diff --git a/pyFTS/common/fts.py b/pyFTS/common/fts.py
index e1f61e8..25a2d9f 100644
--- a/pyFTS/common/fts.py
+++ b/pyFTS/common/fts.py
@@ -239,9 +239,9 @@ class FTS(object):
 
         start = kwargs.get('start_at',0)
 
-        ret = []
+        ret = data[:start+self.max_lag]
         for k in np.arange(start+self.max_lag, steps+start+self.max_lag):
-            tmp = self.forecast(data[k-self.max_lag:k], **kwargs)
+            tmp = self.forecast(ret[k-self.max_lag:k], **kwargs)
 
             if isinstance(tmp,(list, np.ndarray)):
                 tmp = tmp[-1]
@@ -364,7 +364,7 @@ class FTS(object):
             if dump == 'time':
                 print("[{0: %H:%M:%S}] Start training".format(datetime.datetime.now()))
 
-            if num_batches is not None:
+            if num_batches is not None and not self.is_wrapper:
                 n = len(data)
                 batch_size = int(n / num_batches)
                 bcount = 1
diff --git a/pyFTS/tests/multivariate.py b/pyFTS/tests/multivariate.py
index 4ceb69e..ec90ae5 100644
--- a/pyFTS/tests/multivariate.py
+++ b/pyFTS/tests/multivariate.py
@@ -29,12 +29,19 @@ from pyFTS.partitioners import Grid
 
 partitioner = Grid.GridPartitioner(data=train_data, npart=35)
 
-from pyFTS.models import pwfts
+from pyFTS.models import pwfts, hofts
 
-model = pwfts.ProbabilisticWeightedFTS(partitioner=partitioner, order=2)
-model.train(train_data)
+#model = pwfts.ProbabilisticWeightedFTS(partitioner=partitioner, order=2)
+#from pyFTS.models.incremental import TimeVariant
 
-print(model.predict(test_data[:100]))
+#model = TimeVariant.Retrainer(partitioner_method=Grid.GridPartitioner, partitioner_params={'npart': 35},
+#                              fts_method=pwfts.ProbabilisticWeightedFTS, fts_params={}, order=2 ,
+#                              batch_size=100, window_length=500)
+
+model = hofts.HighOrderFTS(partitioner=partitioner, order=2)
+model.fit(train_data)
+
+print(model.predict(test_data, steps_ahead=10))
 
 
 '''