diff --git a/pyFTS/models/multivariate/cmvfts.py b/pyFTS/models/multivariate/cmvfts.py
index 8b0afb5..8bd5167 100644
--- a/pyFTS/models/multivariate/cmvfts.py
+++ b/pyFTS/models/multivariate/cmvfts.py
@@ -67,7 +67,9 @@ class ClusteredMVFTS(mvfts.MVFTS):
 
         ndata = self.check_data(ndata)
 
-        return self.model.forecast(ndata, fuzzyfied=self.pre_fuzzyfy, **kwargs)
+        pre_fuzz = kwargs.get('pre_fuzzyfy', self.pre_fuzzyfy)
+
+        return self.model.forecast(ndata, fuzzyfied=pre_fuzz, **kwargs)
 
     def forecast_multivariate(self, data, **kwargs):
 
diff --git a/pyFTS/models/multivariate/mvfts.py b/pyFTS/models/multivariate/mvfts.py
index 20a3084..d478a88 100644
--- a/pyFTS/models/multivariate/mvfts.py
+++ b/pyFTS/models/multivariate/mvfts.py
@@ -170,11 +170,11 @@ class MVFTS(fts.FTS):
 
         ndata = self.apply_transformations(data)
 
-        start = kwargs.get('start_at', self.order)
+        start = kwargs.get('start_at', self.max_lag)
 
         ret = []
-        for k in np.arange(0, steps):
-            ix = ndata.index[start-self.max_lag+k:k+start]
+        for k in np.arange(start, start+steps):
+            ix = ndata.index[k-self.max_lag:k]
             sample = ndata.loc[ix]
             tmp = self.forecast(sample, **kwargs)
 
@@ -200,9 +200,12 @@ class MVFTS(fts.FTS):
 
             new_data_point[self.target_variable.data_label] = tmp
 
+            print(k)
+            print(new_data_point)
+
             ndata = ndata.append(new_data_point, ignore_index=True)
 
-        return ret[-steps]
+        return ret[-steps:]
 
     def forecast_interval(self, data, **kwargs):
         ret = []
diff --git a/pyFTS/tests/multivariate.py b/pyFTS/tests/multivariate.py
index 784285f..a3ba783 100644
--- a/pyFTS/tests/multivariate.py
+++ b/pyFTS/tests/multivariate.py
@@ -63,9 +63,11 @@ vavg = variable.Variable("Radiation", data_label="glo_avg", alias='rad',
 fs = grid.GridCluster(explanatory_variables=[vmonth, vhour, vavg], target_variable=vavg)
 model = cmvfts.ClusteredMVFTS(explanatory_variables=[vmonth, vhour, vavg], target_variable=vavg, partitioner=fs,
                               order=2, knn=1)
+#model = wmvfts.WeightedMVFTS(explanatory_variables=[vmonth, vhour, vavg], target_variable=vavg)
 
 model.fit(train_mv)
-forecasts = model.predict(test_mv.iloc[:100])
+generator = lambda x : x + pd.to_timedelta(1, unit='h')
+forecasts = model.predict(test_mv.iloc[:3], steps_ahead=48, generators={'data': generator} )
 
 print(forecasts)