diff --git a/pyFTS/models/incremental/Retrainer.py b/pyFTS/models/incremental/Retrainer.py
index 044c2b6..e8f9f54 100644
--- a/pyFTS/models/incremental/Retrainer.py
+++ b/pyFTS/models/incremental/Retrainer.py
@@ -36,6 +36,7 @@ class Retrainer(fts.FTS):
         """The batch interval between each retraining"""
         self.is_high_order = True
         self.uod_clip = False
+        self.max_lag = self.window_length + self.order
 
     def train(self, data, **kwargs):
         self.partitioner = self.partitioner_method(data=data, **self.partitioner_params)
@@ -49,12 +50,11 @@ class Retrainer(fts.FTS):
 
         ret = []
 
-        for k in np.arange(horizon, l):
+        for k in np.arange(horizon, l+1):
             _train = data[k - horizon: k - self.order]
             _test = data[k - self.order: k]
 
             if k % self.batch_size == 0 or self.model is None:
-                print("Treinando {}".format(k))
                 if self.auto_update:
                     self.model.train(_train)
                 else:
diff --git a/pyFTS/tests/general.py b/pyFTS/tests/general.py
index 6021eda..1483cb8 100644
--- a/pyFTS/tests/general.py
+++ b/pyFTS/tests/general.py
@@ -10,7 +10,7 @@ import pandas as pd
 
 from pyFTS.common import Util as cUtil, FuzzySet
 from pyFTS.partitioners import Grid, Entropy, Util as pUtil
-from pyFTS.benchmarks import benchmarks as bchmk
+from pyFTS.benchmarks import benchmarks as bchmk, Measures
 from pyFTS.models import chen, yu, cheng, ismailefendi, hofts, pwfts
 from pyFTS.common import Transformations
 
@@ -27,7 +27,9 @@ model = Retrainer.Retrainer(partitioner_params = {'npart': 30},
                             fts_method=hofts.HighOrderFTS, order = 2,
                   window_length = 500, batch_size = 100)
 
-model.predict(dataset)
+#model.predict(dataset)
+
+Measures.get_point_statistics(dataset, model)
 
 '''
 #dataset = SP500.get_data()[11500:16000]