diff --git a/pyFTS/models/incremental/Retrainer.py b/pyFTS/models/incremental/Retrainer.py
index e211d9d..044c2b6 100644
--- a/pyFTS/models/incremental/Retrainer.py
+++ b/pyFTS/models/incremental/Retrainer.py
@@ -32,7 +32,10 @@ class Retrainer(fts.FTS):
         """The memory window length"""
         self.auto_update = False
         """If true the model is updated at each time and not recreated"""
+        self.batch_size = kwargs.get('batch_size', 10)
+        """The batch interval between each retraining"""
         self.is_high_order = True
+        self.uod_clip = False
 
     def train(self, data, **kwargs):
         self.partitioner = self.partitioner_method(data=data, **self.partitioner_params)
@@ -50,10 +53,12 @@ class Retrainer(fts.FTS):
             _train = data[k - horizon: k - self.order]
             _test = data[k - self.order: k]
 
-            if self.auto_update:
-                self.model.train(_train)
-            else:
-                self.train(_train, **kwargs)
+            if k % self.batch_size == 0 or self.model is None:
+                print("Treinando {}".format(k))
+                if self.auto_update:
+                    self.model.train(_train)
+                else:
+                    self.train(_train, **kwargs)
 
             ret.extend(self.model.predict(_test, **kwargs))
 
diff --git a/pyFTS/tests/general.py b/pyFTS/tests/general.py
index 647b08d..6021eda 100644
--- a/pyFTS/tests/general.py
+++ b/pyFTS/tests/general.py
@@ -16,55 +16,19 @@ from pyFTS.common import Transformations
 
 tdiff = Transformations.Differential(1)
 
-dataset =  pd.read_csv('/home/petronio/Downloads/priceHong')
-dataset['hour'] = dataset.index.values % 24
 
-split = 24 * 800
-#train = data[:split].flatten()
-#test = data[split:].flatten()
-
-#print(train)
-
-from pyFTS.models.multivariate import common, variable, mvfts
-from pyFTS.partitioners import Grid
-from pyFTS.models.seasonal.common import DateTime
-from pyFTS.models.seasonal import partitioner as seasonal
-
-vhour = variable.Variable("Hour", data_label="hour", partitioner=seasonal.TimeGridPartitioner, npart=24,
-                            data=dataset,  partitioner_specific={'seasonality': DateTime.hour_of_day, 'type': 'common'})
-vprice = variable.Variable("Price", data_label="price", partitioner=Grid.GridPartitioner, npart=25,
-                            data=dataset)
-
-
-fig, ax = plt.subplots(nrows=2, ncols=1,figsize=[15,5])
-
-vhour.partitioner.plot(ax[0])
-vprice.partitioner.plot(ax[1])
-
-model = mvfts.MVFTS()
-#model.shortname += ' ' + key
-model.append_variable(vhour)
-model.append_variable(vprice)
-#  model.shortname +=  ' ' + w
-model.target_variable = vprice
-model.fit(dataset.iloc[:split])
-
-'''
 from pyFTS.data import TAIEX, SP500, NASDAQ
 
 dataset = TAIEX.get_data()
 
-partitioner = Grid.GridPartitioner(data=dataset[:800], npart=30) #, transformation=tdiff)
+from pyFTS.models.incremental import Retrainer
 
-model = pwfts.ProbabilisticWeightedFTS(partitioner=partitioner, order=2)
-#model.append_transformation(tdiff)
+model = Retrainer.Retrainer(partitioner_params = {'npart': 30},
+                            fts_method=hofts.HighOrderFTS, order = 2,
+                  window_length = 500, batch_size = 100)
 
-model.fit(dataset[:800])
+model.predict(dataset)
 
-print(model)
-
-ret = model.predict([5000.00, 5200.00, 5400.00], explain=True)
-'''
 '''
 #dataset = SP500.get_data()[11500:16000]
 #dataset = NASDAQ.get_data()