diff --git a/pyFTS/models/incremental/Retrainer.py b/pyFTS/models/incremental/Retrainer.py
index 50af370..455756e 100644
--- a/pyFTS/models/incremental/Retrainer.py
+++ b/pyFTS/models/incremental/Retrainer.py
@@ -40,7 +40,10 @@ class Retrainer(fts.FTS):
 
     def train(self, data, **kwargs):
         self.partitioner = self.partitioner_method(data=data, **self.partitioner_params)
-        self.model = self.fts_method(partitioner=self.partitioner, order=self.order, **self.fts_params)
+        self.model = self.fts_method(partitioner=self.partitioner, **self.fts_params)
+        if self.model.is_high_order:
+            self.model.order = self.model = self.fts_method(partitioner=self.partitioner,
+                                                            order=self.order, **self.fts_params)
         self.model.fit(data, **kwargs)
         self.shortname = self.model.shortname
 
diff --git a/pyFTS/tests/general.py b/pyFTS/tests/general.py
index 1483cb8..1aac53b 100644
--- a/pyFTS/tests/general.py
+++ b/pyFTS/tests/general.py
@@ -23,13 +23,30 @@ dataset = TAIEX.get_data()
 
 from pyFTS.models.incremental import Retrainer
 
-model = Retrainer.Retrainer(partitioner_params = {'npart': 30},
-                            fts_method=hofts.HighOrderFTS, order = 2,
-                  window_length = 500, batch_size = 100)
+from pyFTS.models.incremental import Retrainer
+from pyFTS.benchmarks import benchmarks as bchmk
+
+models = []
+for method in bchmk.get_point_methods():
+  model = Retrainer.Retrainer(partitioner_params = {'npart': 30},
+                    fts_method=method,
+                    window_length = 500, batch_size = 100)
+  models.append(model)
 
 #model.predict(dataset)
 
-Measures.get_point_statistics(dataset, model)
+from pyFTS.partitioners import Grid, Util as pUtil
+from pyFTS.benchmarks import benchmarks as bchmk, naive
+
+tag = 'benchmarks_retrainer'
+
+bchmk.sliding_window_benchmarks(dataset, 2000, train=.1, inc=0.1,
+                              models=[model],
+                              build_methods = False,
+                              benchmark_models=False,
+                              partitions=[35],
+                              progress=False, type='point',
+                              file="nsfts_benchmarks.db", dataset='teste', tag=tag)
 
 '''
 #dataset = SP500.get_data()[11500:16000]