diff --git a/pyFTS/common/FuzzySet.py b/pyFTS/common/FuzzySet.py
index b91aeea..617b519 100644
--- a/pyFTS/common/FuzzySet.py
+++ b/pyFTS/common/FuzzySet.py
@@ -106,7 +106,6 @@ def __binary_search(x, fuzzy_sets, ordered_sets):
                 first = midpoint + 1
 
 
-
 def fuzzyfy(data, partitioner, **kwargs):
     """
     A general method for fuzzyfication.
@@ -117,7 +116,8 @@ def fuzzyfy(data, partitioner, **kwargs):
 
     :keyword alpha_cut: the minimal membership value to be considered on fuzzyfication (only for mode='sets')
     :keyword method: the fuzzyfication method (fuzzy: all fuzzy memberships, maximum: only the maximum membership)
-    :keyword mode: the fuzzyfication mode (sets: return the fuzzy sets names, vector: return a vector with the membership values for all fuzzy sets)
+    :keyword mode: the fuzzyfication mode (sets: return the fuzzy sets names, vector: return a vector with the membership
+    values for all fuzzy sets, both: return a list with tuples (fuzzy set, membership value) )
     :returns a list with the fuzzyfied values, depending on the mode
     """
     alpha_cut = kwargs.get('alpha_cut', 0.)
@@ -126,11 +126,26 @@ def fuzzyfy(data, partitioner, **kwargs):
     if isinstance(data, (list, np.ndarray)):
         if mode == 'vector':
             return fuzzyfy_instances(data, partitioner.sets, partitioner.ordered_sets)
+        elif mode == 'both':
+            mvs = fuzzyfy_instances(data, partitioner.sets, partitioner.ordered_sets)
+            fs = []
+            for mv in mvs:
+                fsets = [(partitioner.ordered_sets[ix], mv[ix])
+                         for ix in np.arange(len(mv))
+                         if mv[ix] >= alpha_cut]
+                fs.append(fsets)
+            return fs
         else:
             return fuzzyfy_series(data, partitioner.sets, method, alpha_cut, partitioner.ordered_sets)
     else:
         if mode == 'vector':
             return fuzzyfy_instance(data, partitioner.sets, partitioner.ordered_sets)
+        elif mode == 'both':
+            mv = fuzzyfy_instances(data, partitioner.sets, partitioner.ordered_sets)
+            fsets = [(partitioner.ordered_sets[ix], mv[ix])
+                     for ix in np.arange(len(mv))
+                     if mv[ix] >= alpha_cut]
+            return fsets
         else:
             return get_fuzzysets(data, partitioner.sets, partitioner.ordered_sets, alpha_cut)
 
diff --git a/pyFTS/common/fts.py b/pyFTS/common/fts.py
index 15325f7..82b602b 100644
--- a/pyFTS/common/fts.py
+++ b/pyFTS/common/fts.py
@@ -464,8 +464,12 @@ class FTS(object):
         """String representation of the model"""
 
         tmp = self.name + ":\n"
-        for r in sorted(self.flrgs, key=lambda key: self.flrgs[key].get_midpoint(self.sets)):
-            tmp = tmp + str(self.flrgs[r]) + "\n"
+        if self.partitioner.type == 'common':
+            for r in sorted(self.flrgs, key=lambda key: self.flrgs[key].get_midpoint(self.partitioner.sets)):
+                tmp = "{0}{1}\n".format(tmp, str(self.flrgs[r]))
+        else:
+            for r in self.model.flrgs:
+                tmp = "{0}{1}\n".format(tmp, str(self.flrgs[r]))
         return tmp
 
     def __len__(self):
diff --git a/pyFTS/models/hofts.py b/pyFTS/models/hofts.py
index 43629c2..4b2e698 100644
--- a/pyFTS/models/hofts.py
+++ b/pyFTS/models/hofts.py
@@ -6,7 +6,8 @@ using Fuzzy Time Series. 2017 IEEE International Conference on Fuzzy Systems. DO
 """
 
 import numpy as np
-from pyFTS.common import FuzzySet, FLR, fts, flrg, tree
+from pyFTS.common import FuzzySet, FLR, fts, flrg
+from itertools import product
 
 class HighOrderFLRG(flrg.FLRG):
     """Conventional High Order Fuzzy Logical Relationship Group"""
@@ -106,30 +107,25 @@ class HighOrderFTS(fts.FTS):
 
     def generate_lhs_flrg(self, sample, explain=False):
 
