador, order = 1, standard_horizon=1, lags=[2])
+modelo = pwfts.ProbabilisticWeightedFTS(partitioner = particionador, order = 1, standard_horizon=3)
+#modelo = hofts.WeightedHighOrderFTS(partitioner = particionador, order = 1, standard_horizon=2)
 #modelo = chen.ConventionalFTS(partitioner = particionador, standard_horizon=3)
 
 modelo.fit(dados)