-        nsample = [FuzzySet.fuzzyfy(k, partitioner=self.partitioner, mode="sets", alpha_cut=self.alpha_cut)
+        nsample = [self.partitioner.fuzzyfy(k, mode="sets", alpha_cut=self.alpha_cut)
                    for k in sample]
 
         return self.generate_lhs_flrg_fuzzyfied(nsample, explain)
 
     def generate_lhs_flrg_fuzzyfied(self, sample, explain=False):
-        lags = {}
-
+        lags = []
         flrgs = []
 
         for ct, o in enumerate(self.lags):
-            lags[ct] = sample[o-1]
+            lhs = sample[o - 1]
+            lags.append(lhs)
 
             if explain:
                 print("\t (Lag {}) {} -> {} \n".format(o, sample[o-1], lhs))
 
-        root = tree.FLRGTreeNode(None)
-
-        tree.build_tree_without_order(root, lags, 0)
-
         # Trace the possible paths
-        for p in root.paths():
+        for path in product(*lags):
             flrg = HighOrderFLRG(self.order)
-            path = list(reversed(list(filter(None.__ne__, p))))
 
             for lhs in path:
                 flrg.append_lhs(lhs)
@@ -141,13 +137,12 @@ class HighOrderFTS(fts.FTS):
     def generate_flrg(self, data):
         l = len(data)
         for k in np.arange(self.max_lag, l):
-            lags = {}
 
             if self.dump: print("FLR: " + str(k))
 
             sample = data[k - self.max_lag: k]
 
-            rhs = FuzzySet.fuzzyfy(data[k], partitioner=self.partitioner, mode="sets", alpha_cut=self.alpha_cut)
+            rhs = self.partitioner.fuzzyfy(data[k], mode="sets", alpha_cut=self.alpha_cut)
 
             flrgs = self.generate_lhs_flrg(sample)
 
@@ -158,6 +153,7 @@ class HighOrderFTS(fts.FTS):
                 for st in rhs:
                     self.flrgs[flrg.get_key()].append_rhs(st)
 
+
     def generate_flrg_fuzzyfied(self, data):
         l = len(data)
         for k in np.arange(self.max_lag, l):
@@ -165,7 +161,6 @@ class HighOrderFTS(fts.FTS):
 
             sample = data[k - self.max_lag: k]
 
-
             rhs = data[k]
 
             flrgs = self.generate_lhs_flrg_fuzzyfied(sample)
@@ -245,24 +240,18 @@ class WeightedHighOrderFTS(HighOrderFTS):
         self.shortname = "WHOFTS"
 
     def generate_lhs_flrg_fuzzyfied(self, sample, explain=False):
-        lags = {}
-
+        lags = []
         flrgs = []
 
         for ct, o in enumerate(self.lags):
-            lags[ct] = sample[o-1]
+            lags.append(sample[o-1])
 
             if explain:
-                print("\t (Lag {}) {} -> {} \n".format(o, sample[o-1], lhs))
-
-        root = tree.FLRGTreeNode(None)
-
-        tree.build_tree_without_order(root, lags, 0)
+                print("\t (Lag {}) {} \n".format(o, sample[o-1]))
 
         # Trace the possible paths
-        for p in root.paths():
+        for path in product(*lags):
             flrg = WeightedHighOrderFLRG(self.order)
-            path = list(reversed(list(filter(None.__ne__, p))))
 
             for lhs in path:
                 flrg.append_lhs(lhs)
diff --git a/pyFTS/models/multivariate/cmvfts.py b/pyFTS/models/multivariate/cmvfts.py
index b5b621f..bea237a 100644
--- a/pyFTS/models/multivariate/cmvfts.py
+++ b/pyFTS/models/multivariate/cmvfts.py
@@ -36,6 +36,8 @@ class ClusteredMVFTS(mvfts.MVFTS):
         self.shortname = "ClusteredMVFTS"
         self.name = "Clustered Multivariate FTS"
 
+        self.pre_fuzzyfy = kwargs.get('pre_fuzzyfy', True)
+
     def fuzzyfy(self,data):
         ndata = []
         for index, row in data.iterrows():
@@ -51,28 +53,29 @@ class ClusteredMVFTS(mvfts.MVFTS):
 
         self.model = self.fts_method(partitioner=self.cluster, **self.fts_params)
         if self.model.is_high_order:
-            self.model.order = self.model = self.fts_method(partitioner=self.cluster,
-                                                            order=self.order, **self.fts_params)
+            self.model.order = self.order
 
-        ndata = self.fuzzyfy(data)
+        if self.pre_fuzzyfy:
+            ndata = self.fuzzyfy(data)
+        else:
+            ndata = [self.format_data(k) for k in data.to_dict('records')]
 
-        self.model.train(ndata, fuzzyfied=True)
+        self.model.train(ndata, fuzzyfied=self.pre_fuzzyfy)
 
         self.cluster.prune()
 
     def forecast(self, ndata, **kwargs):
 
-        ndata = self.fuzzyfy(ndata)
+        if self.pre_fuzzyfy:
+            ndata = self.fuzzyfy(ndata)
+        else:
+            ndata = [self.format(k) for k in ndata.to_dict('records')]
 
-        return self.model.forecast(ndata, fuzzyfied=True, **kwargs)
+        return self.model.forecast(ndata, fuzzyfied=self.pre_fuzzyfy, **kwargs)
 
     def __str__(self):
         """String representation of the model"""
-
-        tmp = self.model.shortname + ":\n"
-        for r in self.model.flrgs:
-            tmp = tmp + str(self.model.flrgs[r]) + "\n"
-        return tmp
+        return str(self.model)
 
     def __len__(self):
         """
diff --git a/pyFTS/models/multivariate/common.py b/pyFTS/models/multivariate/common.py
index e86ca67..43d3bbc 100644
--- a/pyFTS/models/multivariate/common.py
+++ b/pyFTS/models/multivariate/common.py
@@ -42,11 +42,16 @@ def fuzzyfy_instance(data_point, var):
     return [(var.name, fs) for fs in fsets]
 
 
-def fuzzyfy_instance_clustered(data_point, cluster, alpha_cut=0.0):
+def fuzzyfy_instance_clustered(data_point, cluster, **kwargs):
+    alpha_cut = kwargs.get('alpha_cut', 0.0)
+    mode = kwargs.get('mode', 'sets')
     fsets = []
     for fset in cluster.knn(data_point):
         if cluster.sets[fset].membership(data_point) > alpha_cut:
-            fsets.append(fset)
+            if mode == 'sets':
+                fsets.append(fset)
+            elif mode =='both':
+                fsets.append( (fset, cluster.sets[fset].membership(data_point)) )
     return fsets
 
 
diff --git a/pyFTS/models/multivariate/grid.py b/pyFTS/models/multivariate/grid.py
index 2fcc6e8..0441581 100644
--- a/pyFTS/models/multivariate/grid.py
+++ b/pyFTS/models/multivariate/grid.py
@@ -1,5 +1,5 @@
 from pyFTS.partitioners import partitioner
-from pyFTS.models.multivariate.common import MultivariateFuzzySet
+from pyFTS.models.multivariate.common import MultivariateFuzzySet, fuzzyfy_instance_clustered
 from itertools import product
 from scipy.spatial import KDTree
 import numpy as np
@@ -104,3 +104,5 @@ class GridCluster(partitioner.Partitioner):
         else:
             return [self.index[k] for k in ix]
 
+    def fuzzyfy(self, data, **kwargs):
+        return fuzzyfy_instance_clustered(data, self, **kwargs)
diff --git a/pyFTS/models/pwfts.py b/pyFTS/models/pwfts.py
index 32e0fbc..6e31a41 100644
--- a/pyFTS/models/pwfts.py
+++ b/pyFTS/models/pwfts.py
@@ -5,9 +5,10 @@ import numpy as np
 import pandas as pd
 import math
 from operator import itemgetter
-from pyFTS.common import FLR, FuzzySet, tree
+from pyFTS.common import FLR, FuzzySet
 from pyFTS.models import hofts, ifts
 from pyFTS.probabilistic import ProbabilityDistribution
+from itertools import product
 
 
 class ProbabilisticWeightedFLRG(hofts.HighOrderFLRG):
@@ -116,33 +117,33 @@ class ProbabilisticWeightedFTS(ifts.IntervalFTS):
         parameters = kwargs.get('parameters','fuzzy')
 
         if parameters == 'monotonic':
-            tmpdata = FuzzySet.fuzzyfy_series_old(data, self.sets)
+            tmpdata = self.partitioner.fuzzyfy(data, mode='sets', method='maximum')
             flrs = FLR.generate_recurrent_flrs(tmpdata)
-            self.generateFLRG(flrs)
+            self.generate_flrg(flrs)
         else:
             self.generate_flrg(data)
 
     def generate_lhs_flrg(self, sample, explain=False):
-        lags = {}
+        nsample = [self.partitioner.fuzzyfy(k, mode="sets", alpha_cut=self.alpha_cut)
+                   for k in sample]
+
+        return self.generate_lhs_flrg_fuzzyfied(nsample, explain)
+
+    def generate_lhs_flrg_fuzzyfied(self, sample, explain=False):
+        lags = []
 
         flrgs = []
 
         for ct, o in enumerate(self.lags):
-            lhs = FuzzySet.fuzzyfy(sample[o - 1], partitioner=self.partitioner, mode="sets", alpha_cut=self.alpha_cut)
-
-            lags[ct] = lhs
+            lhs = sample[o - 1]
+            lags.append( lhs )
 
             if explain:
                 print("\t (Lag {}) {} -> {} \n".format(o, sample[o-1], lhs))
 
-        root = tree.FLRGTreeNode(None)
-
-        tree.build_tree_without_order(root, lags, 0)
-
         # Trace the possible paths
-        for p in root.paths():
+        for path in product(*lags):
             flrg = ProbabilisticWeightedFLRG(self.order)
-            path = list(reversed(list(filter(None.__ne__, p))))
 
             for lhs in path:
                 flrg.append_lhs(lhs)
@@ -162,14 +163,13 @@ class ProbabilisticWeightedFTS(ifts.IntervalFTS):
 
             for flrg in flrgs:
 
-                lhs_mv = flrg.get_membership(sample, self.sets)
+                lhs_mv = flrg.get_membership(sample, self.partitioner.sets)
 
                 if flrg.get_key() not in self.flrgs:
                     self.flrgs[flrg.get_key()] = flrg;
 
-                fuzzyfied = [(s, self.sets[s].membership(data[k]))
-                             for s in self.sets.keys()
-                             if self.sets[s].membership(data[k]) >  self.alpha_cut]
+                fuzzyfied = self.partitioner.fuzzyfy(data[k], mode='both', method='fuzzy',
+                                                     alpha_cut=self.alpha_cut)
 
                 mvs = []
                 for set, mv in fuzzyfied:
@@ -501,23 +501,19 @@ class ProbabilisticWeightedFTS(ifts.IntervalFTS):
         for k in np.arange(self.max_lag+1, steps+self.max_lag+1):
             dist = ProbabilityDistribution.ProbabilityDistribution(smooth, uod=uod, bins=_bins, **kwargs)
 
-            lags = {}
+            lags = []
 
             # Find all bins of past distributions with probability greater than zero
 
             for ct, d in enumerate(self.lags):
                 dd = ret[k - d]
                 vals = [float(v) for v in dd.bins if round(dd.density(v), 4) > 0]
-                lags[ct] = sorted(vals)
+                lags.append( sorted(vals) )
 
-            root = tree.FLRGTreeNode(None)
-
-            tree.build_tree_without_order(root, lags, 0)
 
             # Trace all possible combinations between the bins of past distributions
 
-            for p in root.paths():
-                path = list(reversed(list(filter(None.__ne__, p))))
+            for path in product(*lags):
 
                 # get the combined probabilities for this path
 
diff --git a/pyFTS/partitioners/partitioner.py b/pyFTS/partitioners/partitioner.py
index ee530a3..9379398 100644
--- a/pyFTS/partitioners/partitioner.py
+++ b/pyFTS/partitioners/partitioner.py
@@ -104,6 +104,9 @@ class Partitioner(object):
         """
         return self.sets[self.ordered_sets[-1]]
 
+    def fuzzyfy(self, data, **kwargs):
+        return FuzzySet.fuzzyfy(data, self, **kwargs)
+
     def plot(self, ax, rounding=0):
         """
         Plot the partitioning using the Matplotlib axis ax
diff --git a/pyFTS/tests/general.py b/pyFTS/tests/general.py
index 6c1debb..78ea4df 100644
--- a/pyFTS/tests/general.py
+++ b/pyFTS/tests/general.py
@@ -25,7 +25,7 @@ p = Grid.GridPartitioner(data=dataset, npart=20)
 
 print(p)
 
-model = hofts.WeightedHighOrderFTS(partitioner=p, order=2)
+model = hofts.HighOrderFTS(partitioner=p, order=2)
 
 model.fit(dataset) #[22, 22, 23, 23, 24])
 
diff --git a/pyFTS/tests/multivariate.py b/pyFTS/tests/multivariate.py
index c47e07c..aff0951 100644
--- a/pyFTS/tests/multivariate.py
+++ b/pyFTS/tests/multivariate.py
@@ -1,11 +1,12 @@
 import pandas as pd
 import matplotlib.pylab as plt
-from pyFTS.data import TAIEX as tx
+from pyFTS.data import TAIEX, Malaysia
 from pyFTS.common import Transformations
 
 from pyFTS.benchmarks import Measures
 from pyFTS.partitioners import Grid, Util as pUtil
 from pyFTS.common import Transformations, Util
+from pyFTS.models import pwfts
 from pyFTS.models.multivariate import common, variable, mvfts, wmvfts
 from pyFTS.models.seasonal import partitioner as seasonal
 from pyFTS.models.seasonal.common import DateTime
@@ -17,7 +18,7 @@ from pyFTS.models.multivariate import common, variable, mvfts, cmvfts
 from pyFTS.models.seasonal import partitioner as seasonal
 from pyFTS.models.seasonal.common import DateTime
 
-
+'''
 model = Util.load_obj('/home/petronio/Downloads/ClusteredMVFTS1solarorder2knn3')
 
 data = [[12, 100], [13, 200]]
@@ -36,10 +37,34 @@ f = lambda x: x + pd.to_timedelta(1, unit='h')
 for ix, row in df.iterrows():
     print(row['data'])
     print(f(row['data']))
-
+'''
 
 # Multivariate time series
 
+dataset = pd.read_csv('https://query.data.world/s/2bgegjggydd3venttp3zlosh3wpjqj', sep=';')
+
+dataset['data'] = pd.to_datetime(dataset["data"], format='%Y-%m-%d %H:%M:%S')
+
+train_mv = dataset.iloc[:24505]
+test_mv = dataset.iloc[24505:]
+
+sp = {'seasonality': DateTime.minute_of_day, 'names': [str(k) for k in range(0,24)]}
+
+vhour = variable.Variable("Hour", data_label="data", partitioner=seasonal.TimeGridPartitioner, npart=24,
+                          data=train_mv, partitioner_specific=sp)
+
+vavg = variable.Variable("Radiation", data_label="glo_avg", alias='rad',
+                         partitioner=Grid.GridPartitioner, npart=30, alpha_cut=.3,
+                         data=train_mv)
+
+model = cmvfts.ClusteredMVFTS(pre_fuzzyfy=False, knn=3, fts_method=pwfts.ProbabilisticWeightedFTS)
+model.append_variable(vhour)
+model.append_variable(vavg)
+model.target_variable = vavg
+model.fit(train_mv)
+
+print(model)
+
 '''
 train_mv = {}
 test_mv = {}